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Page 1: Reliability-Based Design in Geotechnical Engineering
200148c4coverv05bjpg

Reliability-Based Design inGeotechnical Engineering

Also available from Taylor amp Francis

Geological HazardsFred Bell Hb ISBN 0419-16970-9

Pb ISBN 0415-31851-3

Rock Slope EngineeringDuncan Wyllie and Chris Mah Hb ISBN 0415-28000-1

Pb ISBN 0415-28001-X

Geotechnical ModellingDavid Muir Wood Hb ISBN 9780415343046

Pb ISBN 9780419237303

Soil LiquefactionMike Jefferies and Ken Been Hb ISBN 9780419161707

Advanced Unsaturated SoilMechanics and EngineeringCharles WW Ng and Bruce Menzies Hb ISBN 9780415436793

Advanced Soil Mechanics 3rd editionBraja Das Hb ISBN 9780415420266

Pile Design and Construction Practice5th editonMichael Tomlinson and John Woodward Hb ISBN 9780415385824

Reliability-Based Design inGeotechnical Engineering

Computations and Applications

Kok-Kwang Phoon

First published 2008by Taylor amp Francis2 Park Square Milton Park Abingdon Oxon OX14 4RN

Simultaneously published in the USA and Canadaby Taylor amp Francis270 Madison Avenue New York NY 10016 USA

Taylor amp Francis is an imprint of the Taylor amp Francis Groupan informa business

copy Taylor amp Francis

All rights reserved No part of this book may be reprinted or reproducedor utilised in any form or by any electronic mechanical or other meansnow known or hereafter invented including photocopying and recordingor in any information storage or retrieval system without permission inwriting from the publishers

The publisher makes no representation express or implied with regardto the accuracy of the information contained in this book and cannotaccept any legal responsibility or liability for any efforts or omissions thatmay be made

British Library Cataloguing in Publication DataA catalogue record for this book is availablefrom the British Library

Library of Congress Cataloging in Publication DataPhoon Kok-Kwang

Reliability-based design in geotechnical engineering computations andapplicationsKok-Kwang Phoon

p cmIncludes bibliographical references and indexISBN 978-0-415-39630-1 (hbk alk paper) ndash ISBN 978-0-203-93424-1

(e-book) 1 Rock mechanics 2 Soil mechanics 3 Reliability I Title

TA706P48 20086241prime51ndashdc22 2007034643

ISBN10 0-415-39630-1 (hbk)IBSN10 0-213-93424-5 (ebk)

ISBN13 978-0-415-39630-1 (hbk)ISBN13 978-0-203-93424-1 (ebk)

ISBN 0-203-93424-5 Master e-book ISBN

ldquoTo purchase your own copy of this or any of Taylor amp Francis or Routledgersquoscollection of thousands of eBooks please go to wwweBookstoretandfcoukrdquo

This edition published in the Taylor amp Francis e-Library 2008

Contents

List of contributors vii

1 Numerical recipes for reliability analysis ndash a primer 1KOK-KWANG PHOON

2 Spatial variability and geotechnical reliability 76GREGORY B BAECHER AND JOHN T CHRISTIAN

3 Practical reliability approach using spreadsheet 134BAK KONG LOW

4 Monte Carlo simulation in reliability analysis 169YUSUKE HONJO

5 Practical application of reliability-based design indecision-making 192ROBERT B GILBERT SHADI S NAJJAR YOUNG-JAE CHOI AND

SAMUEL J GAMBINO

6 Randomly heterogeneous soils under static anddynamic loads 224RADU POPESCU GEORGE DEODATIS AND JEAN-HERVEacute PREacuteVOST

7 Stochastic finite element methods in geotechnicalengineering 260BRUNO SUDRET AND MARC BERVEILLER

8 Eurocode 7 and reliability-based design 298TREVOR L L ORR AND DENYS BREYSSE

vi Contents

9 Serviceability limit state reliability-based design 344KOK-KWANG PHOON AND FRED H KULHAWY

10 Reliability verification using pile load tests 385LIMIN ZHANG

11 Reliability analysis of slopes 413TIEN H WU

12 Reliability of levee systems 448THOMAS F WOLFF

13 Reliability analysis of liquefaction potential of soilsusing standard penetration test 497CHARNG HSEIN JUANG SUNNY YE FANG AND DAVID KUN LI

Index 527

List of contributors

Gregory B Baecher is Glenn L Martin Institute Professor of Engineeringat the University of Maryland He holds a BSCE from UC Berkeley andPhD from MIT His principal area of work is engineering risk manage-ment He is co-author with JT Christian of Reliability and Statistics inGeotechnical Engineering (Wiley 2003) and with DND Hartford ofRisk and Uncertainty in Dam Safety (Thos Telford 2004) He is recipientof the ASCE Middlebrooks and State-of-the-Art Awards and a memberof the US National Academy of Engineering

Marc Berveiller received his masterrsquos degree in mechanical engineering fromthe French Institute in Advanced Mechanics (Clermont-Ferrand France2002) and his PhD from the Blaise Pascal University (Clermont-FerrandFrance) in 2005 where he worked on non-intrusive stochastic finiteelement methods in collaboration with the French major electrical com-pany EDF He is currently working as a research engineer at EDF onprobabilistic methods in mechanics

Denys Breysse is a Professor of Civil Engineering at Bordeaux 1 UniversityFrance He is working on randomness in soils and building materialsteaches geotechnics materials science and risk and safety He is the chair-man of the French Association of Civil Engineering University Members(AUGC) and of the RILEM Technical Committee TC-INR 207 on Non-Destructive Assessment of Reinforced Concrete Structures He has alsocreated (and chaired 2003ndash2007) a national research scientific networkon Risk Management in Civil Engineering (MR-GenCi)

Young-Jae Choi works for Geoscience Earth amp Marine Services Inc inHouston Texas He received his Bachelor of Engineering degree in 1995from Pusan National University his Master of Science degree from theUniversity of Colorado at Boulder in 2002 and his PhD from TheUniversity of Texas at Austin in 2007 He also has six years of practicalexperience working for Daelim Industry Co Ltd in South Korea

viii List of contributors

John T Christian is a consulting geotechnical engineer in WabanMassachusetts He holds BSc MSc and PhD degrees from MIT Hisprincipal areas of work are applied numerical methods in soil dynamicsand earthquake engineering and reliability methods A secondary inter-est has been the evolving procedures and standards for undergraduateeducation especially as reflected in the accreditation process He was the39th Karl Terzaghi Lecturer of the ASCE He is recipient of the ASCEMiddlebrooks and the BSCE Desmond Fitzgerald Medal and a memberof the US National Academy of Engineering

George Deodatis received his Diploma in Civil Engineering from theNational Technical University of Athens in Greece He holds MS andPhD degrees in Civil Engineering from Columbia University He startedhis academic career at Princeton University where he served as a Post-doctoral Fellow Assistant Professor and eventually Associate ProfessorHe subsequently moved to Columbia University where he is currently theSantiago and Robertina Calatrava Family Professor at the Department ofCivil Engineering and Engineering Mechanics

Sunny Ye Fang is currently a consulting geotechnical engineer withArdaman amp Associates Inc She received her BS degree from NingboUniversity Ningbo China MSCE degree from Zhejiang UniversityHangzhou Zhejiang China and PhD degree in Civil Engineering fromClemson University South Carolina Dr Fang has extensive consultingprojects experience in the field of geoenvironmental engineering

Samuel J Gambino has ten years of experience with URS Corporation inOakland California His specialties include foundation design tunnelinstallations and dams reservoirs and levees Samuel received his bach-elors degree in civil and environmental engineering from the Universityof Michigan at Ann Arbor in 1995 his masters degree in geotechnicalengineering from the University of Texas at Austin in 1998 and has helda professional engineers license in the State of California since 2002

Robert B Gilbert is the Hudson Matlock Professor in Civil Architecturaland Environmental Engineering at The University of Texas at AustinHe joined the faculty in 1993 Prior to that he earned BS (1987) MS(1988) and PhD (1993) degrees in civil engineering from the Universityof Illinois at Urbana-Champaign He also practiced with Golder Asso-ciates Inc as a geotechnical engineer from 1988 to 1993 His expertise isthe assessment evaluation and management of risk in civil engineeringApplications include building foundations slopes pipelines dams andlevees landfills and groundwater and soil remediation systems

Yusuke Honjo is currently a professor and the head of the Civil Engineer-ing Department at Gifu University in Japan He holds ME from Kyoto

List of contributors ix

University and PhD from MIT He was an associate professor and divi-sion chairman at Asian Institute of Technology (AIT) in Bangkok between1989 and 1993 and joined Gifu University in 1995 He is currentlythe chairperson of ISSMGE-TC 23 lsquoLimit state design in geotechnicalengineering practicersquo He has published more than 100 journal papersand international conference papers in the area of statistical analyses ofgeotechnical data inverse analysis and reliability analyses of geotechnicalstructures

Charng Hsein Juang is a Professor of Civil Engineering at Clemson Universityand an Honorary Chair Professor at National Central University TaiwanHe received his BS and MS degrees from National Cheng Kung Universityand PhD degree from Purdue University Dr Juang is a registered Profes-sional Engineering in the State of South Carolina and a Fellow of ASCEHe is recipient of the 1976 Outstanding Research Paper Award ChineseInstitute of Civil and Hydraulic Engineering the 2001 TK Hsieh Awardthe Institution of Civil Engineers United Kingdom and the 2006 BestPaper Award the Taiwan Geotechnical Society Dr Juang has authoredmore than 100 refereed papers in geotechnical related fields and is proudto be selected by his students at Clemson University as the Chi EpsilonOutstanding Teacher in 1984

Fred H Kulhawy is Professor of Civil Engineering at Cornell Univer-sity Ithaca New York He received his BSCE and MSCE from NewJersey Institute of Technology and his PhD from University of Californiaat Berkeley His teaching and research focuses on foundations soil-structure interaction soil and rock behavior and geotechnical computerand reliability applications and he has authored over 330 publicationsHe has lectured worldwide and has received numerous awards fromASCE ADSC IEEE and others including election to Distinguished Mem-ber of ASCE and the ASCE Karl Terzaghi Award and Norman Medal Heis a licensed engineer and has extensive experience in geotechnical practicefor major projects on six continents

David Kun Li is currently a consulting geotechnical engineer with GolderAssociates Inc He received his BSCE and MSCE degrees from ZhejiangUniversity Hangzhou Zhejiang China and PhD degree in Civil Engi-neering from Clemson University South Carolina Dr Li has extensiveconsulting projects experience on retaining structures slope stabilityanalysis liquefaction analysis and reliability assessments

Bak Kong Low obtained his BS and MS degrees from MIT and PhD degreefrom UC Berkeley He is a Fellow of the ASCE and a registered pro-fessional engineer of Malaysia He currently teaches at the NanyangTechnological University in Singapore He had done research while onsabbaticals at HKUST (1996) University of Texas at Austin (1997)

x List of contributors

and Norwegian Geotechnical Institute (2006) His research interest andpublications can be found at httpalummiteduwwwbklow

Shadi S Najjar joined the American University of Beirut (AUB) as an Assis-tant Professor in Civil Engineering in September 2007 Dr Najjar earnedhis BE and ME in Civil Engineering from AUB in 1999 and 2001 respec-tively In 2005 he graduated from the University of Texas at Austin witha PhD in civil engineering Dr Najjarrsquos research involves analytical stud-ies related to reliability-based design in geotechnical engineering Between2005 and 2007 Dr Najjar taught courses on a part-time basis in severalleading universities in Lebanon In addition he worked with Polytech-nical Inc and Dar Al-Handasah Consultants (Shair and Partners) as ageotechnical engineering consultant

Trevor Orr is a Senior Lecturer at Trinity College Dublin He obtainedhis PhD degree from Cambridge University He has been involved inEurocode 7 since the first drafting committee was established in 1981In 2003 he was appointed Chair of the ISSMGE European Technical Com-mittee 10 for the Evaluation of Eurocode 7 and in 2007 was appointeda member of the CEN Maintenance Group for Eurocode 7 He is theco-author of two books on Eurocode 7

Kok-Kwang Phoon is Director of the Centre for Soft Ground Engineering atthe National University of Singapore His main research interest is relatedto development of risk and reliability methods in geotechnical engineer-ing He has authored more than 120 scientific publications includingmore than 20 keynoteinvited papers and edited 15 proceedings He is thefounding editor-in-chief of Georisk and recipient of the prestigious ASCENormal Medal and ASTM Hogentogler Award Webpage httpwwwengnusedusgcivilpeoplecvepkkpkkhtml

Radu Popescu is a Consulting Engineer with URS Corporation and aResearch Professor at Memorial University of Newfoundland CanadaHe earned PhD degrees from the Technical University of Bucharest andfrom Princeton University He was a Visiting Research Fellow at PrincetonUniversity Columbia University and Saitama University (Japan) andLecturer at the Technical University of Bucharest (Romania) andPrinceton University Radu has over 25 years experience in computationaland experimental soil mechanics (dynamic soil-structure interaction soilliquefaction centrifuge modeling site characterization) and publishedover 100 articles in these areas In his research he uses the tools ofprobabilistic mechanics to address various uncertainties manifested in thegeologic environment

Jean H Prevost is presently Professor of Civil and Environmental Engineer-ing at Princeton University He is also an affiliated faculty at the Princeton

List of contributors xi

Materials Institute in the department of Mechanical and AerospaceEngineering and in the Program in Applied and Computational Math-ematics He received his MSc in 1972 and his PhD in 1974 from StanfordUniversity He was a post-doctoral Research Fellow at the NorwegianGeotechnical Institute in Oslo Norway (1974ndash1976) and a ResearchFellow and Lecturer in Civil Engineering at the California Institute ofTechnology (1976ndash1978) He held visiting appointments at the EcolePolytechnique in Paris France (1984ndash1985 2004ndash2005) at the EcolePolytechnique in Lausanne Switzerland (1984) at Stanford University(1994) and at the Institute for Mechanics and Materials at UCSD (1995)He was Chairman of the Department of Civil Engineering and Opera-tions Research at Princeton University (1989ndash1994) His principal areasof interest include dynamics nonlinear continuum mechanics mixturetheories finite element methods XFEM and constitutive theories He isthe author of over 185 technical papers in his areas of interest

Bruno Sudret has a masterrsquos degree from Ecole Polytechnique (France 1993)a masterrsquos degree in civil engineering from Ecole Nationale des Ponts etChausseacutees (1995) and a PhD in civil engineering from the same insti-tution (1999) After a post-doctoral stay at the University of Californiaat Berkeley in 2000 he joined in 2001 the RampD Division of the Frenchmajor electrical company EDF He currently manages a research group onprobabilistic engineering mechanics He is member of the Joint Commit-tee on Structural Safety since 2004 and member of the board of directorsof the International Civil Engineering Risk and Reliability Associationsince 2007 He received the Jean Mandel Prize in 2005 for his work onstructural reliability and stochastic finite element methods

Thomas F Wolff is an Associate Dean of Engineering at Michigan StateUniversity From 1970 to 1985 he was a geotechnical engineer with theUS Army Corps of Engineers and in 1986 he joined MSU His researchand consulting has focused on design and reliability analysis of damslevees and hydraulic structures He has authored a number of Corps ofEngineersrsquo guidance documents In 2005 he served on the ASCE LeveeAssessment Team in New Orleans and in 2006 he served on the InternalTechnical Review (ITR) team for the IPET report which analyzed theperformance of the New Orleans Hurricane Protection System

Tien H Wu is a Professor Emeritus at Ohio State University He receivedhis BS from St Johns University Shanghai China and MS and PhDfrom University of Illinois He has lectured and conducted researchat Norwegian Geotechnical Institute Royal Institute of Technology inStockholm Tonji University in Shanghai National Polytechnical Insti-tute in Quito Cairo University Institute of Forest Research in ChristChurch and others His teaching research and consulting activities

xii List of contributors

involve geotechnical reliability slope stability soil properties glaciologyand soil-bioengineering He is the author of two books Soil Mechanicsand Soil Dynamics He is an Honorary Member of ASCE and has receivedASCErsquos State-of-the-Art Award Peck Award and Earnest Award and theUS Antarctic Service Medal He was the 2008 Peck Lecturer of ASCE

Limin Zhang is an Associate Professor of Civil Engineering and AssociateDirector of Geotechnical Centrifuge Facility at the Hong Kong Universityof Science and Technology His research areas include pile foundationsdams and slopes centrifuge modelling and geotechnical risk and relia-bility He is currently secretary of Technical Committee TC23 on lsquoLimitState Design in Geotechnical Engineeringrsquo and member of TC18 on lsquoDeepFoundationsrsquo of the ISSMGE and Vice Chair of the International Press-InAssociation

Chapter 1

Numerical recipes forreliability analysis ndash a primer

Kok-Kwang Phoon

11 Introduction

Currently the geotechnical community is mainly preoccupied with the tran-sition from working or allowable stress design (WSDASD) to Load andResistance Factor Design (LRFD) The term LRFD is used in a loose wayto encompass methods that require all limit states to be checked using aspecific multiple-factor format involving load and resistance factors Thisterm is used most widely in the United States and is equivalent to LimitState Design (LSD) in Canada Both LRFD and LSD are philosophicallyakin to the partial factors approach commonly used in Europe although adifferent multiple-factor format involving factored soil parameters is usedOver the past few years Eurocode 7 has been revised to accommodate threedesign approaches (DAs) that allow partial factors to be introduced at thebeginning of the calculations (strength partial factors) or at the end of thecalculations (resistance partial factors) or some intermediate combinationsthereof The emphasis is primarily on the re-distribution of the original globalfactor safety in WSD into separate load and resistance factors (or partialfactors)

It is well accepted that uncertainties in geotechnical engineering design areunavoidable and numerous practical advantages are realizable if uncertain-ties and associated risks can be quantified This is recognized in a recentNational Research Council (2006) report on Geological and Geotechni-cal Engineering in the New Millennium Opportunities for Research andTechnological Innovation The report remarked that ldquoparadigms for dealingwith hellip uncertainty are poorly understood and even more poorly practicedrdquoand advocated a need for ldquoimproved methods for assessing the potentialimpacts of these uncertainties on engineering decisions helliprdquo Within the arenaof design code development increasing regulatory pressure is compellinggeotechnical LRFD to advance beyond empirical re-distribution of the orig-inal global factor of safety to a simplified reliability-based design (RBD)framework that is compatible with structural design RBD calls for a willing-ness to accept the fundamental philosophy that (a) absolute reliability is an

2 Kok-Kwang Phoon

unattainable goal in the presence of uncertainty and (b) probability theorycan provide a formal framework for developing design criteria that wouldensure that the probability of ldquofailurerdquo (used herein to refer to exceedingany prescribed limit state) is acceptably small Ideally geotechnical LRFDshould be derived as the logical end-product of a philosophical shift in mind-set to probabilistic design in the first instance and a simplification of rigorousRBD into a familiar ldquolook and feelrdquo design format in the second The needto draw a clear distinction between accepting reliability analysis as a nec-essary theoretical basis for geotechnical design and downstream calibrationof simplified multiple-factor design formats with emphasis on the formerwas highlighted by Phoon et al (2003b) The former provides a consis-tent method for propagation of uncertainties and a unifying frameworkfor risk assessment across disciplines (structural and geotechnical design)and national boundaries Other competing frameworks have been suggested(eg λ-method by Simpson et al 1981 worst attainable value method byBolton 1989 Taylor series method by Duncan 2000) but none has thetheoretical breadth and power to handle complex real-world problems thatmay require nonlinear 3D finite element or other numerical approaches forsolution

Simpson and Yazdchi (2003) proposed that ldquolimit state design requiresanalysis of un-reality not of reality Its purpose is to demonstrate thatlimit states are in effect unreal or alternatively that they are lsquosufficientlyunlikelyrsquo being separated by adequate margins from expected statesrdquo It isclear that limit states are ldquounlikelyrdquo states and the purpose of design is toensure that expected states are sufficiently ldquofarrdquo from these limit states Thepivotal point of contention is how to achieve this separation in numericalterms (Phoon et al 1993) It is accurate to say that there is no consensus onthe preferred method and this issue is still the subject of much heated debatein the geotechnical engineering community Simpson and Yazdchi (2003)opined that strength partial factors are physically meaningful because ldquoit isthe gradual mobilisation of strength that causes deformationrdquo This is con-sistent with our prevailing practice of applying a global factor of safety tothe capacity to limit deformations Another physical justification is that vari-ations in soil parameters can create disproportionate nonlinear variations inthe response (or resistance) (Simpson 2000) In this situation the author feltthat ldquoit is difficult to choose values of partial factors which are applicableover the whole range of the variablesrdquo The implicit assumption here is thatthe resistance factor is not desirable because it is not practical to prescribea large number of resistance factors in a design code However if main-taining uniform reliability is a desired goal a single partial factor for sayfriction angle would not produce the same reliability in different problemsbecause the relevant design equations are not equally sensitive to changes inthe friction angle For complex soilndashstructure interaction problems apply-ing a fixed partial factor can result in unrealistic failure mechanisms In fact

Numerical recipes for reliability analysis 3

given the complexity of the limit state surface it is quite unlikely for thesame partial factor to locate the most probable failure point in all designproblems These problems would not occur if resistance factors are cali-brated from reliability analysis In addition it is more convenient to accountfor the bias in different calculation models using resistance factors How-ever for complex problems it is possible that a large number of resistancefactors are needed and the design code becomes too unwieldy or confusing touse Another undesirable feature is that the engineer does not develop a feelfor the failure mechanism if heshe is only required to analyze the expectedbehavior followed by application of some resistance factors at the end ofthe calculation

Overall the conclusion is that there are no simple methods (factoredparameters or resistances) of replacing reliability analysis for sufficientlycomplex problems It may be worthwhile to discuss if one should insiston simplicity despite all the known associated problems The more recentperformance-based design philosophy may provide a solution for thisdilemma because engineers can apply their own calculations methods forensuring performance compliance without being restricted to following rigiddesign codes containing a few partial factors In the opinion of the authorthe need to derive simplified RBD equations perhaps is of practical impor-tance to maintain continuity with past practice but it is not necessary andit is increasingly fraught with difficulties when sufficiently complex prob-lems are posed The limitations faced by simplified RBD have no bearing onthe generality of reliability theory This is analogous to arguing that limita-tions in closed-form elastic solutions are related to elasto-plastic theory Theapplication of finite element softwares on relatively inexpensive and power-ful PCs (with gigahertz processors a gigabyte of memory and hundreds ofgigabytes ndash verging on terabyte ndash of disk) permit real-world problems to besimulated on an unprecedented realistic setting almost routinely It sufficesto note here that RBD can be applied to complex real-world problems usingpowerful but practical stochastic collocation techniques (Phoon and Huang2007)

One common criticism of RBD is that good geotechnical sense and judg-ment would be displaced by the emphasis on probabilistic analysis (Boden1981 Semple 1981 Bolton 1983 Fleming 1989) This is similar to the on-going criticism of numerical analysis although this criticism seems to havegrown more muted in recent years with the emergence of powerful and user-friendly finite element softwares The fact of the matter is that experiencesound judgment and soil mechanics still are needed for all aspects of geotech-nical RBD (Kulhawy and Phoon 1996) Human intuition is not suited forreasoning with uncertainties and only this aspect has been removed from thepurview of the engineer One example in which intuition can be misleadingis the common misconception that a larger partial factor should be assignedto a more uncertain soil parameter This is not necessarily correct because

4 Kok-Kwang Phoon

the parameter may have little influence on the overall response (eg capacitydeformation) Therefore the magnitude of the partial factor should dependon the uncertainty of the parameter and the sensitivity of the response tothat parameter Clearly judgment is not undermined instead it is focusedon those aspects for which it is most suited

Another criticism is that soil statistics are not readily available becauseof the site-specific nature of soil variability This concern only is true fortotal variability analyses but does not apply to the general approach whereinherent soil variability measurement error and correlation uncertainty arequantified separately Extensive statistics for each component uncertain-ties have been published (Phoon and Kulhawy 1999a Uzielli et al 2007)For each combination of soil type measurement technique and correlationmodel the uncertainty in the design soil property can be evaluated systemat-ically by combining the appropriate component uncertainties using a simplesecond-moment probabilistic approach (Phoon and Kulhawy 1999b)

In summary we are now at the point where RBD really can be used asa rational and practical design mode The main impediment is not theoret-ical (lack of power to deal with complex problems) or practical (speed ofcomputations availability of soil statistics etc) but the absence of simplecomputational approaches that can be easily implemented by practitionersMuch of the controversies reported in the literature are based on qualitativearguments If practitioners were able to implement RBD easily on their PCsand calculate actual numbers using actual examples they would gain a con-crete appreciation of the merits and limitations of RBD Misconceptions willbe dismissed definitively rather than propagated in the literature generatingfurther confusion The author believes that the introduction of powerful butsimple-to-implement approaches will bring about a greater acceptance ofRBD amongst practitioners in the broader geotechnical engineering commu-nity The Probabilistic Model Code was developed by the Joint Committeeon Structural Safety (JCSS) to achieve a similar objective (Vrouwenvelderand Faber 2007)

The main impetus for this book is to explain RBD to students andpractitioners with emphasis on ldquohow to calculaterdquo and ldquohow to applyrdquoPractical computational methods are presented in Chapters 1 3 4 and 7Geotechnical examples illustrating reliability analyses and design are pro-vided in Chapters 5 6 8ndash13 The spatial variability of geomaterials isone of the distinctive aspects of geotechnical RBD This important aspectis covered in Chapter 2 The rest of this chapter provides a primer onreliability calculations with references to appropriate chapters for follow-up reading Simple MATLAB codes are provided in Appendix A andat httpwwwengnusedusgcivilpeoplecvepkkprob_libhtml By focus-ing on demonstration of RBD through calculations and examples thisbook is expected to serve as a valuable teaching and learning resource forpractitioners educators and students

Numerical recipes for reliability analysis 5

12 General reliability problem

The general stochastic problem involves the propagation of input uncer-tainties and model uncertainties through a computation model to arriveat a random output vector (Figure 11) Ideally the full finite-dimensionaldistribution function of the random output vector is desired although par-tial solutions such as second-moment characterizations and probabilities offailure may be sufficient in some applications In principle Monte Carlosimulation can be used to solve this problem regardless of the complexi-ties underlying the computation model input uncertainties andor modeluncertainties One may assume with minimal loss of generality that a com-plex geotechnical problem (possibly 3D nonlinear time and constructiondependent etc) would only admit numerical solutions and the spatialdomain can be modeled by a scalarvector random field Monte Carlosimulation requires a procedure to generate realizations of the input andmodel uncertainties and a numerical scheme for calculating a vector ofoutputs from each realization The first step is not necessarily trivial andnot completely solved even in the theoretical sense Some ldquouser-friendlyrdquomethods for simulating random variablesvectorsprocesses are outlined inSection 13 with emphasis on key calculation steps and limitations Detailsare outside the scope of this chapter and given elsewhere (Phoon 2004a2006a) In this chapter ldquouser-friendlyrdquo methods refer to those that canbe implemented on a desktop PC by a non-specialist with limited pro-gramming skills in other words methods within reach of the generalpractitioner

Model errors

Computationmodel

Uncertain modelparameters

Uncertainoutput vector

Uncertaininput vector

Figure 11 General stochastic problem

6 Kok-Kwang Phoon

The second step is identical to the repeated application of a determin-istic solution process The only potential complication is that a particularset of input parameters may be too extreme say producing a near-collapsecondition and the numerical scheme may become unstable The statisticsof the random output vector are contained in the resulting ensemble ofnumerical output values produced by repeated deterministic runs Fentonand Griffiths have been applying Monte Carlo simulation to solve soil-interaction problems within the context of a random field since the early1990s (eg Griffiths and Fenton 1993 1997 2001 Fenton and Griffiths1997 2002 2003) Popescu and co-workers developed simulation-basedsolutions for a variety of soil-structure interaction problems particularlyproblems involving soil dynamics in parallel Their work is presented inChapter 6 of this book

For a sufficiently large and complex soil-structure interaction problemit is computationally intensive to complete even a single run The rule-of-thumb for Monte Carlo simulation is that 10pf runs are needed to estimatea probability of failure pf within a coefficient of variation of 30 Thetypical pf for a geotechnical design is smaller than one in a thousand andit is expensive to run a numerical code more than ten thousand times evenfor a modest size problem This significant practical disadvantage is wellknown At present it is accurate to say that a computationally efficient andldquouser-friendlyrdquo solution to the general stochastic problem remains elusiveNevertheless reasonably practical solutions do exist if the general stochasticproblem is restricted in some ways for example accept a first-order esti-mate of the probability of failure or accept an approximate but less costlyoutput Some of these probabilistic solution procedures are presented inSection 14

13 Modeling and simulation of stochastic data

131 Random variables

Geotechnical uncertainties

Two main sources of geotechnical uncertainties can be distinguished Thefirst arises from the evaluation of design soil properties such as undrainedshear strength and effective stress friction angle This source of geotechni-cal uncertainty is complex and depends on inherent soil variability degreeof equipment and procedural control maintained during site investigationand precision of the correlation model used to relate field measurementwith design soil property Realistic statistical estimates of the variability ofdesign soil properties have been established by Phoon and Kulhawy (1999a1999b) Based on extensive calibration studies (Phoon et al 1995) threeranges of soil property variability (low medium high) were found to be

Numerical recipes for reliability analysis 7

sufficient to achieve reasonably uniform reliability levels for simplified RBDchecks

Geotechnical parameter Property variability COV ()

Undrained shear strength Low 10ndash30Medium 30ndash50High 50ndash70

Effective stress friction angle Low 5ndash10Medium 10ndash15High 15ndash20

Horizontal stress coefficient Low 30ndash50Medium 50ndash70High 70ndash90

In contrast Reacutethaacuteti (1988) citing the 1965 specification of the AmericanConcrete Institute observed that the quality of concrete can be evaluated inthe following way

Quality COV ()

Excellent lt 10Good 10ndash15Satisfactory 15ndash20Bad gt 20

It is clear that the coefficients of variations or COVs of natural geomaterialscan be much larger and do not fall within a narrow range The ranges ofquality for concrete only apply to the effective stress friction angle

The second source arises from geotechnical calculation models Althoughmany geotechnical calculation models are ldquosimplerdquo reasonable predictionsof fairly complex soilndashstructure interaction behavior still can be achievedthrough empirical calibrations Model factors defined as the ratio of themeasured response to the calculated response usually are used to correctfor simplifications in the calculation models Figure 12 illustrates modelfactors for capacity of drilled shafts subjected to lateral-moment loadingNote that ldquoSDrdquo is the standard deviation ldquonrdquo is the sample size and ldquopADrdquois the p-value from the AndersonndashDarling goodness-of-fit test (gt 005 impliesacceptable lognormal fit) The COVs of model factors appear to fall between30 and 50

It is evident that a geotechnical parameter (soil property or model factor)exhibiting a range of values possibly occurring at unequal frequencies isbest modeled as a random variable The existing practice of selecting onecharacteristic value (eg mean ldquocautiousrdquo estimate 5 exclusion limit etc)is attractive to practitioners because design calculations can be carried outeasily using only one set of input values once they are selected However thissimplicity is deceptive The choice of the characteristic values clearly affects

04 12 20 28 36

HhHu(Reese)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 142 SD = 014 COV = 029

n = 74 pAD = 0186

Hyperbolic capacity (Hh)

04 12 20 28 36HhHu(Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 228 SD = 085 COV = 037

n = 74 pAD = 0149

04 12 20 28 36HhHu(Hansen)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 098 SD = 032 COV = 033

n = 77 pAD = 0229

04 12 20 28 36HhHu(Randolph amp Houlsby)HhHu(Randolph amp Houlsby)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 132 SD = 038 COV = 029

n = 74 pAD = 0270

04 12 20 28 36

HhHu(Simplified Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 130 SD = 049 COV = 038

n = 77 pAD = 0141

00 08 16 24 32

HhHu(Reese)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 092 SD = 027 COV = 029

n = 72 pAD = 0633

Lateral on moment limit (HL)Capacity model

Reese (1958)(undrained)

Broms (1964a)(undrained)

Randolph amp Houlsby(1984) (undrained)

Hansen (1961)(drained)

Broms (1964b)(drained)

00 08 16 24 32HhHu(Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 149 SD = 057 COV = 038

n = 72 pAD = 0122

00 08 16 24 32HhHu(Hansen)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 067 SD = 026 COV = 038

n = 75 pAD = 0468

00 08 16 24 32

HhHu(Simplified Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 088 SD = 036 COV = 041

n = 75 pAD = 0736

00 08 16 24 320

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 085 SD = 024 COV = 028

n = 72 pAD = 0555

Figure 12 Capacity model factors for drilled shafts subjected to lateral-moment loading(modified from Phoon 2005)

Numerical recipes for reliability analysis 9

the overall safety of the design but there are no simple means of ensur-ing that the selected values will achieve a consistent level of safety In factthese values are given by the design point of a first-order reliability anal-ysis and they are problem-dependent Simpson and Driscoll (1998) notedin their commentary to Eurocode 7 that the definition of the characteristicvalue ldquohas been the most controversial topic in the whole process of draftingEurocode 7rdquo If random variables can be included in the design process withminimal inconvenience the definition of the characteristic value is a mootissue

Simulation

The most intuitive and possibly the most straightforward method for per-forming reliability analysis is the Monte Carlo simulation method It onlyrequires repeated execution of an existing deterministic solution process Thekey calculation step is to simulate realizations of random variables This stepcan be carried in a general way using

Y = Fminus1(U) (11)

in which Y is a random variable following a prescribed cumulative distribu-tion F(middot) and U is a random variable uniformly distributed between 0 and 1(also called a standard uniform variable) Realizations of U can be obtainedfrom EXCELtrade under ldquoTools gt Data Analysis gt Random Number Gener-ation gt Uniform Between 0 and 1rdquo MATLABtrade implements U using theldquorandrdquo function For example U = rand(101) is a vector containing 10 real-izations of U Some inverse cumulative distribution functions are availablein EXCEL (eg norminv for normal loginv for lognormal betainv for betagammainv for gamma) and MATLAB (eg norminv for normal logninv forlognormal betainv for beta gaminv for gamma) More efficient methods areavailable for some probability distributions but they are lacking in generality(Hastings and Peacock 1975 Johnson et al 1994) A cursory examinationof standard probability texts will reveal that the variety of classical proba-bility distributions is large enough to cater to almost all practical needs Themain difficulty lies with the selection of an appropriate probability distribu-tion function to fit the limited data on hand A complete treatment of thisimportant statistical problem is outside the scope of this chapter Howeverit is worthwhile explaining the method of moments because of its simplicityand ease of implementation

The first four moments of a random variable (Y) can be calculated quitereliably from the typical sample sizes encountered in practice Theoreticallythey are given by

micro =int

yf (y)dy = E(Y) (12a)

10 Kok-Kwang Phoon

σ 2 = E[(Y minusmicro)2] (12b)

γ1 = E[(Y minusmicro)3]σ 3 (12c)

γ2 = E[(Y minusmicro)4]σ 4 minus 3 (12d)

in which micro = mean σ 2 = variance (or σ = standard deviation) γ1 =skewness γ2 = kurtosis f (middot) = probability density function = dF(y)dy andE[middot] = mathematical expectation The practical feature here is that momentscan be estimated directly from empirical data without knowledge of theunderlying probability distribution [ie f (y) is unknown]

y =

nsumi=1

yi

n(13a)

s2 = 1n minus 1

nsumi=1

(yi minus y)2 (13b)

g1 = n(n minus 1)(n minus 2)s3

nsumi=1

(yi minus y)3 (13c)

g2 = n(n + 1)(n minus 1)(n minus 2)(n minus 3)s4

nsumi=1

(yi minus y)4 minus 3(n minus 1)2

(n minus 2)(n minus 3)(13d)

in which n = sample size (y1y2 yn) = data points y = sample means2 = sample variance g1 = sample skewness and g2 = sample kurtosis Notethat the MATLAB ldquokurtosisrdquo function is equal to g2 + 3 If the sample sizeis large enough the above sample moments will converge to their respectivetheoretical values as defined by Equation (12) under some fairly general con-ditions The majority of classical probability distributions can be determineduniquely by four or less moments In fact the Pearson system (Figure 13)reduces the selection of an appropriate probability distribution function tothe determination of β1 = g2

1 and β2 = g2 + 3 Calculation steps with illus-trations are given by Elderton and Johnson (1969) Johnson et al (1994)provided useful formulas for calculating the Pearson parameters based onthe first four moments Reacutethaacuteti (1988) provided some β1 and β2 values ofSzeged soils for distribution fitting using the Pearson system (Table 11)

Johnson system

A broader discussion of distribution systems (in which the Pearson sys-tem is one example) and distribution fitting is given by Elderton and

0 02 04 06 08 1

β1

β 2

12 14 16 18

VI

VIV

III

Limit of possible distributions

I

2

VII

N

II

1

2

3

4

5

6

7

8

Figure 13 Pearson system of probability distribution functions based on β1 = g21 and

β2 = g2 + 3 N = normal distribution

Table 11 β1 and β2 values for some soil properties from Szeged Hungary (modifiedfrom Reacutethaacuteti 1988)

Soillayer

Watercontent

Liquidlimit

Plasticlimit

Plasticityindex

Consistencyindex

Voidratio

Bulkdensity

Unconfinedcompressivestrength

β1S1 276 412 681 193 013 650 128 002S2 001 074 034 049 002 001 009 094S3 003 096 014 085 000 130 289 506S4 005 034 013 064 003 013 106 480S5 110 002 292 000 098 036 010 272

β2S1 739 810 1230 492 334 1119 398 186S2 345 343 327 269 317 267 387 393S3 762 513 432 446 331 552 1159 1072S4 717 319 347 357 461 403 814 1095S5 670 231 915 217 513 414 494 674

NotePlasticity index (Ip) = wL minus wP in which wL = liquid limit and wP = plastic limit consistency index(Ic)= (wL minusw)Ip = 1minus IL in which w = water content and IL = liquidity index unconfined compressivestrength (qu) = 2su in which su = undrained shear strength

12 Kok-Kwang Phoon

Johnson (1969) It is rarely emphasized that almost all distribution functionscannot be generalized to handle correlated random variables (Phoon 2006a)In other words univariate distributions such as those discussed above cannotbe generalized to multivariate distributions Elderton and Johnson (1969)discussed some interesting exceptions most of which are restricted to bivari-ate cases It suffices to note here that the only convenient solution availableto date is to rewrite Equation (11) as

Y = Fminus1[Φ(Z)] (14)

in which Z = standard normal random variable with mean = 0 andvariance = 1 and Φ(middot) = standard normal cumulative distribution function(normcdf in MATLAB or normsdist in EXCEL) Equation (14) is calledthe translation model It requires all random variables to be related to thestandard normal random variable The significance of Equation (14) iselaborated in Section 132

An important practical detail here is that standard normal random vari-ables can be simulated directly and efficiently using the BoxndashMuller method(Box and Muller 1958)

Z1 =radic

minus2ln(U1)cos(2πU2) (15)

Z2 =radic

minus2ln(U1)sin(2πU2)

in which Z1Z2 = independent standard normal random variables andU1U2 = independent standard uniform random variables Equation (15)is computationally more efficient than Equation (11) because the inversecumulative distribution function is not required Note that the cumulativedistribution function and its inverse are not available in closed-form for mostrandom variables While Equation (11) ldquolooksrdquo simple there is a hiddencost associated with the numerical evaluation of Fminus1(middot) Marsaglia and Bray(1964) proposed one further improvement

1 Pick V1 and V2 randomly within the unit square extending between minus1and 1 in both directions ie

V1 = 2U1 minus 1

V2 = 2U2 minus 1(16)

2 Calculate R2 = V21 + V2

2 If R2 ge 10 or R2 = 00 repeat step (1)

Numerical recipes for reliability analysis 13

3 Simulate two independent standard normal random variables using

Z1 = V1

radicminus2ln(R2)

R2

Z2 = V2

radicminus2ln(R2)

R2

(17)

Equation (17) is computationally more efficient than Equation (15) becausetrigonometric functions are not required

A translation model can be constructed systematically from (β1 β2) usingthe Johnson system

1 Assume that the random variable is lognormal ie

ln(Y minus A) = λ+ ξZ Y gt A (18)

in which ln(middot) is the natural logarithm ξ2 = ln[1 + σ 2(microminus A)2] λ =ln(microminus A) minus 05ξ2 micro = mean of Y and σ 2 = variance of Y The ldquolog-normalrdquo distribution in the geotechnical engineering literature typicallyrefers to the case of A = 0 When A = 0 it is called the ldquoshiftedlognormalrdquo or ldquo3-parameter lognormalrdquo distribution

2 Calculate ω = exp(ξ2)3 Calculate β1 = (ω minus 1)(ω + 2)2 and β2 = ω4 + 2ω3 + 3ω2 minus 3 For the

lognormal distribution β1 and β2 are related as shown by the solid linein Figure 14

4 Calculate β1 = g21 and β2 = g2 + 3 from data If the values fall close to

the lognormal (LN) line the lognormal distribution is acceptable5 If the values fall below the LN line Y follows the SB distribution

ln(

Y minus AB minus Y

)= λ+ ξZ B gt Y gt A (19)

in which λξAB = distribution fitting parameters6 If the values fall above the LN line Y follows the SU distribution

ln

⎡⎣(Y minus A

B minus A

)+radic

1 +(

Y minus AB minus A

)2⎤⎦= sinhminus1

(Y minus AB minus A

)= λ+ ξZ

(110)

Examples of LN SB and SU distributions are given in Figure 15 Simulationof Johnson random variables using MATLAB is given in Appendix A1Carsel and Parrish (1988) developed joint probability distributions for

0 02 04 06 08 1

β1

β 2

12 14 16 18

SU

Limit of possible distributions

Pearson III

Pearson V

SB

2

1

2

3

4

5

6

7

8

LN

N

Figure 14 Johnson system of probability distribution functions based on β1 = g21 and

β2 = g2 + 3 N = normal distribution LN = lognormal distribution

00

01

02

03

04

05

06

07LNSBSU

08

1 2 3 4 5

Figure 15 Examples of LN (λ = 100ξ = 020) SB (λ = 100ξ = 036 A = minus300B = 500) and SU (λ= 100ξ = 009 A = minus188 B = 208) distributions withapproximately the same mean and coefficient of variation asymp 30

Numerical recipes for reliability analysis 15

parameters of soilndashwater characteristic curves using the Johnson system Themain practical obstacle is that the SB Johnson parameters (λξAB) arerelated to the first four moments (ys2g1g2) in a very complicated way(Johnson 1949) The SU Johnson parameters are comparatively easier toestimate from the first four moments (see Johnson et al 1994 for closed-form formulas) In addition both Pearson and Johnson systems require theproper identification of the relevant region (eg SB or SU in Figure 14)which in turn determines the distribution function [Equation (19) or (110)]before one can attempt to calculate the distribution parameters

Hermite polynomials

One-dimensional Hermite polynomials are given by

H0(Z) = 1

H1(Z) = Z

H2(Z) = Z2 minus 1

H3(Z) = Z3 minus 3Z

Hk+1(Z) = ZHk(Z) minus kHkminus1(Z)

(111)

in which Z is a standard normal random variable (mean = 0 andvariance = 1) Hermite polynomials can be evaluated efficiently using therecurrence relation given in the last row of Equation (111) It can be provenrigorously (Phoon 2003) that any random variable Y (with finite variance)can be expanded as a series

Y =infinsum

k=0

akHk(Z) (112)

The numerical values of the coefficients ak depend on the distribution of YThe key practical advantage of Equation (112) is that the randomness ofY is completely accounted for by the randomness of Z which is a knownrandom variable It is useful to observe in passing that Equation (112) maynot be a monotonic function of Z when it is truncated to a finite number ofterms The extrema are located at points with zero first derivatives but non-zero second derivatives Fortunately derivatives of Hermite polynomials canbe evaluated efficiently as well

dHk(Z)dZ

= kHkminus1(Z) (113)

16 Kok-Kwang Phoon

The Hermite polynomial expansion can be modified to fit the first fourmoments as well

Y = y + s k[Z + h3(Z2 minus 1) + h4(Z3 minus 3Z)] (114)

in which y and s = sample mean and sample standard deviation of Y respec-tively and k = normalizing constant = 1(1 + 2h2

3 + 6h24)05 It is important

to note that the coefficients h3 and h4 can be calculated from the sampleskewness (g1) and sample kurtosis (g2) in a relatively straightforward way(Winterstein et al 1994)

h3 = g1

6

(1 minus 0015

∣∣g1

∣∣+ 03g21

1 + 02g2

)(115a)

h4 = (1 + 125g2)13 minus 110

(1 minus 143g2

1

g2

)1minus01(g2+3)08

(115b)

The theoretical skewness (γ1) and kurtosis (γ2) produced by Equation (114)are (Phoon 2004a)

γ1 = k3(6h3 + 36h3h4 + 8h33 + 108h3h2

4) (116a)

γ2 = k4(3 + 48h43 + 3348h4

4 + 24h4 + 1296h34 + 60h2

3 + 252h24

+ 2232h23h2

4 + 576h23h4) minus 3 (116b)

Equation (115) is determined empirically by minimizing the error [(γ1 minusg1)2 + (γ2 minus g2)2] subjected to the constraint that Equation (114) is amonotonic function of Z It is intended for cases with 0 lt g2 lt 12 and0 le g2

1 lt 2g23 (Winterstein et al 1994) It is possible to minimize the errorin skewness and kurtosis numerically using the SOLVER function in EXCELrather than applying Equation (115)

In general the entire cumulative distribution function of Y F(y) canbe fully described by the Hermite expansion using the following simplestochastic collocation method

1 Let (y1y2 yn) be n realizations of Y The standard normal data iscalculated from

zi = Φminus1F(yi) (117)

Numerical recipes for reliability analysis 17

2 Substitute yi and zi into Equation (112) For a third-order expansionwe obtain

yi = a0 + a1zi + a2(z2i minus 1) + a3(z3

i minus 3zi) = a0 + a1hi1

+ a2hi2 + a3hi3 (118)

in which (1 hi1hi2hi3) are Hermite polynomials evaluated at zi3 The four unknown coefficients (a0a1a2a3) can be determined using

four realizations of Y (y1y2y3y4) In matrix notation we write

Ha = y (119)

in which H is a 4 times 4 matrix with ith row given by (1 hi1hi2hi3) y isa 4 times 1 vector with ith component given by yi and a is a 4 times 1 vectorcontaining the unknown numbers (a0a1a2a3)prime This is known as thestochastic collocation method Equation (119) is a linear system andefficient solutions are widely available

4 It is preferable to solve for the unknown coefficients using regression byusing more than four realizations of Y

(HprimeH)a = Hprimey (120)

in which H is an ntimes4 matrix and n is the number of realizations Notethat Equation (120) is a linear system amenable to fast solution as well

Calculation of Hermite coefficients using MATLAB is given in Appendix A2Hermite coefficients can be calculated with ease using EXCEL as well(Chapter 9) Figure 16 demonstrates that Equation (112) is quite efficient ndashit is possible to match very small probabilities (say 10minus4) using a third-orderHermite expansion (four terms)

Phoon (2004a) pointed out that Equations (14) and (112) are theoreti-cally identical Equation (112) appears to present a rather circuitous routeof achieving the same result as Equation (14) The key computational differ-ence is that Equation (14) requires the costly evaluation of Fminus1(middot) thousandsof times in a low-probability simulation exercise (typical of civil engineeringproblems) while Equation (117) requires less than 100 costly evaluationsof minus1F(middot) followed by less-costly evaluation of Equation (112) thousandsof times Two pivotal factors govern the computational efficiency of Equa-tion (112) (a) cheap generation of standard normal random variables usingthe BoxndashMuller method [Equation (15) or (17)] and (b) relatively smallnumber of Hermite terms

The one-dimensional Hermite expansion can be easily extended to ran-dom vectors and processes The former is briefly discussed in the next

18 Kok-Kwang Phoon

100

Theoretical4-term Hermite

Theoretical

4-term Hermite

Cum

ulat

ive

dist

ribut

ion

func

tion

10minus2

10minus4

10minus6minus1 0 1 2

Value3 4 5

08

07

06

05

Pro

babi

lity

dens

ity fu

nctio

n

04

03

02

01

0minus1 0 1 2

Value3 4 5

Theoretical

4-term Hermite

100

Theoretical4-term Hermite

Cum

ulat

ive

dist

ribut

ion

func

tion

10minus2

10minus4

10minus60 1 2 3

Value4 5 6

08

07

06

05

Pro

babi

lity

dens

ity fu

nctio

n

04

03

02

01

00 1 2 3

Value4 5 6

Figure 16 Four-term Hermite expansions for Johnson SB distribution with λ = 100ξ = 036 A = minus300 B = 500 (top row) and Johnson SU distribution withλ = 100ξ = 009 A = minus188 B = 208 (bottom row)

section while the latter is given elsewhere (Puig et al 2002 Sakamoto andGhanem 2002 Puig and Akian 2004) Chapters 5 7 9 and elsewhere(Sudret and Der Kiureghian 2000 Sudret 2007) present applications ofHermite polynomials in more comprehensive detail

132 Random vectors

The multivariate normal probability density function is available analyticallyand can be defined uniquely by a mean vector and a covariance matrix

f (X) = |C|minus 12 (2π )minus

n2 exp

[minus05(X minus micro)primeCminus1(X minus micro)

](121)

in which X= (X1X2 Xn)prime is a normal random vector with n componentsmicro is the mean vector and C is the covariance matrix For the bivariate

Numerical recipes for reliability analysis 19

(simplest) case the mean vector and covariance matrix are given by

micro =micro1micro2

C =[

σ 21 ρσ1σ2

ρσ1σ2 σ 22

] (122)

in which microi and σi = mean and standard deviation of Xi respectively andρ = product-moment (Pearson) correlation between X1 and X2

The practical usefulness of Equation (121) is not well appreciated Firstthe full multivariate dependency structure of a normal random vector onlydepends on a covariance matrix (C) containing bivariate information (cor-relations) between all possible pairs of components The practical advantageof capturing multivariate dependencies in any dimension (ie any number ofrandom variables) using only bivariate dependency information is obviousNote that coupling two random variables is the most basic form of depen-dency and also the simplest to evaluate from empirical data In fact there areusually insufficient data to calculate reliable dependency information beyondcorrelations in actual engineering practice Second fast simulation of cor-related normal random variables is possible because of the elliptical form(X minus micro)primeCminus1(X minus micro) appearing in the exponent of Equation (121) Whenthe random dimension is small the following Cholesky method is the mostefficient and robust

X = LZ + micro (123)

in which Z = (Z1Z2 Zn)prime contains uncorrelated normal random com-ponents with zero means and unit variances These components can besimulated efficiently using the BoxndashMuller method [Equations (15) or (17)]The lower triangular matrix L is the Cholesky factor of C ie

C = LLprime (124)

Cholesky factorization can be roughly appreciated as taking the ldquosquarerootrdquo of a matrix The Cholesky factor can be calculated in EXCEL usingthe array formula MAT_CHOLESKY which is provided by a free add-inat httpdigilanderliberoitfoxesindexhtm MATLAB produces Lprime usingchol (C) [note Lprime (transpose of L) is an upper triangular matrix] Choleskyfactorization fails if C is not ldquopositive definiterdquo The important practical ram-ifications here are (a) the correlation coefficients in the covariance matrixC cannot be selected independently and (b) an erroneous C is automaticallyflagged when Cholesky factorization fails When the random dimension ishigh it is preferable to use the fast fourier transform (FFT) as described inSection 133

20 Kok-Kwang Phoon

As mentioned in Section 131 the multivariate normal distribution playsa central role in the modeling and simulation of correlated non-normalrandom variables The translation model [Equation (14)] for one randomvariable (Y) can be generalized in a straightforward to a random vector(Y1Y2 Yn)

Yi = Fminus1i [Φ(Xi)] (125)

in which (X1X2 Xn) follows a multivariate normal probability densityfunction [Equation (121)] with

micro =

⎧⎪⎪⎪⎨⎪⎪⎪⎩

00

0

⎫⎪⎪⎪⎬⎪⎪⎪⎭

C =

⎡⎢⎢⎢⎣

1 ρ12 middot middot middot ρ1nρ21 1 middot middot middot ρ2n

ρn1 ρn2 middot middot middot 1

⎤⎥⎥⎥⎦

Note that ρij = ρji ie C is a symmetric matrix containing only n(n minus 1)2distinct entries Each non-normal component Yi can follow any arbitrarycumulative distribution function Fi(middot) The cumulative distribution func-tion prescribed to each component can be different ie Fi(middot) = Fj(middot) Thesimulation of correlated non-normal random variables using MATLAB isgiven in Appendix A3 It is evident that ldquotranslationmrdquo can be modifiedto simulate any number of random components as long as a compatiblesize covariance matrix is specified For example if there are three randomcomponents (n = 3) C should be a 3 times 3 matrix such as [1 08 04 081 02 04 02 1] This computational simplicity explains the popularityof the translation model As mentioned previously not all 3 times 3 matri-ces are valid covariance matrices An example of an invalid covariancematrix is C = [108 minus 08081 minus 02minus08 minus 021] An attempt to executeldquotranslationmrdquo produces the following error messages

Error using ==gt cholMatrix must be positive definite

Hence simulation from an invalid covariance matrix will not take placeThe left panel of Figure 17 shows the scatter plot of two correlated nor-mal random variables with ρ = 08 An increasing linear trend is apparentA pair of uncorrelated normal random variables will not exhibit any trendin the scatter plot The right panel of Figure 17 shows the scatter plot of twocorrelated non-normal random variables There is an increasing nonlineartrend The nonlinearity is fully controlled by the non-normal distributionfunctions (SB and SU in this example) In practice there is no reason forthe scatter plot (produced by two columns of numbers) to be related to the

Numerical recipes for reliability analysis 21

4

2

0

minus2

minus4minus4 minus2 0

X1 = normal distribution

X2 =

nor

mal

dis

trib

utio

n

2 4 01

2

3

4

Y2 =

SU

dis

trib

utio

n

Y1 = SB distribution

5

6

1 2 3 4 5

Figure 17 Scatter plots for correlated normal random variables with zero means and unitvariances (left) and correlated non-normal random variables with 1st compo-nent = Johnson SB distribution (λ = 100ξ = 036A = minus300B = 500) and2nd component = Johnson SU distribution (λ = 100ξ = 009 A = minus188B = 208) (right)

distribution functions (produced by treating each column of numbers sepa-rately) Hence it is possible for the scatter plot produced by the translationmodel to be unrealistic

The simulation procedure described in ldquotranslationmrdquo cannot be applieddirectly to practice because it requires the covariance matrix of X as aninput The empirical data can only produce an estimate of the covariancematrix of Y It can be proven theoretically that these covariance matricesare not equal although they can be approximately equal in some cases Thesimplest solution available so far is to express Equation (125) as Hermitepolynomials

Y1 = a10H0(X1) + a11H1(X1) + a12H2(X1) + a13H3(X1) +middotmiddot middotY2 = a20H0(X2) + a21H1(X2) + a22H2(X2) + a23H3(X2) +middotmiddot middot (126)

The relationship between the observed correlation coefficient (ρY1Y2) and the

underlying normal correlation coefficient (ρ) is

ρY1Y2=

infinsumk=1

ka1ka2kρk

radicradicradicradic( infinsumk=1

ka21k

)(infinsum

k=1ka2

2k

) (127)

This complication is discussed in Chapter 9 and elsewhere (Phoon 2004a2006a)

22 Kok-Kwang Phoon

From a practical viewpoint [only bivariate information needed cor-relations are easy to estimate voluminous n-dimensional data compiledinto mere n(n minus 1)2 coefficients etc] and a computational viewpoint(fast robust simple to implement) it is accurate to say that the multivariatenormal model and multivariate non-normal models generated using thetranslation model are already very good and sufficiently general for manypractical scenarios The translation model is not perfect ndash there are impor-tant and fundamental limitations (Phoon 2004a 2006a) For stochasticdata that cannot be modeled using the translation model the hunt forprobability models with comparable practicality theoretical power andsimulation speed is still on-going Copula theory (Schweizer 1991) pro-duces a more general class of multivariate non-normal models but it isdebatable at this point if these models can be estimated empirically andsimulated numerically with equal ease for high random dimensions Theonly exception is a closely related but non-translation approach based onthe multivariate normal copula (Phoon 2004b) This approach is outlinedin Chapter 9

133 Random processes

A natural probabilistic model for correlated spatial data is the randomfield A one-dimensional random field is typically called a random processA random process can be loosely defined as a random vector with an infi-nite number of components that are indexed by a real number (eg depthcoordinate z) We restrict our discussion to a normal random process X(z)Non-normal random processes can be simulated from a normal randomprocess using the same translation approach described in Section 132

The only computational aspect that requires some elaboration is thatsimulation of a process is usually more efficient in the frequency domainRealizations belonging to a zero-mean stationary normal process X(z) canbe generated using the following spectral approach

X(z) =infinsum

k=1

σk(Z1k sin2π fkz+Z2k cos2π fkz) (128)

in which σk =radic2S(fk)f f is the interval over which the spectral densityfunction S(f ) is discretized fk = (2k minus 1)f 2 and Z1k and Z2k are uncor-related normal random variables with zero means and unit variances Thesingle exponential autocorrelation function is commonly used in geostatis-tics R(τ ) = exp(minus2|τ |δ) in which τ is the distance between data points andδ the scale of fluctuation R(τ ) can be estimated from a series of numbers saycone tip resistances sampled at a vertical spacing of z using the method of

Numerical recipes for reliability analysis 23

moments

R(τ = jz) asymp 1(n minus j minus 1)s2

nminusjsumi=1

(xi minus x)(xi+j minus x) (129)

in which xi = x(zi)zi = i(z) x = sample mean [Equation (13a] s2 =sample variance [Equation (13b)] and n = number of data points The scaleof fluctuation is estimated by fitting an exponential function to Equation(129) The spectral density function corresponding to the single exponentialcorrelation function is

S(f ) = 4δ(2π f δ)2 + 4

(130)

Other common autocorrelation functions and their corresponding spectraldensity functions are given in Table 12 It is of practical interest to note thatS(f ) can be calculated numerically from a given target autocorrelation func-tion or estimated directly from x(zi) using the FFT Analytical solutions suchas those shown in Table 12 are convenient but unnecessary The simulationof a standard normal random process with zero mean and unit variance usingMATLAB is given in Appendix A4 Note that the main input to ldquoranpro-cessmrdquo is R(τ ) which can be estimated empirically from Equation (129)No knowledge of S(f ) is needed Some realizations based on the five com-mon autocorrelation functions shown in Table 12 with δ = 1 are given inFigure 18

Table 12 Common autocorrelation and two-sided power spectral density functions

Model Autocorrelation R(τ ) Two-sided power spectraldensity S(f )

Scale offluctuation δ

Singleexponential

exp(minusa |τ |) 2a

ω2 + a22a

Binary noise1 minus a |τ | |τ | le 1a0 otherwise

sin2 (ω2a)

a (ω2a)21a

Cosineexponential

exp(minusa |τ |)cos(aτ ) a(

1

a2 + (ω+ a)2+ 1

a2 + (ωminus a)2

)1a

Second-orderMarkov

(1 + a |τ |)exp(minusa |τ |) 4a3

(ω2 + a2)24a

Squaredexponential

exp[minus(aτ )2

] radicπ

aexp

(minus ω2

4a2

) radicπ

a

ω = 2π f

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(a)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(b)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(c)

Figure 18 Simulated realizations of normal process with mean = 0 variance = 1 scaleof fluctuation = 1 based on autocorrelation function = (a) single exponential(b) binary noise (c) cosine exponential (d) second-order Markov and (e) squareexponential

Numerical recipes for reliability analysis 25

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(d)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(e)

Figure 18 Contrsquod

In geotechnical engineering spatial variability is frequently modeled usingrandom processesfields This practical application was first studied in geol-ogy and mining under the broad area of geostatistics The physical premisethat makes estimation of spatial patterns possible is that points close in spacetend to assume similar values The autocorrelation function (or variogram)is a fundamental tool describing similarities in a statistical sense as a func-tion of distance [Equation (129)] The works of G Matheron DG Krigeand FP Agterberg are notable Parallel developments also took place inmeteorology (LS Gandin) and forestry (B Mateacutern) Geostatistics is math-ematically founded on the theory of random processesfields developed byAY Khinchin AN Kolmogorov P Leacutevy N Wiener and AM Yaglomamong others The interested reader can refer to books by Cressie (1993)and Chilegraves and Delfiner (1999) for details VanMarcke (1983) remarked thatall measurements involve some degree of local averaging and that randomfield models do not need to consider variations below a finite scale becausethey are smeared by averaging VanMarckersquos work is incomplete in one

26 Kok-Kwang Phoon

crucial aspect This crucial aspect is that actual failure surfaces in 2D or 3Dproblems will automatically seek to connect ldquoweakestrdquo points in the randomdomain It is the spatial average of this failure surface that counts in practicenot simple spatial averages along pre-defined surfaces (or pre-specified mea-surement directions) This is an exceptionally difficult problem to solve ndash atpresent simulation is the only viable solution Chapter 6 presents extensiveresults on emergent behaviors resulting from spatially heterogeneous soilsthat illustrate this aspect quite thoroughly

Although the random processfield provides a concise mathematical modelfor spatial variability it poses considerable practical difficulties for statisti-cal inference in view of its complicated data structure All classical statisticaltests are invariably based on the important assumption that the data areindependent (Cressie 1993) When they are applied to correlated data largebias will appear in the evaluation of the test statistics (Phoon et al 2003a)The application of standard statistical tests to correlated soil data is thereforepotentially misleading Independence is a very convenient assumption thatmakes a large part of mathematical statistics tractable Statisticians can goto great lengths to remove this dependency (Fisher 1935) or be contentwith less-powerful tests that are robust to departures from the indepen-dence assumption In recent years an alternate approach involving the directmodeling of dependency relationships into very complicated test statisticsthrough Monte Carlo simulation has become feasible because desktop com-puting machines have become very powerful (Phoon et al 2003a Phoonand Fenton 2004 Phoon 2006b Uzielli and Phoon 2006)

Chapter 2 and elsewhere (Baecher and Christian 2003 Uzielli et al2007) provide extensive reviews of geostatistical applications in geotechnicalengineering

14 Probabilistic solution procedures

The practical end point of characterizing uncertainties in the design inputparameters (geotechnical geo-hydrological geometrical and possibly ther-mal) is to evaluate their impact on the performance of a design Reliabilityanalysis focuses on the most important aspect of performance namely theprobability of failure (ldquofailurerdquo is a generic term for non-performance)This probability of failure clearly depends on both parametric and modeluncertainties The probability of failure is a more consistent and completemeasure of safety because it is invariant to all mechanically equivalent def-initions of safety and it incorporates additional uncertainty informationThere is a prevalent misconception that reliability-based design is ldquonewrdquo Allexperienced engineers would conduct parametric studies when confidencein the choice of deterministic input values is lacking Reliability analysismerely allows the engineer to carry out a much broader range of para-metric studies without actually performing thousands of design checks with

Numerical recipes for reliability analysis 27

different inputs one at a time This sounds suspiciously like a ldquofree lunchrdquobut exceedingly clever probabilistic techniques do exist to calculate theprobability of failure efficiently The chief drawback is that these tech-niques are difficult to understand for the non-specialist but they are notnecessarily difficult to implement computationally There is no consensuswithin the geotechnical community whether a more consistent and com-plete measure of safety is worth the additional efforts or if the significantlysimpler but inconsistent global factor of safety should be dropped Reg-ulatory pressure appears to be pushing towards RBD for non-technicalreasons The literature on reliability analysis and RBD is voluminous Someof the main developments in geotechnical engineering are presented inthis book

141 Closed-form solutions

There is a general agreement in principle that limit states (undesirable statesin which the system fails to perform satisfactorily) should be evaluatedexplicitly and separately but there is no consensus on how to verify thatexceedance of a limit state is ldquosufficiently unlikelyrdquo in numerical terms Dif-ferent opinions and design recommendations have been made but there is alack of discussion on basic issues relating to this central idea of ldquoexceeding alimit staterdquo A simple framework for discussing such basic issues is to imag-ine the limit state as a boundary surface dividing sets of design parameters(soil load andor geometrical parameters) into those that result in satisfac-tory and unsatisfactory designs It is immediately clear that this surface canbe very complex for complex soil-structure interaction problems It is alsoclear without any knowledge of probability theory that likely failure sets ofdesign parameters (producing likely failure mechanisms) cannot be discussedwithout characterizing the uncertainties in the design parameters explicitlyor implicitly Assumptions that ldquovalues are physically boundedrdquo ldquoall valuesare likely in the absence of informationrdquo etc are probabilistic assump-tions regardless of whether or not this probabilistic nature is acknowledgedexplicitly If the engineer is 100 sure of the design parameters then onlyone design check using these fully deterministic parameters is necessary toensure that the relevant limit state is not exceeded Otherwise the situationis very complex and the only rigorous method available to date is reliabilityanalysis The only consistent method to control exceedance of a limit stateis to control the reliability index It would be very useful to discuss at thisstage if this framework is logical and if there are alternatives to it In fact ithas not been acknowledged explicitly if problems associated with strengthpartial factors or resistance factors are merely problems related to simpli-fication of reliability analysis If there is no common underlying purpose(eg to achieve uniform reliability) for applying partial factors and resis-tance factors then the current lack of consensus on which method is better

28 Kok-Kwang Phoon

cannot be resolved in any meaningful way Chapter 8 provides some usefulinsights on the reliability levels implied by the empirical soil partial factorsin Eurocode 7

Notwithstanding the above on-going debate it is valid to question if reli-ability analysis is too difficult for practitioners The simplest example is toconsider a foundation design problem involving a random capacity (Q) anda random load (F) The ultimate limit state is defined as that in which thecapacity is equal to the applied load Clearly the foundation will fail if thecapacity is less than this applied load Conversely the foundation should per-form satisfactorily if the applied load is less than the capacity These threesituations can be described concisely by a single performance function P asfollows

P = Q minus F (131)

Mathematically the above three situations simply correspond to the condi-tions of P = 0P lt 0 and P gt 0 respectively

The basic objective of RBD is to ensure that the probability of failure doesnot exceed an acceptable threshold level This objective can be stated usingthe performance function as follows

pf = Prob(P lt 0) le pT (132)

in which Prob(middot) = probability of an event pf =probability of failure andpT = acceptable target probability of failure A more convenient alternativeto the probability of failure is the reliability index (β) which is defined as

β = minusΦminus1(pf) (133)

in which Φminus1(middot) = inverse standard normal cumulative function The func-tion Φminus1(middot) can be obtained easily from EXCEL using normsinv (pf) orMATLAB using norminv (pf) For sufficiently large β simple approximateclosed-form solutions for Φ(middot) and Φminus1(middot) are available (Appendix B)

The basic reliability problem is to evaluate pf from some pertinent statisticsof F and Q which typically include the mean (microF or microQ) and the standarddeviation (σF or σQ) and possibly the probability density function A simpleclosed-form solution for pf is available if Q and F follow a bivariate normaldistribution For this condition the solution to Equation (132) is

pf = Φ

⎛⎜⎝minus microQ minusmicroFradic

σ 2Q +σ 2

F minus 2ρQFσQσF

⎞⎟⎠= Φ (minusβ) (134)

in which ρQF = product-moment correlation coefficient between Q and FNumerical values for Φ(middot) can be obtained easily using the EXCEL func-tion normsdist(minusβ) or the MATLAB function normcdf(minusβ) The reliability

Numerical recipes for reliability analysis 29

indices for most geotechnical components and systems lie between 1 and5 corresponding to probabilities of failure ranging from about 016 to3 times 10minus7 as shown in Figure 19

Equation (134) can be generalized to a linear performance function con-taining any number of normal components (X1X2 Xn) as long as theyfollow a multivariate normal distribution function [Equation (121)]

P = a0 +nsum

i=1

aiXi (135)

pf = Φ

⎛⎜⎜⎜⎜⎝minus

a0 +nsum

i=1aimicroiradic

nsumi=1

nsumj=1

aiajρijσiσj

⎞⎟⎟⎟⎟⎠ (136)

in which ai = deterministic constant microi = mean of Xiσi = standard devia-tion of Xiρij = correlation between Xi and Xj (note ρii = 1) Chapters 811 and 12 present some applications of these closed-form solutions

Equation (134) can be modified for the case of translation lognormals ieln(Q) and ln(F) follow a bivariate normal distribution with mean of ln(Q) =λQ mean of ln(F) =λF standard deviation of ln(Q) = ξQ standard deviation

Unsatisfactory

Hazardous1E+00

1Eminus01

1Eminus02

1Eminus03

Pro

bab

ility

of

failu

re

1Eminus04

1Eminus05

0 1 2 3

Reliability index4 5

1Eminus06

1Eminus07

PoorBelow average

Above average

Good

High

asymp10minus7

asymp10minus5

asymp10minus3

asymp2asymp7

asymp16

asymp5times10minus3

Figure 19 Relationship between reliability index and probability of failure (classificationsproposed by US Army Corps of Engineers 1997)

30 Kok-Kwang Phoon

of ln(F) = ξF and correlation between ln(Q) and ln(F) = ρprimeQF

pf = Φ

⎛⎜⎝minus λQ minusλFradic

ξ2Q + ξ2

F minus 2ρprimeQFξQξF

⎞⎟⎠ (137)

The relationships between the mean (micro) and standard deviation (σ ) of alognormal and the mean (λ) and standard deviation (ξ ) of the equivalentnormal are given in Equation (18) The correlation between Q and F (ρQF)is related to the correlation between ln(Q) and ln(F) (ρprime

QF) as follows

ρQF =exp(ξQξFρ

primeQF) minus 1radic

[exp(ξ2Q) minus 1][exp(ξ2

F ) minus 1](138)

If ρprimeQF = ρQF = 0 (ie Q and F are independent lognormals) Equation (137)

reduces to the following well-known expression

β =ln(

microQmicroF

radic1+COV2

F1+COV2

Q

)radic

ln[(

1 + COV2Q

)(1 + COV2

F

)] (139)

in which COVQ = σQmicroQ and COVF = σFmicroF If there are physical groundsto disallow negative values the translation lognormal model is more sensibleEquation (139) has been used as the basis for RBD (eg Rosenblueth andEsteva 1972 Ravindra and Galambos 1978 Becker 1996 Paikowsky2002) The calculation steps outlined below are typically carried out

1 Consider a typical Load and Resistance Factor Design (LRFD) equation

φQn = γDDn + γLLn (140)

in which φ = resistance factor γD and γL = dead and live load fac-tors and Qn Dn and Ln = nominal values of capacity dead loadand live load The AASHTO LRFD bridge design specifications rec-ommended γD = 125 and γL = 175 (Paikowsky 2002) The resistancefactor typically lies between 02 and 08

2 The nominal values are related to the mean values as

microQ = bQQn

microD = bDDn

microL = bLLn

(141)

Numerical recipes for reliability analysis 31

in which bQ bD and bL are bias factors and microQ microD and microL are meanvalues The bias factors for dead and live load are typically 105 and 115respectively Figure 12 shows that bQ (mean value of the histogram) canbe smaller or larger than one

3 Assume typical values for the mean capacity (microQ) and DnLn A reason-able range for DnLn is 1 to 4 Calculate the mean live load and meandead load as follows

microL = (φbQmicroQ)bL(γD

DnLn

+ γL

) (142)

microD =(

Dn

Ln

)microLbD

bL(143)

4 Assume typical coefficients of variation for the capacity dead load andlive load say COVQ = 03 COVD = 01 and COVL = 02

5 Calculate the reliability index using Equation (139) with microF = microD +microL and

COVF =radic

(microDCOVD)2 + (microLCOVL)2

microD +microL(144)

Details of the above procedure are given in Appendix A4 For φ = 05 γD =125 γL = 175 bQ = 1bD = 105bL = 115 COVQ = 03 COVD = 01COVL = 02 and DnLn = 2 the reliability index from Equation (139) is299 Monte Carlo simulation in ldquoLRFDmrdquo validates the approximationgiven in Equation (144) It is easy to show that an alternate approximationCOV2

F = COV2D+ COV2

L is erroneous Equation (139) is popular becausethe resistance factor in LRFD can be back-calculated from a target reliabilityindex (βT) easily

φ =bQ(γDDn + γLLn)

radic1+COV2

F1+COV2

Q

(bDDn + bLLn)expβT

radicln[(

1 + COV2Q

)(1 + COV2

F

)] (145)

In practice the statistics of Q are determined by comparing load test resultswith calculated values Phoon and Kulhawy (2005) compared the statisticsof Q calculated from laboratory load tests with those calculated from full-scale load tests They concluded that these statistics are primarily influencedby model errors rather than uncertainties in the soil parameters Hence it islikely that the above lumped capacity approach can only accommodate the

32 Kok-Kwang Phoon

ldquolowrdquo COV range of parametric uncertainties mentioned in Section 131To accommodate larger uncertainties in the soil parameters (ldquomediumrdquo andldquohighrdquo COV ranges) it is necessary to expand Q as a function of the gov-erning soil parameters By doing so the above closed-form solutions are nolonger applicable

142 First-order reliability method (FORM)

Structural reliability theory has a significant impact on the development ofmodern design codes Much of its success could be attributed to the advent ofthe first-order reliability method (FORM) which provides a practical schemeof computing small probabilities of failure at high dimensional space spannedby the random variables in the problem The basic theoretical result wasgiven by Hasofer and Lind (1974) With reference to time-invariant reliabil-ity calculation Rackwitz (2001) observed that ldquoFor 90 of all applicationsthis simple first-order theory fulfills all practical needs Its numerical accuracyis usually more than sufficientrdquo Ang and Tang (1984) presented numer-ous practical applications of FORM in their well known book ProbabilityConcepts in Engineering Planning and Design

The general reliability problem consists of a performance func-tion P(y1y2 yn) and a multivariate probability density functionf (y1y2 yn) The former is defined to be zero at the limit state less thanzero when the limit state is exceeded (ldquofailrdquo) and larger than zero otherwise(ldquosaferdquo) The performance function is nonlinear for most practical prob-lems The latter specifies the likelihood of realizing any one particular setof input parameters (y1y2 yn) which could include material load andgeometrical parameters The objective of reliability analysis is to calculatethe probability of failure which can be expressed formally as follows

pf =int

Plt0f(y1y2 yn

)dy1dy2 dyn (146)

The domain of integration is illustrated by a shaded region in the left panelof Figure 110a Exact solutions are not available even if the multivariateprobability density function is normal unless the performance function islinear or quadratic Solutions for the former case are given in Section 141Other exact solutions are provided in Appendix C Exact solutions are veryuseful for validation of new reliability codes or calculation methods Theonly general solution to Equation (146) is Monte Carlo simulation A simpleexample is provided in Appendix A5 The calculation steps outlined beloware typically carried out

1 Determine (y1y2 yn) using Monte Carlo simulation Section 132has presented fairly general and ldquouser friendlyrdquo methods of simulatingcorrelated non-normal random vectors

Numerical recipes for reliability analysis 33

βminuspoint

β

zlowast = (z1lowastz2

lowast)

PLlt 0

P lt 0FAIL

P gt 0SAFE PLgt 0

Actual P = 0

Approx PL=0

fz1z2(z1z2)

fy1y2(y1y2)

z2

z1

y2

y1

G(y1y2) = 0

Figure 110 (a) General reliability problem and (b) solution using FORM

2 Substitute (y1y2 yn) into the performance function and count thenumber of cases where P lt 0 (ldquofailurerdquo)

3 Estimate the probability of failure using

pf = nf

n(147)

in which nf = number of failure cases and n =number of simulations4 Estimate the coefficient of variation of pf using

COVpf=radic

1 minus pf

pfn(148)

For civil engineering problems pf asymp 10minus3 and hence (1 ndash pf) asymp 1 The samplesize (n) required to ensure COVpf

is reasonably small say 03 is

n = 1 minus pf

pfCOV2pf

asymp 1pf(03)2

asymp 10pf

(149)

It is clear from Equation (149) that Monte Carlo simulation is not prac-tical for small probabilities of failure It is more often used to validateapproximate but more efficient solution methods such as FORM

The approximate solution obtained from FORM is easier to visualize ina standard space spanned by uncorrelated Gaussian random variables withzero mean and unit standard deviation (Figure 110b) If one replaces theactual limit state function (P = 0) by an approximate linear limit state func-tion (PL = 0) that passes through the most likely failure point (also called

34 Kok-Kwang Phoon

design point or β-point) it follows immediately from rotational symmetryof the circular contours that

pf asymp Φ(minusβ) (150)

The practical result of interest here is that Equation (146) simply reduces toa constrained nonlinear optimization problem

β = minradic

zprimez forz G(z) le 0 (151)

in which z = (z1z2 zn)prime The solution of a constrained optimization prob-lem is significantly cheaper than the solution of a multi-dimensional integral[Equation (146)] It is often cited that the β-point is the ldquobestrdquo linearizationpoint because the probability density is highest at that point In actuality thechoice of the β-point requires asymptotic analysis (Breitung 1984) In shortFORM works well only for sufficiently large β ndash the usual rule-of-thumb isβ gt 1 (Rackwitz 2001)

Low and co-workers (eg Low and Tang 2004) demonstrated that theSOLVER function in EXCEL can be easily implemented to calculate thefirst-order reliability index for a range of practical problems Their studiesare summarized in Chapter 3 The key advantages to applying SOLVER forthe solution of Equation (151) are (a) EXCEL is available on almost allPCs (b) most practitioners are familiar with the EXCEL user interface and(c) no programming skills are needed if the performance function can becalculated using EXCEL built-in mathematical functions

FORM can be implemented easily within MATLAB as well Appendix A6demonstrates the solution process for an infinite slope problem (Figure 111)The performance function for this problem is

P =[γ(H minus h

)+ h(γsat minus γw

)]cosθ tanφ[

γ(H minus h

)+ hγsat]sinθ

minus 1 (152)

in which H = depth of soil above bedrock h = height of groundwater tableabove bedrock γ and γsat = moist unit weight and saturated unit weight ofthe surficial soil respectively γw = unit weight of water (981 kNm3) φ =effective stress friction angle and θ = slope inclination Note that the heightof the groundwater table (h) cannot exceed the depth of surficial soil (H)and cannot be negative Hence it is modeled by h = H times U in which U =standard uniform variable The moist and saturated soil unit weights are notindependent because they are related to the specific gravity of the soil solids(Gs) and the void ratio (e) The uncertainties in γ and γsat are characterizedby modeling Gs and e as two independent uniform random variables Thereare six independent random variables in this problem (HUφβe and Gs)

Numerical recipes for reliability analysis 35

ROCKh

SOIL

H

θγ

γsat

Figure 111 Infinite slope problem

Table 13 Summary of probability distributions for input random variables

Variable Description Distribution Statistics

H Depth of soil above bedrock Uniform [28] mh = H times U Height of water table U is uniform [0 1]φ Effective stress friction angle Lognormal mean = 35 cov = 8θ Slope inclination Lognormal mean = 20 cov = 5γ Moist unit weight of soil γsat Saturated unit weight of soil γw Unit weight of water Deterministic 981 kNm3

γ = γw (Gs + 02e)(1 + e) (assume degree of saturation = 20 for ldquomoistrdquo) γsat = γw (Gs + e)(1 + e) (degree of saturation = 100)Assume specific gravity of solids = Gs = uniformly distributed [25 27] and void ratio = e = uniformlydistributed [03 06]

and their probability distributions are summarized in Table 13 The first-order reliability index is 143 The reliability index calculated from MonteCarlo simulation is 157

Guidelines for modifying the code to solve other problems can besummarized as follows

1 Specify the number of random variables in the problem using theparameter ldquomrdquo in ldquoFORMmrdquo

2 The objective function ldquoobjfunmrdquo is independent of the problem3 The performance function ldquoPfunmrdquo can be modified in a straight-

forward way The only slight complication is that the physical vari-ables (eg HUφβe and Gs) must be expressed in terms of thestandard normal variables (eg z1z2 z6) Practical methods for

36 Kok-Kwang Phoon

converting uncorrelated standard normal random variables to correlatednon-normal random variables have been presented in Section 132

Low and Tang (2004) opined that practitioners will find it easier to appre-ciate and implement FORM without the above conversion procedureNevertheless it is well-known that optimization in the standard space is morestable than optimization in the original physical space The SOLVER optionldquoUse Automatic Scalingrdquo only scales the elliptical contours in Figure 110abut cannot make them circular as in Figure 110b Under some circum-stances SOLVER will produce different results from different initial trialvalues Unfortunately there are no automatic and dependable means of flag-ging this instability Hence it is extremely vital for the user to try differentinitial trial values and partially verify that the result remains stable Theassumption that it is sufficient to use the mean values as the initial trialvalues is untrue

In any case it is crucial to understand that a multivariate probabil-ity model is necessary for any probabilistic analysis involving more thanone random variable FORM or otherwise It is useful to recall thatmost multivariate non-normal probability models are related to the mul-tivariate normal probability model in a fundamental way as discussed inSection 132 Optimization in the original physical space does not elim-inate the need for the underlying multivariate normal probability modelif the non-normal physical random vector is produced by the translationmethod It is clear from Section 132 that non-translation methods exist andnon-normal probability models cannot be constructed uniquely from corre-lation information alone Chapters 9 and 13 report applications of FORMfor RBD

143 System reliability based on FORM

The first-order reliability method (FORM) is capable of handling any non-linear performance function and any combination of correlated non-normalrandom variables Its accuracy depends on two main factors (a) the cur-vature of the performance function at the design point and (b) the numberof design points If the curvature is significant the second-order reliabilitymethod (SORM) (Breitung 1984) or importance sampling (Rackwitz 2001)can be applied to improve the FORM solution Both methods are relativelyeasy to implement although they are more costly than FORM The calcu-lation steps for SORM are given in Appendix D Importance sampling isdiscussed in Chapter 4 If there are numerous design points FORM canunderestimate the probability of failure significantly At present no solutionmethod exists that is of comparable simplicity to FORM Note that problemscontaining multiple failure modes are likely to produce more than one designpoint

Numerical recipes for reliability analysis 37

Figure 112 illustrates a simple system reliability problem involving twolinear performance functions P1 and P2 A common geotechnical engineer-ing example is a shallow foundation subjected to inclined loading It isgoverned by bearing capacity (P1) and sliding (P2) modes of failure Thesystem reliability is formally defined by

pf = Prob[(P1 lt 0) cup (P2 lt 0)] (153)

There are no closed-form solutions even if P1 and P2 are linear and if theunderlying random variables are normal and uncorrelated A simple estimatebased on FORM and second-order probability bounds is available and is ofpractical interest The key calculation steps can be summarized as follows

1 Calculate the correlation between failure modes using

ρP2P1= α1 middotα2 = α11α21 +α12α22 = cosθ (154)

in which αi = (αi1αi2) = unit normal at design point for ith performancefunction Referring to Figure 110b it can be seen that this unit normalcan be readily obtained from FORM as αi1 = zlowast

i1βi and αi2 = zlowasti2βi

with (zlowasti1z

lowasti2) = design point for ith performance function

P2(P1 lt 0) cap (P2 lt 0)

fz1z2(z1z2)

z2

z1

θ

P1

β1

β2

α1

α2

Figure 112 Simple system reliability problem

38 Kok-Kwang Phoon

2 Estimate p21 = Prob[(P2 lt 0) cap (P1 lt 0)] using first-order probabilitybounds

pminus21 le p21 le p+

21

max[P(B1)P(B2)] le p21 le P(B1) + P(B2)(155)

in which P(B1) = Φ(minusβ1)Φ[(β1 cosθ minus β2)sinθ ] and P(B2) =Φ(minusβ2)Φ[(β2 cosθ minus β1)sinθ ] Equation (155) only applies forρP2P1

gt 0 Failure modes are typically positively correlated becausethey depend on a common set of loadings

3 Estimate pf using second-order probability bounds

p1 + max[(p2 minus p+21)0] le pf le min[p1 + (p2 minus pminus

21)1] (156)

in which pi = Φ(minusβi)

The advantages of the above approach are that it does not require infor-mation beyond what is already available in FORM and generalization to nfailure modes is quite straightforward

pf ge p1 + max[(p2 minus p+21)0]+ max[(p3 minus p+

31 minus p+320]+ middot middot middot

+ max[(pn minus p+n1 minus p+

n2 minusmiddotmiddot middotminus p+nnminus1)0] (157a)

pf le p1 + minp1 +[p2 minus pminus21]+ [p3 minus max(pminus

31pminus32)]+ middot middot middot

+ [pn minus max(pminusn1p

minusn2 middot middot middotpminus

nnminus1)]1 (157b)

The clear disadvantages are that no point probability estimate is availableand calculation becomes somewhat tedious when the number of failuremodes is large The former disadvantage can be mitigated using the followingpoint estimate of p21 (Mendell and Elston 1974)

a1 = 1

Φ(minusβ1)radic

2πexp

(minusβ2

1

2

)(158)

p21 asymp Φ

⎡⎢⎣ ρP1P2

a1 minusβ2radic1 minusρP1P2

2a1(a1 minusβ1)

⎤⎥⎦Φ(minusβ1) (159)

The accuracy of Equation (159) is illustrated in Table 14 Bold numbersshaded in gray are grossly inaccurate ndash they occur when the reliability indicesare significantly different and the correlation is high

Numerical recipes for reliability analysis 39

Table 14 Estimation of p21 using probability bounds point estimate and simulation

Performancefunctions

Correlation Probability bounds Point estimate Simulation

β1 = 1 01 (29 58) times10minus2 31 times10minus2 31 times10minus2

β2 = 1 05 (45 89) times10minus2 63 times10minus2 63 times10minus2

09 (06 13) times10minus1 12 times10minus1 12 times10minus1

β1 = 1 01 (48 93) times10minus3 50 times10minus3 50 times10minus3

β2 = 2 05 (11 18) times10minus2 13 times10minus2 13 times10minus2

09 (22 23) times10minus2 23 times10minus2 23 times10minus2

β1 = 1 01 (33 61) times10minus4 34 times10minus4 35 times10minus4

β2 = 3 05 (10 13) times10minus3 10 times10minus3 10 times10minus3

09 (13 13) times10minus3 05 times10minus3 13 times10minus3

β1 = 1 01 (86 157) times10minus6 89 times10minus6 85 times10minus6

β2 = 4 05 (28 32) times10minus5 23 times10minus5 28 times10minus5

09 (32 32) times10minus5 007 times10minus5 32 times10sminus5

β1 = 2 01 (80 160) times10minus4 87 times10minus4 88 times10minus4

β2 = 2 05 (28 56) times10minus3 41 times10minus3 41 times10minus3

09 (07 15) times10minus2 14 times10minus2 13 times10minus2

β1 = 2 01 (59 115) times10minus5 63 times10minus5 60 times10minus5

β2 = 3 05 (38 62) times10minus4 45 times10minus4 45 times10minus4

09 (13 13) times10minus3 12 times10minus3 13 times10minus3

β1 = 2 01 (17 32) times10minus6 18 times10minus6 25 times10minus6

β2 = 4 05 (16 22) times10minus5 16 times10minus5 15 times10minus5

09 (32 32) times10minus5 05 times10minus5 32 times10minus5

β1 = 3 01 (45 90) times10minus6 49 times10minus6 45 times10minus6

β2 = 3 05 (56 112) times10minus5 82 times10minus5 80 times10minus5

09 (33 66) times 10minus4 63 times 10minus4 60 times10minus4

Sample size = 5000000

144 Collocation-based stochastic response surfacemethod

The system reliability solution outlined in Section 143 is reasonably prac-tical for problems containing a few failure modes that can be individuallyanalyzed by FORM A more general approach that is gaining wider atten-tion is the spectral stochastic finite element method originally proposed byGhanem and Spanos (1991) The key element of this approach is the expan-sion of the unknown random output vector using multi-dimensional Hermitepolynomials as basis functions (also called a polynomial chaos expansion)The unknown deterministic coefficients in the expansion can be solved usingthe Galerkin or collocation method The former method requires significant

40 Kok-Kwang Phoon

modification of the existing deterministic numerical code and is impossibleto apply for most engineers with no access to the source code of their com-mercial softwares The collocation method can be implemented using a smallnumber of fairly simple computational steps and does not require the mod-ification of existing deterministic numerical code Chapter 7 and elsewhere(Sudret and Der Kiureghian 2000) discussed this important class of methodsin detail Verhoosel and Gutieacuterrez (2007) highlighted challenging difficultiesin applying these methods to nonlinear finite element problems involvingdiscontinuous fields It is interesting to observe in passing that the outputvector can be expanded using the more well-known Taylor series expansionas well The coefficients of the expansion (partial derivatives) can be calcu-lated using the perturbation method This method can be applied relativelyeasily to finite element outputs (Phoon et al 1990 Quek et al 1991 1992)but is not covered in this chapter

This section briefly explains the key computational steps for the morepractical collocation approach We recall in Section 132 that a vector of cor-related non-normal random variables Y = (Y1Y2 Yn)prime can be related to avector of correlated standard normal random variables X = (X1X2 Xn)primeusing one-dimensional Hermite polynomial expansions [Equation (126)]The correlation coefficients for the normal random vector are evaluatedfrom the correlation coefficients of the non-normal random vector usingEquation (127) This method can be used to construct any non-normal ran-dom vector as long as the correlation coefficients are available This is indeedthe case if Y represents the input random vector However if Y representsthe output random vector from a numerical code the correlation coefficientsare unknown and Equation (126) is not applicable Fortunately this practi-cal problem can be solved by using multi-dimensional Hermite polynomialswhich are supported by a known normal random vector with zero meanunit variance and uncorrelated or independent components

The multi-dimensional Hermite polynomials are significantly more com-plex than the one-dimensional version For example the second-order andthird-order forms can be expressed respectively as follows (Isukapalli1999)

Y asympao+nsum

i=1

aiZi +nsum

i=1

aii

(Z2

i minus1)+

nminus1sumi=1

nsumjgti

aijZiZj (160a)

Y asympao+nsum

i=1

aiZi +nsum

i=1

aii

(Z2

i minus1)+

nsumi=1

aiii

(Z3

i minus3Zi

)+

nminus1sumi=1

nsumjgti

aijZiZj

+nsum

i=1

nsumj=1j =i

aijj

(ZiZ

2j minusZi

)+

nminus2sumi=1

nminus1sumjgti

nsumkgtj

aijkZiZjZk (160b)

Numerical recipes for reliability analysis 41

For n = 3 Equation (160) produces the following expansions (indices forcoefficients are re-labeled consecutively for clarity)

Y asymp ao + a1Z1 + a2Z2 + a3Z3 + a4(Z21 minus 1) + a5(Z2

2 minus 1) + a6(Z23 minus 1)

+ a7Z1Z2 + a8Z1Z3 + a9Z2Z3 (161a)

Y asymp ao + a1Z1 + a2Z2 + a3Z3 + a4(Z21 minus 1) + a5(Z2

2 minus 1) + a6(Z23 minus 1)

+ a7Z1Z2 + a8Z1Z3 + a9Z2Z3 + a10(Z31 minus 3Z1) + a11(Z3

2 minus 3Z2)

+ a12(Z33 minus 3Z3) + a13(Z1Z2

2 minus Z1) + a14(Z1Z23 minus Z1)

+ a15(Z2Z21 minus Z2) + a16(Z2Z2

3 minus Z2) + a17(Z3Z21 minus Z3)

+ a18(Z3Z22 minus Z3) + a19Z1Z2Z3 (161b)

In general N2 and N3 terms are respectively required for the second-orderand third-order expansions (Isukapalli 1999)

N2 = 1 + 2n + n(n minus 1)2

(162a)

N3 = 1 + 3n + 3n(n minus 1)2

+ n(n minus 1)(n minus 2)6

(162b)

For a fairly modest random dimension of n = 5 N2 and N3 terms arerespectively equal to 21 and 56 Hence fairly tedious algebraic expressionsare incurred even at a third-order truncation One-dimensional Hermitepolynomials can be generated easily and efficiently using a three-term recur-rence relation [Equation (111)] No such simple relation is available formulti-dimensional Hermite polynomials They are usually generated usingsymbolic algebra which is possibly out of reach of the general practi-tioner This major practical obstacle is currently being addressed by Lianget al (2007) They have developed a user-friendly EXCEL add-in to gener-ate tedious multi-dimensional Hermite expansions automatically Once themulti-dimensional Hermite expansions are established their coefficients canbe calculated following the steps described in Equations (118)ndash(120) Twopractical aspects are noteworthy

1 The random dimension of the problem should be minimized to reducethe number of terms in the polynomial chaos expansion The spectraldecomposition method can be used

X = PD12Z + micro (163)

in which D = diagonal matrix containing eigenvalues in the leading diag-onal and P = matrix whose columns are the corresponding eigenvectors

42 Kok-Kwang Phoon

If C is the covariance matrix of X the matrices D and P can be calcu-lated easily using [PD] = eig(C) in MATLAB The key advantage ofreplacing Equation (123) by Equation (163) is that an n-dimensionalcorrelated normal vector X can be simulated using an uncorrelated stan-dard normal vector Z with a dimension less than n This is achieved bydiscarding eigenvectors in P that correspond to small eigenvalues A sim-ple example is provided in Appendix A7 The objective is to simulate a3D correlated normal vector following a prescribed covariance matrix

C =⎡⎣ 1 09 02

09 1 0502 05 1

⎤⎦

Spectral decomposition of the covariance matrix produces

D =⎡⎣0045 0 0

0 0832 00 0 2123

⎤⎦ P =

⎡⎣ 0636 0467 0614

minus0730 0108 06750249 minus0878 0410

⎤⎦

Realizations of X can be obtained using Equation (163) Results areshown as open circles in Figure 113 The random dimension can bereduced from three to two by ignoring the first eigenvector correspond-ing to a small eigenvalue of 0045 Realizations of X are now simulatedusing

⎧⎨⎩

X1X2X3

⎫⎬⎭=⎡⎣ 0467 0614

0108 0675minus0878 0410

⎤⎦[radic0832 0

0radic

2123

]Z1Z2

Results are shown as crosses in Figure 113 Note that three correlatedrandom variables can be represented reasonably well using only twouncorrelated random variables by neglecting the smallest eigenvalue andthe corresponding eigenvector The exact error can be calculated theo-retically by observing that the covariance of X produced by Equation(163) is

CX = (PD12)(D12Pprime) = PDPprime (164)

If the matrices P and D are exact it can be proven that PDPprime = C iethe target covariance is reproduced For the truncated P and D matrices

Numerical recipes for reliability analysis 43

Random dimension

ThreeTwo

3

2

1

0X3

X2

minus1

minus2

minus3minus3 minus2 minus1 0 2 2 3

Random dimension

ThreeTwo

3

2

1

0X3

X2

minus1

minus2

minus3minus3 minus2 minus1 0 2 2 3

Figure 113 Scatter plot between X1 and X2 (top) and X2 and X3 (bottom)

discussed above the covariance of X is

CX =⎡⎣ 0467 0614

0108 0675minus0878 0410

⎤⎦[0832 0

0 2123

]

times[0467 0108 minus08780614 0675 0410

]=⎡⎣0982 0921 0193

0921 0976 05080193 0508 0997

⎤⎦

44 Kok-Kwang Phoon

The exact error in each element of CX can be clearly seen by comparingwith the corresponding element in C In particular the variances ofX (elements in the leading diagonal) are slightly reduced For randomprocessesfields reduction in the random dimension can be achievedusing the KarhunenndashLoeve expansion (Phoon et al 2002 2004) It canbe shown that the spectral representation given by Equation (128) isa special case of the KarhunenndashLoeve expansion (Huang et al 2001)The random dimension can be further reduced by performing sensitiv-ity analysis and discarding input parameters that are ldquounimportantrdquoFor example the square of the components of the unit normal vec-tor α shown in Figure 112 are known as FORM importance factors(Ditlevsen and Madsen 1996) If the input parameters are independentthese factors indicate the degree of influence exerted by the corre-sponding input parameters on the failure event Sudret (2007) discussedthe application of Sobolrsquo indices for sensitivity analysis of the spectralstochastic finite element method

2 The number of output values (y1y2 ) in Equation (120) should beminimized because they are usually produced by costly finite elementcalculations Consider a problem containing two random dimensionsand approximating the output as a third-order expansion

yi = ao + a1zi1 + a2zi2 + a3(z2i1 minus 1) + a4(z2

i2 minus 1) + a5zi1zi2

+ a6(z3i1 minus 3zi1) + a7(z3

i2 minus 3zi2) + a8(zi1z2i2 minus zi1)

+ a9(zi2z2i1 minus zi2) (165)

Phoon and Huang (2007) demonstrated that the collocation points(zi1zi2) are best sited at the roots of the Hermite polynomial that is oneorder higher than that of the Hermite expansion In this example theroots of the fourth-order Hermite polynomial are plusmnradic

(3plusmnradic6) Although

zero is not one of the roots it should be included because the standardnormal probability density function is highest at the origin Twenty-five collocation points (zi1zi2) can be generated by combining the rootsand zero in two dimensions as illustrated in Figure 114 The roots ofHermite polynomials (up to order 15) can be calculated numerically asshown in Appendix A8

145 Subset simulation method

The collocation-based stochastic response surface method is very efficientfor problems containing a small number of random dimensions and perfor-mance functions that can be approximated quite accurately using low-orderHermite expansions However the method suffers from a rapid proliferation

Numerical recipes for reliability analysis 45

Z1

Z2

Figure 114 Collocation points for a third-order expansion with two random dimensions

of Hermite expansion terms when the random dimension andor order ofexpansion increase A critical review of reliability estimation procedures forhigh dimensions is given by Schueumlller et al (2004) One potentially practicalmethod that is worthy of further study is the subset simulation method (Auand Beck 2001) It appears to be more robust than the importance samplingmethod Chapter 4 and elsewhere (Au 2001) present a more extensive studyof this method

This section briefly explains the key computational steps involved inthe implementation Consider the failure domain Fm defined by the con-dition P(y1y2 yn) lt 0 in which P is the performance function and(y1y2 yn) are realizations of the uncertain input parameters The ldquofail-urerdquo domain Fi defined by the condition P(y1y2 yn) lt ci in which ci isa positive number is larger by definition of the performance function Weassume that it is possible to construct a nested sequence of failure domainsof increasing size by using an increasing sequence of positive numbers iethere exists c1 gt c2 gt gt cm = 0 such that F1 sup F2 sup Fm As shown inFigure 115 for the one-dimensional case it is clear that this is always possibleas long as one value of y produces only one value of P(y) The performancefunction will satisfy this requirement If one value of y produces two valuesof P(y) say a positive value and a negative value then the physical systemis simultaneously ldquosaferdquo and ldquounsaferdquo which is absurd

The probability of failure (pf ) can be calculated based on the above nestedsequence of failure domains (or subsets) as follows

pf = Prob(Fm) = Prob(Fm|Fmminus1)Prob(Fmminus1|Fmminus2)

times Prob(F2|F1)Prob(F1) (166)

46 Kok-Kwang Phoon

P(y)

y

C1

C2

F2 F2

F1

Figure 115 Nested failure domains

At first glance Equation (166) appears to be an indirect and more tediousmethod of calculating pf In actuality it can be more efficient because theprobability of each subset conditional on the previous (larger) subset canbe selected to be sufficiently large say 01 such that a significantly smallernumber of realizations is needed to arrive at an acceptably accurate resultWe recall from Equation (149) that the rule of thumb is 10pf ie only100 realizations are needed to estimate a probability of 01 If the actualprobability of failure is 0001 and the probability of each subset is 01 it isapparent from Equation (166) that only three subsets are needed implying atotal sample size = 3times100 = 300 In contrast direct simulation will require100001 = 10000 realizations

The typical calculation steps are illustrated below using a problemcontaining two random dimensions

1 Select a subset sample size (n) and prescribe p = Prob(Fi|Fiminus1) Weassume p = 01 and n = 500 from hereon

2 Simulate n = 500 realizations of the uncorrelated standard normal vector(Z1Z2)prime The physical random vector (Y1Y2)prime can be determined fromthese realizations using the methods described in Section 132

3 Calculate the value of the performance function gi = P(yi1yi2) associ-ated with each realization of the physical random vector (yi1yi2)prime i =12 500

4 Rank the values of (g1g2 g500) in ascending order The value locatedat the (np + 1) = 51st position is c1

Numerical recipes for reliability analysis 47

5 Define the criterion for the first subset (F1) as P lt c1 By construction atstep (4) P(F1) = npn = p The realizations contained in F1 are denotedby zj = (zj1zj2)prime j = 12 50

6 Simulate 1p = 10 new realizations from zj using the followingMetropolis-Hastings algorithm

a Simulate 1 realization using a uniform proposal distribution withmean located at micro = zj and range = 1 ie bounded by micro plusmn 05 Letthis realization be denoted by u = (u1u2)prime

b Calculate the acceptance probability

α = min(

10I(u)φ(u)φ(micro)

)(167)

in which I(u) = 1 if P(u1u2) lt c1 I(u) = 0 if P(u1u2) ge c1φ(u) = exp(minus05uprimeu) and φ(micro) = exp(minus05microprimemicro)

c Simulate 1 realization from a standard uniform distributionbounded between 0 and 1 denoted by v

d The first new realization is given by

w = u if v lt α

w = micro if v ge α(168)

Update the mean of the uniform proposal distribution in step (a) asmicro = w (ie centred about the new realization) and repeat the algo-rithm to obtain a ldquochainrdquo containing 10 new realizations Fiftychains are obtained in the same way with initial seeds at zj j = 1 50 It can be proven that these new realizations would followProb(middot|F1) (Au 2001)

7 Convert these realizations to their physical values and repeat Step (3)until ci becomes negative (note the smallest subset should correspondto cm = 0)

It is quite clear that the above procedure can be extended to any randomdimension in a trivial way In general the accuracy of this subset simula-tion method depends on ldquotuningrdquo factors such as the choice of np and theproposal distribution in step 6(a) (assumed to be uniform with range = 1)A study of some of these factors is given in Chapter 4 The optimal choice ofthese factors to achieve minimum runtime appears to be problem-dependentAppendix A9 provides the MATLAB code for subset simulation The perfor-mance function is specified in ldquoPfunmrdquo and the number of random variablesis specified in parameter ldquomrdquo Figure 116 illustrates the behavior of thesubset simulation method for the following performance function

P = 2 + 3radic

2 minus Y1 minus Y2 = 2 + 3radic

2 + ln[Φ(minusZ1)]+ ln[Φ(minusZ2)] (169)

minus1 0 1 2 3 4minus1

0

1

2

3

4

Z1

Z2

P lt 0

P lt c1

minus1 0 1 2 3 4minus1

0

1

2

3

4

Z1

Z2

P lt 0

P lt c1

Figure 116 Subset simulation method for P = 2 + 3radic

2 minus Y1 minus Y2 in which Y1 and Y2 areexponential random variables with mean = 1 first subset (top) and secondsubset (bottom)

Numerical recipes for reliability analysis 49

in which Y1Y2 = exponential random variables with mean = 1 and Z1Z2 =uncorrelated standard normal random variables The exact solution for theprobability of failure is 00141 (Appendix C) The solution based on subsetsimulation method solution is achieved as follows

1 Figure 116a Simulate 500 realizations (small open circles) and identifythe first 50 realizations in order of increasing P value (big open circles)The 51st smallest P value is c1 = 2192

2 These realizations satisfy the criterion P lt c1 (solid line) by construc-tion The domain above the solid line is F1

3 Figure 116b Simulate 10 new realizations from each big open circle inFig 116a The total number is 10 times 50 = 500 realizations (small opencircles)

4 There are 66 realizations (big open squares) satisfying P lt 0 (dashedline) The domain above the dashed line is F2 F2 is the actual failuredomain Hence Prob(F2|F1) = 66500 = 0132

5 The estimated probability of failure is pf = Prob(F2|F1)P(F1) = 0132times01 = 00132

Note that only 7 realizations in Figure 116a lie in F2 In contrast 66 real-izations in Figure 116b lie in F2 providing a more accurate pf estimate

15 Conclusions

This chapter presents general and user-friendly computational methods forreliability analysis ldquoUser-friendlyrdquo methods refer to those that can be imple-mented on a desktop PC by a non-specialist with limited programmingskills in other words methods within reach of the general practitionerThis chapter is organized under two main headings describing (a) how tosimulate uncertain inputs numerically and (b) how to propagate uncertaininputs through a physical model to arrive at the uncertain outputs Althoughsome of the contents are elaborated in greater detail in subsequent chaptersthe emphasis on numerical implementations in this chapter should shortenthe learning curve for the novice The overview will also provide a usefulroadmap to the rest of this book

For the simulation of uncertain inputs following arbitrary non-normalprobability distribution functions and correlation structure the translationmodel involving memoryless transform of the multivariate normal probabil-ity distribution function can cater for most practical scenarios Implemen-tation of the translation model using one-dimensional Hermite polynomialsis relatively simple and efficient For stochastic data that cannot be modeledusing the translation model the hunt for probability models with comparablepracticality theoretical power and simulation speed is still on-going Copulatheory produces a more general class of multivariate non-normal models but

50 Kok-Kwang Phoon

it is debatable at this point if these models can be estimated empirically andsimulated numerically with equal ease for high random dimensions Theonly exception is a closely related but non-translation approach based onthe multivariate normal copula

The literature is replete with methodologies on the determination of uncer-tain outputs from a given physical model and uncertain inputs The mostgeneral method is Monte Carlo simulation but it is notoriously tediousOne may assume with minimal loss of generality that a complex geotechni-cal problem (possibly 3D nonlinear time-and construction-dependent etc)would only admit numerical solutions and the spatial domain can be modeledby a scalarvector random field For a sufficiently large and complex problemit is computationally intensive to complete even a single run At present it isaccurate to say that a computationally efficient and ldquouser-friendlyrdquo solutionto the general stochastic problem remains elusive Nevertheless reasonablypractical solutions do exist if the general stochastic problem is restrictedin some ways for example accept a first-order estimate of the probabilityof failure or accept an approximate but less costly output The FORM isby far the most efficient general method for estimating the probability offailure of problems involving one design point (one dominant mode of fail-ure) There are no clear winners for problems beyond the reach of FORMThe collocation-based stochastic response surface method is very efficientfor problems containing a small number of random dimensions and perfor-mance functions that can be approximated quite accurately using low-orderHermite expansions However the method suffers from a rapid prolifera-tion of Hermite expansion terms when the random dimension andor order ofexpansion increase The subset simulation method shares many of the advan-tages of the Monte Carlo simulation method such as generality and tractabil-ity at high dimensions without requiring too many runs At low randomdimensions it does require more runs than the collocation-based stochas-tic response surface method but the number of runs is probably acceptableup to medium-scale problems The subset simulation method has numerousldquotuningrdquo factors that are not fully studied for geotechnical problems

Simple MATLAB codes are provided in the Appendix A to encouragepractitioners to experiment with reliability analysis numerically so as to gaina concrete appreciation of the merits and limitations Theory is discussedwhere necessary to furnish sufficient explanations so that users can modifycodes correctly to suit their purpose and to highlight important practicallimitations

Appendix A ndash MATLAB codes

MATLAB codes are stored in text files with extension ldquomrdquo ndash they are calledM-files M-files can be executed easily by typing the filename (eg hermite)within the command window in MATLAB The location of the M-file should

Numerical recipes for reliability analysis 51

be specified using ldquoFile gt Set Path helliprdquo M-files can be opened and editedusing ldquoFile gt Open helliprdquo Programming details are given under ldquoHelp gt

Contents gt MATLAB gt Programmingrdquo The M-files provided below areavailable at httpwwwengnusedusgcivilpeoplecvepkkprob_libhtml

A1 Simulation of Johnson distributions

Simulation of Johnson distributions Filename Johnsonm Simulation sample size nn = 100000 Lognormal with lambda xilambda = 1xi = 02Z = normrnd(0 1 n 1)X = lambda + ZxiLNY = exp(X) SB with lambda xi A Blambda = 1xi = 036A = -3B = 5X = lambda + ZxiSBY = (exp(X)B+A)(exp(X)+1) SU with lambda xi A Blambda = 1xi = 009A = -188B = 208X = lambda + ZxiSUY = sinh(X)(B-A)+A Plot probability density functions[f x] = ksdensity(LNY)plot(xf)hold[f x] = ksdensity(SBY)plot(xfrsquoredrsquo)[f x] = ksdensity(SUY)plot(xfrsquogreenrsquo)

52 Kok-Kwang Phoon

A2 Calculation of Hermite coefficients using stochasticcollocation method

Calculation of Hermite coefficients using stochasticcollocation method Filename herm_coeffsm Number of realizations nn = 20 Example Lognormal with lambda xilambda = 0xi = 1Z = normrnd(0 1 n 1)X = lambda + ZxiLNY = exp(X) Order of Hermite expansion mm = 6 Construction of Hermite matrix HH = zeros(nm+1)H(1) = ones(n1)H(2) = Zfor k = 3m+1H(k) = ZH(k-1) - (k-2)H(k-2)end Hermite coefficients stored in vector aK = HrsquoHf = HrsquoLNYa = inv(K)f

A3 Simulation of correlated non-normal randomvariables using translation method

Simulation of correlated non-normal random variablesusing translation method Filename translationm Number of random dimension nn = 2 Normal covariance matrix

Numerical recipes for reliability analysis 53

C = [1 08 08 1] Number of realizations mm = 100000 Simulation of 2 uncorrelated normal random variableswith mean = 0 and variance = 1Z = normrnd(0 1 m n) Cholesky factorizationCF = chol(C) Simulation of 2 correlated normal random variableswith with mean = 0 and covariance = CX = ZCF Example simulation of correlated non-normal with Component 1 = Johnson SBlambda = 1xi = 036A = -3B = 5W(1) = lambda + X(1)xiY(1) = (exp(W(1))B+A)(exp(W(1))+1) Component 2 = Johnson SUlambda = 1xi = 009A = -188B = 208W(2) = lambda + X(2)xiY(2) = sinh(W(2))(B-A)+A

A4 Simulation of normal random process using thetwo-sided power spectral density function S(f)

Simulation of normal random process using thetwo-sided power spectral density function S(f) Filename ranprocessm Number of data points based on power of 2 NN = 512

54 Kok-Kwang Phoon

Depth sampling interval delzdelz = 02 Frequency interval delfdelf = 1Ndelz Discretization of autocorrelation function R(tau) Example single exponential functiontau = zeros(1N)tau = -(N2-1)delzdelz(N2)delzd = 2 scale of fluctuationa = 2dR = zeros(1N)R = exp(-aabs(tau)) Numerical calculation of S(f) using FFTH = zeros(1N)H = fft(R) Notes 1 f=0 delf 2delf (N2-1)delf correspondsto H(1) H(2) H(N2) 2 Maximum frequency is Nyquist frequency= N2delf= 12delz 3 Multiply H (discrete transform) by delz to getcontinuous transform 4 Shift R back by tau0 ie multiply H byexp(2piiftau0)f = zeros(1N)S = zeros(1N) Shuffle f to correspond with frequencies orderingimplied in Hf(1) = 0for k = 2N2+1f(k)= f(k-1)+delfendf(N2+2) = -f(N2+1)+delffor k = N2+3Nf(k) = f(k-1)+delfendtau0 = (N2-1)delz

Numerical recipes for reliability analysis 55

for k = 1NS(k) = delzH(k)exp(2piif(k)tau0) i isimaginary numberendS = real(S) remove possible imaginary parts due toroundoff errors Shuffle S to correspond with f in increasing orderf = -(N2-1)delfdelf(N2)delftemp = zeros(1N)for k = 1N2-1temp(k) = S(k+N2+1)endfor k = N2Ntemp(k)=S(k-N2+1)endS = tempclear temp Simulation of normal process using spectralrepresentation mean of process = 0 and variance of process = 1 Maximum possible non-periodic process lengthLmax = 12fmin = 1delf Minimum frequency fmin = delf2Lmax = 1delf Simulation length L = bLmax with b lt 1L = 05Lmax Number of simulated data points nz Depth sampling interval dz Depth coordinates z(1) z(2) z(nz)nz = round(Ldelz)dz = Lnzz = dzdzL Number of realisations mm = 10000 Number of positive frequencies in the spectralexpansion nf = N2

56 Kok-Kwang Phoon

nf = N2 Simulate uncorrelated standard normal randomvariablesrandn(rsquostatersquo 1)Z = randn(m2nf)sigma = zeros(1nf)wa = zeros(1nf) Calculate energy at each frequency using trapezoidalrule 2S(f) for k = 1nfsigma(k) = 205(S(k+nf-1)+S(k+nf))delfwa(k) = 052pi(f(k+nf-1)+f(k+nf))endsigma = sigmaˆ05 Calculate realizations with mean = 0 andvariance = 1X = zeros(mnz)X = Z(1nf)diag(sigma)cos(warsquoz)+Z(nf+12nf)diag(sigma)sin(warsquoz)

A5 Reliability analysis of Load and Resistance FactorDesign (LRFD)

Reliability analysis of Load and Resistance FactorDesign (LRFD) Filename LRFDm Resistance factor phiphi = 05 Dead load factor gD Live load factor gLgD = 125gL = 175 Bias factors for Q D LbR = 1bD = 105bL = 115 Ratio of nominal dead to live load loadratioloadratio = 2 Assume mR = bRRn mD = bDDn mL = bLLn Design equation phi(Rn) = gD(Dn) + gL(Ln)

Numerical recipes for reliability analysis 57

mR = 1000mL = phibRmRbL(gDloadratio+gL)mD = loadratiomLbDbL Coefficients of variation for R D LcR = 03cD = 01cL = 02 Lognormal X with mean = mX and coefficent ofvariation = cXxR = sqrt(log(1+cRˆ2))lamR = log(mR)-05xRˆ2xD = sqrt(log(1+cDˆ2))lamD = log(mD)-05xDˆ2xL = sqrt(log(1+cLˆ2))lamL = log(mL)-05xLˆ2 Simulation sample size nn = 500000Z = normrnd(0 1 n 3)LR = lamR + Z(1)xRR = exp(LR)LD = lamD + Z(2)xDD = exp(LD)LL = lamL + Z(3)xLL = exp(LL) Total load = Dead load + Live LoadF = D + L Mean of FmF = mD + mL Coefficient of variation of F based on second-momentapproximationcF = sqrt((cDmD)ˆ2+(cLmL)ˆ2)mF Failure occurs when R lt Ffailure = 0for i = 1nif (R(i) lt F(i)) failure = failure+1 endend

58 Kok-Kwang Phoon

Probability of failure = no of failuresnpfs = failuren Reliability indexbetas = -norminv(pfs) Closed-form lognormal solutiona1 = sqrt(log((1+cRˆ2)(1+cFˆ2)))a2 = sqrt((1+cFˆ2)(1+cRˆ2))beta = log(mRmFa2)a1pf = normcdf(-beta)

A6 First-order reliability method

First-order reliability method Filename FORMm Number of random variables mm = 6 Starting guess is z0 = 0z0 = zeros(1 m) Minimize objective function exitflag = 1 for normal terminationoptions = optimset(rsquoLargeScalersquorsquooffrsquo)[zfvalexitflagoutput] =fmincon(objfunz0[][][][][][]Pfunoptions) First-order reliability indexbeta1 = fvalpf1 = normcdf(-beta1)

Objective function for FORM Filename objfunmfunction f = objfun(z) Objective function = distance from the origin z = vector of uncorrelated standard normal randomvariables f = norm(z)

Numerical recipes for reliability analysis 59

Definition of performance function P Filename Pfunmfunction [c ceq] = Pfun(z) Convert standard normal random variables to physicalvariables Depth of rock Hy(1) = 2+6normcdf(z(1)) Height of water tabley(2) = y(1)normcdf(z(2)) Effective stress friction angle (radians)xphi = sqrt(log(1+008ˆ2))lamphi = log(35)-05xphiˆ2y(3) = exp(lamphi+xphiz(3))pi180 Slope inclination (radians)xbeta = sqrt(log(1+005ˆ2))lambeta = log(20)-05xbetaˆ2y(4) = exp(lambeta+xbetaz(4))pi180 Specific gravity of solidsGs = 25+02normcdf(z(5)) Void ratioe = 03+03normcdf(z(6)) Moist unit weighty(5) = 981(Gs+02e)(1+e) Saturated unit weighty(6) = 981(Gs+e)(1+e) Nonlinear inequality constraints c lt 0c = (y(5)(y(1)-y(2))+y(2)(y(6)-981))cos(y(4))tan(y(3))((y(5)(y(1)-y(2))+y(2)y(6))sin(y(4)))-1 Nonlinear equality constraintsceq = []

60 Kok-Kwang Phoon

A7 Random dimension reduction using spectraldecomposition

Random dimension reduction using spectraldecomposition Filename eigendecompm Example Target covarianceC = [1 09 02 09 1 05 02 05 1] Spectral decomposition[PD] = eig(C) Simulation sample size nn = 10000 Simulate standard normal random variablesZ = normrnd(01n3) Simulate correlated normal variables following CX = Psqrt(D)ZrsquoX = Xrsquo Simulate correlated normal variables without 1steigen-componentXT = P(1323)sqrt(D(2323))Z(23)rsquoXT=XTrsquo Covariance produced by ignoring 1st eigen-componentCX = P(1323)D(2323)P(1323)rsquo

A8 Calculation of Hermite roots using polynomial fit

Calculation of Hermite roots using polynomial fit Filename herm_rootsm Order of Hermite polynomial m lt 15m = 5 Specification of z values for fittingz = (-412m4)rsquo Number of fitted points nn = length(z)

Numerical recipes for reliability analysis 61

Construction of Hermite matrix HH = zeros(nm+1)H(1) = ones(n1)H(2) = zfor k = 3m+1H(k) =zH(k-1)-(k-2)H(k-2)end Calculation of Hermite expansiony = H(m+1) Polynomial fitp = polyfit(zym) Roots of hermite polynomialr = roots(p) Validation of rootsnn = length(r)H = zeros(nnm+1)H(1) = ones(nn1)H(2) = rfor k = 3m+1H(k) =rH(k-1)-(k-2)H(k-2)endnorm(H(m+1))

A9 Subset Markov Chain Monte Carlo method

Subset Markov Chain Monte Carlo method Filename subsetm copy (2007) Kok-Kwang Phoon Number of random variables mm = 2 Sample size nn = 500 Probability of each subsetpsub = 01 Number of new samples from each seed samplens = 1psub

62 Kok-Kwang Phoon

Simulate standard normal variable zz = normrnd(01nm)stopflag = 0pfss = 1while (stopflag == 0) Values of performance functionfor i=1n g(i) = Pfun(z(i)) end Sort vector g to locate npsub smallest values[gsortindex]=sort(g) Subset threshold gt = npsub+1 smallest value of g if gt lt 0 exit programgt = gsort(npsub+1)if (gt lt 0)

i=1while gsort(i)lt0

i=i+1endpfss=pfss(i-1)nstopflag = 1break

elsepfss = pfsspsub

end npsub seeds satisfying Pfun lt gt stored inz(index(1npsub)) Simulate 1psub samples from each seed to get nsamples for next subsetw = zeros(nm)for i=1npsub

seed = z(index(i))for j=1ns

Proposal density standard uniform with mean =seedu = rand(1m)u = (seed-05)+ u Calculate acceptance probabilitypdf2 = exp(-05sum(seedˆ2))pdf1 = exp(-05sum(uˆ2))

Numerical recipes for reliability analysis 63

I = 0if Pfun(u)ltgt I=1 endalpha = min(1Ipdf1pdf2) Accept u with probability = alphaV = randif V lt alpha

w(ns(i-1)+j)= uelse

w(ns(i-1)+j)= seedend

seed = w(ns(i-1)+j)end

endz=wend Reliability indexbetass = -norminv(pfss)

Appendix B ndash Approximate closed-form solutionsfor Φ(middot) and Φminus1(middot)The standard normal cumulative distribution functionΦ(middot) can be calculatedusing normcdf in MATLAB For sufficiently large β it is also possible to esti-mate Φ(minusβ) quite accurately using the following asymptotic approximation(Breitung 1984)

Φ(minusβ) sim (2π )minus12exp(minusβ22)βminus1 (B1)

An extensive review of estimation methods for the standard normal cumu-lative distribution function is given by Johnson et al (1994) Comparisonbetween Equation (B1) and normcdf(middot) is given in Figure B1 It appearsthat β gt 2 is sufficiently ldquolargerdquo Press et al (1992) reported an extremelyaccurate empirical fit with an error less than 12 times 10minus7 everywhere

t = 1

1 + 025radic

2β05t times exp(minus05β2minus126551223 + t

times (100002368 + t times (037409196 +Φ(minusβ) asymp t times (009678418 + t

times (minus018628806 + t times (027886807 + t times (minus113520398 + t

times (148851587 + t times (minus082215223 + t times 017087277))))))))) (B2)

64 Kok-Kwang Phoon

0 1 2 3 4 510minus7

10minus6

10minus5

10minus4

10minus3

10minus2

10minus1

100

Reliability index β

Pro

babi

lity

of fa

ilure

pf

normcdfEq (B1)

Figure B1 Comparison between normcdf(middot) and asymptotic approximation of the standardnormal cumulative distribution function

However it seems difficult to obtain β = minusΦminus1(pf) which is of signifi-cant practical interest as well Equation (B1) is potentially more useful inthis regard To obtain the inverse standard normal cumulative distributionfunction we rewrite Equation (B1) as follows

β2 + 2ln(radic

2πpf) + 2ln(β) = 0 (B3)

To solve Equation (B3) it is natural to linearize ln(β) using Taylor seriesexpansion ln(β) = minusln[1 + (1β minus 1)] asymp 1 minus 1β with β gt 05 Using thislinearization Equation (B3) simplifies to a cubic equation

β3 + 2[In(radic

2πpf) + 1]β minus 2 = 0 (B4)

The solution is given by

Q = 2[ln(radic

2πpf) + 1]3

R = 1

β =(

R +radic

Q3 + R2)13

+(

R minusradic

Q3 + R2)13

pf lt exp(minus1)radic

(B5)

Numerical recipes for reliability analysis 65

0 1 2 3 4 510minus7

10minus6

10minus5

10minus4

10minus3

10minus2

10minus1

100

Reliability index β

Pro

babi

lity

of fa

ilure

pf

normcdfEq (B5)Eq (B7)

Figure B2 Approximate closed-form solutions for inverse standard normal cumulativedistribution function

Equation (B5) is reasonably accurate as shown in Figure B2 An extremelyaccurate and simpler linearization can be obtained by fitting ln(β) = b0 +b1β

over the range of interest using linear regression Using this linearizationEquation (B3) simplifies to a quadratic equation

β2 + 2b1β + 2[ln(radic

2πpf) + b0] = 0 (B6)

The solution is given by

β = minusb1 +radic

b21 minus 2[ln(

radic2πpf) + b0] pf lt exp(b2

12 minus b0)radic

2π (B7)

The parameters b0 = 0274 and b1 = 0266 determined from linear regressionover the range 2 le β le 6 were found to produce extremely good results asshown in Figure B2 To the authorrsquos knowledge Equations (B5) and (B7)appear to be original

Appendix C ndash Exact reliability solutions

It is well known that exact reliability solutions are available for problemsinvolving the sum of normal random variables or the product of lognormalrandom variables This appendix provides other exact solutions that areuseful for validation of new reliability codescalculation methods

66 Kok-Kwang Phoon

C1 Sum of n exponential random variables

Let Y be exponentially distributed with the following cumulative distributionfunction

F(y) = 1 minus exp(minusyb) (C1)

The mean of Y is b The sum of n independent identically distributed expo-nential random variables follows an Erlang distribution with mean = nband variance = nb2 (Hastings and Peacock 1975) Consider the followingperformance function

P = nb +αbradic

n minusnsum

i=1

Yi = c minusnsum

i=1

Yi (C2)

in which c and α are positive numbers The equivalent performance functionin standard normal space is

P = c +nsum

i=1

bln[Φ(minusZi)

](C3)

The probability of failure can be calculated exactly based on the Erlangdistribution (Hastings and Peacock 1975)

pf = Prob(P lt 0)

= Prob

(nsum

i=1

Y gt c

)(C4)

= exp(minus c

b

)nminus1sumi=0

(cb)i

i

Equation (C2) with b = 1 is widely used in the structural reliabilitycommunity to validate FORMSORM (Breitung 1984 Rackwitz 2001)

C2 Probability content of an ellipse in n-dimensionalstandard normal space

Consider the ldquosaferdquo elliptical domain in 2D standard normal space shownin Figure C1 The performance function is

P = α2 minus(

Z1

b1

)2

minus(

Z2

b2

)2

(C5)

Numerical recipes for reliability analysis 67

Z2

Z1

R + r = αb2

(Z1b1)2 + (Z2b2)2 = α2

R - r = αb1

R + r

R - r

Figure C1 Elliptical safe domain in 2D standard normal space

in which b2 gtb1 gt0 Let R =α(b1+b2)2 and r =α(b2minusb1)2 Johnson andKotz (1970) citing Ruben (1960) provided the following the exact solutionfor the safe domain

Prob(P gt 0) = Prob(χ prime22 (r2) le R2) minus Prob(χ prime2

2 (R2) le r2) (C6)

in which χ prime22 (r2) = non-central chi-square random variable with two degrees

of freedom and non-centrality parameter r2 The cumulative distributionfunction Prob(χ prime2

2 (r2) le R2) can be calculated using ncx2cdf(R22r2) inMATLAB

There is no simple generalization of Equation (C6) to higher dimen-sions Consider the following performance function involving n uncorrelatedstandard normal random variables (Z1Z2 Zn)

P = α2 minus(

Z1

b1

)2

minus(

Z2

b2

)2

minusmiddotmiddot middot(

Zn

bn

)2

(C7)

in which bn gt bnminus1 gt middot middot middot gt b1 gt 0 Johnson and Kotz (1970) citing Ruben(1963) provided a series solution based on the sum of central chi-squarecumulative distribution functions

Prob(P gt 0) =infinsum

r=0

er Prob[χ2

n+2r le (bnα)2] (C8)

in which χ2ν (middot) = central chi-square random variable with ν degrees of free-

dom The cumulative distribution function Prob(χ2ν le y) can be calculated

68 Kok-Kwang Phoon

using chi2cdf(yν) in MATLAB The coefficients er are calculated as

e0 =nprod

j=1

(bj

bn

)

er = (2r)minus1rminus1sumj=0

ejhrminusj r ge 1

(C9)

hr =nsum

j=1

(1 minus

b2j

b2n

)r

(C10)

It is possible to calculate the probabilities associated with n-dimensional non-central ellipsoidal domains as well The performance function is given by

P = α2 minus(

Z1 minus a1

b1

)2

minus(

Z2 minus a2

b2

)2

minusmiddotmiddot middot(

Zn minus an

bn

)2

(C11)

in which bn gt bnminus1 gt gt b1 gt 0 Johnson and Kotz (1970) citing Ruben(1962) provided a series solution based on the sum of non-central chi-squarecumulative distribution functions

Prob(P gt 0) =infinsum

r=0

erProb

⎡⎣χ prime2

n+2r

⎛⎝ nsum

j=1

a2j

⎞⎠le (bnα

)2⎤⎦ (C12)

The coefficients er are calculated as

e0 =nprod

j=1

(bj

bn

)

er = (2r)minus1rminus1sumj=0

ejhrminusj r ge 1

(C13)

h1 =nsum

j=1(1 minus a2

j )

(1 minus

b2j

b2n

)

hr =nsum

j=1

⎡⎣(1 minus

b2j

b2n

)r

+ rb2

na2

j b2j

(1 minus

b2j

b2n

)rminus1⎤⎦ r ge 2

(C14)

Remark It is possible to create a very complex performance function byscattering a large number of ellipsoids in nD space The parameters in

Numerical recipes for reliability analysis 69

Equation (C11) can be visualized geometrically even in nD space Hencethey can be chosen in a relatively simple way to ensure that the ellipsoidsare disjoint One simple approach is based on nesting (a) encase the firstellipsoid in a hyper-box (b) select a second ellipsoid that is outside the box(c) encase the first and second ellipsoids in a larger box and (d) select athird ellipsoid that is outside the larger box Repeated application will yielddisjoint ellipsoids The exact solution for such a performance function ismerely the sum of Equation (C12) but numerical solution can be very chal-lenging An example constructed by this ldquocookie-cutterrdquo approach may becontrived but it has the advantage of being as complex as needed to testthe limits of numerical methods while retaining a relatively simple exactsolution

Appendix D ndash Second-order reliability method(SORM)

The second-order reliability method was developed by Breitung andothers in a series of papers dealing with asymptotic analysis (Breitung1984 Breitung and Hohenbichler 1989 Breitung 1994) The calcula-tion steps are illustrated below using a problem containing three randomdimensions

1 Let the performance function be defined in the standard normal spaceie P(z1z2z3) and let the first-order reliability index calculated fromEquation (150) be β = (zlowastprimezlowast)12 in which zlowast = (zlowast

1zlowast2z

lowast3)prime is the design

point2 The gradient vector at zlowast is calculated as

nablaP(zlowast) =⎧⎨⎩

partP(zlowast)partz1partP(zlowast)partz2partP(zlowast)partz3

⎫⎬⎭ (D1)

The magnitude of the gradient vector is

||nablaP(zlowast)|| = [nablaP(zlowast)primenablaP(zlowast)]12 (D2)

3 The probability of failure is estimated as

pf = Φ(minusβ)|J|minus12 (D3)

in which J is the following 2 times 2 matrix

J =[

1 00 1

]+ β

nablaP(zlowast)[

part2P(zlowast)partz21 part2P(zlowast)partz1partz2

part2P(zlowast)partz2partz1 part2P(zlowast)partz22

](D4)

70 Kok-Kwang Phoon

Z2

Z1

4

201

5 6

3

∆ ∆

Figure D1 Central finite difference scheme

Equations (D1) and (D4) can be estimated using the central finite differencescheme shown in Figure D1 Let Pi be the value of the performance functionevaluated at node i Then the first derivative is estimated as

partPpartz1

asymp P2 minus P1

2(D5)

The second derivative is estimated as

part2P

partz21

asymp P2 minus 2P0 + P1

∆2 (D6)

The mixed derivative is estimated as

part2Ppartz1partz2

asymp P4 minus P3 minus P6 + P5

4∆2 (D7)

References

Ang A H-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Vol 2 (Decision Risk and Reliability) John Wiley and SonsNew York

Au S K (2001) On the solution of first excursion problems by simulation with appli-cations to probabilistic seismic performance assessment PhD thesis CaliforniaInstitute of Technology Pasadena

Au S K and Beck J (2001) Estimation of small failure probabilities in highdimensions by subset simulation Probabilistic Engineering Mechanics 16(4)263ndash77

Numerical recipes for reliability analysis 71

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley amp Sons New York

Becker D E (1996) Limit states design for foundations Part II ndash Developmentfor National Building Code of Canada Canadian Geotechnical Journal 33(6)984ndash1007

Boden B (1981) Limit state principles in geotechnics Ground Engineering14(6) 2ndash7

Bolton M D (1983) Eurocodes and the geotechnical engineer Ground Engineering16(3) 17ndash31

Bolton M D (1989) Development of codes of practice for design In Proceedings ofthe 12th International Conference on Soil Mechanics and Foundation EngineeringRio de Janeiro Vol 3 Balkema A A Rotterdam pp 2073ndash6

Box G E P and Muller M E (1958) A note on the generation of random normaldeviates Annals of Mathematical Statistics 29 610ndash1

Breitung K (1984) Asymptotic approximations for multinormal integrals Journalof Engineering Mechanics ASCE 110(3) 357ndash66

Breitung K (1994) Asymptotic Approximations for Probability Integrals LectureNotes in Mathematics 1592 Springer Berlin

Breitung K and Hohenbichler M (1989) Asymptotic approximations to mul-tivariate integrals with an application to multinormal probabilities Journal ofMultivariate Analysis 30(1) 80ndash97

Broms B B (1964a) Lateral resistance of piles in cohesive soils Journal of SoilMechanics and Foundations Division ASCE 90(SM2) 27ndash63

Broms B B (1964b) Lateral resistance of piles in cohesionless soils Journal of SoilMechanics and Foundations Division ASCE 90(SM3) 123ndash56

Carsel R F and Parrish R S (1988) Developing joint probability distributions ofsoil water retention characteristics Water Resources Research 24(5) 755ndash69

Chilegraves J-P and Delfiner P (1999) Geostatistics ndash Modeling Spatial UncertaintyJohn Wiley amp Sons New York

Cressie N A C (1993) Statistics for Spatial Data John Wiley amp Sons New YorkDitlevsen O and Madsen H (1996) Structural Reliability Methods John Wiley amp

Sons ChichesterDuncan J M (2000) Factors of safety and reliability in geotechnical engineer-

ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

Elderton W P and Johnson N L (1969) Systems of Frequency Curves CambridgeUniversity Press London

Fenton G A and Griffiths D V (1997) Extreme hydraulic gradient statistics in astochastic earth dam Journal of Geotechnical and Geoenvironmental EngineeringASCE 123(11) 995ndash1000

Fenton G A and Griffiths D V (2002) Probabilistic foundation settlement on spa-tially random soil Journal of Geotechnical and Geoenvironmental EngineeringASCE 128(5) 381ndash90

Fenton G A and Griffiths D V (2003) Bearing capacity prediction of spatiallyrandom c- soils Canadian Geotechnical Journal 40(1) 54ndash65

Fisher R A (1935) The Design of Experiments Oliver and Boyd EdinburghFleming W G K (1989) Limit state in soil mechanics and use of partial factors

Ground Engineering 22(7) 34ndash5

72 Kok-Kwang Phoon

Ghanem R and Spanos P D (1991) Stochastic Finite Element A SpectralApproach Springer-Verlag New York

Griffiths D V and Fenton G A (1993) Seepage beneath water retaining structuresfounded on spatially random soil Geotechnique 43(6) 577ndash87

Griffiths D V and Fenton G A (1997) Three dimensional seepage through spa-tially random soil Journal of Geotechnical and Geoenvironmental EngineeringASCE 123(2) 153ndash60

Griffiths D V and Fenton G A (2001) Bearing capacity of spatially random soilthe undrained clay Prandtl problem revisited Geotechnique 54(4) 351ndash9

Hansen J B (1961) Ultimate resistance of rigid piles against transversal forcesBulletin 12 Danish Geotechnical Institute Copenhagen 1961 5ndash9

Hasofer A M and Lind N C (1974) An exact and invariant first order reliabilityformat Journal of Engineering Mechanics Division ASCE 100(EM1) 111ndash21

Hastings N A J and Peacock J B (1975) Statistical Distributions A Handbookfor Students and Practitioners Butterworths London

Huang S P Quek S T and Phoon K K (2001) Convergence study of the truncatedKarhunenndashLoeve expansion for simulation of stochastic processes InternationalJournal of Numerical Methods in Engineering 52(9) 1029ndash43

Isukapalli S S (1999) Uncertainty analysis of transport ndash transformation modelsPhD thesis The State University of New Jersey New Brunswick

Johnson N L (1949) Systems of frequency curves generated by methods oftranslation Biometrika 73 387ndash96

Johnson N L Kotz S and Balakrishnan N (1970) Continuous UnivariateDistributions Vol 2 Houghton Mifflin Company Boston

Johnson N L Kotz S and Balakrishnan N (1994) Continuous UnivariateDistributions Vols 1 and 2 John Wiley and Sons New York

Kulhawy F H and Phoon K K (1996) Engineering judgment in the evolution fromdeterministic to reliability-based foundation design In Uncertainty in the Geo-logic Environment ndash From Theory to Practice (GSP 58) Eds C D ShackelfordP P Nelson and M J S Roth ASCE New York pp 29ndash48

Liang B Huang S P and Phoon K K (2007) An EXCEL add-in implementationfor collocation-based stochastic response surface method In Proceedings of the1st International Symposium on Geotechnical Safety and Risk Tongji UniversityShanghai China pp 387ndash98

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Marsaglia G and Bray T A (1964) A convenient method for generating normalvariables SIAM Review 6 260ndash4

Mendell N R and Elston R C (1974) Multifactorial qualitative traits geneticanalysis and prediction of recurrence risks Biometrics 30 41ndash57

National Research Council (2006) Geological and Geotechnical Engineering inthe New Millennium Opportunities for Research and Technological InnovationNational Academies Press Washington D C

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep founda-tions In Proceedings International Workshop on Foundation Design Codes andSoil Investigation in view of International Harmonization and Performance BasedDesign Hayama Balkema A A Lisse pp 59ndash94

Numerical recipes for reliability analysis 73

Phoon K K (2003) Representation of random variables using orthogonal poly-nomials In Proceedings of the 9th International Conference on Applicationsof Statistics and Probability in Civil Engineering Vol 1 Millpress RotterdamNetherlands pp 97ndash104

Phoon K K (2004a) General non-Gaussian probability models for first-order reli-ability method (FORM) a state-of-the-art report ICG Report 2004-2-4 (NGIReport 20031091-4) International Centre for Geohazards Oslo

Phoon K K (2004b) Application of fractile correlations and copulas to non-Gaussian random vectors In Proceedings of the 2nd International ASRANet(Network for Integrating Structural Safety Risk and Reliability) ColloquiumBarcelona (CDROM)

Phoon K K (2005) Reliability-based design incorporating model uncertainties InProceedings of the 3rd International Conference on Geotechnical EngineeringDiponegoro University Semarang Indonesia pp 191ndash203

Phoon K K (2006a) Modeling and simulation of stochastic data In Geo-Congress 2006 Geotechnical Engineering in the Information Technology AgeEds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K (2006b) Bootstrap estimation of sample autocorrelation functionsIn GeoCongress 2006 Geotechnical Engineering in the Information TechnologyAge Eds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K and Fenton G A (2004) Estimating sample autocorrelation func-tions using bootstrap In Proceedings of the 9th ASCE Specialty Conference onProbabilistic Mechanics and Structural Reliability Albuquerque (CDROM)

Phoon K K and Huang S P (2007) Uncertainty quantification using multi-dimensional Hermite polynomials In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Phoon K K and Kulhawy F H (1999a) Characterization of geotechnicalvariability Canadian Geotechnical Journal 36(4) 612ndash24

Phoon K K and Kulhawy F H (1999b) Evaluation of geotechnical propertyvariability Canadian Geotechnical Journal 36(4) 625ndash39

Phoon K K and Kulhawy F H (2005) Characterization of model uncertainties forlaterally loaded rigid drilled shafts Geotechnique 55(1) 45ndash54

Phoon K K Becker D E Kulhawy F H Honjo Y Ovesen N K and Lo S R(2003b) Why consider reliability analysis in geotechnical limit state design InProceedings of the International Workshop on Limit State design in GeotechnicalEngineering Practice (LSD2003) Cambridge (CDROM)

Phoon K K Kulhawy F H and Grigoriu M D (1993) Observations onreliability-based design of foundations for electrical transmission line struc-tures In Proceedings International Symposium on Limit State Design inGeotechnical Engineering Vol 2 Danish Geotechnical Institute Copenhagenpp 351ndash62

Phoon K K Kulhawy F H and Grigoriu M D (1995) Reliability-based designof foundations for transmission line structures Report TR-105000 Electric PowerResearch Institute Palo Alto

74 Kok-Kwang Phoon

Phoon K K Huang S P and Quek S T (2002) Implementation of KarhunenndashLoeve expansion for simulation using a wavelet-Galerkin scheme ProbabilisticEngineering Mechanics 17(3) 293ndash303

Phoon K K Huang H W and Quek S T (2004) Comparison between KarhunenndashLoeve and wavelet expansions for simulation of Gaussian processes Computersand Structures 82(13ndash14) 985ndash91

Phoon K K Quek S T and An P (2003a) Identification of statistically homo-geneous soil layers using modified Bartlett statistics Journal of Geotechnical andGeoenvironmental Engineering ASCE 129(7) 649ndash59

Phoon K K Quek S T Chow Y K and Lee S L (1990) Reliability anal-ysis of pile settlement Journal of Geotechnical Engineering ASCE 116(11)1717ndash35

Press W H Teukolsky S A Vetterling W T and Flannery B P (1992) Numer-ical Recipes in C The Art of Scientific Computing Cambridge University PressNew York

Puig B and Akian J-L (2004) Non-Gaussian simulation using Hermite polynomialsexpansion and maximum entropy principle Probabilistic Engineering Mechanics19(4) 293ndash305

Puig B Poirion F and Soize C (2002) Non-Gaussian simulation using Hermitepolynomial expansion convergences and algorithms Probabilistic EngineeringMechanics 17(3) 253ndash64

Quek S T Chow Y K and Phoon K K (1992) Further contributions toreliability-based pile settlement analysis Journal of Geotechnical EngineeringASCE 118(5) 726ndash42

Quek S T Phoon K K and Chow Y K (1991) Pile group settlement a proba-bilistic approach International Journal of Numerical and Analytical Methods inGeomechanics 15(11) 817ndash32

Rackwitz R (2001) Reliability analysis ndash a review and some perspectives StructuralSafety 23(4) 365ndash95

Randolph M F and Houlsby G T (1984) Limiting pressure on a circular pileloaded laterally in cohesive soil Geotechnique 34(4) 613ndash23

Ravindra M K and Galambos T V (1978) Load and resistance factor design forsteel Journal of Structural Division ASCE 104(ST9) 1337ndash53

Reese L C (1958) Discussion of ldquoSoil modulus for laterally loaded pilesrdquoTransactions ASCE 123 1071ndash74

Reacutethaacuteti L (1988) Probabilistic Solutions in Geotechnics Elsevier New YorkRosenblueth E and Esteva L (1972) Reliability Basis for some Mexican Codes

Publication SP-31 American Concrete Institute DetroitRuben H (1960) Probability constant of regions under spherical normal distribu-

tion I Annals of Mathematical Statistics 31 598ndash619Ruben H (1962) Probability constant of regions under spherical normal distribu-

tion IV Annals of Mathematical Statistics 33 542ndash70Ruben H (1963) A new result on the distribution of quadratic forms Annals of

Mathematical Statistics 34 1582ndash4Sakamoto S and Ghanem R (2002) Polynomial chaos decomposition for the simu-

lation of non-Gaussian non-stationary stochastic processes Journal of EngineeringMechanics ASCE 128(2) 190ndash200

Numerical recipes for reliability analysis 75

Schueumlller G I Pradlwarter H J and Koutsourelakis P S (2004) A criticalappraisal of reliability estimation procedures for high dimensions ProbabilisticEngineering Mechanics 19(4) 463ndash74

Schweizer B (1991) Thirty years of copulas In Advances in Probability Distribu-tions with Given Marginals Kluwer Dordrecht pp 13ndash50

Semple R M (1981) Partial coefficients design in geotechnics Ground Engineering14(6) 47ndash8

Simpson B (2000) Partial factors where to apply them In Proceedings of theInternational Workshop on Limit State Design in Geotechnical Engineering(LSD2000) Melbourne (CDROM)

Simpson B and Driscoll R (1998) Eurocode 7 A Commentary ConstructionResearch Communications Ltd Watford Herts

Simpson B and Yazdchi M (2003) Use of finite element methods in geotech-nical limit state design In Proceedings of the International Workshop onLimit State design in Geotechnical Engineering Practice (LSD2003) Cambridge(CDROM)

Simpson B Pappin J W and Croft D D (1981) An approach to limit statecalculations in geotechnics Ground Engineering 14(6) 21ndash8

Sudret B (2007) Uncertainty propagation and sensitivity analysis in mechanicalmodels Contributions to structural reliability and stochastic spectral methods1rsquoHabilitation a Diriger des Recherches Universite Blaise Pascal

Sudret B and Der Kiureghian A 2000 Stochastic finite elements and reliabilitya state-of-the-art report Report UCBSEMM-200008 University of CaliforniaBerkeley

US Army Corps of Engineers (1997) Engineering and design introduction to proba-bility and reliability methods for use in geotechnical engineering Technical LetterNo 1110-2-547 Department of the Army Washington D C

Uzielli M and Phoon K K (2006) Some observations on assessment of Gaussianityfor correlated profiles In GeoCongress 2006 Geotechnical Engineering in theInformation Technology Age Eds D J DeGroot J T DeJong J D Frost andL G Braise ASCE Reston (CDROM)

Uzielli M Lacasse S Nadim F and Phoon K K (2007) Soil variability analysisfor geotechnical practice In Proceedings of the 2nd International Workshop onCharacterisation and Engineering Properties of Natural Soils Vol 3 Taylor andFrancis Singapore pp 1653ndash752

VanMarcke E H (1983) Random Field Analysis and Synthesis MIT PressCambridge MA

Verhoosel C V and Gutieacuterrez M A (2007) Application of the spectral stochasticfinite element method to continuum damage modelling with softening behaviourIn Proceedings of the 10th International Conference on Applications of Statisticsand Probability in Civil Engineering Tokyo (CDROM)

Vrouwenvelder T and Faber M H (2007) Practical methods of structural reli-ability In Proceedings of the 10th International Conference on Applications ofStatistics and Probability in Civil Engineering Tokyo (CDROM)

Winterstein S R Ude T C and Kleiven G (1994) Springing and slow driftresponses predicted extremes and fatigue vs simulation Proceedings Behaviourof Offshore Structures Vol 3 Elsevier Cambridge pp 1ndash15

Chapter 2

Spatial variability andgeotechnical reliability

Gregory B Baecher and John T Christian

Quantitative measurement of soil properties differentiated the new disciplineof soil mechanics in the early 1900s from the engineering of earth workspracticed since antiquity These measurements however uncovered a greatdeal of variability in soil properties not only from site to site and stratumto stratum but even within what seemed to be homogeneous deposits Wecontinue to grapple with this variability in current practice although newtools of both measurement and analysis are available for doing so Thischapter summarizes some of the things we know about the variability ofnatural soils and how that variability can be described and incorporated inreliability analysis

21 Variability of soil properties

Table 21 illustrates the extent to which soil property data vary accordingto Phoon and Kulhawy (1996) who have compiled coefficients of variationfor a variety of soil properties The coefficient of variation is the standarddeviation divided by the mean Similar data have been reported by Lumb(1966 1974) Lee et al (1983) and Lacasse and Nadim (1996) amongothers The ranges of these reported values are wide and are only suggestiveof conditions at a specific site

It is convenient to think about the impact of variability on safety byformulating the reliability index

β = E[MS]SD[MS]or

E[FS]minus 1SD[FS] (21)

in which β = reliability index MS = margin of safety (resistance minusload) FS = factor of safety (resistance divided by load) E[middot] = expectationand SD[middot] = standard deviation It should be noted that the two definitionsof β are not identical unless MS = 0 or FS = 1 Equation 21 expressesthe number of standard deviations separating expected performance from afailure state

Spatial variability and geotechnical reliability 77

Table 21 Coefficient of variation for some common field measurements (Phoon andKulhawy 1996)

Test type Property Soil type Mean Units Cov()

qT Clay 05ndash25 MNm2 lt 20CPT qc Clay 05ndash2 MNm2 20ndash40

qc Sand 05ndash30 MNm2 20ndash60VST su Clay 5ndash400 kNm2 10ndash40SPT N Clay and sand 10ndash70 blowsft 25ndash50

A reading Clay 100ndash450 kNm2 10ndash35A reading Sand 60ndash1300 kNm2 20ndash50B reading Clay 500ndash880 kNm2 10ndash35

DMT B Reading Sand 350ndash2400 kNm2 20ndash50ID Sand 1ndash8 20ndash60KD Sand 2ndash30 20ndash60ED Sand 10ndash50 MNm2 15ndash65pL Clay 400ndash2800 kNm2 10ndash35

PMT pL Sand 1600ndash3500 kNm2 20ndash50EPMT Sand 5ndash15 MNm2 15ndash65wn Clay and silt 13ndash100 8ndash30WL Clay and silt 30ndash90 6ndash30WP Clay and silt 15ndash15 6ndash30

Lab Index PI Clay and silt 10ndash40 _a

LI Clay and silt 10 _a

γ γd Clay and silt 13ndash20 KNm3 lt 10Dr Sand 30ndash70 10ndash40

50ndash70b

NotesaCOV = (3ndash12)meanbThe first range of variables gives the total variability for the direct method of determination and thesecond range of values gives the total variability for the indirect determination using SPT values

The important thing to note in Table 21 is how large are the reportedcoefficients of variations of soil property measurements Most are tens ofpercent implying reliability indices between one and two even for conser-vative designs Probabilities of failure corresponding to reliability indiceswithin this range ndash shown in Figure 21 for a variety of common distribu-tional assumptions ndash are not reflected in observed rates of failure of earthstructures and foundations We seldom observe failure rates this high

The inconsistency between the high variability of soil property data andthe relatively low rate of failure of prototype structures is usually attributedto two things spatial averaging and measurement noise Spatial averagingmeans that if one is concerned about average properties within some volumeof soil (eg average shear strength or total compression) then high spotsbalance low spots so that the variance of the average goes down as thatvolume of mobilized soil becomes larger Averaging reduces uncertainty1

78 Gregory B Baecher and John T Christian

1

01

Probability of Failure for Different Distributions

001

0001

00001

Pro

babi

lity

000001

00000010 1 2 3

Beta

Normal

Lognormal COV = 01

Triangular

Lognormal COV = 005

Lognormal COV = 015

4 5

Figure 21 Probability of failure as a function of reliability index for a variety of commonprobability distribution forms

Measurement noise means that the variability in soil property data reflectstwo things real variability and random errors introduced by the processof measurement Random errors reduce the precision with which estimatesof average soil properties can be made but they do not affect the in-fieldvariation of actual properties so the variability apparent in measurementsis larger ndash possibly substantially so ndash than actual in situ variability2

211 Spatial variation

Spatial variation in a soil deposit can be characterized in detail but only witha great number of observations which normally are not available Thus itis common to model spatial variation by a smooth deterministic trend com-bined with residuals about that trend which are described probabilisticallyThis model is

z(x) = t(x) + u(x) (22)

in which z(x) is the actual soil property at location x (in one or more dimen-sions) t(x) is a smooth trend at x and u(x) is residual deviation from thetrend The residuals are characterized as a random variable of zero-meanand some variance

Var(u) = E[z(x) minus t(x)2] (23)

Spatial variability and geotechnical reliability 79

in which Var(x) is the variance The residuals are characterized as randombecause there are too few data to do otherwise This does not presume thatsoil properties actually are random The variance of the residuals reflectsuncertainty about the difference between the fitted trend and the actual valueof soil properties at particular locations Spatial variation is modeled stochas-tically not because soil properties are random but because information islimited

212 Trend analysis

Trends are estimated by fitting lines curves or surfaces to spatially refer-enced data The easiest way to do this is by regression analysis For exampleFigure 22 shows maximum past pressure measurements as a function ofdepth in a deposit of Gulf of Mexico clay The deposit appears homogeneous

Maximum Past Pressure σvm (KSF)

Ele

vatio

n (f

t)

0 2 4 6 8

minus60

minus50

minus40

minus30

minus20

minus10

0

σvo

mean σvm

standard deviations

Figure 22 Maximum past pressure measurements as a function of depth in Gulf of Mexicoclays Mobile Alabama

80 Gregory B Baecher and John T Christian

and mostly normally consolidated The increase of maximum past pressurewith depth might be expected to be linear Data from an over-consolidateddesiccated crust are not shown The trend for the maximum past pressuredata σ prime

vm with depth x is

σ primevm = t(x) + u(x) = α0 +α1x = u (24)

in which t(x) is the trend of maximum past pressure with depth x α0and α1 are regression coefficients and u is residual variation about thetrend taken to be constant with depth (ie it is not a function of x)Applying standard least squares analysis the regression coefficients mini-mizing Var[u] are α0 = 3 ksf (014 KPa) and α1 = 006 ksfft (14 times 10minus3

KPam) yielding Var(u) = 10 ksf (005 KPa) for which the correspondingtrend line is shown The trend t(x) = 3 + 006x is the best estimate or meanof the maximum past pressure as a function of depth (NB ksf = kip persquare foot)

The analysis can be made of data in higher dimensions which in matrixnotation becomes

z = Xα + u (25)

in which z is the vector of the n observations z=z1 hellip zn X=x1 x2 isthe 2 x n matrix of location coordinates corresponding to the observationsα = αα1 hellip αn is the vector of trend parameters and u is the vectorof residuals corresponding to the observations Minimizing the variance ofthe residuals u(x) over α gives the best-fitting trend surface in a frequentistsense which is the common regression surface

The trend surface can be made more flexible for example in the quadraticcase the linear expression is replaced by

z = α0 +α1x +α2x2 + u (26)

and the calculation for α performed the same way Because the quadraticsurface is more flexible than the planar surface it fits the observed datamore closely and the residual variations about it are smaller On the otherhand the more flexible the trend surface the more regression parameters thatneed to be estimated from a fixed number of data so the fewer the degrees offreedom and the greater the statistical error in the surface Examples of theuse of trend surfaces in the geotechnical literature are given by Wu (1974)Ang and Tang (1975) and many others

Historically it has been common for trend analysis in geotechnicalengineering to be performed using frequentist methods Although this istheoretically improper because frequentist methods yield confidence inter-vals rather than probability distributions on parameters the numericalerror is negligible The Bayesian approach begins with the same model

Spatial variability and geotechnical reliability 81

However rather than defining an estimator such as the least squarescoefficients the Bayesian approach specifies an a priori probability distri-bution on the coefficients of the model and uses Bayesrsquos Theorem to updatethat distribution in light of the observed data

The following summarizes Bayesian results from Zellner (1971) for theone-dimensional linear case of Equation (24) Let Var(u)= σ 2 so thatVar(u) = Iσ 2 in which I is the identity matrix The prior probability densityfunction (pdf) of the parameters ασ is represented as f (ασ ) Given a set ofobservations z = z1 hellip zn the updated or posterior pdf of ασ is foundfrom Bayesrsquos Theorem as f (ασ |z) prop f (ασ )L(ασ |z) in which L(ασ |z) isthe Likelihood of the data (ie the conditional probability of the observeddata for various values of the parameters) If variations about the trend lineor surface are jointly Normal the likelihood function is

L(ασ |z) = MN(z|ασ )prop expminus(z minus Xα)primeminus1(z minus Xα) (27)

in which MN() is the Multivariate-Normal distribution having mean Xα

and covariance matrix = Iσ Using a non-informative prior f (α σ ) prop σminus1 and measurements y made

at depths x the posterior pdf of the regression parameters is

f (α0α1σ |xy) prop 1σ n+1 exp

[minus 1

2σ 2

sumn

i=1(y1 minus (α0 +α1x1))2

](28)

The marginal distributions are

f (α0α1|xy) prop [νs2 + n(α0 minusα0) + 2(α0 minusα0)(α1 minusα1)xi

+ (α1 minusα1)2x21]minusn2

f (α0|xy) prop [ν + (xi minus x)2

s2x2i n

(α0 minusα0)2]minus(νminus1)2 (29)

f (α1|xy) prop [ν + (xi minus x)2

s2 (α1 minusα1)2]minus(νminus1)2

f (σ |xy) prop 1σνminus1 exp

(minus νs2

2σ 2

)

in which

ν = n minus 2

α0 = y minusα1x α1 =[sum(

xi minus x)(

yi minus y)][sum(

xi minus x)]

s2 = νminus1sum(

yi minusα0 minusα1xi

)2

82 Gregory B Baecher and John T Christian

y = nminus1sum

yi

x = nminus1sum

xi

The joint and marginal pdfrsquos of the regression coefficients are Student-tdistributed

213 Autocorrelation

In fitting trends to data as noted above the decision is made to dividethe total variability of the data into two parts one part explained by thetrend and the other as variation about the trend Residual variations notaccounted for by the trend are characterized by a residual variance Forexample the overall variance of the blow count data of Figure 23 is 45 bpf2

(475 bpm2) Removing a linear trend reduces this total to a residual varianceof about 11 bpf2(116 bpm2) The trend explains 33 bpf2 (349 bpm2)or about 75 of the spatial variation and 25 is unexplained by thetrend

The spatial structure remaining after a trend is removed usually displayscorrelations among the residuals That is the residuals off the trend are notstatistically independent of one another Positive residuals tend to clumptogether as do negative residuals Thus the probability of encountering a

570

575

580

585

590

595

600

605

610

0 10 20 30 40

STP Blowcount

50

Figure 23 Spatial variation of SPT blow count data in a silty sand (data from Hilldale 1971)

Spatial variability and geotechnical reliability 83

minus25

minus2

minus15

minus1

minus05

0

05

1

15

2

Distance along transect

Figure 24 Residual variations of SPT blow counts

continuous zone of weakness or high compressibility is greater than wouldbe predicted if the residuals were independent

Figure 24 shows residual variations of SPT blow counts measured at thesame elevation every 20 m beneath a horizontal transect at a site The dataare normalized to zero mean and unit standard deviation The dark line is asmooth curve drawn through the observed data The light line is a smoothcurve drawn through artificially simulated data having the same meanand same standard deviation but probabilistically independent Inspectionshows the natural data to be smoothly varying whereas the artificial dataare much more erratic

The remaining spatial structure of variation not accounted for by the trendcan be described by its spatial correlation called autocorrelation Formallyautocorrelation is the property that residuals off the mean trend are notprobabilistically independent but display a degree of association amongthemselves that is a function of their separation in space This degree ofassociation can be measured by a correlation coefficient taken as a functionof separation distance

Correlation is the property that on average two variables are linearlyassociated with one another Knowing the value of one provides informa-tion on the probable value of the other The strength of this association ismeasured by a correlation coefficient ρ that ranges between ndash1 and +1 Fortwo scalar variables z1 and z2 the correlation coefficient is defined as

ρ = Cov(z1z2)radicVar(z1)Var(z2)

= 1σz1

σz2

E[(z1 minusmicroz1)(z2 minusmicroz2

)] (210)

84 Gregory B Baecher and John T Christian

in which Cov(z1z2) is the covariance Var(zi) is the variance σ is thestandard deviation and micro is the mean

The two variables might be of different but related types for examplez1 might be water content and z2 might be undrained strength or the twovariables might be the same property at different locations for examplez1 might be the water content at one place on the site and z2 the watercontent at another place A correlation coefficient ρ = +1 means that tworesiduals vary together exactly When one is a standard deviation above itstrend the other is a standard deviation above its trend too A correlationcoefficient ρ = minus1 means that two residuals vary inversely When one is astandard deviation above its trend the other is a standard deviation belowits trend A correlation coefficient ρ = 0 means that the two residuals areunrelated In the case where the covariance and correlation are calculated asfunctions of the separation distance the results are called the autocovarianceand autocorrelation respectively

The locations at which the blow count data of Figure 23 were measuredare shown in Figure 25 In Figure 26 these data are used to estimate autoco-variance functions for blow count The data pairs at close separation exhibita high degree of correlation for example those separated by 20 m have acorrelation coefficient of 067 As separation distance increases correlationdrops although at large separations where the numbers of data pairs are

A

B

C

D E F G H I

Cprime

Bprime

Aprime

Dprime Eprime Fprime Gprime Hprime

3225

33

21

26

27

2220

37

8

3

210

15

14

169

1

19ST 40033 31 34

302924

23

181265

4 711 13

17

36

ST 40044

28

35

Iprime

Figure 25 Boring locations of blow count data used to describe the site (TW Lambe andAssociates 1982 Earthquake Risk at Patio 4 and Site 400 Longboat Key FLreproduced by permission of TW Lambe)

Spatial variability and geotechnical reliability 85

40

30

20

10

0

minus10

minus20

minus30

minus40

Aut

ocov

aria

nce

0 100 200 300 400 500 600

Separation (m)

8ndash10m

10ndash12m

6ndash8m

Figure 26 Autocorrelation functions for SPT data at at Site 400 by depth interval(TW Lambe and Associates 1982 Earthquake Risk at Patio 4 and Site 400Longboat Key FL reproduced by permission of TW Lambe)

smaller there is much statistical fluctuation For zero separation distancethe correlation coefficient must equal 10 For large separation distancesthe correlation coefficient approaches zero In between the autocorrelationusually falls monotonically from 10 to zero

An important point to note is that the division of spatial variation intoa trend and residuals about the trend is an assumption of the analysis it isnot a property of reality By changing the trend model ndash for example byreplacing a linear trend with a polynomial trend ndash both the variance of theresiduals and their autocorrelation function are changed As the flexibility ofthe trend increases the variance of the residuals goes down and in generalthe extent of correlation is reduced From a practical point of view theselection of a trend line or curve is in effect a decision on how much of thedata scatter to model as a deterministic function of space and how much totreat probabilistically

As a rule of thumb trend surfaces should be kept as simple as possiblewithout doing injustice to a set of data or ignoring the geologic settingThe problem with using trend surfaces that are very flexible (eg high-order polynomials) is that the number of data from which the parameters ofthose equations are estimated is limited The sampling variance of the trendcoefficients is inversely proportional to the degrees of freedom involvedν = (n minus k minus 1) in which n is the number of observations and k is the num-ber of parameters in the trend The more parameter estimates that a trend

86 Gregory B Baecher and John T Christian

surface requires the more uncertainty there is in the numerical values of thoseestimates Uncertainty in regression coefficient estimates increases rapidly asthe flexibility of the trend equation increases

If z(xi) = t(xi)+u(xi) is a continuous variable and the soil deposit is zonallyhomogeneous then at locations i and j which are close together the residualsui and uj should be expected to be similar That is the variations reflectedin u(xi) and u(xj) are associated with one another When the locations areclose together the association is usually strong As the locations becomemore widely separated the association usually decreases As the separationbetween two locations i and j approaches zero u(xi) and u(xj) become thesame the association becomes perfect Conversely as the separation becomeslarge u(xi) and u(xj) become independent the association becomes zeroThis is the behavior observed in Figure 26 for the Standard Peretrationtest(SPT) data

This spatial association of residuals off the trend t(xi) is summarized bya mathematical function describing the correlation of u(xi) and u(xj) asseparation distance increases This description is called the autocorrelationfunction Mathematically the autocorrelation function is

Rz(δ) = 1Varu(x)E[u(xi)u(xi+δ)] (211)

in which Rz(δ) is the autocorrelation function Var[u(x)] is the variance ofthe residuals across the site and E[u(xi)u(xi+δ)]=Cov[u(xi)u(xi+δ)] is thecovariance of the residuals spaced at separation distance δ By definitionthe autocorrelation at zero separation is Rz(0) = 10 and empirically formost geotechnical data autocorrelation decreases to zero as δ increases

If Rz(δ) is multiplied by the variance of the residuals Var[u(x)] theautocovariance function Cz(δ) is obtained

Cz(δ) = E[u(xi)u(xi+δ)] (212)

The relationship between the autocorrelation function and the autocovari-ance function is the same as that between the correlation coefficient and thecovariance except that autocorrelation and autocovariance are functions ofseparation distance δ

214 Example TONEN refinery Kawasaki Japan

The SPT data shown earlier come from a site overlying hydraulic bay fillin Kawasaki (Japan) The SPT data were taken in a silty fine sand betweenelevations +3 and minus7 m and show little if any trend horizontally so aconstant horizontal trend at the mean of the data was assumed Figure 27shows the means and variability of the SPT data with depth Figure 26 shows

Spatial variability and geotechnical reliability 87

Upper Fill

Blow Counts N - BlowsFt

0 5 10 15 20 25 350

2

4

6

8

10

12

14

Dep

th m

Sandy Fill

SandyAlluvium

+σminusσ

mea

n

Figure 27 Soil model and the scatter of blow count data (TW Lambe and Associates1982 Earthquake Risk at Patio 4 and Site 400 Longboat Key FL reproducedby permission of TW Lambe)

autocovariance functions in the horizontal direction estimated for threeintervals of elevation At short separation distances the data show distinctassociation ie correlation At large separation distances the data exhibitessentially no correlation

In natural deposits correlations in the vertical direction tend to have muchshorter distances than in the horizontal direction A ratio of about 1 to 10 forthese correlation distances is common Horizontally autocorrelation may beisotropic (ie Rz(δ) in the northing direction is the same as Rz(δ) in the eastingdirection) or anisotropic depending on geologic history However in prac-tice isotropy is often assumed Also autocorrelation is typically assumed tobe the same everywhere within a deposit This assumption called station-arity to which we will return is equivalent to assuming that the deposit isstatistically homogeneous

It is important to emphasize again that the autocorrelation function is anartifact of the way soil variability is separated between trend and residualsSince there is nothing innate about the chosen trend and since changingthe trend changes Rz(δ) the autocorrelation function reflects a modelingdecision The influence of changing trends on Rz(δ) is illustrated in data

88 Gregory B Baecher and John T Christian

Figure 28 Study area for San Francisco Bay Mud consolidation measurements (Javete1983) (reproduced with the authorrsquos permission)

analyzed by Javete (1983) (Figure 28) Figure 29 shows autocorrelationsof water content in San Francisco Bay Mud within an interval of 3 ft (1 m)Figure 210 shows the autocorrelation function when the entire site is con-sidered The difference comes from the fact that in the first figure the meantrend is taken locally within the 3 ft (1 m) interval and in the latter the meantrend is taken globally across the site

Autocorrelation can be found in almost all spatial data that are analyzedusing a model of the form of Equation (25) For example Figure 211shows the autocorrelation of rock fracture density in a copper porphyrydeposit Figure 212 shows autocorrelation of cone penetration resistancein North Sea Clay and Figure 213 shows autocorrelation of water con-tent in the compacted clay core of a rock-fill dam An interesting aspectof the last data is that the autocorrelations they reflect are more a func-tion of the construction process through which the core of the damwas placed than simply of space per se The time stream of borrowmaterials weather and working conditions at the time the core was

Spatial variability and geotechnical reliability 89

10

05

0rk

minus05

minus100 5 10 15 20

lag k

Figure 29 Autocorrelations of water content in San Francisco Bay Mud within an intervalof 3 ft (1 m) ( Javete 1983) (reproduced with the authorrsquos permission)

0 5 10

Lak k

15 20

rk

10

05

00

minus05

minus10

Figure 210 Autocorrelations of water content in San Francisco Bay Mud within entire siteexpressed in lag intervals of 25 ft ( Javete 1983) (reproduced with the authorrsquospermission)

placed led to trends in the resulting physical properties of the compactedmaterial

For purposes of modeling and analysis it is usually convenient to approx-imate the autocorrelation structure of the residuals by a smooth functionFor example a commonly used function is the exponential

Rz(δ) = exp(minusδδ0) (213)

in which δ0 is a constant having units of length Other functions commonlyused to represent autocorrelation are shown in Table 22 The distance atwhich Rz(δ) decays to 1e (here δ0) is sometimes called the autocorrelation(or autocovariance) distance

90 Gregory B Baecher and John T Christian

minus200

minus100

0

100

200

300

400

500

50

SEPARATION (M)

CO

VA

RIA

NC

E

10 15

Figure 211 Autocorrelation of rock fracture density in a copper porphyry deposit(Baecher 1980)

Distance of Separation (m)

ρ =exp [ minus rb2]

r = 30 m

At Depth 3 m

0 20 40 60 800 20 40 60 80

10

08

06

04

02

00Cor

rela

tion

Coe

ffici

ent10

08

06

04

02

00Cor

rela

tion

Coe

ffici

ent

Distance of Separation (m)

r = 30 m

At Depth 36 m

Figure 212 Autocorrelation of cone penetration resistance in North Sea Clay (Tang1979)

215 Measurement noise

Random measurement error is that part of data scatter attributable toinstrument- or operator-induced variations from one test to another Thisvariability may sometimes increase or decrease a measurement but itseffect on any one specific measurement is unknown As a first approxima-tion instrument and operator effects on measured properties of soils canbe represented by a frequency diagram In repeated testing ndash presumingthat repeated testing is possible on the same specimen ndash measured val-ues differ Sometimes the measurement is higher than the real value ofthe property sometimes it is lower and on average it may systematically

Spatial variability and geotechnical reliability 91

0 20 40 60 80 100

Lag Distance (test number)

050

025

000

minus025

minus050

Aut

ocov

aria

nce

Figure 213 Autocorrelation of water content in the compacted clay core of a rock-filldam (Beacher 1987)

Table 22 One-dimensional autocorrelation models

Model Equation Limits of validity(dimension of relevant space)

White noise Rx(δ) =

1 if δ = 00 otherwise Rn

Linear Rx(δ) =

1 minus|δ|δ0 if δ le δ00 otherwise R1

Exponential Rx(δ) = exp(minusδδ0) R1

Squared exponential(Gaussian)

Rx(δ) = exp2(minusδδ0) Rd

Power Cz(δ) = σ 21(|δ|2δ20)minusβ Rd β gt 0

differ from the real value This is usually represented by a simple model ofthe form

z = bx + e (214)

in which z is a measured value b is a bias term x is the actual propertyand e is a zero-mean independent and identically distributed (IID) errorThe systematic difference between the real value and the average of themeasurements is said to be measurement bias while the variability of themeasurements about their mean is said to be random measurement errorThus the error terms are b and e The bias is often assumed to be uncertainwith mean microb and standard deviation σb The IID random perturbation is

92 Gregory B Baecher and John T Christian

usually assumed to be Normally distributed with zero mean and standarddeviation σe

Random errors enter measurements of soil properties through a varietyof sources related to the personnel and instruments used in soil investiga-tions or laboratory testing Operator or personnel errors arise in many typesof measurements where it is necessary to read scales personal judgment isneeded or operators affect the mechanical operation of a piece of testingequipment (eg SPT hammers) In each of these cases operator differenceshave systematic and random components One person for example mayconsistently read a gage too high another too low If required to make aseries of replicate measurements a single individual may report numbersthat vary one from the other over the series

Instrumental error arises from variations in the way tests are set uploads are delivered or soil response is sensed The separation of mea-surement errors between operator and instrumental causes is not onlyindistinct but also unimportant for most purposes In triaxial tests soilsamples may be positioned differently with respect to loading platens insucceeding tests Handling and trimming may cause differing amounts ofdisturbance from one specimen to the next Piston friction may vary slightlyfrom one movement to another or temperature changes may affect fluidsand solids The aggregate result of all these variables is a number of dif-ferences between measurements that are unrelated to the soil properties ofinterest

Assignable causes of minor variation are always present because a verylarge number of variables affect any measurement One attempts to controlthose that have important effects but this leaves uncontrolled a large numberthat individually have only small effects on a measurement If not identifiedthese assignable causes of variation may influence the precision and possiblythe accuracy of measurements by biasing the results For example hammerefficiency in the SPT test strongly affects measured blow counts Efficiencywith the same hammer can vary by 50 or more from one blow to the nextHammer efficiency can be controlled but only at some cost If uncontrolledit becomes a source of random measurement error and increases the scatterin SPT data

Bias error in measurement arises from a number of reasonably well-understood mechanisms Sample disturbance is among the more importantof these mechanisms usually causing a systematic degradation of average soilproperties along with a broadening of dispersion The second major contrib-utor to measurement bias is the phenomenological model used to interpretthe measurements made in testing and especially the simplifying assump-tions made in that model For example the physical response of the testedsoil element might be assumed linear when in fact this is only an approxima-tion the reversal of principal stress direction might be ignored intermediateprincipal stresses might be assumed other than they really are and so forth

Spatial variability and geotechnical reliability 93

The list of possible discrepancies between model assumptions and the realtest conditions is long

Model bias is usually estimated empirically by comparing predictionsmade from measured values of soil engineering parameters against observedperformance Obviously such calibrations encompass a good deal more thanjust the measurement technique they incorporate the models used to makepredictions of field performance inaccuracies in site characterization and ahost of other things

Bjerrumrsquos (1972 1973) calibration of field vein test results for theundrained strength su of clay is a good example of how measurementbias can be estimated in practice This calibration compares values of sumeasured with a field vane against back-calculated values of su from large-scale failures in the field In principle this calibration is a regression analysisof back-calculated su against field vane su which yields a mean trend plusresidual variance about the trend The mean trend provides an estimate ofmicrob while the residual variance provides an estimate of σb The residual vari-ance is usually taken to be the same regardless of the value of x a commonassumption in regression analysis

Random measurement error can be estimated in a variety of wayssome direct and some indirect As a general rule the direct techniques aredifficult to apply to the soil measurements of interest to geotechnical engi-neers because soil tests are destructive Indirect methods for estimating Veusually involve correlations of the property in question either with otherproperties such as index values or with itself through the autocorrelationfunction

The easiest and most powerful methods involve the autocorrelationfunction The autocovariance of z after the trend has been removed becomes

Cz(δ) = Cx(δ) + Ce(δ) (215)

in which Cx(δ) is from Equation (212) and Cx(δ) is the autocovariancefunction of e However since ei and ej are independent except when i = jthe autocovariance function of e is a spike at δ = 0 and zero elsewhereThus Cx(δ) is composed of two functions By extrapolating the observedautocovariance function to the origin an estimate is obtained of the fractionof data scatter that comes from random error In the ldquogeostatisticsrdquo literaturethis is called the nugget effect

216 Example Settlement of shallow footings on sandIndiana (USA)

The importance of random measurement errors is illustrated by a case involv-ing a large number of shallow footings placed on approximately 10 mof uniform sand (Hilldale 1971) The site was characterized by Standard

94 Gregory B Baecher and John T Christian

Penetration blow count measurements predictions were made of settlementand settlements were subsequently measured

Inspection of the SPT data and subsequent settlements reveals an interest-ing discrepancy Since footing settlements on sand tend to be proportional tothe inverse of average blow count beneath the footing it would be expectedthat the coefficient of variation of the settlements equaled approximatelythat of the vertically averaged blow counts Mathematically settlement ispredicted by a formula of the form ρ prop qNc in which ρ = settlementq = net applied stress at the base of the footing and Nc = average cor-rected blow count (Lambe and Whitman 1979) Being multiplicative thecoefficient of variation of ρ should be the same as that of Nc

In fact the coefficient of variation of the vertically averaged blow countsis about Nc

= 045 while the observed values of total settlements for268 footings have mean 035 inches and standard deviation 012 inchesso ρ = (012035) = 034 Why the difference The explanation maybe found in estimates of the measurement noise in the blow count dataFigure 214 shows the horizontal autocorrelation function for the blowcount data Extrapolating this function to the origin indicates that the noise(or small scale) content of the variability is about 50 of the data scattervariance Thus the actual variability of the vertically averaged blow counts is

aboutradic

12

2N =radic

12 (045)2 = 032 which is close to the observed variability

minus04

minus02

00

02

04

06

08

10

0 200 400 600 800 1000 1200

Separation (ft)

Figure 214 Autocorrelation function for SPT blow count in sand (Adapted from Hilldale1971)

Spatial variability and geotechnical reliability 95

of the footing settlements Measurement noise of 50 or even more of theobserved scatter of in situ test data particularly the SPT has been noted onseveral projects

While random measurement error exhibits itself in the autocorrelation orautocovariance function as a spike at δ = 0 real variability of the soil at ascale smaller than the minimum boring spacing cannot be distinguished frommeasurement error when using the extrapolation technique For this reasonthe ldquonoiserdquo component estimated in the horizontal direction may not be thesame as that estimated in the vertical direction

For many but not all applications the distinction between measure-ment error and small-scale variability is unimportant For any engineeringapplication in which average properties within some volume of soil areimportant the small-scale variability averages quickly and therefore has lit-tle effect on predicted performance Thus for practical purposes it can betreated as if it were a measurement error On the other hand if perfor-mance depends on extreme properties ndash no matter their geometric scale ndashthe distinction between measurement error and small scale is importantSome engineers think that piping (internal erosion) in dams is such a phe-nomenon However few physical mechanisms of performance easily cometo mind that are strongly affected by small-scale spatial variability unlessthose anomalous features are continuous over a large extent in at least onedimension

22 Second-moment soil profiles

Natural variability is one source of uncertainty in soil properties the otherimportant source is limited knowledge Increasingly these are referred to asaleatory and epistemic uncertainty respectively (Hartford 1995)3 Limitedknowledge usually causes systematic errors For example limited numbersof tests lead to statistical errors in estimating a mean trend and if there isan error in average soil strength it does not average out In geotechnical reli-ability the most common sources of knowledge uncertainty are model andparameter selection (Figure 215) Aleatory and epistemic uncertainties canbe combined and represented in a second-moment soil profile The second-moment profile shows means and standard deviations of soil properties withdepth in a formation The standard deviation at depth has two componentsnatural variation and systematic error

221 Example SHANSEP analysis of soft claysAlabama (USA)

In the early 1980s Ideal Basic Industries Inc (IDEAL) constructed a cementmanufacturing facility 11 miles south of Mobile Alabama abutting a shipchannel running into Mobile Bay (Baecher et al 1997) A gantry crane at the

96 Gregory B Baecher and John T Christian

Temporal

Spatial

Natural Variability

Model

Parameters

Knowledge Uncertainty

Objectives

Values

Time Preferences

Decision ModelUncertainty

Risk Analysis

Figure 215 Aleatory epistemic and decision model uncertainty in geotechnical reliabilityanalysis

facility unloaded limestone ore from barges moored at a relieving platformand place the ore in a reserve storage area adjacent to the channel As the sitewas underlain by thick deposits of medium to soft plastic deltaic clay con-crete pile foundations were used to support all facilities of the plant exceptfor the reserve limestone storage area This 220 ft (68 m) wide by 750 ft(230 m) long area provides limestone capacity over periods of interrupteddelivery Although the clay underlying the site was too weak to supportthe planned 50 ft (15 m) high stockpile the cost of a pile supported matfoundation for the storage area was prohibitive

To solve the problem a foundation stabilization scheme was conceivedin which limestone ore would be placed in stages leading to consolidationand strengthening of the clay and this consolidation would be hastened byvertical drains However given large scatter in engineering property data forthe clay combined with low factors of safety against embankment stabilityfield monitoring was essential

The uncertainty in soil property estimates was divided between that causedby data scatter and that caused by systematic errors (Figure 216) These wereseparated into four components

bull spatial variability of the soil depositbull random measurement noisebull statistical estimation error andbull measurement or model bias

The contributions were mathematically combined by noting that the vari-ances of these nearly independent contributions are approximately additive

V[x] asymp Vspatial[x]+ Vnoise[x]+ Vstatistical[x]+ Vbias[x] (216)

Spatial variability and geotechnical reliability 97

Uncertainty

Data Scatter

SpatialVariability

MeasurementNoise

SystematicError

ModelBias

StatisticalError

Figure 216 Sources of uncertainty in geotechnical reliability analysis

E-6400

minus180 minus100

minus20

minus40

minus60

minus80

0

20

0

ELE

VAT

ION

(ft)

100 200 280

E-6500

DRAINED ZONE

DENSE SAND

CLCL

CL

CLCL

CL

SANDY MATERIAL

Edge of primary limestone storage areaand stacker

of Westaccess road

MEDIUM-SOFT TO MEDIUMCL-CH GRAY SILTY CLAY

of drainageditch

of 20ft berm Stockplie of 20ft berm

I-4P

SPS-2I-12I-3

N-5381 RLSANORTH

of East occess road

EAST COORDINATE (ft)

DISTANCE FROM THE STOCKPILE CENTERLINE (ft)

E-6600 E-6700 E-6800

Figure 217 West-east cross-section prior to loading

in which V[x] = variance of total uncertainty in the property x Vspatial[x] =variance of the spatial variation of x Vnoise[x]= variance of the measurementnoise in x Vstatistical[x] = variance of the statistical error in the expectedvalue of x and Vbias[x] = variance of the measurement or model bias in xIt is easiest to think of spatial variation as scatter around the mean trendof the soil property and systematic error as uncertainty in the mean trenditself The first reflects soil variability after random measurement error hasbeen removed the second reflects statistical error plus measurement biasassociated with the mean value

Initial vertical effective stresses σ vo were computed using total unitweights and initial pore pressures Figure 22 shows a simplified profile priorto loading (Figure 217) The expected value σ vm profile versus elevation

98 Gregory B Baecher and John T Christian

was obtained by linear regression The straight short-dashed lines showthe standard deviation of the σ vm profile reflecting data scatter about theexpected value The curved long-dashed lines show the standard deviation ofthe expected value trend itself The observed data scatter about the expectedvalue σ vm profile reflects inherent spatial variability of the clay plus ran-dom measurement error in the determination of σ vm from any one testThe standard deviation about the expected value σ vm profile is about 1 ksf(005 MPa) corresponding to a standard deviation in over-consolidationratio (OCR) from 08 to 02 The standard deviation of the expected valueranges from 02 to 05 ksf (001 to 0024 MPa)

Ten CKoUDSS tests were performed on undisturbed clay samples to deter-mine undrained stressndashstrainndashstrength parameters to be used in the stresshistory and normalized soil engineering properties (SHANSEP) procedure ofLadd and Foott (1974) Reconsolidation beyond the in situ σ vm was used tominimize the influence of sample disturbance Eight specimens were shearedin a normally consolidated state to assess variation in the parameter s withhorizontal and vertical locations The last two specimens were subjected to asecond shear to evaluate the effect of OCR The direct simple shear (DSS) testprogram also provided undrained stressndashstrain parameters for use in finiteelement undrained deformation analyses

Since there was no apparent trend with elevation expected valueand standard deviation values were computed by averaging all data toyield

su = σ vos(σ vm

σ vo

)m

(217)

in which s =(0213 plusmn 0028) and m =(085 plusmn 005) As a first approximationit was assumed that 50 of the variation in s was spatial and 50 wasnoise The uncertainty in m estimated from data on other clays is primarilydue to variability from one clay type to another and hence was assumedpurely systematic It was assumed that the uncertainty in m estimated fromonly two tests on the storage area clay resulted from random measurementerror

The SHANSEP su profile was computed using Equation (217) If σ vo σ vms and m are independent and σ vo is deterministic (ie there is no uncertaintyin σ vo) first-order second-moment error analysis leads to the expressions

E[su] = σ voE[s](

E[σ vo]σ vo

)E[m](218)

2[su] = 2[s]+ E2[m]2[σ vm]+ 1n2(

E[σ vm]σ vo

)V[m] (219)

Spatial variability and geotechnical reliability 99

in which E[X] = expected value of X V[X] = variance of X and [X] =radicV[X]E[X]= coefficient of variation of X The total coefficient of variation

of su is divided between spatial and systematic uncertainty such that

2[su] = 2sp[su]+2

sy[su] (220)

Figure 218 shows the expected value su profile and the standard deviationof su divided into spatial and systematic components

Stability during initial undrained loading was evaluated using two-dimensional (2D) circular arc analyses with SHANSEP DSS undrainedshear strength profiles Since these analyses were restricted to the east andwest slopes of the stockpile 2D analyses assuming plane strain conditionsappeared justified Azzouz et al (1983) have shown that this simplifiedapproach yields factors of safety that are conservative by 10ndash15 for similarloading geometries

Because of differences in shear strain at failure for different modes offailure along a failure arc ldquopeakrdquo shear strengths are not mobilized simul-taneously all along the entire failure surface Ladd (1975) has proposed aprocedure accounting for strain compatibility that determines an averageshear strength to be used in undrained stability analyses Fuleihan and Ladd(1976) showed that in the case of the normally consolidated AtchafalayaClay the CKoUDSS SHANSEP strength was in agreement with the averageshear strength computed using the above procedure All the 2D analyses usedthe Modified Bishop method

To assess the importance of variability in su to undrained stability it isessential to consider the volume of soil of importance to the performanceprediction At one extreme if the volume of soil involved in a failure wereinfinite spatial uncertainty would completely average out and the system-atic component uncertainty would become the total uncertainty At the otherextreme if the volume of soil involved were infinitesimal spatial and sys-tematic uncertainties would both contribute fully to total uncertainty Theuncertainty for intermediate volumes of soil depends on the character ofspatial variability in the deposit specifically on the rapidity with which soilproperties fluctuate from one point to another across the site A convenientindex expressing this scale of variation is the autocorrelation distance δ0which measures the distance to which fluctuations of soil properties abouttheir expected value are strongly associated

Too few data were available to estimate autocorrelation distance for thestorage area thus bounding calculations were made for two extreme casesin the 2D analyses Lδ0 rarr 0 (ie ldquosmallrdquo failure surface) and Lδ0 rarr infin(ie ldquolargerdquo failure surface) in which L is the length of the potential fail-ure surface Undrained shear strength values corresponding to significantaveraging were used to evaluate uncertainty in the factor of safety forlarge failure surfaces and values corresponding to little averaging for small

100 Gregory B Baecher and John T Christian

Symbol

20

08

10

12

14

20 25 30

20 25

=E [FS] minus 10

SD[FS]

FILL HEIGHT Hf (ft)30

Mean minusone standarddeviation (SD)

Mean FS = E [FS]

35

35

RE

LIA

BIL

ITY

IND

EX

bFA

CTO

R O

F S

AF

ET

Y F

S

15

10

05

0

Case Effect of uncertainty in

clay undrained shearstrength su

Lro

Lro

Lro

Lro

Lro

Lro

fill total unit weight gfclay undrained shear strengthsu and fill total unit weight gf

(I)

b

infin

infin

infin

0

0

0

Figure 218 Expected value su profile and the standard deviation of su divided into spatialand systematic components

failure surface The results of the 2D stability analysis were plotted as afunction of embankment height

Uncertainty in the FS was estimated by performing stability analyses usingthe procedure of Christian et al (1994) with expected value and expectedvalue minus standard deviation values of soil properties For a given expected

Spatial variability and geotechnical reliability 101

value of FS the larger the standard deviation of FS the higher the chancethat the realized FS is less than unity and thus the lower the actual safety ofthe facility The second-moment reliability index [Equation (21)] was usedto combine E[FS] and SD[FS] in a single measure of safety and related to aldquonominalrdquo probability of failure by assuming FS Normally distributed

23 Estimating autocovariance

Estimating autocovariance from sample data is the same as making anyother statistical estimate Sample data differ from one set of observa-tions to another and thus the estimates of autocovariance differ Theimportant questions are how much do these estimates differ and howmuch might one be in error in drawing inferences There are two broadapproaches Frequentist and Bayesian The Frequentist approach is morecommon in geotechnical practice For discussion of Bayesian approaches toestimating autocorrelation see Zellner (1971) Cressie (1991) or Berger et al(2001)

In either case a mathematical function of the sample observations is usedas an estimate of the true population parameters θ One wishes to determineθ = g(z1 zn) in which z 1 hellip zn is the set of sample observations and θ which can be a scalar vector or matrix For example the sample mean mightbe used as an estimator of the true population mean The realized value of θfor a particular sample z1 hellip zn is an estimate As the probabilistic proper-ties of the z1hellip zn are assumed the corresponding probabilistic propertiesof θ can be calculated as functions of the true population parameters This iscalled the sampling distribution of θ The standard deviation of the samplingdistribution is called the standard error

The quality of the estimate obtained in this way depends on how variablethe estimator θ is about the true value θ The sampling distribution andhence the goodness of an estimate has to do with how the estimate mighthave come out if another sample and therefore another set of observationshad been made Inferences made in this way do not admit of a probabilitydistribution directly on the true population parameter Put another way theFrequentist approach presumes the state of nature θ to be a constant andyields a probability that one would observe those data that actually wereobserved The probability distribution is on the data not on θ Of coursethe engineer or analyst wants the reverse the probability of θ given the dataFor further discussion see Hartford and Baecher (2004)

Bayesian estimation works in a different way Bayesian theory allowsprobabilities to be assigned directly to states of nature such as θ ThusBayesian methods start with an a priori probability distribution f (θ ) whichis updated by the likelihood of observing the sample using Bayesrsquos Theorem

f (θ |z1 zn)infin f (θ )L(θ |z1 zn) (221)

102 Gregory B Baecher and John T Christian

in which f (θ |z1hellip zn) is the a posteriori pdf of θ conditioned on the obser-vations and L(θ |z1 hellip zn) is the likelihood of θ which is the conditionalprobability of z1 hellip zn as a function of θ Note the Fisherian conceptof a maximum likelihood estimator is mathematically related to Bayesianestimation in that both adopt the likelihood principle that all informationin the sample relevant to making an estimate is contained in the Likelihoodfunction however the maximum likelihood approach still ends up with aprobability statement on the variability of the estimator and not on the stateof nature which is an important distinction

231 Moment estimation

The most common (Frequentist) method of estimating autocovariance func-tions for soil and rock properties is the method of moments This uses thestatistical moments of the observations (eg sample means variances andcovariances) as estimators of the corresponding moments of the populationbeing sampled

Given the measurements z1hellip zn made at equally spaced locationsx1hellip xn along a line as for example in a boring the sample autocovarianceof the measurements for separation is

Cz(δ) = 1(n minus δ)

nminusδsumi=1

[z(xi) minus t(xi)z(xi+δ) minus t(xi+δ)] (222)

in which Cz(δ) is the estimator of the autocovariance function at δ (nminus δ) isthe number of data pairs having separation distance δ and t(xi) is the trendremoved from the data at location xi

Often t(xi) is simply replaced by the spatial mean estimated by the meanof the sample The corresponding moment estimator of the autocorrelationR(δ) is obtained by dividing both sides by the sample variance

Rz(δ) = 1s2z (n minus δ)

nminusδsumi=1

[z(xi) minus t(xi)z(xi+δ) minus t(xi+δ)] (223)

in which sz is the sample standard deviation Computationally this simplyreduces to taking all data pairs of common separation distance d calcu-lating the correlation coefficient of that set then plotting the result againstseparation distance

In the general case measurements are seldom uniformly spaced at least inthe horizontal plane and seldom lie on a line For such situations the sampleautocovariance can still be used as an estimator but with some modificationThe most common way to accommodate non-uniformly placed measure-ments is by dividing separation distances into bands and then taking theaverages within those bands

Spatial variability and geotechnical reliability 103

The moment estimator of the autocovariance function requires no assump-tions about the shape of the autocovariance function except that secondmoments exist The moment estimator is consistent in that as the samplesize becomes large E[(θ minus θ )2]rarr 0 On the other hand the moment estima-tor is only asymptotically unbiased Unbiasedness means that the expectedvalue of the estimator over all ways the sample might have been taken equalsthe actual value of the function being estimated For finite sample sizes theexpected values of the sample autocovariance can differ significantly from theactual values yielding negative values beyond the autocovariance distance(Weinstock 1963)

It is well known that the sampling properties of the moment estima-tor of autocorrelation are complicated and that large sampling variances(and thus poor confidence) are associated with estimates at large sep-aration distances Phoon and Fenton (2004) and Phoon (2006a) haveexperimented with bootstrapping approaches to estimate autocorrelationfunctions with promising success These and similar approaches fromstatistical signal processing should be exploited more thoroughly in thefuture

232 Example James Bay

The results of Figure 219 were obtained from the James Bay data ofChristian et al (1994) using this moment estimator The data are froman investigation into the stability of dykes on a soft marine clay at theJames Bay Project Queacutebec (Ladd et al 1983) The marine clay at the site is

Separation Distance

Aut

ocov

aria

nce

30

0

10

20Maximum Likelihood estimate(curve)

Moment estimates

150m100500

Figure 219 Autocovariance of field vane clay strength data James Bay Project (Christianet al 1994 reproduced with the permission of the American Society of CivilEngineers)

104 Gregory B Baecher and John T Christian

approximately 8 m thick and overlies a lacustrine clay The depth-averagedresults of field vane tests conducted in 35 borings were used for the cor-relation analysis Nine of the borings were concentrated in one location(Figures 220 and 221)

First a constant mean was removed from the data Then the product ofeach pair of residuals was calculated and plotted against separation distanceA moving average of these products was used to obtain the estimated pointsNote the drop in covariance in the neighborhood of the origin and alsothe negative sample moments in the vicinity of 50ndash100 m separation Notealso the large scatter in the sample moments at large separation distanceFrom these estimates a simple exponential curve was fitted by inspectionintersecting the ordinate at about 60 of the sample variance This yieldsan autocovariance function of the form

Cz(δ) =

22 kPa2 for δ = 0

13expminusδ23 m for δgt0(224)

in which variance is in kPa2 and distance in m Figure 222 shows variancecomponents for the factor of safety for various size failures

INDEX PROPERTIES FIELD VANE STRESS HISTORY

DE

PT

H Z

(m

)

0

SOILPROFILE

0

0 1 2 3 0 20 6040 0 100 200 300

10 20 30

2

4

6

8

10

12

14

16

18

20

Mean from8 FV

Selectedcu Profile

Ip

IL

Ip () cu (FV) (kPa) s primevo and s primep (kPa)

IL

Selecteds primep Profile

BlockTube

Till

Lacustrineclay

Marineclay

Crust

s primep

s primevo

gb=90kNm3

gb=90kNm3

gb=105kNm3

Figure 220 Soil property data summary James Bay (Christian et al 1994 reproduced withthe permission of the American Society of Civil Engineers)

160

140

120

100

Y a

xis

(m)

80

200 180 160 140 120 100 80 60 40 20 0 minus20

X axis (m)

Fill properties = 20kNmsup3rsquo= 30

Note All slopes are 3 horizontal to1 vertical

Y 123 m

Stage 2Berm 2Berm 1

Foundation clay Critical wedge - stage 2

Critical circles - single stage

Till Limit of vertical drains

6030

Stage 1

Figure 221 Assumed failure geometries for embankments of three heights (Christianet al 1994 reproduced with the permission of the American Society of CivilEngineers)

Embankment Height

6 m 12 m 23 m0

002

004

006

008

Var

ianc

e of

Fac

tor

of S

afet

y

01Rest of Sp VarAve Spatial VarSystematic Error

F = 1500R = 07

F = 1453R = 02

F = 1427R = 007

Figure 222 Variance components of the factor of safety for three embankment heights(Christian et al 1994 reproduced with the permission of the American Societyof Civil Engineers)

106 Gregory B Baecher and John T Christian

233 Maximum likelihood estimation

Maximum likelihood estimation takes as the estimator that value of theparameter(s) θ leading to the greatest probability of observing the dataz1 hellip zn actually observed This is found by maximizing the likelihoodfunction L(θ |z1 hellip zn) Maximum likelihood estimation is parametricbecause the distributional form of the pdf f (z1 hellip zn|θ ) must be specifiedIn practice the estimate is usually found by maximizing the log-likelihoodwhich because it deals with a sum rather than a product and becausemany common probability distributions involve exponential terms is moreconvenient

The appeal of the maximum likelihood estimator is that it possessesmany desirable sampling properties Among others it has minimum vari-ance (although not necessarily unbiased) is consistent and asymptoticallyNormal The asymptotic variance of θML is

limnrarrinfin Var[θML]=Iz(θ ) = nE[minusδ2LLpartθ2] (225)

in which Iz(θ ) is Fisherrsquos Information (Barnett 1982) and LL is the log-likelihood

Figure 223 shows the results of simulated sampling experiments in whichspatial fields were generated from a multivariate Gaussian pdf with speci-fied mean trend and autocovariance function Samples of sizes n = 36 64and 100 were taken from these simulated fields and maximum likelihoodestimators used to obtain estimates of the parameters of the mean trend andautocovariance function The smooth curves show the respective asymp-totic sampling distributions which in this case conform well with the actualestimates (DeGroot and Baecher 1993)

An advantage of the maximum likelihood estimator over moment esti-mates in dealing with spatial data is that it allows simultaneous estimation ofthe spatial trend and autocovariance function of the residuals Mardia andMarshall (1984) provide an algorithmic procedure finding the maximumDeGroot and Baecher used the Mardia and Marshall approach in analyzingthe James Bay data First they removed a constant mean from the data andestimated the autocovariance function of the residuals as

Cz(δ) =

23forδ = 0133 exp minus δ214 for δ gt 0

(226)

in which variance is in kPa2 and distance is in m Then using estimating thetrend implicitly

β0 = 407 kPa

β1 = minus20 times 10minus3 kPam

Spatial variability and geotechnical reliability 107

20

15

Num

ber

of D

ata

Num

ber

of D

ata

10

5

0

15

10

5

0

20

Num

ber

of D

ata

15

10

5

0

20

04 06 08 10 12 14 16

04 06 08 10 12 14 16

04 06 08 10Variance

12 14 16

0

4

3

2

1

0

1

2 f(x)

f(x)

3

4(a) n=36

(b) n=64

Asymptotic

0

1

2 f(x)

3

4(c) n=100

Asymptotic

Asymptotic

Figure 223 Simulated sampling experiments in which spatial fields were generated froma multivariate Gaussian pdf with specified mean trend and autocovariancefunction (DeGroot and Baecher 1993 reproduced with the permission of theAmerican Society of Civil Engineers)

β2 = minus59 times 10minus3 kPam (227)

Cz(δ) =

23 kPa2 forδ = 0133 kPa2 expminus(δ214 m) forδ gt 0

The small values of β1 and β2 suggest that the assumption of constantmean is reasonable Substituting a squared-exponential model for the

108 Gregory B Baecher and John T Christian

autocovariance results in

β0 = 408 kPa

β1 = minus21 times 10minus3 kPam

β2 = minus61 times 10minus3 kPam (228)

Cz(δ) =

229 kPa2 forδ = 0127 kPa2 expminus(δ373 m)2 forδ gt 0

The exponential model is superimposed on the moment estimates ofFigure 219

The data presented in this case suggest that a sound approach to estimatingautocovariance should involve both the method of moments and maximumlikelihood The method of moments gives a plot of autocovariance versusseparation providing an important graphical summary of the data whichcan be used as a means of determining if the data suggest correlation and forselecting an autocovariance model This provides valuable information forthe maximum likelihood method which then can be used to obtain estimatesof both autocovariance parameters and trend coefficients

234 Bayesian estimation

Bayesian inference for autocorrelation has not been widely used in geotechni-cal and geostatistical applications and it is less well developed than momentestimates This is true despite the fact that Bayesian inference yields theprobability associated with the parameters given the data rather than theconfidence in the data given the probabilistic model An intriguing aspect ofBayesian inference of spatial trends and autocovariance functions is that formany of the non-informative prior distributions one might choose to reflectlittle or no prior information about process parameters (eg the Jeffreysprior the Laplace prior truncated parameter spaces) the posterior pdfrsquoscalculated through Bayesian theorem are themselves improper usually inthe sense that they do not converge toward zero at infinity and thus thetotal probability or area under the posterior pdf is infinite

Following Berger (1993) Boger et al (2001) and Kitanidis (1985 1997)the spatial model is typically written as a Multinomial random process

z(x) =ksum

i=1

fi(x)β + ε(x) (229)

in which fi(x) are unknown deterministic functions of the spatial loca-tions x and ε(x) is a zero-mean spatial random function The random

Spatial variability and geotechnical reliability 109

term is spatially correlated with an isotropic autocovariance function Theautocovariance function is assumed to be non-negative and to decreasemonotonically with distance to zero at infinite separation These assumptionsfit most common autocovariance functions in geotechnical applications TheLikelihood of a set of observations z = z1 hellip zn is then

L(βσ |z) = (2πσ 2)minusn2|Rθ |minus12 expminus 1

2σ 2 (z minus Xβ)tRminus1θ (z minus Xβ)

(230)

in which X is the (n times k) matrix defined by Xij=fj(Xi) Rθ is the matrix ofcorrelations among the observations dependent on the parameters and |Rθ |is the determinant of the correlation matrix of the observations

In the usual fashion a prior non-informative distribution on the param-eters (β σ θ ) might be represented as f (β σ θ )infin(σ 2)minusaf (θ ) for variouschoices of the parameter a and of the marginal pdf f (θ ) The obvious choicesmight be a = 1 f (θ ) = 1 a = 1 f (θ ) = 1θ or a = 1 f (θ ) = 1 but eachof these leads to an improper posterior pdf as does the well-known Jeffreysprior A proper informative prior does not share this difficulty but it iscorrespondingly hard to assess from usually subjective opinion Given thisproblem Berger et al (2001) suggest the reference non-informative prior

f (βσθ ) prop 1σ 2

(|W2

θ |minus |W2θ |

(n minus k)

)12

(231)

in which

W2θ = partRθ

partθRminus1

θ I minus X(XprimeRminus1θ X)minus1X

primeRminus1

θ (232)

This does lead to a proper posterior The posterior pdf is usually evaluatednumerically although depending on the choice of autocovariance functionmodel and the extent to which certain of the parameters of that model areknown closed-form solutions can be obtained Berger et al (2001) presenta numerically calculated example using terrain data from Davis (1986)

235 Variograms

In mining the importance of autocorrelation for estimating ore reserveshas been recognized for many years In mining geostatistics a functionrelated to the autocovariance called the variogram (Matheron 1971) iscommonly used to express the spatial structure of data The variogramrequires a less-restrictive statistical assumption on stationarity than doesthe autocovariance function and it is therefore sometimes preferred for

110 Gregory B Baecher and John T Christian

inference problems On the other hand the variogram is more difficult touse in spatial interpolation and engineering analysis and thus for geotech-nical purposes the autocovariance is used more commonly In practice thetwo ways of characterizing spatial structure are closely related

Whereas the autocovariance is the expected value of the product oftwo observations the variogram 2γ is the expected value of the squareddifference

2γ = E[z(xi) minus z(xj)2] = Var[z(xi) minus z(xj)] (233)

which is a function of only the increments of the spatial properties nottheir absolute values Cressie (1991) points out that in fact the commondefinition of the variogram as the mean squared difference ndash rather thanas the variance of the difference ndash limits applicability to a more restric-tive class of processes than necessary and thus the latter definition is tobe preferred None the less one finds the former definition more commonlyreferred to in the literature The term γ is referred to as the semivariogramalthough caution must be exercised because different authors interchangethe terms The concept of average mean-square difference has been usedin many applications including turbulence (Kolmogorov 1941) and timeseries analysis (Jowett 1952) and is alluded to in the work of Mateacutern(1960)

The principal advantage of the variogram over the autocovariance is thatit makes less restrictive assumptions on the stationarity of the spatial prop-erties being sampled specifically only that their increment and not theirmean is stationary Furthermore the use of geostatistical techniques hasexpanded broadly so that a great deal of experience has been accumulatedwith variogram analysis not only in mining applications but also in environ-mental monitoring hydrology and even geotechnical engineering (Chiassonet al 1995 Soulie and Favre 1983 Soulie et al 1990)

For spatial variables with stationary means and autocovariances (iesecond-order stationary processes) the variogram and autocovariance func-tion are directly related by

γ (δ) = Cz(0) minus Cz(δ) (234)

Common analytical forms for one-dimensional variograms are given inTable 23

For a stationary process as |δ rarr infinCz(δ) rarr 0 thus γ (δ) rarr Cz(0) =Var(z(x)) This value at which the variogram levels off 2Cz(δ) is called thesill value The distance at which the variogram approaches the sill is calledthe range The sampling properties of the variogram are summarized byCressie (1991)

Spatial variability and geotechnical reliability 111

Table 23 One-dimensional variogram models

Model Equation Limits of validity

Nugget g(δ) =

0 if δ = 01 otherwise Rn

Linear g(δ) =

0 if δ = 0c0 + b||δ|| otherwise R1

Spherical g(δ) =

(15)(δa) minus (12)(δa)3 if δ = 01 otherwise

Rn

Exponential g(δ) = 1 minus exp(minus3δa) R1

Gaussian g(δ) = 1 minus exp(minus3δ2a2) Rn

Power g(δ) = hω Rn 0lt δ lt2

24 Random fields

The application of random field theory to spatial variation is based on theassumption that the property of concern z(x) is the realization of a randomprocess When this process is defined over the space x isin S the variable z(x)is said to be a stochastic process In this chapter when S has dimensiongreater than one z(x) is said to be a random field This usage is more or lessconsistent across civil engineering although the geostatistics literature usesa vocabulary all of its own to which the geotechnical literature occasionallyrefers

A random field is defined as the joint probability distribution

Fx1middotmiddotmiddot xn

(z1 zn

) = Pz(x1)le z1 z

(xn

)le zn

(235)

This joint probability distribution describes the simultaneous variation of thevariables z within a space Sx Let E[z(x)] = micro(x) be the mean or trend of z(x)and let Var[z(x)] = σ 2(x) be the variance The covariances of z(x1) hellip z(xn)are defined as

Cov[z(xi)z(xj)] = E[(z(xi) minusmicro(xi)) middot (z(xj) minusmicro(xj))] (236)

A random field is said to be second-order stantionary (weak or wide-sensestationary) if E[z(x)]= micro for all x and Cov[z(xi)z(xj)] depends only onvector separation of xi and xj and not on location Cov[z(xi)z(xj)] =Cz(xi minus xj) in which Cz(xindashxj) is the autocovariance function The randomfield is said to be stationary (strong or strict stationarity) if the complete prob-ability distribution Fx1xn

(z1 zn) is independent of absolute location

112 Gregory B Baecher and John T Christian

depending only on vector separations among the xihellipxn Strong stationarityimplies second-order stationarity In the geotechnical literature stationar-ity is sometimes referred to as statistical homogeneity If the autocovariancefunction depends only on the absolute separation distance and not directionthe random field is said to be isotropic

Ergodicity is a concept originating in the study of time series in whichone observes individual time series of data and wishes to infer proper-ties of an ensemble of all possible time series The meaning and practicalimportance of ergodicity to the inference of unique realizations of spatialrandom fields is less well-defined and is debated in the literature Simplyergodicity means that the probabilistic properties of a random process(field) can be completely estimated from observing one realization of thatprocess For example the stochastic time series z(t) = ν + ε(t) in which ν

is discrete random variable and ε(t) is an autocorrelated random processof time is non-ergotic In one realization of the process there is but onevalue of ν and thus the probability distribution of ν cannot be estimatedOne would need to observe many realizations of zt in order to have suf-ficiently many observations of ν to estimate Fν(ν) Another non-ergoticprocess of more relevance to the spatial processes in geotechnical engi-neering is z(x) = ν + ε(x) in which the mean of z(x) varies linearly withlocation x In this case z(x) is non-stationary Var[z(x)] increases with-out limit as the window within which z(x) is observed increases andthe mean mz of the sample of z(x) is a function of the location of thewindow

The meaning of ergodicity for spatial fields of the sort encountered ingeotechnical engineering is less clear and has not been widely discussed inthe literature An assumption weaker than full erogodicity which none theless should apply for spatial fields is that the observed sample mean mz andsample autocovariance function Cz(δ) converge in mean-squared error to therespective random field mean and autocovariance function as the volumeof space within which they are sampled increases This means that as thevolume of space increases E[(mz minusmicro)2]rarr0 and E[(Cz(δ) minus Cz(δ)2] rarr 0Soong and Grigoriu (1993) provide conditions for checking ergodicity inmean and autocorrelation

When the joint probability distribution Fx1xn(z1 zn) is multivari-

ate Normal (Gaussian) the process z(x) is said to be a Gaussian randomfield A sufficient condition for ergodicity of a Gaussian random field isthat lim|δ|rarrinfinCz(δ) = 0 This can be checked empirically by inspecting thesample moments of the autocovariance function to ensure they convergeto 0 Cressie (1991) notes limitations of this procedure Essentially all theanalytical autocovariance functions common in the geotechnical literatureobey this condition and few practitioners appear concerned about verifyingergodicity Christakos (1992) suggests that in practical situations it isdifficult or impossible to verify ergodicity for spatial fields

Spatial variability and geotechnical reliability 113

A random field that does not meet the conditions of stationarity is saidto be non-stationary Loosely speaking a non-stationary field is statisticallyheterogeneous It can be heterogeneous in a number of ways In the sim-plest case the mean may be a function of location for example if there is aspatial trend that has not been removed In a more complex case the vari-ance or autocovariance function may vary in space Depending on the wayin which the random field is non-stationary sometimes a transformation ofvariables can convert a non-stationary field to a stationary or nearly station-ary field For example if the mean varies with location perhaps a trend canbe removed

In the field of geostatistics a weaker assumption is made on stationaritythan that described above Geostatisticians usually assume only that incre-ments of a spatial process are stationary (ie differences |z1 minus z2|) and thenoperate on the probabilistic properties of those increments This leads to theuse of the variogram rather than the autocovariance function Stationarityof the autocovariance function implies stationarity of the variogram but thereverse is not true

Like most things in the natural sciences stationarity is an assumption ofthe model and may only be approximately true in the world Also station-arity usually depends on scale Within a small region soil properties maybehave as if drawn from a stationary process whereas the same propertiesover a larger region may not be so well behaved

241 Permissible autocovariance functions

By definition the autocovariance function is symmetric meaning

Cz(δ) = Cz(minusδ) (237)

and bounded meaning

Cz(δ) le Cz(0) = σ 2z (238)

In the limit as distance becomes large

lim |δ|rarrinfinCz(δ)

|δ|minus(nminus1)2 = 0 (239)

In general in order for Cz(δ) to be a permissible autocovariance functionit is necessary and sufficient that a continuous mathematical expression ofthe form

msumi=1

msumj=1

kikjCz(δ) ge 0 (240)

114 Gregory B Baecher and John T Christian

be non-negative-definite for all integers m scalar coefficients k1hellipkmand δ This condition follows from the requirement that variances of linearcombinations of the z(xi) of the form

var

[msum

i=1

kiz(xi)

]=

msumi=1

msumj=1

kikjCz(δ) ge 0 (241)

be non-negative ie the matrix is positive definite (Cressie 1991) Christakos(1992) discusses the mathematical implications of this condition on selectingpermissible forms for the autocovariance Suffice it to say that analyticalmodels of autocovariance common in the geotechnical literature usuallysatisfy the condition

Autocovariance functions valid in a space of dimension d are validin spaces of lower dimension but the reverse is not necessarily trueThat is a valid autocovariance function in 1D is not necessarily validin 2D or 3D Christakos (1992) gives the example of the linearly decliningautcovariance

Cz(δ) =σ 2(1 minus δδ0) for 0 le δ le δ0

0 for δ gt δ0(242)

which is valid in 1D but not in higher dimensionsLinear sums of valid autocovariance functions are also valid This means

that if Cz1(δ) and Cz2(δ) are valid then the sum Cz1(δ)+Cz2(δ) is also a validautocovariance function Similarly if Cz(δ) is valid then the product with ascalar αCz(δ) is also valid

An autocovariance function in d-dimensional space is separable if

Cz(δ) =dprod

i=1

Czi(δi) (243)

in which δ is the d-dimensioned vector of orthogonal separation distancesδ1hellipδd and Ci(δi) is the one-dimensional autocovariance function indirection i For example the autocovariance function

Cz(δ) = σ 2 expminusa2|δ|2= σ 2 expminusa2(δ1

2 +middotmiddot middot+ δ2d) (244)

= σ 2dprod

i=1

expminusa2δ2i

is separable into its one-dimensional components

Spatial variability and geotechnical reliability 115

The function is partially separable if

Cz(δ) = Cz(δi)Cz(δj =i) (245)

in which Cz(δj =i) is a (d minus 1) dimension autocovariance function implyingthat the function can be expressed as a product of autocovariance func-tions of lower dimension fields The importance of partial separability togeotechnical applications as noted by VanMarcke (1983) is the 3D case ofseparating autocorrelation in the horizontal plane from that with depth

Cz(δ1δ2δ3) = Cz(δ1δ2)Cz(δ3) (246)

in which δ1 δ2 are horizontal distances and δ3 is depth

242 Gaussian random fields

The Gaussian random field is an important special case because it is widelyapplicable due to the Central Limit Theorem has mathematically conve-nient properties and is widely used in practice The probability densitydistribution of the Gaussian or Normal variable is

fz(z) = minus 1radic2πσ

exp

minus1

2

(x minusmicro

σ

)2

(247)

for minusinfin le z le infin The mean is E[z] = micro and variance Var[z] = σ 2 For themultivariate case of vector z of dimension n the correponding pdf is

fz(z) = (2π )minusn2||minus12 expminus1

2(z minusmicro)primeminus1(z minusmicro)

(248)

in which micro is the mean vector and the covariance matrix

ij =Cov[zi(x)zj(x)

](249)

Gaussian random fields have the following convenient properties (Adler1981) (1) they are completely characterized by the first- and second-ordermoments the mean and autocovarinace function for the univariate caseand mean vector and autocovariance matrix (function) for the multivari-ate case (2) any subset of variables of the vector is also jointly Gaussian(3) the conditional probability distributions of any two variables or vectorsare also Gaussian distributed (4) if two variables z1 and z2 are bivariateGaussian and if their covariance Cov[z1 z2] is zero then the variables areindependent

116 Gregory B Baecher and John T Christian

243 Interpolating random fields

A problem common in site characterization is interpolating among spa-tial observations to estimate soil or rock properties at specific locationswhere they have not been observed The sample observations themselvesmay have been taken under any number of sampling plans random sys-tematic cluster and so forth What differentiates this spatial estimationquestion from the sampling theory estimates in preceding sections of thischapter is that the observations display spatial correlation Thus the assump-tion of IID observations underlying the estimator results is violated in animportant way This question of spatial interpolation is also a problemcommon to the natural resources industries such as forestry (Mateacutern 1986)and mining (Matheron 1971) but also geohydrology (Kitanidis 1997) andenvironmental monitoring (Switzer 1995)

Consider the case for which the observations are sampled from aspatial population with constant mean micro and autocovariance functionCz(δ) = E[z(xi)z(xi+δ)] The set of observations z=zihellip zn thereforehas mean vector m in which all the terms are equal and covariancematrix

=⎡⎢⎣

Var(z1) middot middot middot Cov(z1zn)

Cov(znz1) middot middot middot Var(zn)

⎤⎥⎦ (250)

in which the terms z(xi) are replaced by zi for convenience These termsare found from the autocovariance function as Cov(z(xi)z(xj)) = Cz (δij) inwhich δij is the (vector) separation between locations xi and xj

In principle we would like to estimate the full distribution of z(x0) atan unobserved location x0 but in general this is computationally intensiveif a large grid of points is to be interpolated Instead the most commonapproach is to construct a simple linear unbiased estimator based on theobservations

z(x0) =nsum

i=1

wiz(xi) (251)

in which the weights w=w1hellip wn are scalar values chosen to makethe estimate in some way optimal Usually the criteria of optimality areunbiasedness and minimum variance and the result is sometimes called thebest linear unbiased estimator (BLUE)

The BLUE estimator weights are found by expressing the variance of theestimate z(x0) using a first-order second-moment formulation and minimiz-ing the variance over w using a Lagrange multiplier approach subject to the

Spatial variability and geotechnical reliability 117

condition that the sum of the weights equals one The solution in matrixform is

w = Gminus1h (252)

in which w is the vector of optimal weights and the matrices G and h relatethe covariance matrix of the observations and the vector of covariances of theobservations to the value of the spatial variable at the interpolated locationx0 respectively

G =

⎡⎢⎢⎢⎣

Var(z1) middot middot middot Cov(z1zn) 1

1

Cov(znz1) middot middot middot Var(zn) 11 1 1 0

⎤⎥⎥⎥⎦

h =

⎡⎢⎢⎢⎣

Cov(z1z0)

Cov(znz0)1

⎤⎥⎥⎥⎦

(253)

The resulting estimator variance is

Var(z0) = E[(z0 minus z0)2]

= Var(z0) minusnsum

i=1

wiCov(z0zi) minusλ (254)

in which λ is the Lagrange multiplier resulting from the optimization Thisis a surprisingly simple and convenient result and forms the basis of theincreasingly vast literature on the subject of so-called kriging in the field ofgeostatistics For regular grids of observations such as a grid of borings analgorithm can be established for the points within an individual grid celland then replicated for all cells to form an interpolated map of the largersite or region (Journel and Huijbregts 1978) In the mining industry andincreasingly in other applications it has become common to replace the auto-covariance function as a measure of spatial association with the variogram

244 Functions of random fields

Thus far we have considered the properties of random fields themselvesIn this section we consider the extension to properties of functions of ran-dom fields Spatial averaging of random fields is among the most importantconsiderations for geotechnical engineering Limiting equilibrium stabil-ity of slopes depends on the average strength across the failure surface

118 Gregory B Baecher and John T Christian

Settlements beneath foundations depend on the average compressibility ofthe subsurface soils Indeed many modes of geotechnical performance ofinterest to the engineer involve spatial averages ndash or differences amongspatial averages ndash of soil and rock properties Spatial averages also playa significant role in mining geostatistics where average ore grades withinblocks of rock have important implications for planning As a result thereis a rich literature on the subject of averages of random fields only a smallpart of which can be reviewed here

Consider the one-dimensional case of a continuous scalar stochastic pro-cess (1D random field) z(x) in which x is location and z(x) is a stochasticvariable with mean microz assumed to be constant and autocovariance functionCz(r) in which r is separation distance r = (x1 minusx2) The spatial average ormean of the process within the interval [0X] is

MXz(x) = 1X

int X

0z(x)dx (255)

The integral is defined in the common way as a limiting sum of z(x) valueswithin infinitesimal intervals of x as the number of intervals increasesWe assume that z(x) converges in a mean square sense implying the existenceof the first two moments of z(x) The weaker assumption of convergence inprobability which does not imply existence of the moments could be madeif necessary (see Parzen 1964 1992) for more detailed discussion)

If we think of MXz(x) as a sample observation within one interval of theprocess z(x) then over the set of possible intervals that we might observeMXz(x) becomes a random variable with mean variance and possiblyother moments Consider first the integral of z(x) within intervals of length XParzen (1964) shows that the first two moments of

int X0 z(x)dx are

E

[int X

0z(x)dx

]=int X

0micro(x)dx = microX (256)

Var

[int X

0z(x)dx

]=int X

0

int X

0Cz(xi minus xj)dxidxj = 2

int X

0(X minus r)Cz(r)dr

(257)

and that the autocovariance function of the integralint X

0 z(x)dx as the interval[0 X] is allowed to translate along dimension x is (VanMarcke 1983)

Cint X0 z(x)dx(r) = Cov

[int X

0z(x)dx

int r+X

rz(x)dx

]

=int X

0

int X

0Cz(r + xi minus xj)dxidxj (258)

Spatial variability and geotechnical reliability 119

The corresponding moments of the spatial mean MXz(x) are

E[MXz(x)]= E

[1X

int X

0z(x)dx

]=int X

0

1Xmicro(x)dx = micro (259)

Var[MXz(x)]= Var

[1X

int X

0z(x)dx

]= 2

X2

int X

0(X minus r)Cz(r)dr (260)

CMXz(x)(r) = Cov

[1X

int X

0z(x)dx

1X

int r+X

rz(x)dx

]

= 1X2

int X

0

int X

0Cz(r + xi minus xj)dxidxj (261)

The effect of spatial averaging is to smooth the process The varianceof the averaged process is smaller than that of the original process z(x)and the autocorrelation of the averaged process is wider Indeed averaging issometimes referred to as smoothing (Gelb and Analytic Sciences CorporationTechnical Staff 1974)

The reduction in variance from z(x) to the averaged process MXz(x) canbe represented in a variance reduction function γ (X)

γ (X) = Var[MXz(x)]

var[z(x)] (262)

The variance reduction function is 10 for X = 0 and decays to zero as Xbecomes large γ (X) can be calculated from the autocovariance function ofz(x) as

γ (X) = 2x

xint0

(1 minus r

X

)R2(r)dr (263)

in which Rz(r) is the autocorrelation function of z(x) Note that the squareroot of γ (X) gives the corresponding reduction of the standard deviationof z(x) Table 24 gives one-dimensional variance reduction functions forcommon autocovariance functions It is interesting to note that each of thesefunctions is asymptotically proportional to 1X Based on this observationVanMarcke (1983) proposed a scale of fluctuation θ such that

θ = limXrarrinfin

X γ (X) (264)

or γ (X) = θ X as X rarr infin that is θ X is the asymptote of the variancereduction function as the averaging window expands The function γ (X)

Tabl

e2

4V

aria

nce

redu

ctio

nfu

nctio

nsfo

rco

mm

on1D

auto

cova

rian

ces

(aft

erV

anM

arck

e19

83)

Mod

elAu

toco

rrel

atio

nVa

rianc

ere

duct

ion

func

tion

Scal

eof

fluct

uatio

n

Whi

teno

ise

R x(δ

)= 1

ifδ=

00

othe

rwise

γ(X

)= 1

ifX

=0

0ot

herw

ise0

Line

arR x

(δ)= 1

minus|δ|

δ n

ifδ

leδ 0

0ot

herw

iseγ

(X)= 1

minusX3δ

0if

Xle

δ 0(δ

0X

)[ 1minusδ 03X] ot

herw

iseδ 0

Expo

nent

ial

R x(δ

)=ex

p(minusδ

δ 0

(X)=

2(δ 0X

)2( X δ 0

minus1+

exp2

(minusXδ 0

))4δ

0

Squa

red

expo

nent

ial

(Gau

ssia

n)R x

(δ)=

exp2

(minus|δ|

δ 0

(X)=

(δ0

X)2[ radic π

X δ 0

(minusXδ 0

)+ex

p2(minus

Xδ 0

)minus1]

inw

hich

is

the

erro

rfu

nctio

n

radic πδ 0

Spatial variability and geotechnical reliability 121

converges rapidly to this asymptote as X increases For θ to exist it isnecessary that Rz(r) rarr0 as r rarr infin that is that the autocorrelation func-tion decreases faster than 1r In this case θ can be found from the integralof the autocorrelation function (the moment of Rz(r) about the origin)

θ = 2int infin

0Rz(r)dr =

int infin

minusinfinRz(r)dr (265)

This concept of summarizing the spatial or temporal scale of autocorrelationin a single number typically the first moment of Rz(r) is used by a variety ofother workers and in many fields Taylor (1921) in hydrodynamics calledit the diffusion constant (Papoulis and Pillai 2002) Christakos (1992) ingeoscience calls θ 2 the correlation radius Gelhar (1993) in groundwaterhydrology calls θ the integral scale

In two dimensions the equivalent expressions for the mean and varianceof the planar integral

int X0

int X0 z(x)dx are

E

[int X

0z(x)dx

]=int X

0micro(x)dx = microX (266)

Var

[int X

0z(x)dx

]=int X

0

int X

0Cz(xi minus xj)dxidxj = 2

int X

0(X minus r)Cz(r)dr

(267)

Papoulis and Pillai (2002) discuss averaging in higher dimensions as doElishakoff (1999) and VanMarcke (1983)

245 Stochastic differentiation

The continuity and differentiability of a random field depend on the con-vergence of sequences of random variables z(xa)z(xb) in which xa xb aretwo locations with (vector) separation r = |xa minusxb| The random field is saidto be continuous in mean square at xa if for every sequence z(xa)z(xb)E2[z(xa)minusz(xb)] rarr0 as rrarr0 The random field is said to be continuousin mean square throughout if it is continuous in mean square at every xaGiven this condition the random field z(x) is mean square differentiablewith partial derivative

partz(x)partxi

= lim|r|rarr0

z(x + rδi) minus z(x)r

(268)

in which the delta function is a vector of all zeros except the ith termwhich is unity While stronger or at least different convergence propertiescould be invoked mean square convergence is often the most natural form in

122 Gregory B Baecher and John T Christian

practice because we usually wish to use a second-moment representation ofthe autocovariance function as the vehicle for determining differentiability

A random field is mean square continuous if and only if its autocovariancefunction Cz(r) is continuous at |r| = 0 For this to be true the first derivativesof the autocovariance function at |r|=0 must vanish

partCz(r)partxi

= 0 for all i (269)

If the second derivative of the autocovariance function exists and is finite at|r| = 0 then the field is mean square differentiable and the autocovariancefunction of the derivative field is

Cpartzpartxi(r) = part2Cz(r)partx2

i (270)

The variance of the derivative field can then be found by evaluating the auto-covariance Cpartzpartxi

(r) at |r| = 0 Similarly the autocovariance of the secondderivative field is

Cpart2zpartxipartxj(r) = part4Cz(r)partx2

i partx2j (271)

The cross covariance function of the derivatives with respect to xi and xj inseparate directions is

Cpartzpartxipartzpartxj(r) = minuspart2Cz(r)partxipartxj (272)

Importantly for the case of homogeneous random fields the field itself z(x)and its derivative field are uncorrelated (VanMarcke 1983)

So the behavior of the autocovariance function in the neighborhood ofthe origin is the determining factor for mean-square local properties of thefield such as continuity and differentiability (Crameacuter and Leadbetter 1967)Unfortunately the properties of the derivative fields are sensitive to thisbehavior of Cz(r) near the origin which in turn is sensitive to the choice ofautocovariance model Empirical verification of the behavior of Cz(r) nearthe origin is exceptionally difficult Soong and Grigoriu (1993) discuss themean square calculus of stochastic processes

246 Linear functions of random fields

Assume that the random field z(x) is transformed by a deterministicfunction g() such that

y(x) = g[z(x)] (273)

In this equation g[z(x0)] is a function of z alone that is not of x0 and not ofthe value of z(x) at any x other than x0 Also we assume that the transforma-tion does not depend on the value of x that is the transformation is space- or

Spatial variability and geotechnical reliability 123

time-invariant y(z + δ) = g[z(x + δ)] Thus the random variable y(x) is adeterministic transformation of the random variable z(x) and its probabilitydistribution can be obtained from derived distribution methods Similarlythe joint distribution of the sequence of random variables y(x1) hellip y(xn)can be determined from the joint distribution of the sequence of randomvariables x(x1) hellip x(xn) The mean of y(x) is then

E[y(x)] =infinint

minusinfing(z)fz(z(x))dz (274)

and the autocorrelation function is

Ry(y1y2) = E[y(x1)y(x2)] =infinint

minusinfin

infinintminusinfin

g(z1)g(z2)fz(z(x1)z(x2))dz1dz2

(275)

Papoulis and Pillai(2002) show that the process y(x) is (strictly) stationary ifz(x) is (strictly) stationary Phoon (2006b) discusses limitations and practicalmethods of solving this equation Among the limitations is that such non-Gaussian fields may not have positive definite covariance matrices

The solutions for nonlinear transformations are difficult but for linearfunctions general results are available The mean of y(x) for linear g(z) isfound by transforming the expected value of z(x) through the function

E[y(x)] = g(E[z(x)]) (276)

The autocorrelation of y(x) is found in a two-step process

Ryy(x1x2) = Lx1[Lx2

[Rzz(x1x2)]] (277)

in which Lx1is the transformation applied with respect to the first variable

z(x1) with the second variable treated as a parameter and Lx2is the trans-

formation applied with respect to the second variable z(x2) with the firstvariable treated as a parameter

247 Excursions (level crossings)

A number of applications arise in geotechnical practice for which one isinterested not in the integrals (averages) or differentials of a stochastic pro-cess but in the probability that the process exceeds some threshold eitherpositive or negative For example we might be interested in the probabilitythat a stochastically varying water inflow into a reservoir exceeds some rate

124 Gregory B Baecher and John T Christian

or in the properties of the weakest interval or seam in a spatially varyingsoil mass Such problems are said to involve excursions or level crossings ofa stochastic process The following discussion follows the work of Crameacuter(1967) Parzen (1964) and Papoulis and Pillai (2002)

To begin consider the zero-crossings of a random process the points xi atwhich z(xi) = 0 For the general case this turns out to be a surprisingly diffi-cult problem Yet for the continuous Normal case a number of statementsor approximations are possible Consider a process z(x) with zero mean andvariance σ 2 For the interval [xx+ δ] if the product

z(x)z(x + δ) lt 0 (278)

then there must be an odd number of zero-crossings within the interval forif this product is negative one of the values must lie above zero and the otherbeneath Papoulis and Pillai(2002) demonstrate that if the two (zero-mean)variables z(x) and z(x + δ) are jointly normal with correlation coefficient

r = E [z(x)z(x + δ)]σxσx+δ

(279)

then

p(z(x)z(x + δ) lt 0) = 12

minus arcsin(r)π

= arccos(r)π

p(z(x)z(x + δ) gt 0) = 12

+ arcsin(r)π

= π minus arccos(r)π

(280)

The correlation coefficient of course can be taken from the autocorrelationfunction Rz(δ) Thus

cos[πp(z(x)z(x + δ) lt 0)] = Rz(δ)

Rz(0)(281)

and the probability that the number of zero-crossings is positive is just thecomplement of this result

The probability of exactly one zero-crossing p1(δ) is approximatelyp1(δ) asymp p0(δ) and expanding the cosine in a Fourier series and truncating totwo terms

1 minus π2p21(δ)

2= Rz(δ)

Rz(0)(282)

or

p1(δ) asymp 1π

radic2[Rz(0) minus Rz(δ)]

Rz(0)(283)

Spatial variability and geotechnical reliability 125

In the case of a regular autocorrelation function for which the derivativedRz(0)dδ exists and is zero at the origin the probability of a zero-crossingis approximately

p1(δ) asymp δ

π

radicminusd2Rz(0)dδ2

Rz(0)(284)

The non-regular case for which the derivative at the origin is not zero (egdRz(δ) = exp(δδ0)) is discussed by Parzen (1964) Elishakoff (1999) andVanMarcke (1983) treat higher dimensional results The related probabilityof the process crossing an arbitrary level zlowast can be approximated by notingthat for small δ and thus r rarr1

P[z(x) minus zlowastz(x + δ) minus zlowast lt 0

]asymp P [z(x)z(x + δ) lt 0]eminusarcsin2(r)

2σ2

(285)

For small δ the correlation coefficient Rz(δ) is approximately 1 and thevariances of z(x) and z(x + δ) are approximately Rz(0) thus

p1zlowast (δ) asymp p1zlowast (δ) lt 0]e minusarcsin2(r)2Rz (0) (286)

and for the regular case

p1(δ) asymp δ

π

radicminusd2Rz(0)dδ2

Rz(0)e

minusarcsin2(r)2Rz (0) (287)

Many other results can be found for continuous Normal processes eg theaverage density of the number of crossings within an interval the probabilityof no crossings (ie drought) within an interval and so on A rich literature isavailable of these and related results (Yaglom 1962 Parzen 1964 Crameacuterand Leadbetter 1967 Gelb and Analytic Sciences Corporation TechnicalStaff 1974 Adler 1981 Cliff and Ord 1981 Cressie 1991 Christakos1992 2000 Christakos and Hristopulos 1998)

248 Example New Orleans hurricane protection systemLouisiana (USA)

In the aftermath of Hurricane Katrina reliability analyses were con-ducted on the reconstructed New Orleans hurricane protection system(HPS) to understand the risks faced in future storms A first-excursionor level crossing methodology was used to calculate the probability offailure in long embankment sections following the approach proposed

126 Gregory B Baecher and John T Christian

by VanMarcke (1977) This resulted in fragility curves for a reach oflevee The fragility curve gives the conditional probability of failure forknown hurricane loads (ie surge and wave heights) Uncertainties in thehurricane loads were convolved with these fragility curves in a systems riskmodel to generate unconditional probabilities and subsequently risk whenconsequences were included

As a first approximation engineering performance models and calcu-lations were adapted from the US Army Corps of Engineersrsquo DesignMemoranda describing the original design of individual levee reaches(USACE 1972) Engineering parameter and model uncertainties were prop-agated through those calculations to obtain approximate fragility curvesas a function of surge and wave loads These results were later calibratedagainst analyses which applied more sophisticated stability models and therisk assessments were updated

A typical design profile of the levee system is shown in Figure 224Four categories of uncertainty were included in the reliability analysisgeological and geotechnical uncertainties involving the spatial distribu-tion of soils and soil properties within and beneath the HPS geotechnicalstability modeling of levee performance erosion uncertainties involvingthe performance of levees and fills during overtopping and mechanicalequipment uncertainties including gates pumps and other operating sys-tems and human operator factors affecting the performance of mechanicalequipment

The principal uncertainty contributing to probability of failure of the leveesections in the reliability analysis was soil engineering properties specificallyundrained strength Su measured in Q-tests (UU tests) Uncertainties in soilengineering properties was presumed to be structured as in Figure 216

Figure 224 Typical design section from the USACE Design Memoranda for theNew Orleans Hurricane Protection System (USACE 1972)

Spatial variability and geotechnical reliability 127

and the variance of the uncertainty in soil properties was divided into fourterms

Var(Su) = Var(x) + Var(e) + Var(m) + Var(b) (288)

in which Var() is variance Su is measured undrained strength x is the soilproperty in situ e is measurement error (noise) m is the spatial mean (whichhas some error due to the statistical fluctuations of small sample sizes) andb is a model bias or calibration term caused by systematic errors in mea-suring the soil properties Measured undrained strength for one reach theNew Orleans East lakefront levees are shown as histograms in Figure 225Test values larger than 750 PCF (36 kPa) were assumed to be local effectsand removed from the statistics The spatial pattern of soil variability wascharacterized by autocovariance functions in each region of the system andfor each soil stratum (Figure 226) From the autocovariance analyses twoconclusions were drawn The measurement noise (or fine-scale variation)in the undrained strength data was estimated to be roughly 34 the totalvariance of the data (which was judged not unreasonable given the Q-testmethods) and the autocovariance distance in the horizontal direction forboth the clay and marsh was estimated to be on the order of 500 feetor more

The reliability analysis was based on limiting equilibrium calculationsFor levees the analysis was based on General Design Memorandum (GDM)calculations of factor of safety against wedge instability (USACE 1972)

0

1

2

3

4

5

6

7

8

9

10

0 250 500 750 1000 1250 1500

Undrained Shear Strength Q-test (PCF)

FillMarshDistributary Clay

Figure 225 Histogram of Q-test (UU) undrained soil strengths New Orleans Eastlakefront

128 Gregory B Baecher and John T Christian

C10minus2

29972

19928

9884

minus16

minus10204

minus202480 1025 205 3075 41

Distance h10minus2

5125 615 7175 82

Figure 226 Representative autocovariance function for inter-distributary clay undrainedstrength (Q test) Orleans Parish Louisiana

P(f

|Ele

vatio

n) P

roba

bilit

y of

Fai

lure 10

05

0

Authorization Basis

015 Fractile Level

085 Fractile Level

Median

Elevation (ft)

Range ofthe SystemReliability

Figure 227 Representative fragility curves for unit reach and long reach of levee

using the so-called method of planes The calculations are based onundrained failure conditions Uncertainties in undrained shear strength werepropagated through the calculations to estimate a coefficient of variationin the calculated factor of safety The factor of safety was assumed to beNormally distributed and a fragility curve approximated through threecalculation points (Figure 227)

The larger the failure surface relative to the autocorrelation of the soilproperties the more the variance of the local averages is reduced VanMarcke(1977) has shown that the variance of the spatial average for a unit-widthplain strain cross-section decreases approximately in proportion to (LrL)for Lgt rL in which L is the cross-sectional length of the failure surface and

Spatial variability and geotechnical reliability 129

rL is an equivalent autocovariance distance of the soil properties across thefailure surface weighted for the relative proportion of horizontal and verticalsegments of the surface For the wedge failure modes this is approximatelythe vertical autocovariance distance The variance across the full failure sur-face of width b along the axis of the levee is further reduced by averagingin the horizontal direction by an additional factor (brH) for b gt rH inwhich rH is the horizontal autocovariance distance At the same time thatthe variance of the average strength on the failure surface is reduced bythe averaging process so too the autocovariance function of this averagedprocess stretches out from that of the point-to-point variation

For a failure length of approximately 500 feet along the levee axis and30 feet deep typical of those actually observed with horizontal and ver-tical autocovariance distances of 500 feet and 10 feet respectively thecorresponding variance reduction factors are approximately 075 for aver-aging over the cross-sectional length L and between 073 and 085 foraveraging over the failure length b assuming either an exponential orsquared-exponential (Gaussian) autocovariance The corresponding reduc-tion to the COV of soil strength based on averaging over the failure plane isthe root of the product of these two factors or between 074 and 08

For a long levee the chance of at least one failure is equivalent to the chancethat the variations of the mean soil strength across the failure surface dropbelow that required for stability at least once along the length VanMarckedemonstrated that this can be determined by considering the first crossingsof a random process The approximation to the probability of at least onefailure as provided by VanMarcke was used in the present calculations toobtain probability of failure as a function of levee length

25 Concluding comments

In this chapter we have described the importance of spatial variation ingeotechnical properties and how such variation can be dealt with in aprobabilistic analysis Spatial variation consists essentially of two parts anunderlying trend and a random variation superimposed on it The distri-bution of the variability between the trend and the random variation is adecision made by the analyst and is not an invariant function of nature

The second-moment method is widely used to describe the spatial varia-tion of random variation Although not as commonly used in geotechnicalpractice Bayesian estimation has many advantages over moment-basedestimation One of them is that it yields an estimate of the probabilitiesassociated with the distribution parameters rather than a confidence intervalthat the data would be observed if the process were repeated

Spatially varying properties are generally described by random fieldsAlthough these can become extremely complicated relatively simple modelssuch as Gaussian random field have wide application The last portion of

130 Gregory B Baecher and John T Christian

the chapter demonstrates how they can be manipulated by differentiationby transformation through linear processes and by evaluation of excursionsbeyond specified levels

Notes

1 It is true that not all soil or rock mass behaviors of engineering importance aregoverned by averages for example block failures in rock slopes are governed by theleast favorably positioned and oriented joint They are extreme values processesNevertheless averaging soil or rock properties does reduce variance

2 In early applications of geotechnical reliability a great deal of work was focusedon appropriate distributional forms for soil property variability but this no longerseems a major topic First the number of measurements typical of site characteri-zation programs is usually too few to decide confidently whether one distributionalform provides a better fit than another and second much of practical geotechnicalreliability work uses second-moment characterizations rather than full distribu-tional analysis so distributional assumptions only come into play at the endof the work Second-moment characterizations use only means variances andcovariances to characterize variability not full distributions

3 In addition to aleatory and epistemic uncertainties there are also uncertainties thathave little to do with engineering properties and performance yet which affectdecisions Among these is the set of objectives and attributes considered importantin reaching a decision the value or utility function defined over these attributesand discounting for outcomes distributed in time These are outside the presentscope

References

Adler R J (1981) The Geometry of Random Fields Wiley ChichesterAng A H-S and Tang W H (1975) Probability Concepts in Engineering Planning

and Design Wiley New YorkAzzouz A Baligh M M and Ladd C C (1983) Corrected field vane strength

for embankment design Journal of Geotechnical Engineering ASCE 109(3)730ndash4

Baecher G B(1980) Progressively censored sampling of rock joint traces Journalof the International Association of Mathematical Geologists 12(1) 33ndash40

Baecher G B (1987) Statistical quality control for engineered fills EngineeringGuide US Army Corps of Engineers Waterways Experiment Station GL-87-2

Baecher G B Ladd C C Noiray L and Christian J T (1997) Formal obser-vational approach to staged loading Transportation Research Board AnnualMeeting Washington DC

Barnett V (1982) Comparative Statistical Inference John Wiley and Sons LondonBerger J O (1993) Statistical Decision Theory and Bayesian Analysis Springer

New YorkBerger J O De Oliveira V and Sanso B (2001) Objective Bayesian analysis of

spatially correlated data Journal of the American Statistical Association 96(456)1361ndash74

Bjerrum L (1972) Embankments on soft ground In ASCE Conference on Perfor-mance of Earth and Earth-Supported Structures Purdue pp 1ndash54

Spatial variability and geotechnical reliability 131

Bjerrum L (1973) Problems of soil mechanics and construction on soft clays InEighth International Conference on Soil Mechanics and Foundation EngineeringMoscow Spinger New York pp 111ndash59

Chiasson P Lafleur J Soulie M and Law K T (1995) Characterizing spatialvariability of a clay by geostatistics Canadian Geotechnical Journal 32 1ndash10

Christakos G (1992) Random Field Models in Earth Sciences Academic PressSan Diego

Christakos G (2000) Modern Spatiotemporal Geostatistics Oxford UniversityPress Oxford

Christakos G and Hristopulos D T (1998) Spatiotemporal Environmental HealthModelling A Tractatus Stochasticus Kluwer Academic Boston MA

Christian J T Ladd C C and Baecher G B (1994) Reliability applied toslope stability analysis Journal of Geotechnical Engineering ASCE 120(12)2180ndash207

Cliff A D and Ord J K (1981) Spatial Processes Models amp Applications PionLondon

Crameacuter H and Leadbetter M R (1967) Stationary and Related StochasticProcesses Sample Function Properties and their Applications Wiley New York

Cressie N A C (1991) Statistics for Spatial Data Wiley New YorkDavis J C (1986) Statistics and Data Analysis in Geology Wiley New YorkDeGroot D J and Baecher G B (1993) Estimating autocovariance of in situ soil

properties Journal Geotechnical Engineering Division ASCE 119(1) 147ndash66Elishakoff I (1999) Probabilistic Theory of Structures Dover Publications

Mineola NYFuleihan N F and Ladd C C (1976) Design and performance of Atchafalaya

flood control levees Research Report R76-24 Department of Civil EngineeringMassachusetts Institute of Technology Cambridge MA

Gelb A and Analytic Sciences Corporation Technical Staff (1974) Applied OptimalEstimation MIT Press Cambridge MA

Gelhar L W (1993) Stochastic Subsurface Hydrology Prentice-Hall EnglewoodCliffs NJ

Hartford D N D (1995) How safe is your dam Is it safe enough MEP11-5 BCHydro Burnaby BC

Hartford D N D and Baecher G B (2004) Risk and Uncertainty in Dam SafetyThomas Telford London

Hilldale C (1971) A probabilistic approach to estimating differential settle-ment MS thesis Civil Engineering Massachusetts Institute of TechnologyCambridge MA

Javete D F (1983) A simple statistical approach to differential settlements on clayPh D thesis Civil Engineering University of California Berkeley

Journel A G and Huijbregts C (1978) Mining Geostatistics Academic PressLondon

Jowett G H (1952) The accuracy of systematic simple from conveyer belts AppliedStatistics 1 50ndash9

Kitanidis P K (1985) Parameter uncertainty in estimation of spatial functionsBayesian analysis Water Resources Research 22(4) 499ndash507

Kitanidis P K (1997) Introduction to Geostatistics Applications to HydrogeologyCambridge University Press Cambridge

132 Gregory B Baecher and John T Christian

Kolmogorov A N (1941) The local structure of turbulence in an incompress-ible fluid at very large Reynolds number Doklady Adademii Nauk SSSR 30301ndash5

Lacasse S and Nadim F (1996) Uncertainties in characteristic soil properties InUncertainty in the Geological Environment ASCE specialty conference MadisonWI ASCE Reston VA pp 40ndash75

Ladd CC and Foott R (1974) A new design procedure for stability of soft claysJournal of Geotechnical Engineering 100(7) 763ndash86

Ladd C C (1975) Foundation design of embankments constructed on con-necticut valley varved clays Report R75-7 Department of Civil EngineeringMassachusetts Institute of Technology Cambridge MA

Ladd C C Dascal O Law K T Lefebrve G Lessard G Mesri G andTavenas F (1983) Report of the subcommittee on embankment stability__annexe II Committe of specialists on Sensitive Clays on the NBR Complex SocieteacutedrsquoEnergie de la Baie James Montreal

Lambe TW and Associates (1982) Earthquake risk to patio 4 and site 400 Reportto Tonen Oil Corporation Longboat Key FL

Lambe T W and Whitman R V (1979) Soil Mechanics SI Version WileyNew York

Lee I K White W and Ingles O G (1983) Geotechnical Engineering PitmanBoston

Lumb P (1966) The variability of natural soils Canadian Geotechnical Journal3 74ndash97

Lumb P (1974) Application of statistics in soil mechanics In Soil Mechanics NewHorizons Ed I K Lee Newnes-Butterworth London pp 44ndash112

Mardia K V and Marshall R J (1984) Maximum likelihood estimation of modelsfor residual covariance in spatial regression Biometrika 71(1) 135ndash46

Mateacutern B (1960) Spatial Variation 49(5) Meddelanden fran Statens Skogsforskn-ingsinstitut

Mateacutern B (1986) Spatial Variation Springer BerlinMatheron G (1971) The Theory of Regionalized Variables and Its Application ndash

Spatial Variabilities of Soil and Landforms Les Cahiers du Centre de Morphologiemathematique 8 Fontainebleau

Papoulis A and Pillai S U (2002) Probability Random Variables and StochasticProcesses 4th ed McGraw-Hill Boston MA

Parzen E (1964) Stochastic Processes Holden-Day San Francisco CAParzen E (1992) Modern Probability Theory and its Applications Wiley

New YorkPhoon K K (2006a) Bootstrap estimation of sample autocorrelation functions

In GeoCongress Atlanta ASCEPhoon K K (2006b) Modeling and simulation of stochastic data In GeoCongress

Atlanta ASCEPhoon K K and Fenton G A (2004) Estimating sample autocorrelation func-

tions using bootstrap In Proceedings Ninth ASCE Specialty Conference onProbabilistic Mechanics and Structural Reliability Albuquerque

Phoon K K and Kulhawy F H (1996) On quantifying inherent soil variabilityUncertainty in the Geologic Environment ASCE specialty conference MadisonWI ASCE Reston VA pp 326ndash40

Spatial variability and geotechnical reliability 133

Soong T T and Grigoriu M (1993) Random Vibration of Mechanical andStructural Systems Prentice Hall Englewood Cliffs NJ

Soulie M and Favre M (1983) Analyse geostatistique drsquoun noyau de barrage telque construit Canadian Geotechnical Journal 20 453ndash67

Soulie M Montes P and Silvestri V (1990) Modeling spatial variability of soilparameters Canadian Geotechnical Journal 27 617ndash30

Switzer P (1995) Spatial interpolation errors for monitoring data Journal of theAmerican Statistical Association 90(431) 853ndash61

Tang W H (1979) Probabilistic evaluation of penetration resistance Journal ofthe Geotechnical Engineering Division ASCE 105(10) 1173ndash91

Taylor G I (1921) Diffusion by continuous movements Proceeding of the LondonMathematical Society (2) 20 196ndash211

USACE (1972) New Orleans East Lakefront Levee Paris Road to South Point LakePontchartrain Barrier Plan DM 2 Supplement 5B USACE New Orleans DistrictNew Orleans

VanMarcke E (1983) Random Fields Analysis and Synthesis MIT PressCambridge MA

VanMarcke E H (1977) Reliability of earth slopes Journal of the GeotechnicalEngineering Division ASCE 103(GT11) 1247ndash65

Weinstock H (1963) The description of stationary random rate processes E-1377Massachusetts Institute of Technology Cambridge MA

Wu T H (1974) Uncertainty safety and decision in soil engineering JournalGeotechnical Engineering Division ASCE 100(3) 329ndash48

Yaglom A M (1962) An Introduction to the Theory of Random FunctionsPrentice-Hall Englewood Cliffs NJ

Zellner A (1971) An Introduction to Bayesian Inference in Econometrics WileyNew York

Chapter 3

Practical reliability approachusing spreadsheet

Bak Kong Low

31 Introduction

In a review on first-order second-moment reliability methods USACE (1999)rightly noted that a potential problem with both the Taylorrsquos series methodand the point estimate method is their lack of invariance for nonlinear perfor-mance functions The document suggested the more general HasoferndashLindreliability index (Hasofer and Lind 1974) as a better alternative but con-ceded that ldquomany published analyses of geotechnical problems have notused the HasoferndashLind method probably due to its complexity especiallyfor implicit functions such as those in slope stability analysisrdquo and that ldquothemost common method used in Corps practice is the Taylorrsquos series methodbased on a Taylorrsquos series expansion of the performance function about theexpected valuesrdquo

A survey of recent papers on geotechnical reliability analysis reinforces theabove USACE observation that although the HasoferndashLind index is perceivedto be more consistent than the Taylorrsquos series mean value method the latteris more often used

This chapter aims to overcome the computational and conceptual barriersof the HasoferndashLind index for correlated normal random variables and thefirst-order reliability method (FORM) for correlated nonnormals in thecontext of three conventional geotechnical design problems Specificallythe conventional bearing capacity model involving two random variablesis first illustrated to elucidate the procedures and concepts This is fol-lowed by a reliability-based design of an anchored sheet pile wall involvingsix random variables which are first treated as correlated normals thenas correlated nonnormals Finally reliability analysis with search for crit-ical noncircular slip surface based on a reformulated Spencer method ispresented This probabilistic slope stability example includes testing therobustness of search for noncircular critical slip surface modeling lognor-mal random variables deriving probability density functions from reliabilityindices and comparing results inferred from reliability indices with MonteCarlo simulations

Practical reliability approach 135

The expanding ellipsoidal perspective of the HasoferndashLind reliability indexand the practical reliability approach using object-oriented constrained opti-mization in the ubiquitous spreadsheet platform were described in Low andTang (1997a 1997b) and extended substantially in Low and Tang (2004)by testing robustness for various nonnormal distributions and complicatedperformance functions and by providing enhanced operational convenienceand versatility

Reasonable statistical properties are assumed for the illustrative cases pre-sented in this chapter actual determination of the statistical properties is notcovered Only parametric uncertainty is considered and model uncertaintyis not dealt with Hence this chapter is concerned with reliability methodand perspectives and not reliability in its widest sense The focus is onintroducing an efficient and rational design approach using the ubiquitousspreadsheet platform

The spreadsheet reliability procedures described herein can be appliedto stand-alone numerical (eg finite element) packages via the establishedresponse surface method (which itself is straightforward to implement inthe ubiquitous spreadsheet platform) Hence the applicability of the relia-bility approach is not confined to models which can be formulated in thespreadsheet environment

32 Reliability procedure in spreadsheet andexpanding ellipsoidal perspective

321 A simple hands-on reliability analysis

The proposed spreadsheet reliability evaluation approach will be illustratedfirst for a case with two random variables Readers who want a betterunderstanding of the procedure and deeper appreciation of the ellipsoidalperspective are encouraged to go through the procedure from scratch on ablank Excel worksheet After that some Excel files for hands-on and deeperappreciation can be downloaded from httpalummiteduwwwbklow

The example concerns the bearing capacity of a strip footing sustain-ing non-eccentric vertical load Extensions to higher dimensions and morecomplicated scenarios are straightforward

With respect to bearing capacity failure the performance function (PerFn)for a strip footing in its simplest form is

PerFn = qu minus q (31a)

where qu = cNc + poNq + B2γNγ (31b)

in which qu is the ultimate bearing capacity q the applied bearing pressurec the cohesion of soil po the effective overburden pressure at foundation

136 Bak Kong Low

level B the foundation width γ the unit weight of soil below the base offoundation and Nc Nq and Nγ are bearing capacity factors which areestablished functions of the friction angle (φ) of soil

Nq = eπ tanφ tan2(

45 + φ

2

)(32a)

Nc =(Nq minus 1

)cot (φ) (32b)

Nγ = 2(Nq + 1

)tanφ (32c)

Several expressions for Nγ exist The above Nγ is attributed to Vesic inBowles (1996)

The statistical parameters and correlation matrix of c and φ are shown inFigure 31 The other parameters in Equations (31a) and (31b) are assumedknown with values q = QvB = (200 kNm)B po = 18 kPa B = 12 m andγ = 20 kNm3 The parameters c and φ in Equations (31) and (32) readtheir values from the column labeled xlowast which were initially set equal tothe mean values These xlowast values and the functions dependent on them

qu = cNc + PoNq + B gNγ2

Units m kNm2kNm3 degrees as appropriate

X Qv q

Corr Matrix

PerFn

Solution procedure

nxnx =

xi ndash mi

C

Nq

NC

Ng

B Pogsm

f

b

63391463

3804

1074 1

00

= qu(x) - q

3268

1minus05

minus2732

minus0187

minus05

2507

2015

5 200 1667 12 20

Qv B

182

si

=si

xi ndash mi

si

xi ndash miT

[R ]minus1

1 Initially x values = mean values m

2 Invoke Solver to minimize b by changing x values subject to PerFn = 0 amp x values ge 0

Figure 31 A simple illustration of reliability analysis involving two correlated randomvariables which are normally distributed

Practical reliability approach 137

change during the optimization search for the most probable failure pointSubsequent steps are

1 The formula of the cell labeled β in Figure 31 is Equation (35b)in Section 323 ldquo=sqrt(mmult(transpose(nx) mmult(minverse(crmat)nx)))rdquo The arguments nx and crmat are entered by selecting thecorresponding numerical cells of the column vector

(xi minusmicroi

)σi and

the correlation matrix respectively This array formula is then enteredby pressing ldquoEnterrdquo while holding down the ldquoCtrlrdquoand ldquoShiftrdquo keysMicrosoft Excelrsquos built-in matrix functions mmult transpose andminverse have been used in this step Each of these functions containsprogram codes for matrix operations

2 The formula of the performance function is g (x) = qu minus q wherethe equation for qu is Equation (31b) and depends on the xlowastvalues

3 Microsoft Excelrsquos built-in constrained optimization program Solveris invoked (via ToolsSolver) to Minimize β By Changing the xvalues Subject To PerFn le 0 and x values ge 0 (If used for thefirst time Solver needs to be activated once via ToolsAdd-insSolverAdd-in)

The β value obtained is 3268 The spreadsheet approach is simple andintuitive because it works in the original space of the variables It does notinvolve the orthogonal transformation of the correlation matrix and iter-ative numerical partial derivatives are done automatically on spreadsheetobjects which may be implicit or contain codes

The following paragraphs briefly compares lumped factor of safetyapproach partial factors approach and FORM approach and provideinsights on the meaning of reliability index in the original space of therandom variables More details can be found in Low (1996 2005a) Lowand Tang (1997a 2004) and other documents at httpalummiteduwwwbklow

322 Comparing lumped safety factor and partial factorsapproaches with reliability approach

For the bearing capacity problem of Figure 31 a long-established determin-istic approach evaluates the lumped factor of safety (Fs) as

Fs = qu minus po

q minus po= f (cφ ) (33)

where the symbols are as defined earlier If c = 20 kPa and φ = 15 andwith the values of Qv B γ and po as shown in Figure 31 then the factor of

138 Bak Kong Low

45 Limit state surface boundary between safeand unsafe domains

40

35

30

25

20

Coh

esio

n c

15

10

5

0

SAFE

one-sigmadispersion ellipse

Mean-valuepointDesign point

Friction angle φ (degrees)0 10 20 30

UNSAFER

r

β-ellipse

b = R rsc

mc

Figure 32 The design point the mean-value point and expanding ellipsoidal perspectiveof the reliability index in the original space of the random variables

safety is Fs asymp 20 by Equation (33) In the two-dimensional space of c andφ (Figure 32) one can plot the Fs contours for different combinations ofc andφ including the Fs =10 curve which separates the unsafe combinationsfrom the safe combinations of c and φ The average point (c = 20 kPa andφ = 15) is situated on the contour (not plotted) of Fs = 20 Design by thelumped factor of safety approach is considered satisfactory with respect tobearing capacity failure if the factor of safety by Equation (33) is not smallerthan a certain value (eg when Fs ge 20)

A more recent and logical approach (eg Eurocode 7) applies partial fac-tors to the parameters in the evaluation of resistance and loadings Designis acceptable if

Bearing capacity (based on reduced c and φ) ge Applied pressure

(amplified) (34)

A third approach is reliability-based design where the uncertainties andcorrelation structure of the parameters are represented by a one-standard-deviation dispersion ellipsoid (Figure 32) centered at the mean-value pointand safety is gaged by a reliability index which is the shortest distance(measured in units of directional standard deviations Rr) from the safemean-value point to the most probable failure combination of parame-ters (ldquothe design pointrdquo) on the limit state surface (defined by Fs = 10for the problem in hand) Furthermore the probability of failure (Pf )

Practical reliability approach 139

can be estimated from the reliability index β using the established equa-tion Pf = 1 minus(β) = (minusβ) where is the cumulative distribution (CDF)of the standard normal variate The relationship is exact when the limitstate surface is planar and the parameters follow normal distributions andapproximate otherwise

The merits of a reliability-based design over the lumped factor-of-safetydesign is illustrated in Figure 33a in which case A and case B (with dif-ferent average values of effective shear strength parameters cprime and φprime) showthe same values of lumped factor of safety yet case A is clearly safer thancase B The higher reliability of case A over case B will correctly be revealedwhen the reliability indices are computed On the other hand a slope mayhave a computed lumped factor of safety of 15 and a particular foun-dation (with certain geometry and loadings) in the same soil may have acomputed lumped factor of safety of 25 as in case C of Figure 33(b)Yet a reliability analysis may show that they both have similar levels ofreliability

The design point (Figure 32) is the most probable failure combination ofparametric values The ratios of the respective parametric values at the cen-ter of the dispersion ellipsoid (corresponding to the mean values) to those atthe design point are similar to the partial factors in limit state design exceptthat these factored values at the design point are arrived at automatically(and as by-products) via spreadsheet-based constrained optimization Thereliability-based approach is thus able to reflect varying parametric sensitiv-ities from case to case in the same design problem (Figure 33a) and acrossdifferent design realms (Figure 33b)

one-standard-deviationdispersion ellipsoidFs = 14

Fs = 10

Unsafe Fs lt 10

Fs = 12

Fs = 30

Fs = 10

Fs = 10

Slope

(Slope)Unsafe

(Foundation)

Foundation

Safe

A

B

20 15

12C

cprimecprime

φprime φprime(a) (b)

Figure 33 Schematic scenarios showing possible limitations of lumped factor of safety(a) Cases A and B have the same lumped Fs = 14 but Case A is clearly morereliable than Case B (b) Case C may have Fs = 15 for a slope and Fs = 25 fora foundation and yet have similar levels of reliability

140 Bak Kong Low

323 HasoferndashLind index reinterpreted via expandingellipsoid perspective

The matrix formulation (Veneziano 1974 Ditlevsen 1981) of the HasoferndashLind index β is

β = minxisinF

radic(x minusmicro)T Cminus1 (x minusmicro) (35a)

or equivalently

β = minxisinF

radic[xi minusmicroi

σi

]T

[R]minus1[

xi minusmicroi

σi

](35b)

where x is a vector representing the set of random variables xi micro the vec-tor of mean values microi C the covariance matrix R the correlation matrixσi the standard deviation and F the failure domain Low and Tang (1997b2004) used Equation (35b) in preference to Equation (35a) because the cor-relation matrix R is easier to set up and conveys the correlation structuremore explicitly than the covariance matrix C Equation (35b) was enteredin step (1) above

The ldquoxlowastrdquo values obtained in Figure 31 represent the most probable failurepoint on the limit state surface It is the point of tangency (Figure 32) of theexpanding dispersion ellipsoid with the bearing capacity limit state surfaceThe following may be noted

(a) The xlowast values shown in Figure 31 render Equation (31a) (PerFn) equalto zero Hence the point represented by these xlowast values lies on the bearingcapacity limit state surface which separates the safe domain from theunsafe domain The one-standard-deviation ellipse and the β-ellipse inFigure 32 are tilted because the correlation coefficient between c and φ isminus05 in Figure 31 The design point in Figure 32 is where the expandingdispersion ellipse touches the limit state surface at the point representedby the xlowast values of Figure 31

(b) As a multivariate normal dispersion ellipsoid expands its expandingsurfaces are contours of decreasing probability values according tothe established probability density function of the multivariate normaldistribution

f (x) = 1

(2π )n2 |C|05

exp[minus1

2(x minusmicro)T Cminus1 (x minusmicro)

](36a)

= 1

(2π )n2 |C|05

exp[minus1

2β2]

(36b)

Practical reliability approach 141

where β is defined by Equation (35a) or (35b) without the ldquominrdquoHence to minimize β (or β2 in the above multivariate normal distribu-tion) is to maximize the value of the multivariate normal probabilitydensity function and to find the smallest ellipsoid tangent to thelimit state surface is equivalent to finding the most probable failurepoint (the design point) This intuitive and visual understanding ofthe design point is consistent with the more mathematical approachin Shinozuka (1983 equations 4 41 and associated figure) in whichall variables were transformed into their standardized forms and thelimit state equation had also to be written in terms of the standard-ized variables The differences between the present original space versusShinozukarsquos standardized space of variables will be further discussed in(h) below

(c) Therefore the design point being the first point of contact betweenthe expanding ellipsoid and the limit state surface in Figure 32is the most probable failure point with respect to the safe mean-value point at the centre of the expanding ellipsoid where Fs asymp 20against bearing capacity failure The reliability index β is the axisratio (Rr) of the ellipse that touches the limit state surface and theone-standard-deviation dispersion ellipse By geometrical properties ofellipses this co-directional axis ratio is the same along any ldquoradialrdquodirection

(d) For each parameter the ratio of the mean value to the xlowast value is similarin nature to the partial factors in limit state design (eg Eurocode 7)However in a reliability-based design one does not specify the par-tial factors The design point values (xlowast) are determined automaticallyand reflect sensitivities standard deviations correlation structure andprobability distributions in a way that prescribed partial factors cannotreflect

(e) In Figure 31 the mean value point at 20 kPa and 15 is safe againstbearing capacity failure but bearing capacity failure occurs when thec and φ values are decreased to the values shown (6339 1463) Thedistance from the safe mean-value point to this most probable failurecombination of parameters in units of directional standard deviationsis the reliability index β equal to 3268 in this case

(f) The probability of failure (Pf ) can be estimated from the reliabilityindex β Microsoft Excelrsquos built-in function NormSDist() can be usedto compute () and hence Pf Thus for the bearing capacity problemof Figure 31 Pf = NormSDist(minus3268) = 0054 This value com-pares remarkably well with the range of values 0051minus0060 obtainedfrom several Monte Carlo simulations each with 800000 trials usingthe commercial simulation software RISK (httpwwwpalisadecom)The correlation matrix was accounted for in the simulation The excel-lent agreement between 0054 from reliability index and the range

142 Bak Kong Low

0051minus0060 from Monte Carlo simulation is hardly surprisinggiven the almost linear limit state surface and normal variates shownin Figure 32 However for the anchored wall shown in the nextsection where six random variables are involved and nonnormal dis-tributions are used the six-dimensional equivalent hyperellispoid andthe limit state hypersurface can only be perceived in the mindrsquos eyeNevertheless the probabilities of failure inferred from reliability indicesare again in close agreement with Monte Carlo simulations Com-puting the reliability index and Pf = (minusβ) by the present approachtakes only a few seconds In contrast the time needed to obtain theprobability of failure by Monte Carlo simulation is several ordersof magnitude longer particularly when the probability of failure issmall and many trials are needed It is also a simple matter to inves-tigate sensitivities by re-computing the reliability index β (and Pf ) fordifferent mean values and standard deviations in numerous what-ifscenarios

(g) The probability of failure as used here means the probability that inthe presence of parametric uncertainties in c and φ the factor of safetyEquation (33) will be le 10 or equivalently the probability that theperformance function Equation (31a) will be le 0

(h) Figure 32 defines the reliability index β as the dimensionless ratio Rrin the direction from the mean-value point to the design point This isthe axis ratio of the β-ellipsoid (tangential to the limit state surface) tothe one-standard-deviation dispersion ellipsoid This axis ratio is dimen-sionless and independent of orientation when R and r are co-directionalThis axis-ratio interpretation in the original space of the variables over-comes a drawback in Shinozukarsquos (1983) standardized variable spacethat ldquothe interpretation of β as the shortest distance between the ori-gin (of the standardized space) and the (transformed) limit state surfaceis no longer validrdquo if the random variables are correlated A furtheradvantage of the original space apart from its intuitive transparency isthat it renders feasible and efficient the two computational approachesinvolving nonnormals as presented in Low amp Tang (2004) and (2007)respectively

33 Reliability-based design of an anchored wall

This section illustrates reliability-based design of anchored sheet pile walldrawing material from Low (2005a 2005b) The analytical formulationsin a deterministic anchored wall design are the basis of the performancefunction in a probabilistic-based design Hence it is appropriate to brieflydescribe the deterministic approach prior to extending it to a probabilistic-based design An alternative design approach is described in BS8002(1994)

Practical reliability approach 143

331 Deterministic anchored wall design based on lumpedfactor and partial factors

The deterministic geotechnical design of anchored walls based on the freeearth support analytical model was lucidly presented in Craig (1997) Anexample is the case in Figure 34 where the relevant soil properties are theeffective angle of shearing resistance φprime and the interface friction angle δ

between the retained soil and the wall The characteristic values are cprime = 0φprime = 36 and δ = frac12φprime The water table is the same on both sides of thewall The bulk unit weight of the soil is 17 kNm3 above the water table and20 kNm3 below the water table A surcharge pressure qs = 10 kNm2 actsat the top of the retained soil The tie rods act horizontally at a depth 15 mbelow the top of the wall

In Figure 34 the active earth pressure coefficient Ka is based on theCoulomb-wedge closed-form equation which is practically the same asthe KeriselndashAbsi active earth pressure coefficient (Kerisel and Absi 1990)The passive earth pressure coefficient Kp is based on polynomial equations

17

20

10qs

36 (Characteristic value)

Surcharge qs = 10 kNm2

18

200

M5

M1 4

02

Ka Kah Kp Kph

0225 6966 662 329

Boxed cells contain equations

15 m

Water table

TA

d Fs

d

γsat

γ φprime δ

γ

γsat

64 m

24 m

45

1 -272

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-782

-139

-371

3657

4545

2767

7745

8693

949

-123

-216

-1077

-322

3472

1733

2

3

4

5

3

1

2

δ

φprime

Figure 34 Deterministic design of embedment depth d based on a lumped factor ofsafety of 20

144 Bak Kong Low

(Figure 35) fitted to the values of KeriselndashAbsi (1990) for a wall with avertical back and a horizontal retained soil surface

The required embedment depth d of 329 m in Figure 34 ndash for alumped factor of safety of 20 against rotational failure around anchor pointA ndash agrees with Example 69 of Craig (1997)

If one were to use the limit state design approach with a partial factor of12 for the characteristic shear strength one enters tanminus1(tanφprime12) = 31in the cell of φprime and changes the embedment depth d until the summationof moments is zero A required embedment depth d of 283 m is obtainedin agreement with Craig (1997)

Function KpKeriseIAbsi(phi del)lsquoPassive pressure coefficient Kp for vertical wall back and horizontal retained filllsquoBased on Tables in Kerisel amp Absi (1990) for beta = 0 lamda = 0 andlsquo delphi = 0033 05 066 100x = del phiKp100=000007776 phi ^ 4 - 0006608 phi ^ 3 + 02107 phi ^ 2 - 2714 phi + 1363Kp66=000002611 phi ^ 4 - 0002113 phi ^ 3 + 006843 phi ^ 2 - 08512 phi + 5142Kp50 = 000001559 phi ^ 4 - 0001215 phi ^ 3 + 003886 phi ^ 2 - 04473 phi + 3208Kp33 = 0000007318 phi ^ 4 - 00005195 phi ^ 3 + 00164 phi ^ 2 - 01483 phi + 1798Kp0 = 0000002636 phi ^ 4 - 00002201 phi ^ 3 + 0008267 phi ^ 2 - 00714 phi + 1507Select Case xCase 066 To 1 Kp = Kp66 + (x - 066) 1 - 066) (Kp100 - Kp66)Case 05 To 066 Kp = Kp50 + (x - 05) (066 - 05) (Kp66 - Kp50)Case 033 To 05 Kp = Kp33 + (x - 033) (05 - 033) (Kp50 - Kp33)Case 0 To 033 Kp = Kp0 + x 033 (Kp33 - Kp0)End SelectKpKeriselAbsi = KpEnd Function

Value from Kerisel-Absi tables

Polynomial curves equations as givenin the above Excel VBA code

40

35

30

25

20

Pas

sive

ear

th p

ress

ure

coef

ficie

nt K

p

15

10

5

00 10 20

Friction angle φ30

0

033

050

066

40 50

d f = 10

Figure 35 User-created Excel VBA function for Kp

Practical reliability approach 145

The partial factors in limit state design are applied to the characteristicvalues which are themselves conservative estimates and not the most proba-ble or average values Hence there is a two-tier nested safety first during theconservative estimate of the characteristic values and then when the partialfactors are applied to the characteristic values This is evident in Eurocode 7where Section 243 clause (5) states that the characteristic value of a soil orrock parameter shall be selected as a cautious estimate of the value affectingthe occurrence of the limit state Clause (7) further states that character-istic values may be lower values which are less than the most probablevalues or upper values which are greater and that for each calculation themost unfavorable combination of lower and upper values for independentparameters shall be used

The above Eurocode 7 recommendations imply that the characteristicvalue of φprime (36) in Figure 34 is lower than the mean value of φprime Hence inthe reliability-based design of the next section the mean value of φprime adoptedis higher than the characteristic value of Figure 34

While characteristic values and partial factors are used in limit state designmean values (not characteristic values) are used with standard deviations andcorrelation matrix in a reliability-based design

332 From deterministic to reliability-based anchoredwall design

The anchored sheet pile wall will be designed based on reliability analy-sis (Figure 36) As mentioned earlier the mean value of φprime in Figure 36is larger ndash 38 is assumed ndash than the characteristic value of Figure 34In total there are six normally distributed random variables with meanand standard deviations as shown Some correlations among parametersare assumed as shown in the correlation matrix For example it is judgedlogical that the unit weights γ and γsat should be positively correlatedand that each is also positively correlated to the angle of friction φprime sinceγ prime = γsat minus γw

The analytical formulations based on force and moment equilibrium in thedeterministic analysis of Figure 34 are also required in a reliability analysisbut are expressed as limit state functions or performance functions ldquo= Sum(Moments1rarr5)rdquo

The array formula in cell β of Figure 36 is as described in step 1 of thebearing capacity example earlier in this chapter

Given the uncertainties and correlation structure in Figure 36 wewish to find the required total wall height H so as to achieve a relia-bility index of 30 against rotational failure about point ldquoArdquo Initiallythe column xlowast was given the mean values Microsoft Excelrsquos built-inconstrained optimization tool Solver was then used to minimize β bychanging (automatically) the xlowast column subject to the constraint that

146 Bak Kong Low

Surcharge qs

03

Ka Kah Kp Kph

0249 5904 5637 279 000

d

d = H ndash 64 ndash z

1215

Boxed cells containequations

15 m

Water table

TA

H PerFn

crmatrix (Correlation matrix)

nX

d

γsat

γ φprime δ64 m

300

b

z

45

1 -311

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-828

-149

-356

1892

4575

2767

7775

8733

972

-142

-229

-1156

-311

1839

Normal

Normal

Normal

Normal

Normal

Normal

1844

1028

162

3351

1729

2963

20

10

17

Mean StDev

38

19

24

1

1

05

0

05

0

0

05

1

0

05

0

0

0

0

1

0

0

0

05

05

0

1

08

0

0

0

0

08

1

0

0

0

0

0

0

1

2

085 -09363

-15572

013807

-22431

-17077

187735

2

1

03

γsat

qs

γ

x

φprime

δ

z

γsat

γsat

qs

qs

γ

γ

φprime

φprime

δ

δ

z

z

2

3

4

5 3

1

2

Dredge level

Figure 36 Design total wall height for a reliability index of 30 against rotational failureDredge level and hence z and d are random variables

the cell PerFn be equal to zero The solution (Figure 36) indicates thata total height of 1215 m would give a reliability index of 30 againstrotational failure With this wall height the mean-value point is safeagainst rotational failure but rotational failure occurs when the mean val-ues descendascend to the values indicated under the xlowast column Thesexlowast values denote the design point on the limit state surface and rep-resent the most likely combination of parametric values that will causefailure The distance between the mean-value point and the design pointin units of directional standard deviations is the HasoferndashLind reliabilityindex

Practical reliability approach 147

As noted in Simpson and Driscoll (1998 81 158) clause 8321 ofEurocode 7 requires that an ldquooverdigrdquo allowance shall be made for wallswhich rely on passive resistance This is an allowance ldquofor the unforeseenactivities of nature or humans who have no technical appreciation of thestability requirements of the wallrdquo For the case in hand the reliability anal-ysis in Figure 36 accounts for uncertainty in z requiring only the mean valueand the standard deviation of z (and its distribution type if not normal) to bespecified The expected embedment depth is d = 1215minus64minusmicroz = 335 mAt the failure combination of parametric values the design value of z iszlowast = 29632 and dlowast = 1215 minus 64 minus zlowast = 279 m This corresponds to anldquooverdigrdquo allowance of 056 m Unlike Eurocode 7 this ldquooverdigrdquo is deter-mined automatically and reflects uncertainties and sensitivities from case tocase in a way that specified ldquooverdigrdquo cannot Low (2005a) illustrates anddiscusses this automatic probabilistic overdig allowance in a reliability-baseddesign

The nx column indicates that for the given mean values and uncer-tainties rotational stability is not surprisingly most sensitive to φprime andthe dredge level (which affects z and d and hence the passive resistance)It is least sensitive to uncertainties in the surcharge qs because the aver-age value of surcharge (10 kNm2) is relatively small when compared withthe over 10 m thick retained fill Under a different scenario where thesurcharge is a significant player its sensitivity scale could conceivably bedifferent It is also interesting to note that at the design point where thesix-dimensional dispersion ellipsoid touches the limit state surface bothunit weights γ and γsat (1620 and 1844 respectively) are lower thantheir corresponding mean values contrary to the expectation that higherunit weights will increase active pressure and hence greater instability Thisapparent paradox is resolved if one notes that smaller γsat will (via smaller γ prime)reduce passive resistance smaller φprime will cause greater active pressure andsmaller passive pressure and that γ γsat and φprime are logically positivelycorrelated

In a reliability-based design (such as the case in Figure 36) one doesnot prescribe the ratios meanxlowast ndash such ratios or ratios of (characteristicvalues)xlowast are prescribed in limit state design ndash but leave it to the expand-ing dispersion ellipsoid to seek the most probable failure point on the limitstate surface a process which automatically reflects the sensitivities of theparameters The ability to seek the most-probable design point without pre-suming any partial factors and to automatically reflect sensitivities from caseto case is a desirable feature of the reliability-based design approach Thesensitivity measures of parameters may not always be obvious from a pri-ori reasoning A case in point is the strut with complex supports analyzedin Low and Tang (2004 85) where the mid-span spring stiffness k3 andthe rotational stiffness λ1 at the other end both turn out to have surpris-ingly negligible sensitivity weights this sensitivity conclusion was confirmed

148 Bak Kong Low

by previous elaborate deterministic parametric plots In contrast reliabilityanalysis achieved the same conclusion relatively effortlessly

The spreadsheet-based reliability-based design approach illustrated inFigure 36 is a more practical and relatively transparent intuitive approachthat obtains the same solution as the classical HasoferndashLind method forcorrelated normals and FORM for correlated nonnormals (shown below)Unlike the classical computational approaches the present approach doesnot need to rotate the frame of reference or to transform the coordinatespace

333 Positive reliability index only if mean-value point isin safe domain

In Figure 36 if a trial H value of 10 m is used and the entire ldquoxlowastrdquo columngiven the values equal to the ldquomeanrdquo column values the performance func-tion PerFn exhibits a value of minus4485 meaning that the mean value point isalready inside the unsafe domain Upon Solver optimization with constraintPerFn = 0 a β index of 134 is obtained which should be regarded as anegative index ie minus134 meaning that the unsafe mean value point is atsome distance from the nearest safe point on the limit state surface that sep-arates the safe and unsafe domains In other words the computed β indexcan be regarded as positive only if the PerFn value is positive at the meanvalue point For the case in Figure 36 the mean value point (prior to Solveroptimization) yields a positive PerFn for H gt 106 m The computed β indexincreases from 0 (equivalent to a lumped factor of safety equal to 10 ie onthe verge of failure) when H is 106 m to 30 when H is 1215 m as shownin Figure 37

4

3

2

1

0

8 9 10 11 12 13

Total wall height H

Rel

iabi

lity

inde

x b

minus1

minus2

minus3

minus4

minus5

Figure 37 Reliability index is 300 when H = 1215 m For H smaller than 106 m themean-value point is in the unsafe domain for which the reliability indices arenegative

Practical reliability approach 149

334 Reliability-based design involving correlatednonnormals

The two-parameter normal distribution is symmetrical and theoretically hasa range from minusinfin to +infin For a parameter that admits only positive valuesthe probability of encroaching into the negative realm is extremely remoteif the coefficient of variation (Standard deviationMean) of the parameter is020 or smaller as for the case in hand Alternatively the lognormal dis-tribution has often been suggested in lieu of the normal distribution sinceit excludes negative values and affords some convenience in mathematicalderivations Figure 38 shows an efficient reliability-based design when therandom variables are correlated and follow lognormal distributions Thetwo columns labeled microN and σN contain the formulae ldquo=EqvN(hellip 1)rdquoand ldquoEqvN(hellip 2)rdquo respectively which invoke the user-created functionsshown in Figure 38 to perform the equivalent normal transformation(when variates are lognormals) based on the following RackwitzndashFiesslertwo-parameter equivalent normal transformation (Rackwitz and Fiessler1978)

Equivalent normal standard deviation σN = φminus1 [F (x)]

f (x)

(37a)

Equivalent normal mean microN = x minusσN timesminus1 [F (x)] (37b)

where x is the original nonnormal variateminus1[] is the inverse of the cumula-tive probability (CDF) of a standard normal distribution F(x) is the originalnonnormal CDF evaluated at x φ is the probability density function (PDF)of the standard normal distribution and f (x) is the original nonnormalprobability density ordinates at x

For lognormals closed form equivalent normal transformation is avail-able and has been used in the VBA code of Figure 38 Efficient Excel VBAcodes for equivalent normal transformations of other nonnormal distribu-tions (including Gumbel uniform exponential gamma Weibull triangularand beta) are given in Low and Tang (2004 2007) where it is shown thatreliability analysis can be performed with various distributions merely byentering ldquoNormalrdquo ldquoLognormalrdquo ldquoExponentialrdquo ldquoGammardquo hellip in thefirst column of Figure 38 and distribution parameters in the columns to theleft of the xlowast column In this way the required RackwitzndashFiessler equivalentnormal evaluations (for microN and σN) are conveniently relegated to functionscreated in the VBA programming environment of Microsoft Excel

Therefore the single spreadsheet cell object β in Figure 38 contains severalExcel function objects and substantial program codes

For correlated nonnormals the ellipsoid perspective (Figure 32) and theconstrained optimization approach still apply in the original coordinate

150 Bak Kong Low

Lognormal

Lognormal

Lognormal

Lognormal

Lognormal

Lognormal

03

Ka Kah Kp Kph

0239 6307 601 261 000

d

d = H ndash 64 ndash z

122

Boxed cells containequations

H PerFn

Correlation matrix

300

b

1 -293

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-802

-145

-36

1835

4595

2767

7795

8760

982

-135

-222

-1131

-311

1802

Mean

17 085 1636 1697

1994

9803

3779

1893

226

0817

0938

1988

1814

0927

0396

-074

-124

0119

-181

-139

2327

1878

1004

345

1763

3182

1

2

2

1

03

20

10

38

19

24

StDev

1

05

0

05

0

0

05

1

0

05

0

0

0

0

1

0

0

0

05

05

0

1

08

0

0

0

0

08

1

0

0

0

0

0

0

1

γsat

qs

γ

x

φprime

δ

z

γsat

γsat

qs

qs

γ

γ

φprime

φprime

δ

δsNmN

z

z

2

3

4

5

nX

The microN and σN column invoke theuser-defined function EqvN_LNbelow to obtain the equivalentnormal mean microN and equivalentnormal standard deviation σN of thelognormal variates

More nonnormal options availablein Low amp Tang (2004 2007)

Function EqvN_LN(mean StDev x code)lsquoReturns the equivalent mean of the lognormal variateif if code is 1lsquoReturns the equivalent standard deviation of the lognormal variates if code is 2del = 00001 lsquovariable lower limitIf x lt del Then x = dellamda = Log(mean) - 05 Log(1 + (StDev mean) ^ 2)If code = 1 Then EqvN_LN = x (1 - Log(x) + lamda)If code = 2 Then EqvN_LN = x Sqr(Log(1 + (StDev mean) ^ 2))End Function

Figure 38 Reliability-based design of anchored wall correlated lognormals

system except that the nonnormal distributions are replaced by an equiv-alent normal ellipsoid centered not at the original mean of the nonnormaldistributions but at an equivalent normal mean microN

β = minxisinF

radicradicradicradic[xi minusmicroNi

σNi

]T

[R]minus1

[xi minusmicroN

i

σNi

](38)

Practical reliability approach 151

as explained in Low and Tang (2004 2007) One Excel file associated withLow (2005a) for reliability analysis of anchored sheet pile wall involving cor-related nonnormal variates is available for download at httpalummiteduwwwbklow

For the case in hand the required total wall height H is practically thesame whether the random variables are normally distributed (Figure 36)or lognormally distributed (Figure 38) Such insensitivity of the design tothe underlying probability distributions may not always be expected par-ticularly when the coefficient of variation (standard deviationmean) or theskewness of the probability distribution is large

If desired the original correlation matrix (ρij) of the nonnormalscan be modified to ρprime

ij in line with the equivalent normal transforma-tion as suggested in Der Kiureghian and Liu (1986) Some tables ofthe ratio ρprime

ijρij are given in Appendix B2 of Melchers (1999) includ-ing a closed-form solution for the special case of lognormals For thecases illustrated herein the correlation matrix thus modified differs onlyslightly from the original correlation matrix Hence for simplicity theexamples of this chapter retain their original unmodified correlationmatrices

This section has illustrated an efficient reliability-based design approachfor an anchored wall The correlation structure of the six variables wasdefined in a correlation matrix Normal distributions and lognormal dis-tributions were considered in turn (Figures 36 and 38) to investigatethe implication of different probability distributions The procedure is ableto incorporate and reflect the uncertainty of the passive soil surface ele-vation Reliability index is the shortest distance between the mean-valuepoint and the limit state surface ndash the boundary separating safe and unsafecombinations of parameters ndash measured in units of directional standarddeviations It is important to check whether the mean-value point is inthe safe domain or unsafe domain before performing reliability analysisThis is done by noting the sign of the performance function (PerFn) inFigures 36 and 38 when the xlowast columns were initially assigned the meanvalues If the mean value point is safe the computed reliability index ispositive if the mean-value point is already in the unsafe domain the com-puted reliability index should be considered a negative entity as illustratedin Figure 37

The differences between reliability-based design and design based on spec-ified partial factors were briefly discussed The merits of reliability-baseddesign are thought to lie in its ability to explicitly reflect correlation struc-ture standard deviations probability distributions and sensitivities and toautomatically seek the most probable failure combination of parametric val-ues case by case without relying on fixed partial factors Corresponding toeach desired value of reliability index there is also a reasonably accuratesimple estimate of the probability of failure

152 Bak Kong Low

34 Practical probabilistic slope stability analysisbased on reformulated Spencer equations

This section presents a practical procedure for implementing Spencer methodreformulated for a computer age first deterministically then probabilisti-cally in the ubiquitous spreadsheet platform The material is drawn fromLow (2001 2003 both available at the authorrsquos website) and includes test-ing the robustness of search for noncircular critical slip surface modelinglognormal random variables deriving probability density functions fromreliability indices and comparing results inferred from reliability indices withMonte Carlo simulations The deterministic modeling is described first as itunderlies the limit state function (ie performance function) of the reliabilityanalysis

341 Deterministic Spencer method reformulated

Using the notations in Nash (1987) the sketch at the top of Figure 39(below columns I and J) shows the forces acting on a slice (slice i) thatforms part of the potential sliding soil mass The notations are weight Wibase length li base inclination angle αi total normal force Pi at the baseof slice i mobilized shearing resistance Ti at the base of slice i horizontaland vertical components (Ei Eiminus1 λiEi λiminus1Eiminus1) of side force resultantsat the left and right vertical interfaces of slice i where λiminus1 and λi are thetangents of the side force inclination angles (with respect to horizontal) atthe vertical interfaces Adopting the same assumptions as Spencer (1973)but reformulated for spreadsheet-based constrained optimization approachone can derive the following from MohrndashCoulomb criterion and equilibriumconsiderations

Ti = [cprimeili +(Pi minus uili

)tanφprime

i

]F (MohrndashCoulomb criteria) (39)

Pi cosαi = Wi minusλiEi +λiminus1Eiminus1 minus Ti sinαi (vertical equilibrium)(310)

Ei = Eiminus1 + Pi sinαi minus Ti cosαi (horizontal equilibrium) (311)

Pi =

[Wi minus

(λi minusλiminus1

)Eiminus1

minus1F

(cprime

ili minus uili tanφprimei

)(sinαi minusλi cosαi

)]

[λi sinαi + cosαi

+1F

tanφprimei

(sinαi minusλi cosαi

)]

(from above three equations) (312)

Undrained shear strength profile of soft clay Embankment

depth

cu

0

40

15

28

3

20

5

20

7

26

100 25 50

Cm

10(kPa) (deg) (kNm3)

30 20

φm γm

γclay

16 (kNm3)

0

2

4

6Dep

th

cu (kPa)

8

10

37

(m)

(kPa)

1 minus10

Xn

γw

Pw

Mw X

cX

minX

max

(XiminusX

0)

(XnminusX

0)

Xn

X0

yc

Rru

X0

minus5 0 5 10

ytop

Ω

Ω H

27

X ybot

ytop

γave C φ W α

rad u l P T E λ σprime Lx

Ly

Units m kNm3 kNm2 kNor other consistent set of units

5 173 10 15 61518 02

hc

ybot

slope angle

Soft clay

10

5

0

minus5

minus10

15 20

A B C D E F G H I J K L M N O P Q R

234567

8

9

10

111213

14

15

1617181920212223242526272829303132333435363738394041424344

454647484950

Embankment

Reformulated Spencer method

λiE

i

λ

λiminus1Eiminus1

αi

Eiminus1

Ei

F

0105

Array formulas

λ

Varying side-force angle λ

000 000

1287 1287

DumEq

Varying λ

λ=λ sin π

TRUE

ΣM ΣForces

Ti

Wi

l

Pi

491 minus589795 1340

Center of Rotation

Framed cells contain quations

17467

0 1747 327 500 15 0000

1 1658 137 500 2000 1000 30 474 1136 1073 210 4972 2850 481 0012 1298 1212 563

2 1570 000 500 2000 1000 30 763 0997 1727 163 8096 3636 963 0025 3249 1124 726

3 1472 minus118 500 1958 3526 0 1074 0879 2189 154 11201 4214 1557 0038 5094 1030 854

4 1374 minus214 500 1900 2715 0 1242 0770 2531 137 13712 2884 2305 0050 7497 932 961

5 1276 minus292 500 1866 2252 0 1378 0673 2809 125 14925 2195 3063 0062 9090 834 1047

6 1178 minus356 500 1843 2000 0 1490 0582 3037 117 15635 1825 3770 0072 10275 735 1119

7 1079 minus410 500 1827 2000 0 1583 0496 3225 112 16090 1734 4384 0082 11192 638 1178

8 981 minus453 500 1815 2000 0 1658 0415 3380 107 16506 1666 4896 0090 12014 540 1226

9 883 minus487 450 1801 2000 0 1670 0336 3404 104 16403 1615 5285 0096 12377 442 1264

10 785 minus513 400 1784 2000 0 1619 0259 3300 102 15816 1578 5538 0101 12279 343 1295

11 687 minus531 350 1767 2066 0 1556 0184 3171 100 15237 1603 5659 0103 12093 245 1317

12 589 minus542 300 1751 2110 0 1481 0110 3018 099 14654 1619 5659 0105 11826 147 1331

13 491 minus546 250 1734 2132 0 1394 0037 2841 098 14044 1627 5548 0104 11461 049 1339

14 393 minus542 200 1717 2132 0 1296 minus0037 2641 098 13380 1627 5337 0101 10985 minus049 1339

15 294 minus531 150 1698 2110 0 1186 minus0110 2418 099 12636 1619 5037 0097 10381 minus147 1331

16 196 minus513 100 1677 2066 0 1065 minus0184 2171 100 11786 1603 4663 0091 9636 minus245 1317

17 098 minus487 050 1652 2000 0 932 minus0259 1900 102 10799 1578 4234 0083 8737 minus343 1295

18 000 minus453 000 1620 2000 0 787 minus0336 1604 104 9674 1615 3763 0074 7703 minus442 1264

19 minus098 minus410 000 1600 2000 0 677 minus0415 1380 107 8912 1666 3251 0064 6932 minus540 1226

20 minus196 minus356 000 1600 2000 0 601 minus0496 1225 112 8534 1734 2692 0053 6422 minus638 1178

21 minus294 minus292 000 1600 2000 0 509 minus0582 1037 117 7974 1825 2101 0040 5752 minus736 1119

22 minus393 minus214 000 1600 2252 0 397 minus0673 809 125 7392 2195 1469 0027 5084 minus834 1047

23 minus491 minus118 000 1600 2715 0 261 minus0770 531 137 6839 2884 786 0014 4470 minus932 961

24 minus589 000 000 1600 3526 0 93 minus0879 189 154 6712 4214 00 0000 4175 minus1030 854

Figure 39 Deterministic analysis of a 5 m high embankment on soft ground with depth-dependent undrained shear strength The limit equilibrium method of slices isbased on reformulated Spencer method with half-sine variation of side forceinclination

154 Bak Kong Low

sum[Ti cosαi minus Pi sinαi

]minus Pw = 0 (overall horizontal equilibrium)

(313)

sum[(Ti sinαi + Pi cosαi minus Wi

)lowast Lxi+(Ti cosαi minus Pi sinαi

)lowast Lyi

]minus Mw = 0

(overall moment equilibrium) (314)

Lxi = 05(xi + ximinus1

)minus xc (horizontal lever arm of slice i) (315)

Lyi = yc minus 05(yi + yiminus1

)(vertical lever arm of slice i) (316)

where ci φi and ui are cohesion friction angle and pore water pressurerespectively at the base of slice i Pw is the water thrust in a water-filledvertical tension crack (at x0) of depth hc and Mw the overturning momentdue to Pw Equations (315) and (316) required for noncircular slip surfacegive the lever arms with respect to an arbitrary center The use of both λiand λiminus1 in Equation (312) allows for the fact that the right-most slice(slice 1) has a side that is adjacent to a water-filled tension crack henceλ0 = 0 (ie the direction of water thrust is horizontal) and for differentλ values (either constant or varying) on the other vertical interfaces

The algebraic manipulation that results in Equation (312) involves open-ing the term (Pi minus uili)tanφprime of Equation (39) an action legitimate only if(Pi minus uili) ge 0 or equivalently if the effective normal stress σ prime

i (= Pili minus ui)at the base of a slice is nonnegative Hence after obtaining the critical slipsurface in the section to follow one needs to check that σ prime

i ge 0 at the baseof all slices and Ei ge 0 at all the slice interfaces Otherwise one should con-sider modeling tension cracks for slices near the upper exit end of the slipsurface

Figure 39 shows the spreadsheet set-up for deterministic stability analysisof a 5 m high embankment on soft ground The undrained shear strength pro-file of the soft ground is defined in rows 44 and 45 The subscript m in cellsP44R44 denotes embankment Formulas need be entered only in the firstor second cell (row 16 or 17) of each column followed by autofilling downto row 40 The columns labeled ytop γave and c invoke the functions shownin Figure 310 created via ToolsMacroVisualBasicEditorInsertModuleon the Excel worksheet menu The dummy equation in cell P2 is equalto Flowast1 This cell unlike cell O2 can be minimized because it contains aformula

Initially xc = 6 yc = 8 R = 12 in cells I11K11 and λprime = 0 F = 1in cells N2O2 Microsoft Excelrsquos built-in Solver was then invoked to settarget and constraints as shown in Figure 311 The Solver option ldquoUseAutomatic Scalingrdquo was also activated The critical slip circle and factor ofsafety F = 1287 shown in Figure 39 were obtained automatically withinseconds by Solver via cell-object oriented constrained optimization

Practical reliability approach 155

Function Slice_c(ybmid dmax dv cuv cm)lsquocomment dv = depth vectorlsquocuv = cu vectorIf ybmid gt 0 Then Slice_c = cm Exit FunctionEnd Ifybmid = Abs(ybmid)If ybmid gt dmax Then lsquoundefined domain Slice_c = 300 lsquohence assume hard stratum Exit FunctionEnd IfFor j = 2 T o dvCount lsquoarray size=dvCount If dv(j) gt= ybmid Then interp = (ybmid - dv(j - 1)) (dv(j) - dv(j - 1)) Slice_c = cuv(j - 1) + (cuv(j) - cuv(j - 1)) interp Exit For End IfNext jEnd Function

Function ytop(x omega H)grad = Tan(omega 314159 180)If x lt 0 Then ytop = 0If x gt=0 And x lt H grad Then ytop = x gradIf x gt=H grad Then ytop = HEnd Function

Function AveGamma(ytmid ybmid gm gclay)If ybmid lt 0 Then Sum = (ytmid gm + Abs(ybmid) gclay) AveGamma = Sum (ytmid - ybmid) Else AveGamma = gmEnd IfEnd Function

Figure 310 User-defined VBA functions called by columns ytop γave and c of Figure 39

Noncircular critical slip surface can also be searched using Solver asin Figure 311 except that ldquoBy Changing Cellsrdquo are N2O2 B16 B18B40 C17 and C19C39 and with the following additional cell constraintsB16 ge B11tan(radians(A11)) B16 ge B18 B40 le 0 C19C39 le D19D39O2 ge 01 and P17P40 ge 0

Figure 312 tests the robustness of the search for noncircular critical sur-face Starting from four arbitrary initial circles the final noncircular criticalsurfaces (solid curves each with 25 degrees of freedom) are close enoughto each other though not identical Perhaps more pertinent their factors ofsafety vary narrowly within 1253 ndash 1257 This compares with the minimumfactor of safety 1287 of the critical circular surface of Figure 39

Figure 311 Excel Solver settings to obtain the solution of Figure 39

8

4

0

-4

-8

-12

-20 -10 0 10

Circular initial

Noncircular critical

20

FS =125-126

Figure 312 Testing the robustness of search for the deterministic critical noncircular slipsurface

Practical reliability approach 157

342 Reformulated Spencer method extendedprobabilistically

Reliability analyses were performed for the embankment of Figure 39as shown in Figure 313 The coupling between Figures 39 and 313 isbrought about simply by entering formulas in cells C45H45 and P45R45of Figure 39 to read values from column vi of Figure 313 The matrixform of the HasoferndashLind index Equation (35b) will be used except thatthe symbol vi is used to denote random variables to distinguish it from thesymbol xi (for x-coordinate values) used in Figure 39

Spatial correlation in the soft ground is modeled by assuming an autocor-relation distance (δ) of 3 m in the following established negative exponentialmodel

ρij = eminus∣∣Depth(i) minus Depth(j)

∣∣δ (317)

mean StDev Correlation matrixldquocrmatrdquo

Dist Name vi microi σi microiN σi

N (vi-microiN)σi

N

lognormal Cm 104815 10 150 9872 1563 0390

lognormal φm 3097643 30 300 29830 3090 0371

lognormal γm 2077315 20 100 19959 1038 0784

lognormal Cu1 3426899 40 600 39187 5112 -0962

lognormal Cu2 2287912 28 420 27246 3413 -1279

lognormal Cu3 1598835 20 300 19390 2385 -1426

lognormal Cu4 1583775 20 300 19357 2362 -1490

lognormal Cu5 2207656 26 390 25442 3293 -1022

lognormal Cu6 3459457 37 555 36535 5160 -0376

nv

EqvN(1)

Array formula Ctrl+Shift then enter 1961PrFailβ

00249 =normsdist(-β)

Probability of failure

EqvN(2)

Function EqvN(Distributions Name v mean StDev code)Select Case UCase(Trim(Distribution Name)) lsquotrim leadingtrailing spaces amp convert to uppercaseCase ldquoNORMALrdquo If code = 1 Then EqvN = mean If code = 2 Then EqvN = StDevCase ldquoLOGNORMALrdquo If vlt0000001 Then v=0000001 lamda = Log(mean) - 05 Log(1+(StDevmean)and2) If code=1 Then EqvN=v(1-Log(v) +lamda) If code=2 Then EqvN=vSqr(Log(1+(StDevmean)and2))End selectEnd Function

1

-03

05

0

0

0

0

0

0

-03

1

05

0

0

0

0

0

0

05

05

1

0

0

0

0

0

0

0

0

0

1

06065

03679

01889

0097

00357

0

0

0

06065

1

06065

03114

01599

00588

0

0

0

03679

06065

1

05134

02636

0097

0

0

0

01889

03114

05134

1

05134

01889

0

0

0

0097

01599

02636

05134

1

03679

0

Cm -φm γm Cu1 Cu2 Cu3 Cu4 Cu4 Cu1

0

0

00357

00588

0097

01889

03679

1

0 15 3 5 7 10

0

15

3

5

7

10

depth

Equation 16 autofilled

=SQRT(MMULT(TRANSPOSE(nv) MMULT(MINVERSE(crmat)nv)))

Figure 313 Reliability analysis of the case in Figure 39 accounting for spatial variation ofundrained shear strength The two templates are easily coupled by replacingthe values cm φm γm and the cu values of Figure 39 with formulas that pointto the values of the vi column of this figure

158 Bak Kong Low

(Reliability analysis involving horizontal spatial variations of undrainedshear strengths and unit weights is illustrated in Low et al 2007 for a clayslope in Norway)

Only normals and lognormals are illustrated Unlike a widely documentedclassical approach there is no need to diagonalize the correlation matrix(which is equivalent to rotating the frame of reference) Also the iterativecomputations of the equivalent normal mean (microN) and equivalent normalstandard deviation (σN) for each trial design point are automatic during theconstrained optimization search

Starting with a deterministic critical noncircular slip surface of Figure 312the λprime and F values of Figure 39 were reset to 0 and 1 respectively The vivalues in Figure 313 were initially assigned their respective mean valuesThe Solver routine in Microsoft Excel was then invoked to

bull Minimize the quadratic form (a 9-dimensional hyperellipsoid in originalspace) ie cell ldquoβrdquo

bull By changing the nine random variables vi the λprime in Figure 39 and the 25coordinate values x0 x2 x24 yb1 yb3 yb23 of the slip surface (F remainsat 1 ie at failure or at limit state)

bull Subject to the constraints minus1 le λprime le 1 x0 ge Htan(radians()) x0 ge x2x24 le 0 yb3 yb23 le yt3 yt23

sumForces = 0

sumM = 0 and σ prime

1 σ prime24 ge 0

The Solver option ldquoUse automatic scalingrdquo was also activated

The β index is 1961 for the case with lognormal variates (Figure 313)The corresponding probability of failure based on the hyperplane assump-tion is 249 The reliability-based noncircular slip surface is shown inFigure 314 The nine vi values in Figure 313 define the most probablefailure point where the equivalent dispersion hyperellipsoid is tangent tothe limit state surface (F = 1) of the reliability-based critical slip surfaceAt this tangent point the values of cu are as expected smaller thantheir mean values but the cm and φm values are slightly higher thantheir respective mean values This peculiarity is attributable to cm and φmbeing positively correlated to the unit weight of the embankment γm andalso reflects the dependency of tension crack depth hc (an adverse effect)on cm since the equation hc = 2cm (γm

radicKa) is part of the model in

Figure 39By replacing ldquolognormalrdquo with ldquonormalrdquo in the first column of

Figure 313 re-initializing column vi to mean values and invoking Solverone obtains a β index of 1857 with a corresponding probability of fail-ure equal to 316 compared with 249 for the case with lognormalvariates The reliability-based noncircular critical slip surface of the casewith normal variates is practically indistinguishable from the case with log-normal variates Both are however somewhat different (Figure 314) fromthe deterministic critical noncircular surface from which they evolved via

Practical reliability approach 159

(m)

(m)

Deterministic critical

64

20

-2 0 5 10 15 20-5-10-4-6-8

Figure 314 Comparison of reliability-based critical noncircular slip surfaces (the two uppercurves for normal variates and lognormal variates) with the deterministiccritical noncircular slip surface (the lower dotted curve)

Table 31 Reliability indices for the example case

[Lognormal variates] [Normal variates]

Reliability index 1961 1857(1971)lowast (1872)lowast

Prob of failure 249 316(244)lowast (306)lowast

lowast at deterministic critical noncircular surface

Table 32 Solverrsquos computing time on a computer

F for specified slip surface asymp 03 sF search for noncircular surface 15 sβ for specified slip surface 2 sβ search for noncircular surface 20 s

25 degrees of freedom during Solverrsquos constrained optimization search Thisdifference in slip surface geometry matters little however for the followingreason If the deterministic critical noncircular slip surface is used for relia-bility analysis the β index obtained is 1971 for the case with lognormalsand 1872 for the case with normals These β values are only slightlyhigher (lt1) than the β values of 1961 and 1857 obtained earlier with aldquofloatingrdquo surface Hence for the case in hand performing reliability anal-ysis based on the fixed deterministic critical noncircular slip surface willyield practically the same reliability index as the reliability-based critical slipsurface Table 31 summarizes the reliability indices for the case in handTable 32 compares the computing time

160 Bak Kong Low

343 Deriving probability density functions fromreliability indices and comparison withMonte Carlo simulations

The reliability index β in Figure 313 has been obtained with respect tothe limit state surface defined by F = 10 The mean value point repre-sented by column microi is located on the 1253 factor-of-safety contour ascalculated in the deterministic noncircular slip surface search earlier A dis-persion hyperellipsoid expanding from the mean-value point will touchthe limit state surface F = 10 at the design point represented by thevalues in the column labeled vi The probability of failure 249 approx-imates the integration of probability density in the failure domain F le 10If one defines another limit state surface F = 08 a higher value of β isobtained with a correspondingly smaller probability that F le 08 reflect-ing the fact that the limit state surface defined by F = 08 is further awayfrom the mean-value point (which is on the factor-of-safety contour 1253)In this way 42 values of reliability indices (from positive to negative) cor-responding to different specified limit states (from F = 08 to F = 18)were obtained promptly using a short VBA code that automatically invokesSolver for each specified limit state F The series of β indices yields thecumulative distribution function (CDF) of the factor of safety based onPr[F le FLimitState] asymp (minusβ) where () is the standard normal cumula-tive distribution function The probability density function (PDF) plots inFigure 315 (solid curves) were then obtained readily by applying cubicspline interpolation (eg Kreyszig 1988) to the CDF during which pro-cess the derivatives (namely the PDF) emerged from the tridiagonal splinematrix The whole process is achieved easily using standard spreadsheetmatrix functions Another alternative was given in Lacasse and Nadim(1996) which examined pile axial capacity and approximated the CDFby appropriate probability functions

For comparison direct Monte Carlo simulations with 20000 realiza-tions were performed using the commercial software RISK with LatinHypercube sampling The random variables were first assumed to be nor-mally distributed then lognormally distributed The mean values standarddeviations and correlation structure were as in Figure 313

The solid PDF curves derived from the β indices agree remarkably wellwith the Monte Carlo PDF curves (the dashed curves in Figure 315)

Using existing state-of-the-art personal computer the time taken by Solverto determine either the factor of safety F or the reliability index β of theembankment in hand is as shown in Table 32

The computation time for 20000 realizations in Monte Carlo simula-tions would be prohibitive (20000times15 s or 83 h) if a search for thecritical noncircular slip surface is carried out for each random set (cm φmγm and the cu values) generated Hence the PDF plots of Monte Carlo

Practical reliability approach 161

3

from β indices Normalvariates

Factor of safety

from Monte Carlosimulation20000 trials

PD

F

25

2

15

1

05

006 08 1 12 14 16 18

3

from β indices Lognormalvariates

Factor of safety

from simulation20000 trials

25

2

15

1

05

006 08 1 12 14 16 18

3

Normal variates

Lognormals

Factor of safety

Both curves basedon β indices

PD

F

25

2

15

1

05

006 08 1 12 14 16 18

3

Normals

Lognormals

Factor of safety

Both curves fromMonte Carlosimulationseach 20000 trials

25

2

15

1

05

006 08 1 12 14 16 18

Figure 315 Comparing the probability density functions (PDF) obtained from reliabilityindices with those obtained from Monte Carlo simulations ldquoNormalsrdquo meansthe nine input variables are correlated normal variates and ldquoLognormalsrdquomeans they are correlated lognormal variates

simulations shown in Figure 315 have been obtained by staying put atthe reliability-based critical noncircular slip surface found in the previoussection In contrast computing 42 values of β indices (with fresh non-circular surface search for each limit state) will take only about 14 minNevertheless for consistency of comparison with the Monte Carlo PDFplots the 42 β values underlying each solid PDF curve of Figure 315have also been computed based on the same reliability-based critical non-circular slip surface shown in Figure 314 Thus it took about 14 min(42times2 s) to produce each reliability indices-based PDF curve but about70 times as long (asymp 20000 times 03 s) to generate each simulation-basedPDF curve What-if scenarios can be investigated more efficiently using thepresent approach of first-order reliability method than using Monte Carlosimulations

The probabilities of failure (F le 1) from Monte Carlo simulations with20000 realizations are 241plusmn for lognormal variates and 328plusmn fornormal variates These compare well with the 249 and 316 shown inTable 31 Note that only one β index value is needed to compute each prob-ability of failure in Table 31 Only for PDF does one need about 30 or 40β values of different limit states in order to define the CDF from which thePDF are obtained

162 Bak Kong Low

35 On the need for a positive definitecorrelation matrix

Although the correlation coefficient between two random variables has arange minus1 le ρij le 1 one is not totally free in assigning any values within thisrange for the correlation matrix This was explained in Ditlevsen (1981 85)where a 3times3 correlation matrix with ρ12 = ρ13 = ρ23 = minus1 implies a contra-diction and is hence inconsistent A full-rank (ie no redundant variables)correlation matrix has to be positive definite One may note that HasoferndashLind index is defined as the square-root of a quadratic form (Equation (35a)or (35b)) and unless the quadratic form and its covariance matrix and cor-relation matrix are positive-definite one is likely to encounter the square rootof a negative entity which is meaningless whether in the dispersion ellipsoidperspective or in the perspective of minimum distance in the reduced variatespace

Mathematically a symmetric matrix R is positive definite if and only ifall the eigenvalues of R are positive For present purposes one can createthe equation QForm = uTRminus1u side by side with β = radic

(QForm) DuringSolverrsquos searches if the cell Qform shows negative value it means that thequadratic form is not positive definite and hence the correlation matrix isinconsistent

The Monte Carlo simulation program RISK (httpwwwpalisadecom)will also display a warning of ldquoInvalid correlation matrixrdquo and offer tocorrect the matrix if simulation is run involving an inconsistent correlationmatrix

That a full-rank correlation or covariance matrix should be positive def-inite is an established theoretical requirement although not widely knownThis positive-definiteness requirement is therefore not a limitation of thepaperrsquos proposed procedure

The joint PDF of an n-dimensional multivariate normal distributionis given by Equation (36a) The PDF has a peak at mean-value pointand decreasing ellipsoidal probability contours surrounding micro only if thequadratic form and hence the covariance matrix and its inverse are positivedefinite (By lemmas of matrix theory C positive definite implies Cminus1 is pos-itive definite and also means the correlation matrix R and its inverse arepositive definite)

Without assuming any particular probability distributions it is docu-mented in books on multivariate statistical analysis that a covariance matrix(and hence a correlation matrix) cannot be allowed to be indefinite becausethis would imply that a certain weighted sum of its variables has a negativevariance which is inadmissible since all real-valued variables must havenonnegative variances (The classical proof can be viewed as reduction adabsurdum ie a method of proving the falsity of a premise by showing thatthe logical consequence is absurd)

Practical reliability approach 163

An internet search using the Google search engine with ldquopositive defi-nite correlation matrixrdquo as search terms also revealed thousands of relevantweb pages showing that many statistical computer programs (in addi-tion to the RISK simulation software) will check for positive definitenessof correlation matrix and offer to generate the closest valid matrix toreplace the entered invalid one For example one such web site titledldquoNot Positive Definite Matrices ndash Causes and Curesrdquo at httpwwwgsuedusimmkteernpdmatrihtml refers to the work of Wothke (1993)

36 Finite element reliability analysis via responsesurface methodology

Programs can be written in spreadsheet to handle implicit limit state func-tions (eg Low et al 1998 Low 2003 and Low and Tang 2004 87)However there are situations where serviceability limit states can only beevaluated using stand-alone finite element or finite difference programs Inthese circumstances reliability analysis and reliability-based design by thepresent approach can still be performed provided one first obtains a responsesurface function (via the established response surface methodology) whichclosely approximates the outcome of the stand-alone finite element or finitedifference programs Once the closed-form response functions have beenobtained performing reliability-based design for a target reliability index isstraightforward and fast Performing Monte-Carlo simulation on the closedform approximate response surface function also takes little time Xu andLow (2006) illustrate the finite element reliability analysis of embankmentson soft ground via the response surface methodology

37 Limitations and flexibilities of object-orientedconstrained optimization in spreadsheet

If equivalent normal transformation is used for first-order reliability methodinvolving nonnormal distributions other than the lognormal it is neces-sary to compute the inverse of the standard normal cumulative distributions(Equations (37a) and (37b)) There is no closed form equation for this pur-pose Numerical procedures such as that incorporated in Excelrsquos NormSInvis necessary In Excel 2000 and earlier versions NormSInv(CDF) returnscorrect values only for CDF between about 000000035 and 099999965ie within about plusmn5 standard deviations from the mean There are unde-sirable and idiosyncratic sharp kinks at both ends of the validity rangeIn EXCEL XP (2002) and later versions the NormSInv (CDF) functionhas been refined and returns correct values for a much broader band ofCDF 10minus16 lt CDF lt (1 minus 10minus16) or about plusmn8 standard deviations fromthe mean In general robustness and computation efficiency will be bet-ter with the broader validity band of NormSInv(CDF) in Excel XP This is

164 Bak Kong Low

because during Solverrsquos trial solutions using the generalized reduced gradientmethod the trial xlowast values may occasionally stray beyond 5 standard devia-tions from the mean as dictated by the directional search before returningto the real design point which could be fewer than 5 standard deviationsfrom the mean

Despite its simple appearance the single spreadsheet cell that contains theformula for reliability index β is in fact a conglomerate of program routinesIt contains Excelrsquos built-in matrix operating routines ldquommultrdquo ldquotransposerdquoand ldquominverserdquo which can operate on up to 50times50 matrices The membersof the ldquonxrdquo vector in the formula for β are each functions of their respec-tive VBA codes in the user-created function EqvN (Figure 313) Similarlythe single spreadsheet cell for the performance function g(x) can also con-tain many nested functions (built-in or user-created) In addition up to 100constraints can be specified in the standard Excel Solver Regardless of theircomplexities and interconnections they (β index g(x) constraints) appear asindividual cell objects on which Excelrsquos built-in Solver optimization programperforms numerical derivatives and iterative directional search using the gen-eralized reduced gradient method as implemented in Lasdon and WarenrsquosGRG2 code (httpwwwsolvercomtechnology4htm)

The examples of Low et al (1998 2001) with implicit performance func-tions and numerical methods coded in Excelrsquos programming environmentinvolved correlated normals only and have no need for the Excel NormSInvfunction and hence are not affected by the limitations of NormSInv in Excel2000 and earlier versions

38 Summary

This chapter has elaborated on a practical object-oriented constrained opti-mization approach in the ubiquitous spreadsheet platform based on thework of Low and Tang (1997a 1997b 2004) Three common geotechni-cal problems have been used as illustrations namely a bearing capacityproblem an anchored sheet pile wall embedment depth design and areformulated Spencer method Slope reliability analyses involving spatiallycorrelated normal and lognormal variates were demonstrated with searchfor the critical noncircular slip surface The HasoferndashLind reliability indexwas re-interpreted using the perspective of an expanding equivalent dis-persion ellipsoid centered at the mean in the original space of the randomvariables When nonnormals are involved the perspective is one of equiva-lent ellipsoids The probabilities of failure inferred from reliability indices arein good agreement with those from Monte Carlo simulations for the exam-ples in hand The probability density functions (of the factor of safety) werealso derived from reliability indices using simple spreadsheet-based cubicspline interpolation and found to agree well with those generated by the farmore time-consuming Monte Carlo simulation method

Practical reliability approach 165

The coupling of spreadsheet-automated cell-object oriented constrainedoptimization and minimal macro programming in the ubiquitous spreadsheetplatform together with the intuitive expanding ellipsoid perspective ren-ders a hitherto complicated reliability analysis problem conceptually moretransparent and operationally more accessible to practicing engineers Thecomputational procedure presented herein achieves the same reliability indexas the classical first order reliability method (FORM) ndash well-documented inAng and Tang (1984) Madsen et al (1986) Haldar and Mahadevan (1999)Baecher and Christian (2003) for example ndash but without involving the con-cepts of eigenvalues eigenvectors and transformed space which are requiredin a classical approach It also affords possibilities yet unexplored or difficulthitherto Although bearing capacity anchored wall and slope stability aredealt with in this chapter the perspectives and techniques presented couldbe useful in many other engineering problems

Although only correlated normals and lognormals are illustrated the VBAcode shown in Figure 313 can be extended (Low and Tang 2004 and 2007)to deal with the triangular the exponential the gamma the Gumbel andthe beta distributions for example

In reliability-based design it is important to note whether the mean-valuepoint is in the safe domain or in the unsafe domain When the mean-valuepoint is in the safe domain the distance from the safe mean-value pointto the most probable failure combination of parametric values (the designpoint) on the limit state surface in units of directional standard deviationsis a positive reliability index When the mean-value point is in the unsafedomain (due to insufficient embedment depth of sheet pile wall for example)the distance from the unsafe mean-value point to the most probable safecombination of parametric values on the limit state surface in units of direc-tional standard deviations is a negative reliability index This was shownin Figure 37 for the anchored sheet pile wall example It is also importantto appreciate that the correlation matrix to be consistent must be positivedefinite

The meaning of the computed reliability index and the inferred probabilityof failure is only as good as the analytical model underlying the performancefunction Nevertheless even this restrictive sense of reliability or failure ismuch more useful than the lumped factor of safety approach or the partialfactors approach both of which are also only as good as their analyticalmodels

In a reliability-based design the design point reflects sensitivities standarddeviations correlation structure and probability distributions in a way thatprescribed partial factors cannot

The spreadsheet-based reliability approach presented in this chaptercan operate on stand-alone numerical packages (eg finite element) viathe response surface method which is itself readily implementable inspreadsheet

166 Bak Kong Low

The present chapter does not constitute a comprehensive risk assessmentapproach but it may contribute component blocks necessary for such afinal edifice It may also help to overcome a language barrier that hamperswider adoption of the more consistent HasoferndashLind reliability index andreliability-based design Among the issues not covered in this chapter aremodel uncertainty human uncertainty and estimation of statistical parame-ters These and other important issues were discussed in VanMarcke (1977)Christian et al (1994) Whitman (1984 1996) Morgenstern (1995) Baecherand Christian (2003) among others

Acknowledgment

This chapter was written at the beginning of the authorrsquos four-monthsabbatical leave at NGI Norway summer 2006

References

Ang H S and Tang W H (1984) Probability Concepts in Engineering Planningand Design Vol 2 ndash Decision Risk and Reliability John Wiley New York

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley Chichester UK

Bowles J E (1996) Foundation Analysis and Design 5th ed McGraw-HillNew York

BS 8002 (1994) Code of Practice for Earth Retaining Structures British StandardsInstitution London

Christian J T Ladd C C and Baecher G B (1994) Reliability applied toslope stability analysis Journal of Geotechnical Engineering ASCE 120(12)2180ndash207

Craig R F (1997) Soil Mechanics 6th ed Chapman amp Hall LondonDer Kiureghian A and Liu P L (1986) Structural reliability under incom-

plete probability information Journal of Engineering Mechanics ASCE 112(1)85ndash104

Ditlevsen O (1981) Uncertainty Modeling With Applications to MultidimensionalCivil Engineering Systems McGraw-Hill New York

ENV 1997-1 (1994) Eurocode 7 Geotechnical Design Part 1 General RulesCENEuropean Committee for Standardization Brussels

Haldar A and Mahadevan S (1999) Probability Reliability and StatisticalMethods in Engineering Design John Wiley New York

Hasofer A M and Lind N C (1974) Exact and invariant second-moment codeformat Journal of Engineering Mechanics ASCE 100 111ndash21

Kerisel J and Absi E (1990) Active and Passive Earth Pressure Tables 3rd edAA Balkema Rotterdam

Kreyszig E (1988) Advanced Engineering Mathematics 6th ed John Wiley amp SonsNew York pp 972ndash3

Lacasse S and Nadim F (1996) Model uncertainty in pile axial capacity cal-culations Proceedings of the 28th Offshore Technology Conference Texaspp 369ndash80

Practical reliability approach 167

Low B K (1996) Practical probabilistic approach using spreadsheet Geotech-nical Special Publication No 58 Proceedings Uncertainty in the GeologicEnvironment From Theory to Practice ASCE USA Vol 2 pp 1284ndash302

Low B K (2001) Probabilistic slope analysis involving generalized slip sur-face in stochastic soil medium Proceedings 14th Southeast Asian Geotech-nical Conference December 2001 Hong Kong AA Balkema Rotterdampp 825ndash30

Low B K (2003) Practical probabilistic slope stability analysis In Proceedings Soiland Rock America MIT Cambridge MA June 2003 Verlag Gluumlckauf GmbHEssen Germany Vol 2 pp 2777ndash84

Low B K (2005a) Reliability-based design applied to retaining walls Geotech-nique 55(1) 63ndash75

Low B K (2005b) Probabilistic design of anchored sheet pile wall Proceedings16th International Conference on Soil Mechanics and Geotechnical Engineering12ndash16 September 2005 Osaka Japan Millpress pp 2825ndash8

Low B K and Tang W H (1997a) Efficient reliability evaluation using spread-sheet Journal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K and Tang W H (1997b) Reliability analysis of reinforced embankmentson soft ground Canadian Geotechnical Journal 34(5) 672ndash85

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Low B K and Tang W H (2007) Efficient spreadsheet algorithm for first-orderreliability method Journal of Engineering Mechanics ASCE 133(12) 1378ndash87

Low B K Lacasse S and Nadim F (2007) Slope reliability analysis accountingfor spatial variation Georisk Assesment and Management of Risk for EngineeringSystems and Geohazards Taylor amp Francis 1(4) 177ndash89

Low B K Gilbert R B and Wright S G (1998) Slope reliability analysis usinggeneralized method of slices Journal of Geotechnical and GeoenvironmentalEngineering ASCE 124(4) 350ndash62

Low B K Teh C I and Tang W H (2001) Stochastic nonlinear pndashy analysisof laterally loaded piles Proceedings of the Eight International Conference onStructural Safety and Reliability ICOSSAR lsquo01 Newport Beach California 17ndash22June 2001 8 pages AA Balkema Rotterdam

Madsen H O Krenk S and Lind N C (1986) Methods of Structural SafetyPrentice Hall Englewood Cliffs NJ

Melchers R E (1999) Structural Reliability Analysis and Prediction 2nd edJohn Wiley New York

Morgenstern N R (1995) Managing risk in geotechnical engineering ProceedingsPan American Conference ISSMFE

Nash D (1987) A comparative review of limit equilibrium methods of stability anal-ysis In Slope Stability Eds M G Anderson and K S Richards Wiley New Yorkpp 11ndash75

Rackwitz R and Fiessler B (1978) Structural reliability under combined randomload sequences Computers and Structures 9 484ndash94

Shinozuka M (1983) Basic analysis of structural safety Journal of StructuralEngineering ASCE 109(3) 721ndash40

Simpson B and Driscoll R (1998) Eurocode 7 A Commentary ARUPBREConstruction Research Communications Ltd London

168 Bak Kong Low

Spencer E (1973) Thrust line criterion in embankment stability analysis Geotech-nique 23 85ndash100

US Army Corps of Engineers (1999) ETL 1110-2-556 Risk-based analysis ingeotechnical engineering for support of planning studies Appendix A pagesA11 and A12 (httpwwwusacearmymilpublicationseng-tech-ltrsetl1110-2-556tochtml)

VanMarcke E H (1977) Reliability of earth slopes Journal of GeotechnicalEngineering ASCE 103(11) 1247ndash66

Veneziano D (1974) Contributions to Second Moment Reliability Research ReportNo R74ndash33 Department of Civil Engineering MIT Cambridge MA

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110(2) 145ndash88

Whitman R V (1996) Organizing and evaluating uncertainnty in geotechnicalengineering Proceedings Uncertainty in Geologic Environment From Theoryto Practice ASCE Geotechnical Special Publication 58 ASCE New York V1pp 1ndash28

Wothke W (1993) Nonpositive definite matrices in structural modeling In TestingStructural Equation Models Eds KA Bollen and J S Long Sage Newbury ParkCA pp 257ndash93

Xu B and Low B K (2006) Probabilistic stability analyses of embankmentsbased on finite-element method Journal of Geotechnical and GeoenvironmentalEngineering ASCE 132(11) 1444ndash54

Chapter 4

Monte Carlo simulationin reliability analysis

Yusuke Honjo

41 Introduction

In this chapter Monte Carlo Simulation (MCS) techniques are described inthe context of reliability analysis of structures Special emphasis is placedon the recent development of MCS for reliability analysis In this contextgeneration of random numbers by low-discrepancy sequences (LDS) andsubset Markov Chain Monte Carlo (MCMC) techniques are explained insome detail The background to the introduction of such new techniques isalso described in order for the readers to make understanding of the contentseasier

42 Random number generation

In Monte Carlo Simulation (MCS) it is necessary to generate random num-bers that follow arbitrary probability density functions (PDF) It is howeverrelatively easy to generate such random numbers once a sequence of uniformrandom numbers in (00 10) is given (this will be described in section 23)

Because of this the generation of a sequence of random numbers in (0010) is discussed first in this section Based on the historical developmentsthe methods can be classified to pseudo random number generation andlow-discrepancy sequences (or quasi random number) generation

421 Pseudo random numbers

Pseudo random numbers are a sequence of numbers that look like randomnumbers by deterministic calculations This is a suitable method for acomputer to generate a large number of reproducible random numbers andis used commonly today (Rubinstein 1981 Tsuda 1995)

170 Yusuke Honjo

Pseudo random numbers are usually assumed to satisfy the followingconditions

1 A great number of random numbers can be generated instantaneously2 If there is a cycle in the generation (which usually exists) it should be

long enough to be able to generate a large number of random numbers3 The random numbers should be reproducible4 The random numbers should have appropriate statistical properties

which can usually be examined by statistical testings for example Chisquare goodness of fit test

The typical methods of pseudo random number generation include themiddle-square method and linear recurrence relations (sometimes called thecongruential method)

The origin of the middle-square method goes back to Von Neumann whoproposed generating a random number of 2a digits from xn of 2a digitsby squaring xn to obtain 4a digits number and then cut the first and lasta digits of this number to obtain xn+1 This method however was foundto have relatively short cycle and also does not satisfy satisfactory statisticalcondition This method is rarely used today

On the other hand the general form of linear recurrence relations can begiven by the following equation

xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b (mod P) (41)

where mod P implies that xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b minus Pknand kn = [(xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b)P] denotes the largestpositive integer in (xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b)P

The simplest form of Equation (41) may be given as

xn+1 = xn + xnminus1 (mod P) (42)

This is called the Fibonacci method If the generated random numbers are2 digits integer and suppose the initial two numbers are x1 = 11 and x2 = 36and P = 100 the generated numbers are 4783301443

The modified versions of Equation (41) are multiplicative congruentialmethod and mixed congruential method which are still popularly used andprograms are easily available (see for example Koyanagi 1989)

422 Low-discrepancy sequences (quasi random numbers)

One of the most important applications of MCS is to solve multi-dimensionalintegration problems which includes reliability analysis The problem can be

Monte Carlo simulation 171

generally described as a multi-dimensional integration in a unit hyper cube

I =int 1

0

int 1

0f (x)dx [x = (x1x2 xk)] (43)

The problem solved by MCS can be described as the following equation

S(N) = 1N

Nsumi=1

f (xi) (44)

where xirsquos are N randomly generated points in the unit hyper cube

The error of |S(N) minus I| is proportional to O(Nminus 12 ) which is related to

the sample number N Therefore if one wants to improve the accuracy ofthe integration by one digit one needs to increase the sample point number100 times

Around 1960 some mathematicians showed that if one can generate thepoints sequences with some special conditions the accuracy can be improvedby O(Nminus1) or even O(Nminus2) Such points sequences were termed quasirandom numbers (Tsuda 1995)

However it was found later that quasi random numbers are not so effec-tive as the dimensions of the integration increase to more than 50 On theother hand there was much demand especially in financial engineeringto solve integrations of more than 1000 dimensions which expeditedresearch in this area in 1990s

One of the triggers that accelerated the research in this area was theKoksmandashHlawka theorem which related the convergence of the integrationand discrepancy of numbers generated in the unit hyper cube According tothis theorem the convergence of integration is faster as the discrepancy ofthe points sequences is smaller Based on this theorem the main objectiveof the research in the 1990s was to generate point sequences in the veryhigh dimensional hyper cube with as low discrepancy as possible In otherwords how to generate point sequences with maximum possible uniformitybecame the main target of the research Such point sequences were termedlow-discrepancy sequences (LDS) In fact LDS are point sequences that allowthe accuracy of the integration in k dimensions to improve in proportion to

O(

(logN)k

N

)or less (Tezuka 1995 2003)

Based on this development the term LDS is more often used than quasirandom numbers in order to avoid confusion between pseudo and quasirandom numbers

The discrepancy is defined as follows (Tezuka 1995 2003)

DN(k) = supyisin[01]k

∣∣∣∣∣∣([0y)N)

Nminus

kprodi=1

yi

∣∣∣∣∣∣ (45)

172 Yusuke Honjo

10

0000

10y1

y2

Figure 41 Concept of discrepancy in 2-dimensional space

where ([0y]N) denotes number of points in [0y) Therefore DN(k)

indicates discrepancy between the distribution of N points xn in thek dimensional hyper cube [01]k from ideally uniform points distributions

In Figure 41 discrepancy in 2-dimensional space is illustrated conceptu-ally where N = 10 If the number of the points in a certain area is always inproportion to the area rate DN

(k) is 0 otherwise DN(k) becomes larger as

points distribution is biasedA points sequences xi is said to be LDS when it satisfies the conditions

below

|S(N) minus I| le VfDN(k) (46)

DN(k) = c(k)

(logN)k

N

where Vf indicates the fluctuation of function f (x) within the domain of theintegration and c(k) is a constant which only depends on dimension k

Halton Faure and Sobolrsquo sequences are known to be LSD Variousalgorithms are developed to generate LSD (Tezuka 1995 2003)

In Figure 42 1000 samples generated by the mixed congruential methodand LSD are compared It is understood intuitively from the figure that pointsgenerated by LSD are more uniformly distributed

423 Random number generation following arbitrary PDF

In this section generation of random numbers following arbitrary PDF isdiscussed The most popular method to carry out this is to employ inversetransformation method

Monte Carlo simulation 173

1

08

06

04

02

00 02 04 06 08 1

1

08

06

04

02

00 02 04 06 08 1

(a) (b)

Figure 42 (a) Pseudo random numbers by mixed congruential method and (b) pointsequences by LDS 1000 generated sample points in 2-dimensional unit space

Fu (u ) Fx (x )1

0 11u xu1 u3 u2 u4

u4

u2

u3

u1

x4 x2x3x1

Figure 43 Concept of generation of random number xi by uniform random number uibased on the inverse transformation

Suppose X is a random variable whose CDF is FX(x) = pFX(x) is a non-decreasing function and 0 le p le 10 Thus the following

inverse transformation always exists for any p as (Rubinstein 1981)

Fminus1X (p) = min

[x FX(x) ge p

]0 le p le 10 (47)

By using this inverse transformation any random variable X that followsCDF FX(x) can be generated based on uniform random numbers in (01)(Figure 43)

X = Fminus1X (U) (48)

174 Yusuke Honjo

Some of the frequently used inverse transformations are summarizedbelow (Rubinstein 1981 Ang and Tang 1985 Reiss and Thomas 1997)Note that U is a uniform random variable in (01)

Exponential distribution

FX(x) = 1 minus exp[minusλx] (x ge 0)

X = Fminus1X (U) = minus1

λln(1 minus U)

The above equation can be practically rewritten as follows

X = Fminus1X (U) = minus1

λln(U)

Cauchy distribution

fX(x) = α

πα2 + (x minusλ)2 α gt 0λ gt 0minusinfin lt x lt infin

FX(x) = 12

+πminus1tanminus1(

x minusλ

α

)

X = Fminus1X (U) = λ+α tan

(U minus 1

2

)]

Gumbel distribution

FX(x) = exp[minusexpminusa(x minus b)] (minusinfin lt x lt infin)

X = Fminus1X (U) = minus1

aln(minusln(U)

)+ b

Frechet distribution

FX(x) = exp

[minus(

ν

x minus ε

)k]

(ε lt x lt infin)

X = Fminus1X (U) = ν

(minusln(U))minus1k + ε

Monte Carlo simulation 175

Weibull distribution

FX(x) = exp

[minus(ωminus xωminus ν

)k]

(minusinfin lt x ltω)

X = Fminus1X (U) = (ωminus ν)

(ln(U)

)1k +ω

Generalized Pareto distribution

FX(x) = 1 minus(

1 + γx minusmicro

σ

)minus1γ

(micro le x)

X = Fminus1X (U) = σ

γ

[(1 minus U)minusγ minus 1

]+micro

The normal random numbers which are considered to be used mostfrequently next to the uniform random numbers do not have inverse trans-formation in an explicit form One of the most common ways to generate thenormal random numbers is to employ the Box and Muller method which isdescribed below (see Rubinstein 1981 86ndash7 for more details)

U1 and U2 are two independent uniform random variables Based on thesevariables two independent standard normal random numbers Z1 and Z2can be generated by using following equations

Z1 = (minus2 ln(U1))12 cos(2πU2) (49)

Z2 = (minus2 ln(U1))12 sin(2πU2)

A program based on this method can be obtained easily from standardsubroutine libraries (eg Koyanagi 1989)

424 Applications of LDS

In Yoshida and Sato (2005a) reliability analyses by MCS are presentedwhere results by LDS and by OMCS (ordinary Monte Carlo simulation) arecompared The performance function they employed is

g(xS) =5sum

i=1

xi minus(1 + x1

2500

)S (410)

where xi are independent random variables which follow identically a lognormal distribution with mean 250 and COV 10

The convergence of the failure probability with the number of simulationruns are presented in Figure 44 for LSD and 5 OMCS runs (which are

176 Yusuke Honjo

10minus2

10minus3

Failu

re P

roba

bilit

y

10minus4

100 101 102

Number103

LDSCrude

Figure 44 Relationship between calculated Pf and number of simulation runs (Yoshida andSato 2005a)

indicated by ldquoCruderdquo in the figure) It is observed that LDS has fasterconvergence than OMCS

43 Some methods to improve the efficiencyof MCS

431 Accuracy of ordinary Monte Carlo Simulation(OMCS)

We consider the integration of the following type in this section

Pf =int

intI [g (x) le 0] fX (x)dx (411)

where fX is the PDF of basic variables X and I is an indicator functiondefined below

I (x) = 1 g(x) le 0

0 g(x) gt 0

The indicator function identifies the domain of integration Function g(x)is a performance function in reliability analysis thus the integration provides

Monte Carlo simulation 177

the failure probability Pf Note that the integration is carried out on all thedomains where basic variables are defined

When this integration is evaluated by MCS Pf can be evaluated as follows

Pf asymp 1N

Nsumj=1

I[g(xj

)le 0]

(412)

where xj is j-th sample point generated based on PDF fx()One of the important aspects of OMCS is to find the necessary number of

simulation runs to evaluate Pf of required accuracy This can be generallydone in the following way (Rubinstein 1981 115ndash18)

Let N be the total number of runs and Nf be the ones which indicatedthe failure Then the failure probability Pf is estimated as

Pf = Nf

N(413)

The mean and variance of Pf can be evaluated as

E[Pf

]= 1

NE[NF

]= NPf

N= Pf (414)

Var[Pf

]= Var

[Nf

N

]= 1

N2 Var[Nf]

= 1N2 NPf

(1 minus Pf

)= 1N

Pf(1 minus Pf

)(415)

Note that here these values are obtained by recognizing the fact that Nffollows binominal distribution of N trials where the mean and the varianceare given as NPf and NPf (1 minus Pf ) respectively

Suppose one wants to obtain a necessary number of simulation runs for

probability∣∣∣Pf minus Pf

∣∣∣le ε to be more than 100α

Prob[∣∣∣Pf minus Pf

∣∣∣ le ε]

ge α (416)

On the other hand the following relationship is given based onChebyshevrsquos inequality formula

Prob[∣∣∣Pf minus Pf

∣∣∣lt ε]

ge 1 minusVar[Pf

]ε2 (417)

178 Yusuke Honjo

From Equations (416) and (417) and using Equation (415) thefollowing relationship is obtained

α le 1 minusVar[Pf

]ε2 = 1 minus 1

Nε2 Pf(1 minus Pf

)1 minusα ge 1

Nε2 Pf(1 minus Pf

)

N ge Pf(1 minus Pf

)(1 minusα)ε2 (418)

Based on Equation (418) the necessary number of simulation runs N canbe obtained for any given ε and α Let ε = δPf and one obtains for N

N ge Pf(1 minus Pf

)(1 minusα)δ2P2

f

= (1Pf ) minus 1(1 minusα)δ2 asymp 1

(1 minusα)δ2Pf(419)

The necessary numbers of simulation runs N are calculated in Table 41for ε = 01Pf ie δ = 01 and α = 095 In other words N obtained inthis table ensures the error of Pf to be within plusmn10 for 95 confidenceprobability

The evaluation shown in Table 41 is considered to be very conservativebecause it is based on the very general Chebyshevrsquos inequality relationshipBroding (Broding et al 1964) gives necessary simulation runs as below(Melchers 1999)

N gtminus ln(1 minusα)

Pf(420)

Table 41 Necessary number of simulation runs to evaluate Pf of required accuracy

δ α Pf N by Chebyshev N by Bording

01 095 100E-02 200000 300100E-04 20000000 29957100E-06 2000000000 2995732

05 095 100E-02 8000 300100E-04 800000 29957100E-06 80000000 2995732

10 095 100E-02 2000 300100E-04 200000 29957100E-06 20000000 2995732

50 095 100E-02 80 300100E-04 8000 29957100E-06 800000 2995732

Monte Carlo simulation 179

where N is necessary number of simulation runs for one variable α is givenconfidence level and Pf is the failure probability For example if α = 095and Pf = 10minus3 the necessary N is by Equation (420) about 3000 In thecase of more than one independent random variable one should multiplythis number by the number of variables N evaluated by Equation (420) isalso presented in Table 41

432 Importance sampling (IMS)

As can be understood from Table 41 the required number of simulation runsin OMCS to obtain Pf of reasonable confidence level is not small especiallyfor cases of smaller failure probability In most practical reliability analysesthe order of Pf is of 10minus4 or less thus this problem becomes quite seriousOne of the MCS methods to overcome this difficulty is the ImportanceSampling (IMS) technique which is discussed in this section

Theory

Equation (411) to calculate Pf can be rewritten as follows

Pf =int

intI[g(x) le 0] fX(x)

hV (x)hV (x)dx (421)

where v is a random variable following PDF hV

Pf asymp 1N

⎧⎨⎩

Nsumj=1

I[g(vj) le 0] fX(vj)

hV (vj)

⎫⎬⎭ (422)

where hV is termed the Importance Sampling function and acts as a con-trolling function for sampling Furthermore the optimum hV is known tobe the function below (Rubinstein 1981 122ndash4)

hV (v) = I [g(v) le 0] fX(v)Pf

(423)

This function hV (v) in the case of a single random variable is illustratedin Figure 45

It can be shown that the estimation variance of Pf is null for the samplingfunction hV (v) given in Equation (423) In other words Pf can be obtained

180 Yusuke Honjo

fx (x ) hv (v )

G(x ) = x minus a

apf

x v

Figure 45 An illustration of the optimum Importance Function in the case of a singlevariable

exactly by using this sample function as demonstrated below

Pf =int int

intI [g(x) le 0]

fX(x)hV (x)

hV (x)dx

=int int

intI [g(x) le 0] fX(x)

[I [g(x) le 0] fX(x)

Pf

]minus1

hV (x)dx

= Pf

int int

inthV (x)dx = Pf

Therefore Pf can be obtained without carrying out MCS if the importancefunction of Equation (423) is employed

However Pf is unknown in practical situations and the result obtainedhere does not have an actual practical implication Nevertheless Equation(423) provides a guideline on how to select effective important samplingfunctions a sampling function that has a closer form to Equation (423) maybe more effective It is also known that if an inappropriate sampling func-tion is chosen the evaluation becomes ineffective and estimation varianceincreases

Selection of importance sampling functions in practice

The important region in reliability analyses is the domain where g(x) le 0The point which has the maximum fX(x) in this domain is known as thedesign point which is denoted by xlowast One of the most popular ways to sethV is to set the mean value of this function at this design point (Melcher1999) Figure 46 conceptually illustrates this situation

Monte Carlo simulation 181

Failure

Z (x)=0

Limit State Surface

Safe

0

hv (x )fx (x )

x2

x1

Figure 46 A conceptual illustration of importance sampling function set at the design point

If g(x) is a linear function and the space is standardized normal space thesample points generated by this hV fall in the failure region by about 50which is considered to give much higher convergence compared to OMCSwith a smaller number of sample points

However if the characteristic of a performance function becomes morecomplex and also if the number of basic variables increases it is not a sim-ple task to select an appropriate sample function Various so-called adaptivemethods have been proposed but most of them are problem-dependentIt is true to say that there is no comprehensive way to select an appro-priate sample function at present that can guarantee better results in allcases

433 Subset MCMC (Markov Chain Monte Carlo) method

Introduction to the subset MCMC method

The subset MCMC method is a reliability analysis method by MCS proposedby Au and Beck (2003) They combined the subset concept with the MCMCmethod which had been known as a very flexible MCS technique to generaterandom numbers following any arbitrary PDF (see for example Gilks et al1996) They combined these two concepts to develop a very effective MCStechnique for structural reliability analysis

The basic concept of this technique is explained in this subsection whereasa more detailed calculation procedure is presented in the next subsectionSome complementary mathematical proofs are presented in the appendix ofthis chapter and some simple numerical examples in subsection 433

182 Yusuke Honjo

Let F be a set indicating the failure region Also let F0 be the total set andFi(I = 1 middot middot middotm) be subsets that satisfy the relationship below

F0 sup F1 sup F2 sup sup Fm sup F (424)

where Fm is the last subset assumed in the Pf calculation Note that thecondition Fm sup F is always satisfied

The failure probability can be calculated by using these subsets as follows

Pf = P(F) = P(F | Fm)P(Fm | Fmminus1) P(F1 | F0) (425)

Procedure to calculate Pf by subset MCMC method

(1) Renewal of subsets

The actual procedure to set a subset is as follows where zi = g(xi) is thecalculated value of a given performance function at sample point xi Nt isthe total number of generated points in each subset Ns are number of pointsselected among Nt points from the smaller zi values and Nf is number offailure points (ie zi is negative) in Nt Also note that x used in this sectioncan be a random variable or a random vector that follows a specified PDF

Step 1 In the first cycle Nt points are generated by MCS that follow thegiven PDF

Step 2 Order the generated samples in ascending order by the value of ziand select smaller Ns(lt Nt) points A new subset Fk+1 is defined bythe equation below

Fk+1 =

x | z(x) le zs + zs+1

2

(426)

Note that zirsquos in this equation is assembled in ascending order Theprobability that a point generated in subset Fk+1 for points in subsetFk is calculated to be P(Fk+1 | Fk) = NsNt

Step 3 By repeating Step 2 for a range of subsets Equation (425) can beevaluatedNote that xi (i = 1 Ns) are used as seed points in MCMC togenerate the next Nt samples which implies that NtNs samplesare generated from each of these seed points In other words NtNspoints are generated from each seed point by MCMCFurthermore the calculation is ceased when Nf reaches certainnumber with respect to Nt

Monte Carlo simulation 183

(2) Samples generation by MCMC

By MCMC method Nt samples are generated in subset Fk+1 This method-ology is described in this subsection

Let π (x) be a PDF of x that follows PDF fX(x) and yet conditioned to bewithin the subset Fk PDF π (x) is defined as follows

π (x) = c middot fX(x) middot ID(x) (427)

where

ID(x) =

1 if (x sub Fk)0 otherwise

where c is a constant for PDF π to satisfy the condition of a PDFPoints following this PDF π (x) can be generated by MCMC by the

following procedure (Gilks et al 1996 Yoshida and Sato 2005)

Step 1 Determine any arbitrary PDF q(xprime | xi) which is termed proposaldensity In this research a uniform distribution having mean xiand appropriate variance is employed (The author tends to usethe variance of the original PDF to this uniform distribution Thesmaller variance tends to give higher acceptance rate (ie Equation(429)) but requires more runs due to slow movement of the subsetboundary)

Step 2 The acceptance probability α(xprimexi) is calculated Then set xi+1 = xprimewith probability α or xi+1 = xi with probability 1 minus α Theacceptance probability α is defined as the following equation

α(xprimexi) = min

10q(xi | xprime) middotπ (xprime)q(xprime | xi)π (xi)

= min

10q(xi | xprime) middot fX(xprime) middot ID(xprime)

q(xprime | xi)fX(xi)

(428)

Equation (428) can be rewritten as below considering the fact thatq(xprime | xi) is a uniform distribution and point xi is certainly in thesubset under consideration

α = min

10fX(xprime) middot ID(xprime)

fX(xi)

(429)

Step 3 Repeat Step 2 for necessary cycles

184 Yusuke Honjo

s

Failure Region

Performance Function

Stable Region

R

Z = R minus S

Design Point

Figure 47 A conceptual illustration of the subset MCMC method

(3) Evaluation of the failure probability Pf

In this study the calculation is stopped when Nf reaches certain number withrespect to Nt otherwise the calculation in Step 2 is repeated for a renewedsubset which is smaller than the previous one

Some of more application examples of the subset MCMC to geotechnicalreliability problems can be seen in Yoshida and Sato (2005b)

The failure probability can be calculated as below

Pf = P (z lt 0) =

Ns

Nt

mminus1 Nf

Nt(430)

The mathematical proof of this algorithm is presented in the nextsection

The conceptual illustration of this procedure is presented in Figure 47It is understood that by setting the subsets more samples are generated tothe closer region to the performance function which is supposed to improvethe efficiency of MCS considerably compared to OMCS

Examples of subset MCMC

Two simple reliability analyses are presented here to demonstrate the natureof subset MCMC method

Monte Carlo simulation 185

8

6

4S

R

2

00 2 4 6 8 10

k = 1

k = 2

k = 3

Z = 0

Zsub_1

Zsub_2

Figure 48 A calculation example of subset MCMC method when Z = R minus S

(1) An example on a linear performance function

The first example employs the simplest performance function Z = R ndash SThe resistance R follows a normal distribution N(7010) whereas theexternal force S a normal distribution N(3010) The true value of thefailure probability of this problem is Pf = 234E minus 03

Figure 48 exhibits a calculation example of this reliability analysis In thiscalculation Nt = 100 and two subsets are formed before the calculation isceased It is observed that more samples are generated closer to the limitstate line as the calculation proceeds

In order to examine the performance of subset MCMC method the num-ber of selected sample points to proceed to the next subset Ns and stoppingcriteria of the calculation are altered Actually Ns is set to 251020 or 50The stopping criteria are set based on the rate of Nf to Nt as Nf ge050020010 or 005Nt Also a criterion Nf ge Ns is added Nt is setto 100 in all cases

Table 42 lists the results of the calculation 1000 calculations are madefor each case where mean and COV of log10(calculated Pf ) log10(truePf )are presented

In almost all cases the means are close to 10 which exhibit very littlebias in the present calculation for evaluating Pf COV seems to be somewhatsmaller for case Ns is larger

186 Yusuke Honjo

Table 42 Accuracy of Pf estimation for Z = R minus S

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 0992 0995 0997 0976 0943Cov 0426 0448 0443 0390 0351

➁Nf 005N Mean 0995 0993 0989 0989 0971Cov 0428 0378 0349 0371 0292

➂Nf 010N Mean 1033 0994 0991 0985 0985Cov 0508 0384 0367 0312 0242

➃Nf 020N Mean 1056 1074 1006 0993 0990Cov 0587 0489 0348 0305 0165

➄Nf 050N Mean 1268 1111 1056 1020 0996Cov 0948 0243 0462 0319 0162

Table 43 Number of performance function calls for Z = R minus S example

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 1719 1881 2077 2329 3432sd 251 371 438 561 1092

➁Nf 005N Mean 1858 2090 2291 2716 4400sd 313 562 445 540 882

➂Nf 010N Mean 2029 2218 2518 2990 5066sd 324 616 396 532 754

➃Nf 020N Mean 2129 2406 2706 3265 5694sd 312 486 389 480 681

➄Nf 050N Mean 2260 2580 2930 3611 6547sd 257 371 426 484 627

Table 43 presents a number of performance function calls in each caseThe mean and sd (standard deviation) of these numbers are shown It isobvious from this table that the numbers of the function calls increase as Nsincreases It is understandable because as Ns increases the speed of subsetsconverging to the limit state line decreases which makes the number of callslarger

As far as the present calculation is concerned the better combinationof Ns and the stopping criterion seems to be around Ns = 20sim50 andNf ge 020sim050 Nt However the number of performance function callsalso increases considerably for these cases

(2) An example on a parabolic performance function

The second example employs a parabolic performance function Z =(R minus 110)2 minus (S minus 6) The resistance R follows a normal distribution

Monte Carlo simulation 187

10

8

6

k = 1

k = 3

k = 4

k = 5

Z = 0

k = 2

Zsub_1

Zsub_2

Zsub_3

Zsub_4S

4

24 6 8 10

R12 14

Figure 49 A calculation example of subset MCMC method when Z = (Rminus110)2 minus (Sminus6)

N(8507072) whereas the external force S follows a normal distributionN(5007072) The true value of the failure probability of this problemwhich is obtained by one million runs of OMCS is Pf = 320E minus 04

Figure 49 exhibits a calculation example of this reliability analysis In thiscalculation Nt = 100 and four subsets were formed before the calculationwas ceased It is observed that more samples are generated closer to thelimit state line as the calculation proceeds which is the same as for the firstexample

All the cases that have been calculated in the previous example are repeatedin this example as well where Nt = 100

Table 44 exhibits the same information as in the previous example Themean value is practically 10 in most of the cases which suggests there islittle bias in the evaluation On the other hand COV tends to be smaller forlarger Ns

Table 45 also provides the same information as the previous examplesThe number of function calls increases as Ns is set larger It is difficult toidentify the optimum Ns and the stopping criterion

44 Final remarks

Some of the recent developments in Monte Carlo simulation (MCS) tech-niques namely low-discrepancy sequences (LDS) and the subset MCMC

188 Yusuke Honjo

Table 44 Accuracy of Pf estimation for Z = (R minus 110)2 minus (S minus 6)

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 1097 1101 1078 1069 0997Cov 1027 0984 0839 0847 0755

➁Nf 005N Mean 1090 1092 1071 1056 1002Cov 0975 0924 0865 0844 0622

➂Nf 010N Mean 1135 1083 1055 1051 1024Cov 1056 0888 0857 0737 0520

➃Nf 020N Mean 1172 1077 1089 1054 1013Cov 1129 0706 0930 0777 0387

➄Nf 050N Mean 1309 1149 1160 1100 0997Cov 1217 0862 1015 0786 0366

Table 45 Number of performance function calls for Z = (R minus 110)2 minus (S minus 6) example

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 2173 2501 2825 3416 5515sd 709 735 789 830 1254

➁Nf 005N Mean 2343 2703 3091 3707 6388sd 519 628 794 678 1113

➂Nf 010N Mean 2457 2866 3235 3999 7124sd 361 775 696 656 926

➃Nf 020N Mean 2627 2997 3475 4308 7727sd 621 848 732 908 880

➄Nf 050N Mean 2679 3187 3703 4673 8427sd 392 816 716 851 923

method that are considered to be useful in structural reliability problemsare highlighted in this chapter

There are many other topics that need to be introduced for MCS in struc-tural reliability analysis These include generation of a three-dimensionalrandom field MCS technique for code calibration and partial factorsdetermination

It is the authorrsquos observation that classic reliability analysis tools such asFORM are now facing difficulties when applied to recent design methodswhere the calculations become more and more complex and sophisticatedOn the other hand our computational capabilities have progressed tremen-dously compared to the time when these classic methods were developedWe need to develop more flexible and more user-friendly reliability analysistools and MCMS is one of the strong candidate methods to be used forthis purpose More development however is still necessary to fulfill theserequirements

Monte Carlo simulation 189

Appendix Mathematical proof of MCMC byMetropolisndashHastings algorithm

(1) Generation of random numbers by MCMC basedon MetropolisndashHastings algorithm

Let us generate x that follows PDF π (x) Note that x used in this sectioncan be a random variable or a random vector that follows the specifiedPDF π (x)

Let us consider a Markov chain process x(t) rarr x(t+1) The procedure togenerate this chain is described as follows (Gilks et al 1996)

Step 1 Fix an appropriate proposal density which can generally bedescribed as

q(

xprime∣∣x(t))

(431)

Step 2 Calculate the acceptance probability which is defined by the equa-tion below

α(xprimex(t)

)= min

1

q(x(t)∣∣xprime)π (xprime)

q(xprime|x(t)

)π(x(t))

(432)

α is the acceptance probability in (0010)Step 3 x(t) is advanced to x(t+1) by the condition below based on the

acceptance probability calculated in the previous step

x(t+1) =

xprime with probability α

x(t) with probability 1 minusα

x(t+1) generated by this procedure surely depends on the previous stepvalue x(t)

(2) Proof of the MetropolisndashHastings algorithm

It is proved in this subsection that x generated following the proceduredescribed above actually follows PDF π (x) The proof is given in the threesteps

1 First the transition density which is to show the probability to moveform x to xprime in this Markov chain is defined

p(xprime∣∣x)= q

(xprime∣∣x)α (xprimex

)(433)

190 Yusuke Honjo

2 It is shown next that the transition density used in the MetropolisndashHastings algorithm satisfies so-called detailed balance This implies thatp(xprime | x) is reversible with respect to time

p(xprime∣∣x)π (x) = p

(x|xprime)π (xprime) (434)

3 Finally it is proved that generated sequences of numbers by this Markovchain follow a stationary distribution π (x) which is expressed by thefollowing relationshipint

p(xprime∣∣x)π (x)dx = π

(xprime) (435)

It is proved here that the transition density used in MetropolisndashHastingsalgorism satisfies the detailed balance

It is obvious that Equation (434) is satisfied when x = xprimeWhen x = xprime

p(xprime∣∣x)π (x) = q

(xprime∣∣x)α (xprimex

)π (x)

= q(xprime∣∣x)min

1

q (x|xprime)π (xprime)q (xprime|x)π (x)

π (x)

= minq(xprime∣∣x)π (x) q

(x|xprime)π (xprime) (436)

Therefore p (xprime|x)π (x) is symmetric with respect to x and xprime Thus

p(xprime∣∣x)π (x) = q

(xprime∣∣x)π (xprime) (437)

From this relationship the detailed balance of Equation (434) is provedFinally the stationarity of generated x and xprime are proved based on the

detailed balance of Equation (434) This can be proved if the results beloware obtained

x sim π (x)

xprime sim π(xprime) (438)

This is equivalent to prove the stationarity of the Markov chainintp(xprime | x

)π (x)dx = π

(xprime) (439)

which is obtained as followsintp(xprime∣∣x)π (x)dx =

intp(x|xprime)π (xprime)dx

= π(xprime)int p

(x|xprime)dx

= π(xprime)

Monte Carlo simulation 191

Thus a sequence of numbers generated by the MetropolisndashHastings algo-rithm follow PDF π (x)

References

Ang A and Tang W H (1984) Probability Concepts in Engineering Planning andDesign Vol II Decision Risk and Reliability John Wiley amp Sons

Au S-K and Beck J L (2003) Subset Simulation and its Appplication to SeismicRisk Based Dynamic Analysis Journal of Engineering Mechanics ASCE 129 (8)901ndash17

Broding W C Diederich F W and Parker P S (1964) Structural optimizationand design based on a reliability design criterion J Spacecraft 1(1) 56ndash61

Gilks W R Richardson S and Spiegelhalter D J (1996) Markov Chain MonteCarlo in Practice Chapman amp HallCRC London

Koyanagi Y (1989) Random Numbers Numerical Calculation Softwares byFORTRAN 77 Eds T Watabe M Natori and T Oguni Maruzen Co Tokyopp 313ndash22 (in Japanese)

Melcher R E (1999) Structure Reliability Analysis and Prediction 2nd ed JohnWiley amp Sons Ltd England

Reiss R D and Thomas M (1997) Statistical Analysis of Extreme ValuesBirkhauser Basel

Rubinstein R Y (1981) Simulation and the Monte Carlo Method John Wiley ampSons Ltd New York

Tezuka S (1995) Uniform Random Numbers Theory and Practice Kluwer BostonTezuka S (2003) Mathematics in Low Discrepancy Sequences In Computational

Statistics I Iwanami Publisher Tokyo pp 65ndash120 (in Japanese)Tsuda T (1995) Monte Carlo Methods and Simulation 3rd ed Baifukan Tokyo

(in Japanese)Yoshida I and Sato T (2005a) A Fragility Analysis by Low Discrepancy Sequences

Journal of Structural Engineering 51A 351ndash6 (in Japanese)Yoshida I and Sato T (2005b) Effective Estimation Method of Low Failure Prob-

ability by using Markov Chain Monte Carlo Journal of Structural EngineeringNo794I-72m 43ndash53 (in Japanese)

Chapter 5

Practical application ofreliability-based designin decision-making

Robert B Gilbert Shadi S Najjar Young-Jae Choi andSamuel J Gambino

51 Introduction

A significant advantage of reliability-based design (RBD) approaches is tofacilitate and improve decision-making While the attention in RBD hasgenerally been focused on the format and calibration of design-checkingequations the basic premise of a reliability-based design is that the tar-get of the design should achieve an acceptable level of reliability With thispremise in mind designers and stakeholders can utilize a range of possiblealternatives in order to achieve the desired reliability The objective of thischapter is to present practical methods and information that can be used tothis end

This chapter begins with a discussion of decision trees and decisioncriteria Next the methods for conducting reliability analyses and the resultsfrom reliability analyses are considered in the context of decision-makingFinally the calibration of mathematical models with data is addressed forthe purposes of reliability analyses and ultimately decision-making

The emphasis in presenting the material in this chapter is on illustrat-ing basic concepts with practical examples These examples are all basedon actual applications where the concepts were used in solving real-worldproblems to achieve more reliable andor more cost-effective designs

In order to provide consistency throughout the chapter the examples areintentionally narrowed in scope to the design of foundations for offshorestructures For context two types of structures are considered herein fixedjacket platforms and floating production systems Fixed jackets consist ofa steel frame with legs supported at the sea floor with driven pipe piles(Figure 51) Fixed jackets are used in water depths up to several hundredmeters with driven piles that are approximately 1 m in diameter and 100 mlong Floating production systems consist of a steel hull moored to the seafloor with 8ndash16 mooring lines anchored by steel caissons that are jackedinto the soil using under-pressure and are called suction caissons Floatingsystems are used in water depths of 1000 m or more with suction caissonsthat are approximately 5 m in diameter and 30 m long Both structures

Practical application of RBD 193

Ocean Surface

Mudline

Mudline

Driven SteelPipe Pile

Foundation

SuctionCaisson

Foundation

Jacket

MooringLine

TopsidesFacilities

TopsidesFacilities

Hull

FixedJacket

Platform

FloatingProduction

System

Figure 51 Schematic of typical offshore structures

provide a platform for the production and processing of oil and gas offshoreThe structures typically cost between $100 million for jackets up to morethan $1 billion for floating systems and the foundation can cost anywherefrom 5 to 50 of the cost of the structure

52 Decision trees

Decision trees are a useful tool for structuring and making decisions(Benjamin and Cornell 1970 Ang and Tang 1984) They organize theinformation that is needed in making a decision provide for a repeatableand consistent process allow for an explicit consideration of uncertainty andrisk and facilitate communication both in eliciting input from stakeholdersto the decision and in presenting and explaining the basis for a decision

There are two basic components in a decision tree alternatives andoutcomes A basic decision tree in reliability-based design is shown inFigure 52 to illustrate these components There are two alternatives eg twodifferent pile lengths For each alternative there are two possible outcomesthe design functions adequately eg the settlements in the foundation donot distress the structure or it functions inadequately eg the settlementsare excessive In a decision tree the alternatives are represented by limbsthat are joined by a decision node while the outcomes are represented by

194 R B Gilbert et al

limbs that are joined by an outcome node (Figure 52) The sequence of thelimbs from left to right indicates the order of events in the decision processIn the example in Figure 52 the decision about the design alternative willbe made before knowing whether or not the foundation will function ade-quately Finally the outcomes are associated with consequences which areexpressed as costs in Figure 52

The following examples taken from actual projects illustrate a variety ofapplications for decision trees in RBD

Design B

Design A

FunctionsInadequately

Outcome Node

Decision Node

Inadequately

Adequately

Adequately

Functions

Functions

Functions

Consequences

Cost forConstruction

and Corrective Action

Cost forConstruction

and CorrectiveAction

Cost forConstruction

Cost forConstruction

Figure 52 Basic decision tree

Box 51 Example I ndash Decision tree for design of new facility

The combination of load and resistance factors to be used for the design ofa caisson foundation for a floating offshore structure was called into ques-tion due to the unique nature of the facility the loads on the foundation weredominated by the sustained buoyant load of the structure versus transientenvironmental loads and the capacity of the foundation was dominated byits weight versus the shear resistance of the soil The design-build contractorproposed to use a relatively small safety margin defined as the ratio of theload factor divided by the resistance factor due to the relatively small uncer-tainty in both the load and the capacity The owner wanted to consider thisproposed safety margin together with a typical value for more conventionaloffshore structures which would be higher and an intermediate value Thetwo considerations in this decision were the cost of the foundation whichwas primarily affected by needing to use larger vessels for installation as theweight and size of the caisson increased and the cost associated with a pull-out failure of the caisson if its capacity was not adequate The decision treein Figure 53 shows the structure of decision alternatives and outcomes thatwere considered

Practical application of RBD 195

Box 51 contrsquod

Relative Costs

Use Contractorrsquos ProposedSafety Margin (Small)

No Caisson Failure

Caisson Failure

Use Intermediate SafetyMargin (Medium)

No Caisson Failure

Caisson Failure

No Caisson FailureUse Conventional SafetyMargin (High)

None(Base Case)

Facility Damage

Larger Foundation

Larger Foundation + Facility Damage

Largest Foundation

Largest Foundation +Facility Damage Caisson Failure

Figure 53 Decision tree for Example I ndash Design of new facility

53 Decision criteria

Once the potential alternatives have been established through use of deci-sion trees or by some other means the criterion for making a decision amongpotential alternatives should logically depend on the consequences for possi-ble outcomes and their associated probabilities of happening A rational anddefensible criterion to compare alternatives when the outcomes are uncer-tain is the value of the consequence that is expected if that alternative isselected (Benjamin and Cornell 1970 Ang and Tang 1984) The expectedconsequence E(C) is obtained mathematically as follows

E (C) =sumallci

ciPC

(ci

)or

intallc

cfC (c)dc (51)

where C is a random variable representing the consequence and PC

(ci

)or

fC (c) is the probability distribution for c in discrete or continuous formrespectively

The consequences of a decision outcome generally include a variety ofattributes such as monetary value human health and safety environmentalimpact social and cultural impact and public perception Within thecontext of expected consequence (Equation (51)) a simple approach toaccommodate multiple attributes is to assign a single value such as a

Box 52 Example II ndash Decision tree for site investigation in mature field

The need for drilling geotechnical borings in order to design new structures ina mature offshore field where considerable geologic and geotechnical infor-mation was already available was questioned The trade-off for not drillinga site-specific boring was that an increased level of conservatism would beneeded in design to account for the additional uncertainty The decision tree inFigure 54 shows the structure of decision alternatives and outcomes that wereconsidered In this case the outcome of drilling the boring ie the geotech-nical design properties derived from the boring data is not known beforedrilling the boring In addition there is a range of possible design propertiesthat could be obtained from the boring and therefore a range of possible piledesigns that would be needed for a given set of geotechnical properties Thesemi-circles in Figure 54 indicate that there is a continuous versus a discreteset of possibilities at the outcome and decision nodes

Relative Costs

No Pile Failure New BoringDrill New Site-SpecificBoring

GeotechnicalProperties

DevelopConventional

Design

Pile Failure New Boring + Facility Damage

No Pile Failure Added Conservatism in Design

AddConservatism

to Design

Pile Failure

Use AvailableGeotechnicalInformation Added Conservatism in Design

+ Facility Damage

Figure 54 Decision tree for Example II ndash Site investigation in mature field

Box 53 Example III ndash Decision tree for pile monitoring in frontier field

The pile foundation for an offshore structure in a frontier area was designedbased on a preliminary analysis of the site investigation data The geotechnicalproperties of the site were treated in design as if the soil conditions were similarto other offshore areas where the experience base was large The steel wasthen ordered Subsequently a more detailed analysis of the geotechnical prop-erties showed that the soil conditions were rather unusual calling into question

Box 53 contrsquod

how the properties should be used in design and leading to relatively largeuncertainty in the estimated pile capacity The owner was faced with a seriesof decisions First should they stay with the original pile design or change itgiven the uncertainty in the pile capacity considering that changing the piledesign after the steel had been ordered would substantially impact the costand schedule of the project Second if they decided to stay with the originalpile design should they monitor the installation to confirm the capacity wasacceptable considering that this approach required a flexible contract wherethe pile design may need to be updated after the pile is installed The decisiontree in Figure 55 shows the structure of decision alternatives and outcomesthat were considered all of the possibilities are shown for completeness eventhough some such as keeping the design in spite of evidence that the capacityis unacceptable could intuitively be ruled out

Relative CostsNone (Base Case)No Pile Failure

Make Design More Conservative

Keep OriginalDesign

AcceptableCapacity

Do Not Monitor Pile Driving

MonitorPile Driving

UnacceptableCapacity

Facility Damage Pile Failure Keep

DesignNo Pile Failure Monitoring + Contract Flexibility

Monitoring + Contract Flexibility + Facility Damage

Pile Failure

Monitoring + Contract Flexibility+ Design Change

No Pile Failure

ChangeDesign Monitoring + Contract Flexibility +

Design Change + Facility DamagePile Failure No Pile Failure Monitoring + Contract FlexibilityKeep

Design Monitoring + ContractFlexibility + Facility Damage

Pile Failure

Monitoring + Contract Flexibility+ Design Change

No Pile FailureChangeDesign Monitoring + Contract Flexibility +

Design Change + Facility DamageDesign Change

Pile Failure No Pile Failure

Pile Failure Design Change + Facility Damage

Figure 55 Decision tree for Example III ndash Pile monitoring in frontier field

Box 54 Decision criterion for Example I ndash Design of new facility

The decision tree for the design example described in Box 51 (Figure 53)is completed in Figure 56 with information for the values and probabilitiesof the consequences The probabilities and costs for caisson failure corre-spond to the event that the caisson will pull out of the mudline at some timeduring the 20-year design life for the facility The costs are reported as negative

Continued

198 R B Gilbert et al

Box 54 contrsquod

values in order to denote monetary loss It is assumed that a caisson failurewill result in a loss of $500 million The cost of redesigning and installingthe caisson to achieve an intermediate safety margin is $1 million while thecost of redesigning and installing the caisson to achieve a high safety margin is$5 million The cost of the caisson increases significantly if the highest safetymargin is used because a more costly installation vessel will be required Inthis example the maximum expected consequence (or the minimum expectedcost) is obtained for the intermediate safety margin Note that the contributionof the expected cost of failure to the total expected cost becomes relativelyinsignificant for small probabilities of failure

Relative Costs (Millions $)

Use Contractorrsquos ProposedSafety Margin (Small)

No Caisson Failure 0(099)

(001)E(C) = 0(099) ndash 500(001) = minus$5 Millionminus500Caisson Failure

minus1No Caisson Failure(0999)

Use Intermediate SafetyMargin (Medium)

(0001)E(C) = minus1 ndash 500(0001) = minus$15 Million Caisson Failure minus(1 + 500)

No Caisson Failure(09999)

minus5Use Conventional SafetyMargin (High)

(00001)E(C) = minus5 ndash 500(00001) = minus$505 MillionCaisson Failure minus(5 + 500)

Figure 56 Completed decision tree for Example I ndash Design of new facility

monetary value that implicitly considers all of the possible attributes for anoutcome

A second approach to consider factors that cannot necessarily be directlyrelated to monetary value such as human fatalities is to establish tolerableprobabilities of occurrence for an event as a function of the consequencesThese tolerable probabilities implicitly account for all of the attributesassociated with the consequences A common method of expressing thisinformation is on a risk tolerance chart which depicts the annual probabil-ity of occurrence for an event versus the consequences expressed as humanfatalities These charts are sometimes referred to as FndashN charts where Fstands for probability or frequency of exceedance typically expressed on anannual basis and N stands for number of fatalities (Figure 57) These chartsestablish a level of risk expressed by an envelope or line which will betolerated by society in exchange for benefits such as economical energy

Practical application of RBD 199

1E-08

1E-07

1E-06

1E-05

1E-04

1E-03

1E-02

1E-01

1 10 100 1000

Number of Fatalities (N)

Lower Bound(Nuclear Power

Plants)

Target for OffshoreStructures

Ann

ual P

roba

bilit

y of

Inci

dent

s w

ith N

or

Mor

e F

atal

ities

Upper Bound(IndustrialFacilities)

Figure 57 Risk tolerance chart for an engineered facility

The risk is considered acceptable if the combination of probability and conse-quences falls below the envelope Excellent discussions concerning the basisfor applications of and limitations with these types of charts are provided inFischhoff et al (1981) Whitman (1984) Whipple (1985) ANCOLD (1998)Bowles (2001) USBR (2003) and Christian (2004)

The chart on Figure 57 includes an upper and lower bound for risktolerance based on a range of FndashN curves that have been published Thelower bound (most stringent criterion) on Figure 57 corresponds to a pub-lished curve estimating the risk tolerated by society for nuclear power plants(USNRC 1975) For comparison purposes government agencies in both theUnited States and Australia have established risk tolerance curves for damsthat are one to two orders of magnitude above the lower bound on Figure 57(ANCOLD 1998 and USBR 2003) The upper bound (least stringent crite-rion) on Figure 57 envelopes the risks that are being tolerated for a varietyof industrial applications such as refineries and chemical plants based onpublished data (eg Whitman 1984) As an example the target levels of riskthat are used by the oil industry for offshore structures are shown as a shadedrectangular box on Figure 57 (Bea 1991 Stahl et al 1998 Goodwin et al2000)

The level of risk that is deemed acceptable for the same consequences onFigure 57 is not a constant and depends on the type of facility If expectedconsequences are calculated for an event which results in a given number

200 R B Gilbert et al

of fatalities the points along the upper bound on Figure 57 correspondto 01 fatalities per year and those along the lower bound correspond to1 times 10minus5 fatalities per year Facilities where the consequences affect thesurrounding population versus on-site workers ie the risks are imposedinvoluntarily versus voluntarily and where there is a potential for catas-trophic consequences such as nuclear power plants and dams are generallyheld to higher standards than other engineered facilities

The risk tolerance curve for an individual facility may have a steeper slopethan one to one which is the slope of the bounding curves on Figure 57A steeper slope indicates that the tolerable expected consequence in fatalitiesper year decreases as the consequence increases Steeper curves reflect anaversion to more catastrophic events

Expressing tolerable risk in terms of costs is helpful for facilities wherefatalities are not necessarily possible due to a failure The Economist (2004)puts the relationship between costs and fatalities at approximately in orderof magnitude terms $10000000 US per fatality For example a typicaltolerable risk for an offshore structure is 0001 to 01 fatalities per year(shaded box Figure 57) which corresponds approximately to a tolerableexpected consequence of failure between $10000 and $1000000 per yearA reasonable starting point for a target value in a preliminary evaluation ofa facility is $100000 per year

The effect of time is not shown explicitly in a typical risk tolerance curvesince the probability of failure is expressed on an annual basis Howevertime can be included if the annual probabilities on Figure 57 are assumed toapply for failure events that occur randomly with time such as explosionshurricanes and earthquakes Based on the applications for which these curveswere developed this assumption is reasonable In this way the tolerabilitycurve can be expressed in terms of probability of failure in the lifetime ofthe facility meaning that failure events that may or may not occur randomlywith time can be considered on a consistent basis

Box 55 Tolerable risk for Example I ndash Design of new facility

Revisiting the example for the design of a floating system in Boxes 51and 54 (Figures 53 and 56) the event of ldquocaisson failurerdquo does not leadto fatalities The primary consequences for the failure of this very large andexpensive facility are the economic damage to the operator and the associatednegative perception from the public In addition the event of ldquocaissonfailurerdquo is not an event that occurs randomly with time over the 20-yearlifetime of the facility The maximum load on the foundation occurs whena maintenance operation is performed which happens once every five yearsof operation The probability of ldquocaisson failurerdquo in Figure 56 corresponds

Practical application of RBD 201

Box 55 contrsquod

to the probability that the caisson will be overloaded in at least one of theseoperations over its 20-year life The information on Figure 57 has beenre-plotted on Figure 58 for this example by assuming that incidents occurrandomly according to a Poisson process eg an incident with an annualprobability of 0001 per year on Figure 57 corresponds to probability ofoccurrence of 1 minus eminus(0001year)(20 years) = 002 in 20 years Figure 58 showsthat an acceptable probability of failure in the 20-year lifetime for this facilityis between 0002 and 002 Therefore the first design alternative on Figure 56(use contractorrsquos proposed factor of safety (small) with a probability of caissonfailure equal to 001) would provide a level of risk that is marginal concerningwhat would generally be tolerated

1E-07

1E-06

1E-05

1E-04

1E-03

1E-02

1E-01

1E+00

10 100 1000 10000

Cost of Failure C ($ Millions)

Lower Bound(Nuclear Power

Plants)

Upper Bound(IndustrialFacilities)

Target for OffshoreStructures

Pro

babi

lity

of In

cide

nt w

ith C

or

Mor

e D

amag

e in

20

Yea

rs

Figure 58 Risk tolerance chart for Example I ndash Design of floating system

A third approach to consider factors in decision making that cannot nec-essarily be directly related to monetary value is to use multi-attribute utilitytheory (Kenney and Raiffa 1976) This approach provides a rational andsystematic means of combining consequences expressed in different mea-sures to be combined together into a single scale of measure a utility valueWhile flexible and general implementation of multi-attribute utility theoryis cumbersome and not commonly used in engineering practice

202 R B Gilbert et al

54 Reliability analysis

Reliability analyses are an integral part of the decision-making processbecause they establish the probabilities for outcomes in the decision trees(eg the probability of caisson failure in Figure 56) The following sectionsillustrate practical applications for relating reliability analyses to decisionmaking

541 Simplified first-order analyses

The probability of failure for a design can generally be expressed as the prob-ability that a load exceeds a capacity A useful starting point for a reliabilityanalysis is to assume that the load on the foundation and the capacity ofthe foundation are independent random variables with lognormal distribu-tions In this way the probability of failure can be analytically related tothe first two moments of the probability distributions for the load and thecapacity This simplified first-order analysis can be expressed in a convenientmathematical form as follows (eg Wu et al 1989)

P(Load gt Capacity

)sim= Φ

⎛⎜⎝minus ln

(FSmedian

)radicδ2

load + δ2capacity

⎞⎟⎠ (52)

where P(Load gt Capacity) is the probability that the load exceeds the capac-ity in the design life which is also referred to as the lifetime probability offailure FSmedian is the median factor of safety which is defined as the ratioof the median capacity to the median load δ is the coefficient of variation(cov) which is defined as the standard deviation divided by the mean valuefor that variable and Φ () is the cumulative distribution function for a stan-dard normal variable The median factor of safety in Equation (52) can berelated to the factor of safety used in design as follows

FSmedian = FSdesign times

(capacitymedian

capacitydesign

)(

loadmedian

loaddesign

) (53)

where the subscript ldquodesignrdquo indicates the value used to design thefoundation The ratios of the median to design values represent biasesbetween the median value (or the most likely value since the mode equals themedian for a lognormal distribution) in the design life and the value that isused in the design check with the factor of safety or with load and resistancefactors The coefficients of variation in Equation (52) represent uncertainty

Practical application of RBD 203

in the load and the capacity The denominator in Equation (52) is referredto as the total coefficient of variation

δtotal =radicδ2

load + δ2capacity (54)

An exact solution for Equation (52) is obtained if the denominator iethe total cov in Equation (54) is replaced by the following expressionradic

ln(1 + δ2

load

)+ ln(1 + δ2

capacity

) the approximation in Equation (52) is

reasonable for values of the individual coefficients of variation that are lessthan about 03

The relationship between the probability of failure and the median factorof safety and total cov is shown in Figure 59 An increase in the medianfactor of safety and a decrease in the total cov both reduce the probabilityof failure

542 Physical bounds

In many practical applications of reliability analyses there are physicalbounds on the maximum load that can be applied to the foundation orthe minimum capacity that will be available in response to the load It is

0

01

02

03

04

05

06

07

08

1 2 3 4 5 6 7 8 9 10

Tot

al c

ov

Median FS

0001

000001

00001

001Failure Probability = 01

0000001

FixedJackets

Floating Systems

FixedJackets

Floating Systems

Figure 59 Graphical solutions for simplified first-order reliability analysis

204 R B Gilbert et al

Box 56 Reliability benchmarks for Example I ndash Design of new facility

In evaluating the appropriate level of conservatism for the design of a newfloating system as described in Box 51 (Figure 53) it is important to con-sider the reliability levels achieved for similar types of structures Fixed steeljacket structures have been used for more than 50 years to produce oil and gasoffshore in relatively shallow water depths up to about 200 m In recent yearsfloating systems have been used in water depths ranging from 1000 to 3000 mTo a large extent the foundation design methods developed for driven pilesin fixed structures have been applied directly to the suction caissons that areused for floating systems

For a pile in a fixed jacket with a design life of 20 years the median factorof safety is typically between three and five and the total cov is typicallybetween 05 and 07 (Tang and Gilbert 1993) Hence the resulting probabil-ity of failure in the lifetime is on the order of 001 based on Figure 59 Notethat the event of foundation failure ie axial overload of a single pile in thefoundation does not necessarily lead to collapse of a jacket failure probabil-ities for the foundation system are ten to 100 times smaller than those for asingle pile (Tang and Gilbert 1993)

For comparison typical values were determined for the median factor ofsafety and the total cov for a suction caisson foundation in a floating pro-duction system with a 20-year design life (Gilbert et al 2005a) The medianfactor of safety ranges from three to eight The median factor of safety tendsto be higher for the floating versus fixed systems for two reasons First a newsource of conservatism was introduced for floating systems in that the foun-dations are checked for a design case where the structure is damaged (ieone line is removed from the mooring system) Second the factors of safetyfor suction caissons were generally increased above those for driven piles dueto the relatively small experience base with suction caissons In addition to ahigher median factor of safety the total cov value for foundations in floatingsystems tends to be smaller with values between 03 and 05 This decreasecompared to fixed jackets reflects that there is generally less uncertainty inthe load applied to a mooring system foundation compared to that applied toa jacket foundation The resulting probabilities of foundation failure tend tobe smaller for floating systems compared to fixed jackets by several orders ofmagnitude (Figure 59) From the perspective of decision making this simpli-fied reliability analysis highlights a potential lack of consistency in how thedesign code is applied to offshore foundations and provides insight into whysuch an inconsistency exists and how it might be addressed in practice

important to consider these physical bounds in decision making applicationbecause they can have a significant effect on the reliability

One implication of the significant role that a lower-bound capacity canhave on reliability is that information about the estimated lower-boundcapacity should possibly be included in design-checking equations for

Box 57 Lower-bound capacity for Example I ndash Design of new facility

In evaluating the appropriate level of conservatism for the design of a newfloating system as described in Box 51 (Figure 53) it is important to con-sider the range of possible values for the capacity of the caisson foundationsA reasonable estimate of a lower-bound on the capacity for a suction caissonin normally consolidated clay can be obtained using the remolded strengthof the clay to calculate side friction and end bearing Based on an analysisof load-test data for suction caissons Najjar (2005) found strong statisticalevidence for the existence of this physical lower bound and that the ratio oflower-bound capacities to measured capacities ranged from 025 to 10 withan average value of 06

The effect of a lower-bound capacity on the reliability of a foundationfor a floating production system is shown in Figure 510 The structure isanchored with suction caisson foundations in a water depth of 2000 m detailsof the analysis are provided in Gilbert et al (2005a) Design informationfor the caissons is as follows loadmedianloaddesign = 07 δload = 014capacitymediancapacitydesign = 13 and δcapacity = 03 The lognormaldistribution for capacity is assumed to be truncated at the lower-bound valueusing a mixed lognormal distribution where the probability that the capac-ity is equal to the lower bound is set equal to the probability that the capacity is

100E-07

100E-06

100E-05

100E-04

100E-03

100E-02

100E-01

100E+00

0 02 04 06 08

Ratio of Lower-Bound to Median Capacity

Pro

babi

lity

of F

ailu

re in

Life

time

FSdesign = 15

FSdesign = 20

FSdesign = 25

Figure 510 Effect of lower-bound capacity on foundation reliability for Example I ndashDesign of floating system

Continued

206 R B Gilbert et al

Box 57 contrsquod

less than or equal to the lower bound for the non-truncated distribution Theprobability of failure is calculated through numerical integration

The results on Figure 510 show the significant role that a lower-boundcapacity can have on the reliability For a lower-bound capacity that is 06times the median capacity the probability of failure is more than 1000 timessmaller with the lower-bound than without it (when the ratio of the lower-bound to median capacity is zero) for a design factor of safety of 15

RBD codes For example an alternative code format would be to have twodesign-checking equations

φcapacitycapacitydesign ge γloadloaddesign

or

φcapacitylower boundcapacitylower bound ge γloadloaddesign

(55)

where φcapacity and γload are the conventional resistance and load factorsrespectively and φcapacitylower bound

is an added resistance factor that is appliedto the lower-bound capacity Provided that one of the two equations is satis-fied the design would provide an acceptable level of reliability Gilbert et al(2005b) show that a conservative value of 075 for φcapacitylower bound

wouldcover a variety of typical conditions in foundation design

Box 58 Lower-bound capacity for Example III ndash Pile monitoring in frontier field

The example described in Box 53 (Figure 55) illustrates a practical applica-tion where a lower-bound value for the foundation capacity provides valuableinformation for decision-making Due to the relatively large uncertainty inthe pile capacity at this frontier location the reliability for this foundationwas marginal using the conventional design check even though a relativelylarge value had been used for the design factor of safety FSdesign = 225 Theprobability of foundation failure in the design life of 20 years was slightlygreater than the target value of 1 times 10minus3 therefore the alternative of usingthe existing design without additional information (the top design alternativeon Figure 55) was not acceptable However the alternative of supplementingthe existing design with information from pile installation monitoring wouldonly be economical if the probability that the design would need to be changedafter installation was small Otherwise the preferred alternative for the ownerwould be to modify the design before installation

Box 58 contrsquod

In general pile monitoring information for piles driven into normal toslightly overconsolidated marine clays cannot easily be related to the ultimatepile capacity due to the effects of set-up following installation The capacitymeasured at the time of installation may only be 20ndash30 of the capacity afterset-up However the pile capacity during installation does arguably providea lower-bound on the ultimate pile capacity In order to establish the proba-bility of needing to change the design after installation the reliability of thefoundation was related to the estimated value for the lower-bound capacitybased on pile driving as shown on Figure 511 In this analysis the pile capac-ity estimated at the time of driving was considered to be uncertain it wasmodeled as a normally distributed random variable with a mean equal to theestimated value and a coefficient of variation of 02 to account for errors inthe estimated value The probability of foundation failure in the design lifewas then calculated by integrating the lower-bound value over the range ofpossible values for a given estimate The result is shown in Figure 511 Notethat even though there is uncertainty in the lower-bound value it still can havea large effect on the reliability

The expected value of driving resistance to be encountered and mea-sured during installation is indicated by the arrow labeled ldquoinstallationrdquo in

1E-05

1E-04

1E-03

1E-020 01 02 03 04 05 06

Estimated Lower Bound of Pile Capacity based on DrivingResistance

(Expressed as Ratio to Median Capacity)

Installation

Pro

babi

lity

of F

ound

atio

n F

ailu

re in

Des

ign

Life

Re-Tap

Target

Figure 511 Reliability versus lower-bound capacity for Example III ndash Pile monitoringin frontier field

Continued

208 R B Gilbert et al

Box 58 contrsquod

Figure 511 While it was expected that the installation information would justbarely provide for an acceptable level of reliability (Figure 511) the conse-quence of having to change the design at that point was very costly In orderto provide greater assurance the owner decided to conduct a re-tap analysis 5days after driving The arrow labeled ldquore-taprdquo in Figure 511 shows that thisinformation was expected to provide the owner with significant confidencethat the pile design would be acceptable The benefits of conducting the re-tapanalysis were considered to justify the added cost

543 Systems versus components

Nearly all design codes whether reliability-based or not treat foundationdesign on the basis of individual components However from a decision-making perspective it is insightful to consider how the reliability of anindividual foundation component is related to the reliability of the overallfoundation and structural system

Box 59 System reliability analysis for Example I ndash Design of new facility

The mooring system for a floating offshore facility such as that describedin Box 51 (Figure 53) includes multiple lines and foundation anchors Theresults from a component reliability analysis are shown in Figure 512 fora facility moored in three different water depths (Choi et al 2006) Theseresults correspond to the most heavily loaded line in the system where theprimary source of loading is from hurricanes Each mooring line consists ofsegments of steel chain and wire rope or polyester The points labeled ldquorope ampchainrdquo in Figure 512 correspond to a failure anywhere within the segmentsof the mooring line the points labeled ldquoanchorrdquo correspond to a failure at thefoundation and the points labeled ldquototalrdquo correspond to a failure anywherewithin the line and foundation The probability that the foundation fails isabout three orders of magnitude smaller than that for the line

The reliability of the system of lines was also related to that for the mostheavily loaded line (Choi et al 2006) In this analysis the event that thesystem fails is related to that for an individual line in two ways First the loadswill be re-distributed to the remaining lines when a single line fails Secondthe occurrence of a single line failure indicates information about the loads onthe system if a line has failed it is more likely that the system is being exposedto a severe hurricane (although it is still possible that the loads are relativelysmall and the line capacity for that particular line was small) The results forthis analysis are shown in Figure 513 where the probability that the mooringsystem fails is expressed as a conditional probability for the event that a

Box 59 contrsquod

single line in the system fails during a hurricane For the mooring systems in2000 and 3000 m water depths the redundancy is significant in that there isless than a 10 chance the system will fail even if the most heavily loaded linefails during a hurricane Also the mooring system in the 1000 m water depthhas substantially less redundancy than those in deeper water

10E-08

10E-07

10E-06

10E-05

10E-04

10E-03

10E-02

Water Depth (m)

1000 2000 3000

Rope ampChain

Anchor

Total

Pro

babi

lity

of F

ailu

re in

Des

ign

Life

Figure 512 Comparison of component reliabilities for most heavily loaded line inoffshore mooring system

70

Pro

babi

lity

of S

yste

m F

ailu

regi

ven

Fai

lure

of S

ingl

e Li

ne

0

10

20

50

60

40

30

1000 2000 3000Water Depth (m)

Figure 513 System redundancy for offshore mooring system

Continued

210 R B Gilbert et al

Box 59 contrsquod

The results in Figures 512 and 513 raise a series of questions forconsideration by the writers of design codes

(1) Is it preferable to have a system fail in the line or at the foundation Thebenefit of a failure in the foundation is that the weight of the foundationattached to the line still provides a restoring force to the entire mooringsystem even after it has pulled out from the seafloor The cost of failurein the foundation is that the foundation may cause collateral damage toother facilities if it is dragged across the seafloor

(2) Would the design be more effective if the reliability of the foundationwere brought closer to that of the line components It is costly to designfoundations that are significantly more conservative than the line Inaddition to the cost a foundation design that is excessively conserva-tive may pose problems in constructability and installation due to itslarge size

(3) Should the reliability for individual lines and foundations be consistentfor different water depths

(4) Should the system reliability be consistent for the different waterdepths

(5) How much redundancy should be achieved in the mooring system

55 Model calibration

The results from a reliability analysis need to be realistic to be of practicalvalue in decision-making Therefore the calibration of mathematical modelswith real-world data is very important

A general framework for calibrating a model with a set of data is obtainedfrom the following assumptions

bull The variation for an individual measurement about its mean value isdescribed by a Hermite Polynomial transformation of a standard normaldistribution which can theoretically take on any possible shape as theorder of the transformation approaches infinity (Journel and Huijbregts1978 Wang 2002)

bull The relationship between an individual data point with other data pointsis described by a linear correlation

bull The shape of the Hermite Polynomial transformation is consistentbetween data points ie affine correlation as described in Journel andHuijbregts (1978)

Practical application of RBD 211

In mathematical terms the probability distribution to describe variationsbetween a single measurement yi and a model of that measurement Yi isdescribed by a mean value a standard deviation correlation coefficientsand a conditional probability distribution

microYi= gmicro

(xYi

θ1 θnmicro

)(56)

σYi= gσ

(xYi

microYiθnmicro+1 θnmicro+nσ

)(57)

ρYiYj= gρ

(xYi

xYjθnmicro+nσ+1

θnmicro+nσ+nρ

)(58)

FYi|y1yiminus1

(yi

∣∣y1 yiminus1)= gF

(xYi

microYi|y1yiminus1σYi|y1yiminus1

θnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

)(59)

where microYiis the mean value and gmicro () is a model with nmicro model param-

eters θ1 θnmicro that relate the mean to attributes of the measurementxYi

σYiis the standard deviation and gσ () is a model with nσ model

parameters θnmicro+1 θnmicro+nσ that relate the standard deviation to attributes

and the mean value of the measurement ρYiYjis the correlation coeffi-

cient between measurements i and j and gρ () is a model with nρ modelparameters θnmicro+nσ+1 θnmicro+nσ+nρ

that relate the correlation coefficient to

the attributes for measurements i and j and FYi|y1yiminus1

(yi

∣∣y1 yiminus1)

isthe cumulative distribution function for measurement i conditioned on themeasurements y1 to yiminus1 and gF () is a model with nF model parametersθnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

that relate the cumulative distribution func-tion to the data attributes and the conditional mean value microYi|y1yiminus1

andstandard deviation σYi|y1yiminus1

which are obtained from the mean valuesstandard deviations and correlations coefficients for measurements 1 to iand the measurements y1 to yiminus1 The nF model parameters in gF () are thecoefficients in the Hermite Polynomial transformation function where theorder of the transformation is nF + 1

gF

(xYi

microYi|y1yiminus1σYi|y1yiminus1

θnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

)

=int

ϕ(u)leyi minusmicroYi|y1yiminus1

σYi|y1yiminus1

1radic2π

eminus 12 u2

du (510)

212 R B Gilbert et al

where

ϕ (u) =

[H1 (u) +

nF+1summ=2

ψmm Hm (u)

]

minusradic

1 +nF+1summ=2

ψ2m

m

=y minusmicroYi|y1yiminus1

σYi|y1yiminus1

(511)

in which ψm are polynomial coefficients that are expressed as modelparameters ψm = θnmicro+nσ+nρ+mminus1 and Hm (u) are Hermite polynomialsHm+1 (u) = minusuHm (u)minusmHmminus1 (u) with H0 (u) = 1 and H1 (u) = minusu Thepractical implementation of Equation (510) involves first finding all of

the intervals of u in Equation (511) where ϕ (u) le yiminusmicroYi|y1yiminus1σYi|y1yiminus1

and then

integrating the standard normal distribution over those intervals If thedata points are assumed to follow a normal distribution then the order

of the polynomial transformation is one nF = 0 ϕ (u) = u = yminusmicroYi|y1yiminus1σYi|y1yiminus1

and FYi|y1yiminus1

(yi

∣∣y1 yiminus1)= (u) where (u) is the standard normal

functionThe framework described above contains nθ = nmicro + nσ + nρ + nF model

parameters θ1 θnmicro+nσ+nρ+nF that need to be estimated or calibrated

based on available information It is important to bear in mind that thesemodel parameters have probability distributions of their own since they arenot known with certainty For a given set of measurements the calibrationof the various model parameters follows a Bayesian approach

f1nθ |y1yn

(θ1 θnθ

∣∣y1 yn

)(512)

=P(y1 yn

∣∣∣θ1 θnθ

)f1nθ

(θ1 θnθ

)P(y1 yn

)where f1nθ |y1yn

(θ1 θnθ

∣∣y1 yn

)is the calibrated or updated

probability distribution for the model parameters P(y1 yn

∣∣∣θ1 θnθ

)is

the probability of obtaining this specific set of measurements for a given set

of model parameters and f1nθ

(θ1 θnθ

)is the probability distribu-

tion for the model parameters based on any additional information that isindependent of the measurements The probability of obtaining the mea-

surements P(y1 yn

∣∣∣θ1 θnθ

) can include both point measurements

as well as interval measurements such as proof loads or censored values

Practical application of RBD 213

(Finley 2004) While approximations exist for solving Equation (512) (egGilbert 1999) some form of numerical integration is generally required

The information from model calibration can be used in decision makingin two ways First the calibrated distribution from Equation (512) providesmodels that are based on all available information at the time of making adecision uncertainty in the calibrated parameters can be included directlyin the decision since the model parameters are represented by a probabilitydistribution and not deterministic point estimates Second the effect andtherefore value of obtaining additional information before making a decision(eg Figure 54) can be assessed

Box 510 Probability distribution of capacity for Example I ndash Design of new facility

The probability distribution for axial pile capacity plays an important role inreliability-based design such as described in Box 51 because it dictates theprobability of foundation failure due to overloading A database of pile loadtests with 45 driven piles in clay was analyzed by Najjar (2005) to investi-gate the shape of the probability distribution First a conventional analysiswas conducted on the ratio of measured to predicted capacity where the dis-tribution for this ratio was assumed to be lognormal Individual data pointswere assumed to be statistically independent In terms of the framework pre-sented in Equations (56) through (511) the attribute for each data point xiis the predicted capacity the function gmicro () is microInYi

= ln(θ1xi) the function

gσ () is σlnYi=radic

ln(1 + θ2

2

) the data points are statistically independent ie

the correlation coefficients between data points are zero and the cumulativedistribution function for lnYi gF () is a normal distribution with mean microlnYiand standard deviation σlnYi

Therefore there are two model parameters tobe calibrated with the data θ1 and θ2

In calibrating the model parameters with Equation (512) the only infor-mation used was the measured data points in the load test database ief12

(θ1θ2

)is taken as a non-informative diffuse prior distribution The cal-

ibrated expected values for θ1 and θ2 from the updated probability distributionobtained with Equation (512) are 096 and 024 respectively The resultingprobability distribution is shown on Figure 514 and labeled ldquoconventionallognormalrdquo

In Boxes 57 and 58 the effect of a lower bound on the pile capacity wasshown to be significant In order to investigate the existence of a lower-boundvalue an estimate for a lower-bound capacity was established for each datapoint based on the site-specific soil properties and pile geometry Specificallyremolded undrained shear strengths were used to calculate a lower-boundcapacity for driven piles in normally consolidated to slightly overconsolidated

Continued

Box 510 contrsquod

00001

0001

001

01

1

050 151

Actual CapacityPredicted Capacity

Cum

ulat

ive

Dis

tribu

tion

Fun

ctio

n

Hermite PolynomialTransformation

ConventionalnormalLog

Lower Bound

Figure 514 Calibrated probability distributions for axial pile capacity

clays and residual drained shear strengths and at-rest conditions were used tocalculate a lower-bound capacity for driven piles in highly overconsolidatedclays (Najjar 2005) For all 45 data points the measured capacity was abovethe calculated value for the lower-bound capacity To put this result in per-spective the probability of obtaining this result if the distribution of capacityreally is a lognormal distribution (with a lower bound of zero) is essentiallyzero meaning that a conventional lognormal distribution is not plausible

The model for the probability distribution of the ratio of measured to pre-dicted capacity was therefore refined to account for a more complicated shapethan a simple lognormal distribution The modifications are the following anadditional attribute the predicted lower-bound capacity is included for eachdata point and a 7th-order Hermite Polynomial transformation is used as ageneral model for the shape of the probability distribution In terms of Equa-tions (56) through (511) the attributes for each data point are the predictedcapacity x1i and the calculated lower-bound capacity x2i the function gmicro ()is microYi

= θ1x1i the function gσ () is σYi= θ2microYi

the correlation coefficientsbetween data points were zero and the cumulative distribution function forlnYi gF () is obtained by combining Equations (511) and (512) as follows

FY(yi)=

⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩

0 yi lt x2i

Φ(ui)where

H1(ui)+ 7sum

m=2

θm+1m Hm

(ui)

minusradicradicradicradic1 +

7summ=2

θ2m+1m

= yi minusmicroYi

σYi

yi ge x2i

(513)

Box 510 contrsquod

Therefore there are now eight model parameters to be calibrated with thedata θ1 to θ8

The calibrated expected values for the model parameters from the updatedprobability distribution obtained with Equation (512) are θ1 = 096θ2 = 026 θ3 = minus0289 θ4 = 0049 θ5 = minus0049 θ6 = 0005 θ7 = 005 andθ8 = 0169 The resulting probability distribution is shown on Figure 514 andlabeled ldquoHermite polynomial transformationrdquo

There are two practical conclusions from this calibrated model First theleft-hand tail of the calibrated distribution with the 7th-order Hermite Poly-nomial transformation is substantially different than that for the conventionallognormal distribution (Figure 514) This difference is significant because itis the lower percentiles of the distribution for capacity say less than 10which govern the probability of failure Second truncating the conventionallognormal distribution at the calculated value for the lower-bound capacityprovides a reasonable and practical fit to the more complicated 7th-orderHermite Polynomial transformation

Box 511 Value of new soil boring for Example II ndash Site investigation in mature field

A necessary input to the decision about whether or not to drill an additional soilboring in a mature field as described in Box 52 and illustrated in Figure 54is a model describing spatial variability in pile capacity across the field Thegeology for the field in this example is relatively uniform marine clays thatare normally to slightly overconsolidated The available data are soil boringswhere the geotechnical properties were measured and a design capacity wasdetermined Two types of soil borings are available modern borings wherehigh-quality soil samples were obtained and older borings where soil sampleswere of lower quality Pile capacity for these types of piles is governed by sideshear capacity

The model for the spatial variability in design pile capacity expressed interms of the average unit side shear over a length of pile penetration is givenby Equations (514) through (517)

microYi=(θ1 + θ2x1i + θ3x2

1i

)ex2i θ4 (514)

where Yi is the average unit side shear which is the total capacity due to sideshear divided by the circumference of the pile multiplied by its length x1i isthe penetration of the pile and x2i is 0 for modern borings and 1 for older

Continued

Box 511 contrsquod

borings (the term ex2i θ4 accounts for biases due to the quality of soil samples)

σYi= eθ4ex2i θ5 (515)

where the term eθ4 accounts for a standard deviation that does not dependon pile penetration and that is non-negative and the term ex2i θ5 accounts forany effects on the spatial variability caused by the quality of the soil samplesincluding greater or smaller variability

ρYiYj=⎛⎝e

minusradic(

x3iminusx3j)2+(x4iminusx4j

)2(eθ6+x1ieθ7)⎞⎠e

minus∣∣x2iminusx2j∣∣eθ8

(516)

where x3i and x4i are the coordinates describing the horizontal position

of boring i the term eminusradic(

x3iminusx3j)2+(x4iminusx4j

)2(eθ6+x1ieθ7)

accounts for apositive correlation between nearby borings that is described by a positive cor-

relation distance of eθ6 +x1ieθ7 and the term eminus∣∣x2iminusx2j

∣∣eθ8 accounts for asmaller positive correlation between borings with different qualities of samplesthan those with the same qualities of samples and the probability distributionfor the average side friction is modeled by a normal distribution

FYi|y1yiminus1

(yi∣∣y1 yiminus1

)= Φ

(yi minusmicroYi|y1yiminus1

σYi|y1yiminus1

)(517)

Therefore there are eight parameters to be calibrated with the data θ1 to θ8The detailed results for this calibration are provided in Gambino and Gilbert(1999)

Before considering the decision at hand there are several practicalconclusions that can be obtained from the calibrated model in its own rightThe expected values for the mean and standard deviation of average unitside shear from modern borings are 98 kPa and 72 kPa respectively Thecoefficient of variation for pile capacity is then 7298 or 007 meaning thatthe magnitude of spatial variability in pile capacity across this field is relativelysmall The expected horizontal correlation distance is shown with respect topile penetration in Figure 515 it is on the order of thousands of meters andit increases with pile penetration The expected bias due to older soil boringsis equal to 091 (ex2iθ4 in Equation (514)) meaning that designs based onolder borings tend to underestimate the pile capacity that would be obtainedfrom a modern boring Also the expected effect on the spatial variability dueto older soil borings is to reduce the standard deviation by 086 (ex2iθ5 ) in

Box 511 contrsquod

0

20

40

60

80

100

120

140

160

0 1000 2000 3000 4000

Horizontal Correlation Distance (m)

Pile

Pen

etra

tion

(m)

Figure 515 Calibrated horizontal correlation distance for pile capacities betweenborings

Equation (515) meaning that the data from the older soil borings tends tomask some of the naturally occurring spatial variations that are picked upwith modern borings thereby reducing the apparent standard deviation Fur-thermore the expected correlation coefficient between an older boring and a

modern boring at the exact same location is 058 (eminus∣∣x2iminusx2j∣∣eθ8 in Equa-

tion (516) and notably less than the ideal value of 10 meaning that it is notpossible to deterministically predict the pile capacity obtained from a modernboring by using information from an older boring

The results for this calibrated model are illustrated in Figures 516a and516b for an example 4000 m times 4000 m block in this field Within this blockthere are two soil borings available a modern one and an older one at therespective locations shown in Figures 516a and 516b The three-dimensionalsurface shown in Figure 1516a denotes the expected value for the calculateddesign capacity at different locations throughout the block The actual designpile capacities at the two boring locations are both above the average for thefield (Figure 516a) Therefore the expected values for the design pile capac-ity at other locations of an offshore platform within this block are aboveaverage however as the location moves further away from the existing bor-ings the expected value tends toward the unconditional mean for the fieldapproximately 30 MN

The uncertainty in the pile capacity due to not having a site-specificmodern soil boring is expressed as a coefficient of variation on Figure 516b

Continued

Box 511 contrsquod0

500

1000

2000

2500

3500

4000

1500

3000 0

1500

2000

2500

3000

3500

4000

500

1000

26

27

28

29

30

31

32

33

34

x Coordinate (m)

Exp

ecte

d P

ile C

apac

ity (

MN

)

y Coordinate (m)

Unconditional Mean

Older BoringModern Boring

Figure 516a Expected value for estimated pile capacity versus location for a 100 mlong 1 m diameter steel pipe pile

At the location of the modern boring this cov is zero since a modern boringis available at that location and the design capacity is known However at thelocation of the older boring the cov is greater than zero since the correlationbetween older and modern borings is not perfect (Figure 516b) therefore eventhough the design capacity is known at this location the design capacity thatwould have been obtained based on a modern soil boring is not known Asthe platform location moves away from both borings the cov approachesthe unconditional value for the field approximately 0075

The final step is to put this information from the calibrated model for spatialvariability into the decision tree in Figure 54 The added uncertainty in nothaving a site-specific modern boring is included in the reliability analysis byadding uncertainty to the capacity the greater the uncertainty the lower thereliability for the same set of load and resistance factors in a reliability-baseddesign code In order to achieve the same reliability as if a modern soil boring

Box 511 contrsquod

0

500

1000

1500

2000

2500

3000

3500

4000

0500

1000

1500

2000

2500

3000

3500

40000000

0020

0040

0060

0080

x Coordinate (m) y Coordinate (m)

co

v in

Pile

Cap

acity

Older Boring

Modern Boring

Unconditional cov

Figure 516b Coefficient of variation for estimated pile capacity versus location fora 100 m long 1 m diameter steel pipe pile

were available the resistance factor needs to be smaller as expressed in thefollowing design check format

φspatial

(φcapacitycapacitydesign

)ge γloadloaddesign (517)

where φspatial is a partial resistance factor that depends on the magnitudeof spatial variability obtained from Figure 516b and the design capacityis obtained from Figure 516a Figure 517 shows the relationship betweenφspatial and the coefficient of variation due to spatial variability for thefollowing assumptions design capacity is normally distributed with a cov of03 when a site-specific boring is available load is normally distributed witha cov of 05 and the target probability of failure for the pile foundationis 6 times 10minus3 (Gilbert et al 1999) To use this chart a designer would first

Continued

Box 511 contrsquod

075

080

085

090

095

100

0 002 004 006 008 01

Coefficient of Variation due to Spatial Variability

Partial ResistanceFactor

due to SpatialVariabilityφspatial

Figure 517 Partial resistance factor versus coefficient of variation for spatialvariability

quantify the spatial variability as a cov value using the approach describedabove as shown in Figure 516b The designer would then read the corre-sponding partial resistance factor directly from Figure 517 as a functionof this cov due to spatial variability When there is not additional uncer-tainty because a modern soil boring is available at the platform location(ie the cov due to spatial variability is 0) the partial resistance factoris 10 and does not affect the design As the cov due to spatial variabilityincreases the partial resistance factor decreases and has a larger effect on thedesign

The information in Figures 516a 516b and 517 can be combined togetherto support the decision-making process For example consider a location in theblock where the cov due to spatial variability is 006 from Figure 516b Therequired partial resistance factor to account for this spatial variability is 095or 95 of the design capacity could be relied upon in this case comparedto what is expected if a site-specific boring is drilled Since pile capacity isapproximately proportional to pile length over a small change in length thisreduction in design capacity is roughly equivalent to a required increase in pilelength of about 5 Therefore the cost of drilling a site-specific boring couldbe compared to the cost associated with increasing the pile lengths by 5 inthe decision tree in Figure 54 to decide whether or not to drill an additionalsoil boring

Practical application of RBD 221

56 Summary

There can be considerable value in using the reliability-based designapproach to facilitate and improve decision-making This chapter hasdemonstrated the practical potential of this concept Several different levelsof decisions were addressed

bull project-specific decisions such as how long to make a pile foundationor how many soil borings to drill

bull design code decisions such as what resistance factors should be usedand

bull policy decisions such as what level of reliability should be achieved forcomponents and systems in different types of facilities

An emphasis has been placed on first establishing what is important in thecontext of the decisions that need to be made before developing and applyingmathematical tools An emphasis has also been placed on practical meth-ods to capture the realistic features that will influence decision-making It ishoped that the real-world examples described in this chapter motivate thereader to consider how the role of decision making can be utilized in thecontext of reliability-based design for a variety of different applications

Acknowledgments

We wish to acknowledge gratefully the following organizations for support-ing the research and consulting upon which this paper is based AmericanPetroleum Institute American Society of Civil Engineers BP ExxonMobilNational Science Foundation Offshore Technology Research Center ShellState of Texas Advanced Research and Technology Programs Unocal andUnited States Minerals Management Service In many cases these organiza-tions provided real-world problems and data as well as financial supportWe would also like to acknowledge the support of our colleagues andstudents at The University of Texas at Austin The views and opinionsexpressed herein are our own and do not reflect any of the organizationsparties or individuals with whom we have worked

References

ANCOLD (1998) Guidelines on Risk Assessment Working Group on Risk Assess-ment Australian National Committee on Large Dams Sydney New South WalesAustralia

Ang A A-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Volume II ndash Decision Risk and Reliability John Wiley amp SonsNew York

222 R B Gilbert et al

Bea R G (1991) Offshore platform reliability acceptance criteria DrillingEngineering Society of Petroleum Engineers June 131ndash6

Benjamin J R and Cornell C A (1970) Probability Statistics and Decision forCivil Engineers McGraw-Hill New York

Bowles D S (2001) Evaluation and use of risk estimates in dam safetydecisionmaking In Proceedings Risk-Based Decision-Making in Water ResourcesASCE Santa Barbara California 17 pp

Choi Y J Gilbert R B Ding Y and Zhang J (2006) Reliability ofmooring systems for floating production systems Final Report for MineralsManagement Service Offshore Technology Research Center College StationTexas 90 pp

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental EngineeringASCE 130 (10) 985ndash1003

Finley C A (2004) Designing and analyzing test programs with censored datafor civil engineering applications PhD dissertation The University of Texas atAustin

Fischhoff B Lichtenstein S Slovic P Derby S L and Keeney R L (1981)Acceptable Risk Cambridge University Press Cambridge

Gambino S J and Gilbert RB (1999) Modeling spatial variability in pile capacityfor reliability-based design Analysis Design Construction and Testing of DeepFoundations ASCE Geotechnical Special Publication No 88 135ndash49

Gilbert R B (1999) First-order second-moment bayesian method for data analysisin decision making Geotechnical Engineering Center Report Department of CivilEngineering The University of Texas at Austin Austin Texas 50 pp

Gilbert R B Gambino S J and Dupin R M (1999) Reliability-based approachfor foundation design without with-specific soil borings In Proceedings Off-shore Technology Conference Houston Texas OTC 10927 Society of PetroleumEngineers Richardson pp 631ndash40

Gilbert R B Choi Y J Dangyach S and Najjar S S (2005a) Reliability-baseddesign considerations for deepwater mooring system foundations In Proceed-ings ISFOG 2005 Frontiers in Offshore Geotechnics Perth Western AustraliaTaylor amp Francis London pp 317ndash24

Gilbert R B Najjar S S and Choi Y J (2005b) Incorporating lower-boundcapacities into LRFD codes for pile foundations In Proceedings Geo-FrontiersAustin TX ASCE Virginia pp 361ndash77

Goodwin P Ahilan R V Kavanagh K and Connaire A (2000) Integratedmooring and riser design target reliabilities and safety factors In ProceedingsConference on Offshore Mechanics and Arctic Engineering New Orleans TheAmerican Society of Mechanical Engineers (ASME) New York 785ndash92

Journel A G and Huijbregts Ch J (1978) Mining Geostatistics Academic PressSan Diego

Kenney R L and Raiffa H (1976) Decision with Multiple Objectives Preferencesand Value Tradeoffs John Wiley and Sons New York

Najjar S S (2005) The importance of lower-bound capacities in geotech-nical reliability assessments PhD dissertation The University of Texas atAustin

Practical application of RBD 223

Stahl B Aune S Gebara J M and Cornell C A (1998) Acceptance criteria foroffshore platforms In Proceedings Conference on Offshore Mechanics and ArcticEngineering OMAE98-1463

Tang W H and Gilbert R B (1993) Case study of offshore pile system reliabilityIn Proceedings Offshore Technology Conference OTC 7196 Houston Societyof Petroleum Engineers pp 677ndash83

The Economist (2004) The price of prudence A Survey of Risk 24 January 6ndash8USBR (2003) Guidelines for Achieving Public Protection in Dam Safety Decision

Making Dam Safety Office United States Bureau of Reclamation Denver COUSNRC (1975) Reactor safety study an assessment of accident risks in US com-

mercial nuclear power plants United States Nuclear Regulatory CommissionNUREG-75014 Washington D C

Wang D (2002) Development of a method for model calibration with non-normaldata PhD dissertation The University of Texas at Austin

Whipple C (1985) Approaches to acceptable risk In Proceedings Risk-Based Deci-sion Making in Water Resources Ed Y Y Haimes and EZ Stakhiv ASCENew York CA pp 31ndash45

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110(2) 145ndash88

Wu T H Tang W H Sangrey D A and Baecher G (1989) Reliability of offshorefoundations Journal of Geotechnical Engineering ASCE 115(2) 157ndash78

Chapter 6

Randomly heterogeneous soilsunder static and dynamicloads

Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

61 Introduction

The values of soil properties used in geotechnical engineering and geome-chanics involve a significant level of uncertainty arising from several sourcessuch as inherent random heterogeneity (also referred to as spatial variabil-ity) measurement errors statistical errors (due to small sample sizes) anduncertainty in transforming the index soil properties obtained from soil testsinto desired geomechanical properties (eg Phoon and Kulhawy 1999) Inthis chapter only the first source of uncertainty will be examined (inher-ent random heterogeneity) as it can have a dramatic effect on soil responseunder loading (in particular soil failure) as will be demonstrated in the fol-lowing As opposed to lithological heterogeneity (which can be describeddeterministically) the aforementioned random soil heterogeneity refers tothe natural spatial variability of soil properties within geologically distinctlayers Spatially varying soil properties are treated probabilistically and areusually modeled as random fields (also referred to as stochastic fields) In thelast decade or so the probabilistic characteristics of the spatial variabilityof soil properties have been quantified using results of various sets of in-situtests

The first effort in accounting for uncertainties in the soil mass in geotech-nical engineering and geomechanics problems involved the random variableapproach According to this approach each soil property is modeled bya random variable following a prescribed probability distribution function(PDF) Consequently the soil property is constant over the analysis domainThis approach allowed the use of established deterministic analysis meth-ods developed for uniform soils but neglected any effects of their spatialvariability It therefore reduced the natural spatial variation of soil prop-erties to an uncertainty in their mean values only This rather simplisticapproach was quickly followed by a more sophisticated one modeling thespatial heterogeneity of various soil properties as random fields This becamepossible from an abundance of data from in-situ tests that allowed the

Soils under static and dynamic loads 225

establishment of various probabilistic characteristics of the spatial variabilityof soil properties

In the last decade or so a series of papers have appeared in the litera-ture dealing with the effect of inherent random soil heterogeneity on themechanical behavior of various problems in geomechanics and geotechnicalengineering using random field theory A few representative papers are men-tioned here grouped by the problem examined (1) foundation settlementsBrzakala and Pula (1996) Paice et al (1996) Houy et al (2005) (2) soilliquefaction Popescu et al (1997 2005ab) Fenton and Vanmarke (1998)Koutsourelakis et al (2002) Elkateb et al (2003) Hicks and Onisiphorou(2005) (3) slope stability El Ramly et al (2002 2005) Griffiths and Fenton(2004) and (4) bearing capacity of shallow foundations Cherubini (2000)Nobahar and Popescu (2000) Griffiths et al (2002) Fenton and Griffiths(2003) Popescu et al (2005c) The methodology used in essentially all ofthese studies was Monte Carlo Simulation (MCS)

First-order reliability analysis approaches (see eg Christian (2004) fora review) as well as perturbationexpansion techniques postulate the exis-tence of an ldquoaverage responserdquo that depends on the average values of thesoil properties This ldquoaverage responserdquo is similar to that obtained froma corresponding uniform soil having properties equal to the average prop-erties of the randomly variable soil The inherent soil variability (randomheterogeneity) is considered to induce uncertainty in the computed responseonly as a random fluctuation around the ldquoaverage responserdquo For exam-ple in problems involving a failure surface it is quite common to assumethat the failure mechanism is deterministic depending only on the averagevalues of the soil properties More specifically in most reliability analysesof slopes involving spatially variable soil deterministic analysis methodsare first used to estimate the critical slip surface (corresponding to a mini-mum factor of safety and calculated assuming uniform soil properties) Afterthat the random variability of soil properties along this pre-determinedslip surface is used in the stochastic analysis (eg El Ramly et al 20022005)

In contrast to the type of work mentioned in the previous paragrapha number of recent MCS-based studies that did not impose any restric-tions on the type and geometry of the failure mechanism (eg Paice et al1996 for foundation settlements Popescu et al 1997 2005ab for soilliquefaction Griffiths and Fenton 2004 for slope stability and Popescuet al 2005c for bearing capacity of shallow foundations) observed thatthe inherent spatial variability of soil properties can significantly modifythe failure mechanism of spatially variable soils compared to the corre-sponding mechanism of uniform (deterministic) soils For example Fochtand Focht (2001) state that ldquothe actual failure surface can deviate fromits theoretical position to pass through weaker material so that the averagemobilized strength is less than the apparent average strengthrdquo The deviation

226 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

of the failure surface from its deterministic configuration can be dramaticas there can be a complete change in the topology of the surface Further-more the average response of spatially variable soils (obtained from MCS)was observed to be significantly different from the response of the corre-sponding uniform soil For example Paice et al (1996) predicted an up to12 increase in average settlements for an elastic heterogeneous soil withcoefficient of variation CV = 42 compared to corresponding settlementsof a uniform soil deposit with the same mean soil properties Nobahar andPopescu (2000) and Griffiths et al (2002) found a 20ndash30 reduction in themean bearing capacity of heterogeneous soils with CV = 50 comparedto the corresponding bearing capacity of a uniform soil with the same aver-age properties Popescu et al (1997) predicted an increase of about 20 inthe amount of pore-water pressure build-up for a heterogeneous soil depositwith CV = 40 compared to the corresponding results of uniform soilwith the same mean properties It should be noted that the effects of spatialvariability were generally stronger for phenomena governed by highly non-linear constitutive laws (such as bearing capacity and soil liquefaction) thanfor phenomena governed by linear laws (eg settlements of foundations onelastic soil)

This chapter discusses the effects of inherent spatial variability (randomheterogeneity) of soil properties on two very different phenomena bearingcapacity failure of shallow foundations under static loads and seismicallyinduced soil liquefaction The first implies a limit equilibrium type of failureinvolving a large volume of soil while the second is based on a micro-scale(soil grain level) mechanism Based on results of numerical work previouslypublished by the authors it is shown that the end effects of spatial variabilityon limit loads are qualitatively similar for both phenomena

The probabilistic characteristics of the spatial variability of soils are brieflydiscussed in the first part of this chapter A general MCS approach forgeotechnical systems exhibiting random variation of their properties is alsopresented The second part focuses on the effects of inherent soil spatialvariability Based on examples dealing with the bearing capacity of shallowfoundations and seismically induced soil liquefaction it is shown that thespatial variability of soil properties can change the soil behavior from thewell known theoretical results obtained for uniform (deterministic) soils dra-matically The mechanisms of this intriguing behavior are analyzed in somedetail and interpreted using experimental and numerical evidence It is alsoshown that in some situations the resulting factors of safety and failureloads for randomly heterogeneous soils can be considerably lower than thecorresponding ones determined from some current reliability approachesused in geotechnical engineering with potentially significant implicationsfor design The concept of characteristic values or ldquoequivalent uniformrdquo soilproperties is discussed in the last part of the chapter as a means of accountingfor the effects of soil variability in practical applications

Soils under static and dynamic loads 227

62 Representing the random spatial variability ofsoil properties

The natural heterogeneity in a supposedly homogeneous soil layer may bedue to small-scale variations in mineral composition environmental condi-tions during deposition past stress history variations in moisture contentetc (eg Tang 1984 Lacasse and Nadim 1996) From a geotechnical designviewpoint this small-scale heterogeneity is manifested as spatial variation ofrelevant soil properties (such as relative density shear strength hydraulicconductivity etc)

An example of soil spatial variability is presented in Figure 61 in terms ofrecorded cone tip resistance values in a number of piezocone (CPTu) profilesperformed for core verification at one of the artificial islands (namely TarsiutP-45) constructed in the Canadian Beaufort Shelf and used as drilling plat-forms for oil exploration in shallow waters (Gulf Canada Resources 1984)The cone tip resistance qc is directly related to shear strength and to liq-uefaction potential of sandy soils The profiles shown in Figure 61 werelocated on a straight line at 9 m center-to-center distances Those recordsare part of a more extended soil investigation program consisting of 32piezocone tests providing an almost uniform coverage of the area of inter-est (72 mtimes72 m) The soil deposit at Tarsuit P-45 consists of two distinct

9m

MAC31

dept

h -

z(m

)

0minus5

minus10

minus15

minus20

0 10 20 0 10 20 0 10 20cone tip resistance (MPa)

averagevalues

example of loosesand pocket

example of densesand pocket

0 10 20 0 10 20 0 10 20

MAC04 MAC05 MAC06 MAC07 MAC08

9m 9m 9m 9m

Figure 61 Cone tip resistance recorded in the sand core at Tarsiut P-45 (after Popescuet al 1997)

228 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

a50m

P3

P2 P5 P8

P6

P9

P12

clay

sandclay

sandclay

sand

clay

sandclay

sand

clay

sand

clay

sandclay

sandclay

sand

0 25 50 0 25

Stress normalized standard penetration resistance - (N1)60recorded in the dense sand layer

50 0 25 50 0 25 50

clay

sand

clay 25 50

25 50

250 50

b

P11

sand profile P10

finesandwithsilt

siltyclay

densesand

clay

sandde

pth

(m)

dept

h (m

)55

m

55 m

50m 50m

minus45

minus40

minus35

minus30

minus25

P1 P4

P10P7

minus45

minus40

minus35

minus30

minus25

dept

h (m

)

minus45

minus40

minus35

minus30

minus25

minus20

minus15

minus10

minus5

dept

h (m

)

minus45

minus40

minus35

minus30

minus25

(N1)60

Figure 62 Standard Penetration Test results from a 2D measurement array in the TokyoBay area Japan (after Popescu et al 1998a)

layers an upper layer termed the ldquocorerdquo and a base layer termed the ldquobermrdquo(see eg Popescu et al 1998a for more details) Selected qc records fromthe core are shown in Figure 61 Records from both layers are shown inFigure 63a

A second example is presented in Figure 62 in terms of StandardPenetration Test (SPT) results recorded at a site in the Tokyo Bay areaJapan where an extended in situ soil test program had been performed forliquefaction risk assessment A two-dimensional measurement array consist-ing of 24 standard penetration test profiles was performed in a soil depositformed of three distinct soil layers (Figure 62b) a fine sand with silt inclu-sions a silty clay layer and a dense to very dense sand layer The resultsshown in Figure 62a represent stress normalized SPT blowcounts (N1)60recorded in the dense sand layer in 12 of the profiles Results from all soillayers are shown in Figure 63b

Soils under static and dynamic loads 229

c Probability density functions fitted to normalized fluctuations of recorded data

Gaussian distribution(for comparison)

Beta distribution (Tarsiut - core)Beta distribution (Tarsiut - berm)

Lognormal distrib(Tokyo Bay - Fine sand) Gamma distribution(Tokyo Bay - Dense sand)

a CPT data from Tarsiut P-45 (hydraulic fill) b SPT data from Tokyo Bay (natural deposit)

0 5 10Cone tip resistance - qc (MPa) Normalized SPT blowcount - (N1)60

15 20 25 0

minus50

minus30

05

04

03

02

01

0

minus4 minus3 minus2 minus1 0Normalized data (zero-mean unit standard deviation)

1 2 3 4

minus25

minus20

minus15

minus10

minus50

minus40

minus30

SILTY CLAY

Dep

th -

z(m

)

Dep

th -

z(m

)

FINE SAND

minus20mminus16m dry

DENSESAND

minus20

saturated

CORE

BERM

minus10

0

20 40 60 80 100

Figure 63 In-situ penetration test results at two sites (after Popescu et al 1998a) and cor-responding fitted probability distribution functions for the normalized data (zeromean unit standard deviation) (a) cone penetration resistance from 8 CPTprofiles taken at an artificial island in the Beaufort Sea (Gulf Canada Resources1984) (b) normalized standard penetration data from 24 borings at a site in theTokyo Bay area involving a natural soil deposit (c) fitted PDFs to normalizedfluctuations of field data Thick straight lines in (a) and (b) represent averagevalues (spatial trends)

It can be easily observed from Figures 61 and 62 that soil shear strength ndashwhich is proportional to qc and (N1)60 ndash is not uniform over the test area butvaries from one location to another (both in the horizontal and vertical direc-tions) As deterministic descriptions of this spatial variability are practicallyimpossible ndash owing to the prohibitive cost of sampling and to uncertaintiesinduced by measurement errors (eg VanMarcke 1989) ndash it has becomestandard practice today in the academic community to model this smallscale random heterogeneity using stochastic field theory The probabilistic

230 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

characteristics of a stochastic field describing spatial variability of soilproperties are briefly discussed in the following

621 Mean value

For analysis purposes the inherent spatial variability of soil propertiesis usually separated into an average value and fluctuations about it (egVanMarcke 1977 DeGroot and Baecher 1993) The average values of cer-tain index soil properties (eg qc) are usually assumed to vary linearly withdepth (eg Rackwitz 2000) reflecting the dependence of shear strength onthe effective confining stress They are usually called ldquospatial trendsrdquo andcan be estimated from a reasonable amount of in-situ soil data (using forexample a least square fit) In a 2D representation denoting the horizontaland vertical coordinates by x and z respectively the recorded qc values areexpressed as

qc(xz) = qavc (z) + uf (xz) (61)

where uf(xz) are the random fluctuations around the spatial trend qavc (z)

Assuming isotropy in the horizontal plane extension to 3D is straightfor-ward The spatial trends qav

c (z) are shown in Figures 61 and 63ab bythick straight lines representing a linear increase with depth of the aver-age recorded index values qc and (N1)60 It is mentioned that the thick linesin all plots in Figure 61 correspond to the same trend for all profiles (oneaverage calculated from all records) Several authors (eg Fenton 1999bEl-Ramly et al 2002) note the importance of correct identification of thespatial trends Phoon and Kulhawy (2001) state that ldquodetrending is as muchan art as a sciencerdquo and recommend using engineering judgment

622 Degree of variability

The degree of variability ndash or equivalently the magnitude of the randomfluctuations around the mean values ndash is quantified by the variance or thestandard deviation As the systematic trends can be identified and expresseddeterministically they are usually removed from the subsequent analysis anditrsquos only the local fluctuations that are modeled using random field theoryFor mathematical convenience the fluctuations uf(xz) in Equation (61) arenormalized by the standard deviation σ (z)

u(xz) = [qc(xz) minus qavc (z)]σ (z) (62)

so that the resulting normalized fluctuations u(xz) can be described by azero-mean unit standard deviation random field

As implied by Equation (62) the standard deviation σ (z) may be a func-tion of the depth z A depth-dependent standard deviation can be inferred

Soils under static and dynamic loads 231

by evaluating it in smaller intervals over which it is assumed to be constantrather than using the entire soil profile at one time For example the standarddeviation of the field data shown in Figure 61 was calculated over 1 m longmoving intervals and was found to vary between 1 and 4 MPa (Popescu1995)

The most commonly used parameter for quantifying the magnitude offluctuations in soil properties around their mean values is the coefficient ofvariation (CV) For the data in Figure 61 CV(z) = σ (z)qav

c (z) was found tovary between 20 and 60 The average CV of SPT data from the densesand layer in Figure 62 was about 40

It should be mentioned at this point that the CPT and SPT records shown inFigures 61 and 62 include measurement errors and therefore the degreesof variability discussed here may be larger than the actual variability ofsoil properties The measurement errors are relatively small for the caseof CPT records (eg for the electrical cone the average standard devia-tion of measurement errors is about 5 American Society for Testing andMaterials 1989) but they can be significant for SPT records which areaffected by many sources of errors (discussed by Schmertmann 1978 amongothers)

623 Correlation structure

The fluctuations of soil properties around their spatial trends exhibit in gen-eral some degree of coherencecorrelation as a function of depth as can beobserved in Figures 61 and 62 For example areas with qc values consis-tently larger than average indicate the presence of dense (stronger) pocketsin the soil mass while areas with qc values consistently smaller than aver-age indicate the presence of loose (less-resistant) soil Examples of denseand loose soil zones inferred from the fluctuations of qc are identified inFigure 61 Similarly the presence of a stronger soil zone at a depth of about35 m and spanning profiles P5 and P8ndashP12 can be easily observed from theSPT results presented in Figure 62

The similarity between fluctuations recorded at two points as a function ofthe distance between those two points is quantified by the correlation struc-ture The correlation between values of the same material property measuredat different locations is described by the auto-correlation function A signif-icant parameter associated with the auto-correlation function is called thescale of fluctuation (or correlation distance) and represents a length overwhich significant coherence is still manifested Owing to the geological soilformation processes for most natural soil deposits the correlation distancein the horizontal direction is significantly larger than the one in the verticaldirection This can be observed in Figure 61 by the shape of the dense andloose soil pockets In this respect separable correlation structure models (egVanMarcke 1983) seem appropriate to model different spatial variability

232 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

characteristics in the vertical direction (normal to soil strata) and in thehorizontal one

There are several one-dimensional theoretical auto-correlation functionmodels that can be combined into separable correlation structures and usedto describe natural soil variability (eg VanMarcke 1983 Shinozuka andDeodatis 1988 Popescu 1995 Rackwitz 2000) Inferring the parame-ters of a theoretical correlation model from field records is straightfor-ward for the vertical (depth) direction where a large number of closelyspaced data are usually available (eg from CPT records) However it ismuch more challenging for the horizontal direction where sampling dis-tances are much larger DeGroot and Baecher (1993) and Fenton (1999a)review the most common methods for estimating the auto-correlationfunction of soil variability in the vertical direction Przewlocki (2000)and Jaksa (2007) present methods for estimating the two-dimensionalcorrelation of soil spatial variability based on close-spaced field measure-ments in both vertical and horizontal directions As close-spaced recordsin horizontal direction are seldom available in practice Popescu (1995)and Popescu et al (1998a) introduce a method for estimating the hor-izontal auto-correlation function based on a limited number of verticalprofiles

As a note of caution it is mentioned here that various different definitionsfor the correlation distance exist in the literature yielding different values forthis parameter (see eg Popescu et al 2005c for a brief discussion) Anotherproblem stems from the fact that when estimating correlation distances fromdiscrete data the resulting values are dependent on the sampling distance(eg Der Kiureghian and Ke 1988) and the length scale or the distance overwhich the measurements are taken (eg Fenton 1999b)

When two or more different soil properties can be recorded simultane-ously it is interesting to assess the spatial interdependence between themand how it affects soil behavior For example from piezocone test resultsone can infer another index property ndash besides qc ndash called soil classifica-tion index which is related to grain size and soil type (see eg Jefferies andDavies 1993 Robertson and Wride 1998) The spatial dependence betweentwo different properties measured at different locations is described by thecross-correlation function In this case the variability of multiple soil prop-erties is modeled by a multivariate random field (also known as a vectorfield) and the ensemble of auto-correlation and cross-correlation functionsform the cross-correlation matrix

As the field data u(xz) employed to estimate the correlation structurehave been detrended and normalized (refer to Equation (62)) the result-ing field is stationary It is always possible to model directly non-stationarycharacteristics in spatial correlation but this is very challenging because oflimited data and modeling difficulties (eg Rackwitz 2000) Consequentlystationarity is almost always assumed (at least in a piecewise manner)

Soils under static and dynamic loads 233

624 Probability distribution

Based on several studies reported in the literature soil properties can followdifferent probability distribution functions (PDFs) for different types of soilsand sites but due to physical reasons they always have to follow distribu-tions defined only for nonnegative values of the soil properties (excludingthus the Gaussian distribution) Unlike Gaussian stochastic fields wherethe first two moments provide complete probabilistic information non-Gaussian fields require knowledge of moments of all orders As it is extremelydifficult to estimate moments higher than order two from actual data (egLumb 1966 Phoon 2006) the modeling and subsequent simulation of soilproperties that are represented as homogeneous non-Gaussian stochasticfields are usually done using their cross-correlation matrix (or equiva-lently cross-spectral density matrix) and non-Gaussian marginal probabilitydistribution functions

While there is no clear evidence pointing to any specific non-Gaussianmodel for the PDF of soil properties one condition that has to be satisfiedis for the PDF to have a lower (nonnegative) bound The beta gamma andlognormal PDFs are commonly used for this purpose as they all satisfy thiscondition For prescribed values of the mean and standard deviation thegamma and lognormal PDFs are one-parameter distributions (eg the valueof the lower bound can be considered as this parameter) They are bothskewed to the left Similarly for prescribed values of the mean and standarddeviation the beta PDF is a two-parameter distribution and consequentlymore flexible in fitting in-situ data Moreover it can model data that aresymmetrically distributed or skewed to the right Based on penetration datafrom an artificial and a natural deposit Popescu et al (1998a) observed thatPDFs of soil strength in shallow layers are skewed to the left while for deepersoils the corresponding PDFrsquos tend to follow more symmetric distributions(refer to Figure 63)

As will be discussed later the degree of spatial variability (expressed bythe coefficient of variation) is a key factor influencing the behavior of het-erogeneous soils as compared to uniform ones It has also been determinedthat both the correlation structure and the marginal probability distributionfunctions of soil properties affect the response behavior of heterogeneoussoils to significant degrees (eg Popescu et al 1996 2005a Fenton andGriffiths 2003)

63 Analysis method

631 Monte Carlo Simulation approach

Though expensive computationally Monte Carlo Simulation (MCS) isthe only currently available universal methodology for accurately solving

234 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

problems in stochastic mechanics involving strong nonlinearities and largevariations of non-Gaussian uncertain system parameters as is the case ofspatially variable soils As it will be shown later in this chapter failuremechanisms of heterogeneous soils may be different from the theoreticalones (derived for assumed deterministic uniform soil) and can change signif-icantly from one realization (sample function) of the random soil propertiesto another This type of behavior prevents the use of stochastic analysismethods such as the perturbationexpansion ones that are based on theassumption of an average failure mechanism and variations (fluctuations)around it

MCS is a means of solving numerically problems in mathematics physicsand other sciences through sampling experiments and basically consists ofthree steps (eg Elishakoff 1983) (1) simulation of the random quanti-ties (variables fields) (2) solution of the resulting deterministic problemfor a large number of realizations in step 1 and (3) statistical analysis ofresults For the case of spatially variable soils the realizations of the ran-dom quantities in step 1 consist of simulated sample functions of stochasticfields with probabilistic characteristics estimated from field data as discussedin Section 62 Each such sample function represents a possible realizationof the relevant soil index properties over the analysis domain The deter-ministic problem in step 2 is usually solved by finite element analysis Onesuch analysis is performed for each realization of the (spatially variable) soilproperties

632 Simulation of homogeneous non-Gaussian stochasticfields

Stochastic fields modeling spatially variable soil properties are non-Gaussianmulti-dimensional (2D or 3D) and can be univariate (describing the variabil-ity of a single soil property) or multivariate (referring to the variability of sev-eral soil properties and their interdependence) Among the various methodsthat have been developed to simulate homogeneous non-Gaussian stochas-tic fields the following representative ones are mentioned here Yamazakiand Shinozuka (1988) Grigoriu (1995 1998) Gurley and Kareem (1998)Deodatis and Micaletti (2001) Puig et al (2002) Sakamoto and Ghanem(2002) Masters and Gurley (2003) Phoon et al (2005)

As discussed in Section 624 it is practically impossible to estimate non-Gaussian joint PDFs from actual soil data Therefore a full descriptionof the random field representing the spatial variability of soil propertiesat a given site is not achievable Under these circumstances the simu-lation methods considered generate sample functions matching a targetcross-correlation structure (CCS) ndash or equivalently cross-spectral densitymatrix (CSDM) in the wave number domain ndash and target marginal PDFsthat can be inferred from the available field information It is mentioned

Soils under static and dynamic loads 235

here that certain simulation methods match only lower order moments(such as mean variance skewness and kurtosis) instead of marginal PDFs(the reader is referred to Deodatis and Micaletti 2001 for a review anddiscussion)

In most methods for non-Gaussian random field simulation a Gaussiansample function is first generated starting from the target CCS (or targetCSDM) and then mapped into the desired non-Gaussian sample functionusing a transformation procedure that involves nonlinear mapping (egGrigoriu 1995 Phoon 2006) While the target marginal PDFs are matchedafter such a mapping the nonlinearity of this transformation modifies theCCS and the resulting non-Gaussian sample function is no longer compati-ble with the target CCS Cases involving small CVs and non-Gaussian PDFsthat are not too far from Gaussianity result in general in changes in the CCSthat are small compared to the uncertainties involved in estimating the tar-get CCS from field data However for highly skewed marginal PDFs thedifferences between target and resulting CCSs may become significant Forexample Popescu (2004) discussed such an example where mapping from aGaussian to a lognormal random field for very large CVs of soil variabilityproduced differences of up to 150 between target and resulting correlationdistances

There are several procedures for correcting this problem and generat-ing sample functions that are compatible with both the target CCS andmarginal PDFs These methods range from iterative correction of the tar-get CCS (or target CSDM) used to generate the Gaussian sample function(eg Yamazaki and Shinozuka 1988) to analytical transformation methodsthat are applicable to certain combinations of target CCS ndash target PDF (egGrigoriu 1995 1998) A review of these methods is presented by Deodatisand Micaletti (2001) together with an improved algorithm for simulatinghighly skewed non-Gaussian fields

The methodology used for the numerical examples presented in this studycombines work done on the spectral representation method by Yamazaki andShinozuka (1988) Shinozuka and Deodatis (1996) and Deodatis (1996) andextends it to simulation of multi-variate multi-dimensional (mVndashnD) homo-geneous non-Gaussian stochastic fields According to this methodology asample function of an mVndashnD Gaussian vector field is first generated usingthe classic spectral representation method Then this Gaussian vector fieldis transformed into a non-Gaussian one that is compatible with a prescribedcross-spectral density matrix and with prescribed (non-Gaussian) marginalPDFs assumed for the soil properties This is achieved through the clas-sic memoryless nonlinear transformation of ldquotranslation fieldsrdquo (Grigoriu1995) and the iterative scheme proposed by Yamazaki and Shinozuka(1988) For a detailed presentation of this simulation algorithm and a dis-cussion on the convergence of the iterative scheme the reader is referredto Popescu et al (1998b) The reason that a more advanced methodology

236 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

such as that of Deodatis and Micaletti (2001) is not used in this chapteris the fact that the random fields describing various soil properties areusually only slightly skewed and consequently the iterative correctionproposed by Yamazaki and Shinozuka (1988) gives sufficiently accurateresults

633 Deterministic finite element analyses with generatedsoil properties as input

Solutions of boundary value problems can be readily obtained through finiteelement analysis (FEA) Along the lines of the MCS approach a deterministicFEA is performed for every generated sample function representing a pos-sible realization of soil index properties over the analysis domain In eachsuch analysis the relevant material (soil) properties are obtained at eachspatial location (finite element centroid) as a function of the simulated soilindex properties Two important issues related to such analyses are worthmentioning

1 The size of finite elements should be selected to accurately reproducethe simulated boundary value problem and at the same time to ade-quately capture the essential features of the stochastic spatial variabilityof soil properties Regarding modeling the spatial variability the opti-mum mesh refinement depends on both correlation distance and type ofcorrelation structure For example Der Kireghian and Ke (1988) rec-ommend the following upper bounds for the size of finite elements tobe used with an exponential correlation structure xk le (025minus05)θkwhere xk is the finite element size in the spatial direction ldquokrdquo and θkis the correlation distance in the same spatial direction

2 Quite often the mesh used for generating sample functions of the ran-dom field representing the soil properties is different from the finiteelement mesh Therefore the generated random soil properties have tobe transferred from one mesh to another There are several methods foraccomplishing this data transfer (see eg Brenner 1991 for a review)Two of them are mentioned here (see Popescu 1995 for a compari-son study) (1) the spatial averaging method proposed by VanMarcke(1977) which assigns to each element a value obtained as an averageof stochastic field values over the element domain and (2) the midpointmethod (eg Shinozuka and Dasgupta 1986 Der Kiureghian and Ke1988 Deodatis 1989) in which the random field is represented by itsvalues at the centroid of each finite element The midpoint method isused in all numerical examples presented here as it better preserves thenon-Gaussian characteristics describing the variability of different soilproperties

Soils under static and dynamic loads 237

64 Understanding mechanisms and effects

641 A simple example

To provide a first insight on how spatial variability of material propertiescan modify the mechanical response and which are the main factors affectingstructural behavior a simple example is presented in Figure 64 The responseof an axially loaded linear elastic bar with initial length L0 unit area A0 = 1and uniform Youngrsquos modulus E = 1 is compared to that of another linearelastic bar with the same L0 and A0 but made of two different materialswith Youngrsquos moduli equal to E1 = 12 and E2 = 08 The uniform barand the non-uniform one are shown in Figures 64a and 64b respectivelyNote that the non-uniform bar in Figure 64b has an ldquoaveragerdquo modulus(E1 + E2)2 = 1 equal to the Youngrsquos modulus of the uniform bar Thelinear stressndashstrain relationships of the three materials E E1 and E2 areshown in Figure 64c The two bars are now subjected to an increasingtensile axial stress σ The ratio of the resulting axial strains ε-variableε-uniform (variable denoting the non-uniform bar) is plotted versus a rangeof values for σ in Figure 64e In this case involving linear elastic materialsonly a 4 difference is observed between ε-variable and ε-uniform for allvalues of σ (continuous line in Figure 64e) This small difference is mainly

L02

L02

uniform(E)

stiffer(E1=12E)

softer(E2=08E)

s s

E=10

E1=12

E2=08

s

e

c Linear material

E1=12E=10E2=08

s

e

d Bi-linear material

Eacute=01

Eacute1=012

Eacute2=008

ep = 1

a

0 05 1 15 20

05

1

15

2

25

Axial stress ndash s

104

sp2

sp2

sp1

sp1

LinearBi-linear

e

b

ε-va

riabl

e ε

-uni

form

Figure 64 Simple example illustrating the effects of material heterogeneity on mechanicalresponse (a) uniform bar (b) non-uniform bar (c) linear stressndashstrain relation-ships (d) bi-linear stressndashstrain relationships (e) resulting strain ratios for arange of axial stresses

238 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

induced by the type of averaging considered for Youngrsquos modulus and itwould vanish if geometric mean were used for the elastic moduli instead ofarithmetic mean

The situation is significantly different for the same bars when the mate-rial behavior becomes nonlinear Assuming bilinear material behavior for allthree materials as shown in Figure 64d the resulting differences betweenthe responses in terms of axial strains are now significant As indicated bythe dotted line in Figure 64e the ratio ε-variableε-uniform now exceeds200 for some values of σ The reason for this different behavior is thatfor a certain range of axial stresses the response of the non-uniform bar iscontrolled by the softer material that reaches the plastic state (with resultingvery large strains) before the material of the uniform bar From this simpleexample it can be concluded that (1) mechanical effects induced by mate-rial heterogeneity are more pronounced for phenomena governed by highlynonlinear laws (2) loose zones control the deformation mechanisms and(3) the effects of material heterogeneity are stronger for certain values of theload intensity All these observations will be confirmed later based on morerealistic examples of heterogeneous soil behavior

642 Problems involving a failure surface

In a small number of recent MCS-based studies that did not impose anyrestrictions on the geometry of the failure mechanism it has been observedthat the inherent spatial variability of soil properties could significantlymodify the failure mechanism of spatially variable soils as compared tothe corresponding mechanism of uniform (deterministic) soils

Along these lines Figure 65 presents results of finite element analyses(FEA) of bearing capacity (BC) for a purely cohesive soil with elastic-plasticbehavior (the analysis details are presented in Popescu et al 2005c) Theresults of a so-called ldquodeterministic analysisrdquo assuming uniform soil strengthover the analysis domain are shown in Figure 65a in terms of maximumshear strain contours Under increasing uniform vertical pressure the foun-dation settles with no rotations and induces bearing capacity failure in asymmetrical pattern essentially identical to that predicted by Prandtlrsquos the-ory The symmetric pattern for the maximum shear strain can be clearly seenin Figure 65a The corresponding pressurendashsettlement curve is shown witha dotted line in Figure 65d

Figure 65b displays one sample function of the spatially variable soil shearstrength having the same mean value as the value used in the deterministic(uniform soil) analysis (cav

u =100 kPa) and a coefficient of variation of 40The undrained shear strength is modeled here as a homogeneous randomfield with a symmetric beta probability distribution function and a separablecorrelation structure with correlation distances θH =5 m in the horizontaldirection and θV =1 m in the vertical direction Lighter areas in Figure 65b

Soils under static and dynamic loads 239

Initial soillevel

δB =01

B =4m

Max shearstrain ()

011020

a

δB =01

Max shearstrain ()

011020

c

b

15010050

cu (kPa)

d

Settlement δ(m)

Spatially varying soil shown in Fig 65bUniform soil (deterministic)

Nor

mal

ized

pre

ssur

e q

c uav

0 01 02 03 04 050

20

40

60

Figure 65 Comparison between finite element computations of bearing capacity for a uni-form vs a heterogeneous soil deposit (a) contours of maximum shear strain fora uniform soil deposit with undrained shear strength cu =100 kPa (b) contoursof undrained shear strength (cu) obtained from a generated sample function ofa random field modeling the spatial variability of soil (c) contours of maximumshear strain for the variable soil shown in Figure 5(b) (d) computed normalizedpressure vs settlement curves Note that the settlement δ is measured at themidpoint of the width B of the foundation (after Popescu et al 2005c)

indicate weaker zones of soil while darker areas indicate stronger zonesFigure 65c displays results for the maximum shear strain corresponding tothe spatially variable soil deposit shown in Figure 65b An unsymmetric fail-ure surface develops passing mainly through weaker soil zones as illustratedby the dotted line in Figure 65b The resulting pressurendashsettlement curve(continuous line in Figure 65d) indicates a lower ultimate BC for the spa-tially variable soil than that for the uniform one (dotted line in Figure 65d)It is important to emphasize here that the failure surface for the spatially vari-able soil passes mainly through weak soil zones indicated by lighter patchesin Figure 65b It should be noted that other sample functions of the spatiallyvariable soil shear strength different from the one shown in Figure 65b willproduce different failure surfaces from the one shown in Figure 65c (anddifferent from the deterministic failure pattern shown in Figure 65a)

Figure 66 also taken from Popescu et al (2005c) presents results involv-ing 100 sample functions in a Monte Carlo simulation type of analysis Theproblem configuration here is essentially identical to that in Figure 65 the

240 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

20 10 0 10 200

25

50

75

Foundation slope ()

b

Nor

mal

ized

pre

ssur

e q

c uav

Settlements (m)

0 01 02 03 04 050

20

40

60

Monte Carlo simulation resultsDeterministic analysis results

Average from Monte Carlo simulations

a

Nor

mal

ized

pre

ssur

e q

c uav

Settlement at ultimate bearing capacity(δB =001 B = 4m)

Figure 66 Monte Carlo simulation results involving 100 sample functions for a strip founda-tion on an overconsolidated clay deposit with variable undrained shear strengthProblem configuration identical to that in Figure 65 except marginal PDF of soilproperties (a) normalized bearing pressures vs settlements (b) normalizedbearing pressures vs footing rotations (after Popescu et al 2005c)

only difference being that the marginal PDF of soil properties is now modeledby a Gamma distribution (compared to a beta distribution in Figure 65)The significant variation between different sample functions becomes imme-diately obvious as well as the fact that on the average the ultimate BC ofspatially variable soils is considerably lower than the corresponding valueof uniform (deterministic) soils Figure 66b indicates also that the spatialvariability of soils induces footing rotations (differential settlements) forcentrally loaded symmetric foundations

The aforementioned results suggest the following behavior for problemsinvolving the presence of a failure surface (1) the consideration of the spatialvariability of soil properties leads to different failure mechanisms (surfaces)for different realizations of the soil properties These failure surfaces canbecome dramatically different from the classic ones predicted by existingtheories for uniform (deterministic) soils (eg PrandtlndashReisner solution forBC critical slip surface resulting from limit equilibrium analysis for slopestability) (2) an immediate consequence is that the failure loadsfactors ofsafety for the case of spatially variable soils can become significantly lower(on the average) than the corresponding values for uniform (deterministic)soils

643 Problems involving seismically induced soilliquefaction

Marcuson (1978) defines liquefaction as the transformation of a granularmaterial from a solid to a liquefied state as a consequence of increased pore

Soils under static and dynamic loads 241

water pressure and reduced effective stress This phenomenon occurs mostreadily in loose to medium dense granular soils that have a tendency tocompact when sheared In saturated soils pore water pressure drainagemay be prevented due to the presence of silty or clayey seam inclusionsor may not have time to occur due to rapid loading ndash such as in the caseof seismic events In this situation the tendency to compact is translatedinto an increase in pore water pressure This leads to a reduction in effec-tive stress and a corresponding decrease of the frictional shear strengthIf the excess pore water pressure (EPWP) generated at a certain locationin a purely frictional soil (eg sand) reaches the initial value of the effec-tive vertical stress then theoretically all shear strength is lost at thatlocation and the soil liquefies and behaves like a viscous fluid Liquefaction-induced large ground deformations are a leading cause of disasters duringearthquakes

Regarding the effects of soil spatial variability on seismically induced lique-faction both experimental (eg Budiman et al 1995 Konrad and Dubeau2002) and numerical results indicate that more EPWP is generated in a het-erogeneous soil than in the corresponding uniform soil having geomechanicalproperties equal to the average properties of the variable soil

To illustrate the effects of soil heterogeneity on seismically induced EPWPbuild-up some of the results obtained by Popescu et al (1997) for a loose tomedium dense saturated soil deposit subjected to seismic loading are repro-duced in Figure 67 The geomechanical properties and spatial variabilitycharacteristics were estimated based on the piezocone test results shown inFigure 61 The results in Figure 67b show the computed contours of theEPWP ratio with respect to the initial effective vertical stress for six samplefunctions of a stochastic field representing six possible realizations of soilproperties over the analysis domain (see Popescu et al 1997 for more detailson the soil properties) Soil liquefaction (EPWP ratio larger than approxi-mately 09) was predicted for most sample functions shown in Figure 67bAnalysis of an assumed uniform (deterministic) soil deposit with strengthcharacteristics corresponding to the average strength of the soil samples usedin MCS resulted in no soil liquefaction (the maximum predicted EPWP ratiowas 044 as shown in the upper-left plot of Figure 67c) It can be concludedfrom these results that both the pattern and the amount of dynamicallyinduced EPWP build-up are strongly affected by the spatial variability ofsoil properties For the same average values of the soil parameters moreEPWP build-up was predicted in the stochastic analysis (MCS) accountingfor spatial variability than in the deterministic analysis considering uniformsoil properties

It was postulated by Popescu et al (1997) that the presence of loosesoil pockets leads to earlier initiation of EPWP and to local liquefactioncompared to the corresponding uniform soil After that the pressure gra-dient between loose and dense zones would induce water migration into

242 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

20 one-phase finiteelements (2 dofnode)

Water table

Vertical scale is twotimes larger thanhorizontal scale

090100070090050070000050

Grey scale rangefor the excess porepressure ratio (usrsquovo)

480 two- phase finite elementsfor porous media (4 dofnode)with dimensions 3m x 05mfree field boundary

conditions

Sample function 1 Sample function 2 Sample function 3

Sample function 4 Sample function 5 Sample function 6

Deterministic - average 50 - percentile 60 - percentile

70 - percentile 80 - percentile 90 - percentile

a Finite element analysis set- up

600m

b Excess pore pressure ratio predicated using six sample functions of a stochastic vector field withcross- correlation structure and probability distribution functions estimated from piezocone test results

c Excess pore pressure ratio predicted using deterministic input soil parameters

120

m1

6m

(u srsquov0)max = 044

Figure 67 Monte Carlo simulations of seismically induced liquefaction in a saturated soildeposit accounting for natural variability of the soil properties (after Popescuet al 1997) (a) finite element analysis setup (b) contours of EPWP ratio for sixsample functions used in the Monte Carlo simulations (c) contours of EPWPratio using deterministic (ie uniform) soil parameters and various percentilesof soil strength

neighboring denser soil zones followed by softening and liquefaction ofthe dense sand This assumption ndash mainly that the presence of loose soilpockets is responsible for lower liquefaction resistance ndash was further ver-ified by two sets of numerical results First for a given average value ofthe soil strength and a given earthquake intensity the resulting amount ofEPWP build-up in a variable soil increased with the degree of variability ofsoil properties (expressed by the coefficient of variation) This degree of soilvariability directly controls the amount of loose pockets in the soil mass (egPopescu et al 1998c) Second for the same coefficient of variation of spatial

Soils under static and dynamic loads 243

variability more EPWP was predicted when the soil strength fluctuationsfollowed a probability distribution function (PDF) with a fatter left tail thatyields directly a larger amount of loose soil pockets (eg Popescu et al1996)

There are very few experimental studies dealing with liquefaction of spa-tially variable soil Particularly interesting are the results obtained by Konradand Dubeau (2002) They performed a series of undrained cyclic triaxialtests with various cyclic stress ratios on uniform dense sand uniform siltand layered soil (a silt layer sandwiched between two sand layers) The soilsin layered samples were prepared at the same void ratios as the correspond-ing soils in the uniform samples It was concluded from the results that thecyclic strength (expressed as number of cycles to liquefaction) of the layered(non-homogeneous) samples was considerably lower than the one of theuniform silt and the uniform sand samples For example at a cyclic stressratio (CSR) = 0166 the numbers of cycles to liquefaction were NL = 150for uniform dense sand at relative density 77 NL = 90 for silt preparedat void ratio 078 and NL = 42 for the layered soil Chakrabortty et al(2004) reproduced the cyclic undrained triaxial tests on uniform and lay-ered samples made of dense sand and silt layers described by Konrad andDubeau (2002) and studied the mechanism by which a sample made of twodifferent soils liquefies faster than each of the soils tested separately in uni-form samples Their explanation ndash resulting from a detailed analysis of thenumerical results ndash was that water was squeezed from the more deformablesilt layer and injected into the neighboring sand leading to liquefaction ofthe dense sand

Regarding liquefaction mechanisms of soil deposits involving the samematerial but with spatially variable strength Ghosh and Madabhushi (2003)performed a series of centrifuge experiments to analyze the effects of local-ized loose patches in a dense sand deposit subjected to seismic loads Theyobserved that EPWP is first generated in the loose sand patches and thenwater migrates into the neighboring dense sand reducing the effective stressand loosening the dense soil that can subsequently liquefy As discussedbefore it is believed that a similar phenomenon is responsible for the lowerliquefaction resistance of continuously heterogeneous soils compared to thatof corresponding uniform soils To further verify this assumed mechanismPopescu et al (2006) calibrated a numerical finite element model for liquefac-tion analysis (Preacutevost 1985 2002) to reproduce the centrifuge experimentalresults of Ghosh and Madabhushi (2003) and then used it for a detailedanalysis of a structure founded on a hypothetical chess board-like heteroge-neous soil deposit subjected to earthquake loading From careful analysis ofthe EPWP calculated at the border between loose and dense zones it wasclearly observed how pore water pressures built up first in the loose areasand then transferred into the dense sand zones that eventually experiencedthe same increase in EPWP Water ldquoinjectionrdquo into dense sand loosened the

244 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

strong pockets and the overall liquefaction resistance of the heterogeneoussoil deposit became much lower than that of a corresponding uniform soilalmost as low as the liquefaction resistance of a deposit made entirely ofloose sand

65 Quantifying the effects of random soilheterogeneity

651 General considerations

Analyzing the effects of inherent soil variability by MCS involves the solu-tion of a large number of nonlinear boundary value problems (BVPs) aswas described earlier in this chapter Such an approach is too expensivecomputationally and too complex conceptually to be used in current designapplications The role of academic research is to eventually provide thegeotechnical practice with easy to understandeasy to use guidelines in theform of charts and characteristic values that account for the effects of soilvariability In this respect MCS results contain a wealth of information thatcan be processed in various ways to provide insight into the practical aspectsof the problem at hand Two such design guidelines estimated from MCSresults are presented here

1 ldquoEquivalent uniformrdquo soil properties representing values of certain soilproperties which ndash when used in a deterministic analysis assuming uni-form soil properties over the analysis domain ndash would provide a responseldquoequivalentrdquo to the average response resulting from computationallyexpensive MCS accounting for soil spatial variability The responseresulting from deterministic analysis is termed ldquoequivalentrdquo to the MCSresponse since only certain components of it can be similar For exam-ple when studying BC failure of symmetrically loaded foundations onecan obtain in a deterministic analysis an ultimate BC equal to the aver-age one from MCS but cannot simulate footing rotations Similarlyfor the case of soil liquefaction a deterministic analysis can reproducethe average amount of EPWP resulting from MCS but cannot predictany kind of spatially variable liquefaction pattern (see eg Figures 67band c) In conclusion an equivalent uniform analysis would only matchthe average response provided by a set of MCS accounting for spatialvariability of soil properties and nothing more The equivalent uniformproperties are usually expressed as percentiles of field test recorded soilindex properties and are termed ldquocharacteristic percentilesrdquo

2 Fragility curves which are an illustrative and practical way of express-ing the probability of exceeding a prescribed threshold in the response(or damage level) as a function of load intensity They can be useddirectly by practicing engineers without having to perform any complex

Soils under static and dynamic loads 245

computations and form the basis for all risk analysisloss estimationriskreduction calculations of civil infrastructure systems performed by emer-gency management agencies and insurance companies Fragility curvescan include effects of multiple sources of uncertainty related to materialresistance or load characteristics The fragility curves are usually rep-resented as shifted lognormal cumulative distribution functions For aspecific threshold in the response each MCS case analyzed is treatedas a realization of a Bernoulli experiment The Bernoulli random vari-able resulting from each MCS case is assigned to the unity probabilitylevel if the selected threshold is exceeded or to zero if the computedresponse is less than the threshold Next the two parameters of theshifted lognormal distribution are estimated using the maximum like-lihood method and the results of all the MCS cases For a detaileddescription of this methodology the reader is referred to Shinozukaet al (2000) and Deodatis et al (2000)

Such guidelines have obvious qualitative value indicating the most impor-tant effects of soil heterogeneity for a particular BVP and type of responsewhich probabilistic characteristics of spatial variability have major effectsand which are the directions where more investigations would provide maxi-mum benefit However several other aspects have to be carefully consideredas will be demonstrated in the following when such guidelines are used inquantitative analyses

652 Liquefaction of level ground randomheterogeneous soil

Regarding liquefaction analysis of natural soil deposits exhibiting a certaindegree of spatial variability in their properties Popescu et al (1997) sug-gested that in order to predict more accurate values of EPWP build-up ina deterministic analysis assuming uniform soil properties one has to use amodified (or equivalent uniform) soil strength The results of deterministicanalyses presented in Figure 67c illustrate this idea of an equivalent uniformsoil strength for liquefaction analysis of randomly heterogeneous soils Forevery such analysis in Figure 67c the uniform (deterministic) soil propertiesare determined based on a certain percentile of the in-situ recorded qc (asliquefaction strength is proportional to qc) From visual examination andcomparison of Figures 67b and 67c one can select a percentile value some-where between 70 and 80 that would lead to an equivalent amountof EPWP in a deterministic analysis to the one predicted by multiple MCSThis value is termed ldquocharacteristic percentilerdquo A comparison between theresults presented in Figures 67b and 67c also shows clearly the differencesin the predicted liquefaction pattern of natural heterogeneous soil versusthat of assumed uniform soil This fact somehow invalidates the use of an

246 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

equivalent uniform soil strength estimated solely on the basis of averageEPWP build-up as liquefaction effects may be controlled also by the pres-ence of a so called ldquomaximal planerdquo defined by Fenton and VanMarcke(1988) as the horizontal plane having the highest value of average EPWPratio This aspect is discussed in detail by Popescu et al (2005a)

It should be mentioned that establishing the characteristic percentile byldquovisual examination and comparisonrdquo of results is exemplified here onlyfor illustrative purposes Popescu et al (1997) compared the range of MCSpredictions with deterministic analysis results in terms of several indicescharacterizing the severity of liquefaction and in terms of horizontal dis-placements Furthermore these comparisons were made for seismic inputswith various ranges of maximum spectral amplitudes For the type of soildeposit analyzed and for the probabilistic characteristics of spatial variabil-ity considered in that study it was determined that the 80-percentile of soilstrength was a good equivalent uniform value to be used in deterministicanalyses

The computed characteristic percentile of soil strength is valid only for thespecific situation analyzed and can be affected by a series of factors such assoil type probabilistic characteristics of spatial variability seismic motionintensity and frequency content etc

Popescu et al (1998c) studied the effect of the ldquointensityrdquo of spatial vari-ability on the characteristic percentile of soil strength A fine sand depositwith probabilistic characteristics of its spatial variability derived from thefield data from Tokyo Bay area (upper soil layer in Figures 62b and 63b)has been analyzed in a parametric study involving a range of values for theCV of soil strength Some of these results are shown in Figure 68 in theform of range of average EPWP ratios predicted by MCS as a function ofCV (shaded area) The EPWP ratio was averaged over the entire analysisdomain (12 m deep times 60 m long) As only 10 sample functions of spatialvariability have been used for each value of CV these results have onlyqualitative value It can be observed that for the situation analyzed in thisexample the effects of spatial variability are insignificant for CVlt02 andare strongly dependent on the degree of soil variability for CV between 02and 08 It can be therefore concluded that for the soil type and seismicintensity considered in this example CV=02 is a threshold beyond whichspatial variability has an important effect on liquefaction The range of aver-age EPWP ratios predicted by MCS is compared in Figure 68 with averageEPWP ratios computed from deterministic analyses assuming uniform soilproperties and using various percentiles of in-situ recorded soil strength Thiscomparison offers some insight about the relation between the characteristicpercentile of soil strength and the CV

Intensity of seismic ground motion is another important factor Popescuet al (2005a) studied the effect of soil heterogeneity on liquefaction fora soil deposit subjected to a series of earthquake ground accelerations

Soils under static and dynamic loads 247

05

04

03

02

0 04 08

Coefficient of variation of soil strength - CV

range from Monte Carlo simulations

90-percentile

80-percentile

70-percentile

60-percentile

averageAve

rage

EP

WP

rat

io

12 16

Figure 68 Range of average excess pore water pressure (EPWP) ratio from Monte Carlosimulations as a function of the coefficient of variation of soil strength (afterPopescu et al 1998c) Horizontal lines represent average EPWP ratios com-puted from deterministic analyses assuming uniform soil strength equal to theaverage and four percentiles of the values used in MCS

corresponding to a wide range of seismic intensities A saturated soil depositwith randomly varying soil strength was considered in a range of full 3D anal-yses (accounting for soil variability in all three spatial directions) as well asin a set of corresponding 2D analyses under a plane strain assumption (andtherefore assuming infinite correlation distance in the third direction) Thesoil variability was modeled by a three-dimensional two-variate randomfield having as components the cone tip resistance qc and the soil classifica-tion index A value of CV=05 was assumed for qc For all other probabilisticcharacteristics considered in that study the reader is referred to Popescu et al(2005a) Deterministic analyses assuming average uniform soil propertieswere also performed for comparison

The resulting EPWP ratios were compared in terms of their averages overthe entire analysis domain (Figure 69a) and averages over the ldquomaximalplanerdquo (Figure 69b) A point on each line in Figure 69 represents the com-puted average EPWP ratio for a specific input earthquake intensity expressedby the Arias Intensity (eg Arias 1970) which is a measure of the totalenergy delivered per unit mass of soil during an earthquake The corre-sponding approximate peak ground accelerations (PGA) are also indicatedon Figure 69 Similar to results of previous studies it can be observed thatgenerally higher EPWP ratios were predicted (on average) by MCS than bydeterministic analysis It is important to remark however that for the type ofsoil and input accelerations used in that study significant differences between

01 015 02 03025

Approx PGA (grsquos)

0 05 1 15

0 05 1 15

0

02

04

06

08

1

0

02

04

06

08

1

Arias Intensity (ms)

Arias Intensity (ms)

Ave

rage

EP

WP

rat

ioA

vera

ge E

PW

P r

atio

Deterministic

Stochastic - 2D

Stochastic - 3D

a

0 05 1 150

2

4

6

8

Arias Intensity (ms)

Max

imum

set

tlem

ent (

cm) Deterministic

Stochastic - 2D

Stochastic - 3D

c

Deterministic

Stochastic - 2D

Stochastic - 3D

b

Figure 69 Comparison between stochastic (2D and 3D) and deterministic analysis resultsin terms of predicted (a) EPWP ratios averaged over the entire analysis domain(b) EPWP ratios averaged over the maximal plane (c) maximum settlements(after Popescu et al 2005a)

Soils under static and dynamic loads 249

MCS and deterministic analyses occurred only for seismic inputs with AriasIntensity less than 1 ms (or PGA less than 025 g) while for strongerearthquakes the results were very similar It is obvious therefore that theldquoequivalent uniformrdquo soil strengths or characteristic percentiles developedfor liquefaction of heterogeneous soils are dependent on the seismic loadintensity

As discussed in Section 62 another important probabilistic characteris-tic of the spatial variability is the correlation distance Figure 69 indicatesthat MCS using full 3D analysis yielded practically the same results interms of average EPWP ratios as MCS using 2D analysis in conjunctionwith the plane strain assumption While the 3D analyses account for soilspatial variability in both horizontal directions in 2D plane strain calcula-tions the soil is assumed uniform in one horizontal direction (normal to theplane of analysis) and the correlation distance in that direction is there-fore considered to be infinite Therefore it can be concluded from theseresults that the value of horizontal correlation distance does not affect theaverage amount of EPWP build-up in this problem In contrast the hori-zontal correlation distance affects other factors of the response in this sameproblem as shown in Figure 69c displaying settlements Maximum set-tlements predicted by MCS using 3D analyses are about 40 larger thanthose resulting from MCS using 2D analyses A similar conclusion was alsoreached by Popescu (1995) based on a limited study on the effects of hori-zontal correlation distance on liquefaction of heterogeneous soils a fivefoldincrease in horizontal correlation distance did not change the predicted aver-age EPWP build-up but resulted in significant changes in the pattern ofliquefaction

653 Seismic response of structures on liquefiable randomheterogeneous soil

Some of the effects of inherently spatially variable soil properties on theseismic response of structures founded on potentially liquefiable soils havebeen studied by Popescu et al (2005b) who considered a tall structure ona saturated sand deposit The structure was modeled as a single degree-of-freedom oscillator with a characteristic frequency of 14 Hz corresponding toa seven-storey building The surrounding soil (and consequently the structuretoo) was subjected to a series of 100 earthquake acceleration time historiesscaled according to their Arias Intensities with the objective of establishingfragility curves for two response thresholds exceeding average settlementsof 20 cm and differential settlements of 5 cm These fragility curves areplotted in Figures 610a and 610b for two different assumptions related tosoil properties (1) variable soils with properties modeled by a random fieldwith coefficient of variation CV = 05 correlation distances θH = 8 m in thehorizontal direction and θV = 2 m in the vertical direction and gamma PDF

250 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

0 05 1 15 20

02

04

06

08

1

Arias Intensity (ms)

Pro

b th

at s

ettle

men

t exc

eeds

20

cm

Uniform soilVariable soil

a

0 05 1 15 20

02

04

06

08

1

Arias Intensity (ms)

Pro

bth

at d

iffs

ettle

men

t exc

eeds

5cm

b

Uniform soilVariable soil

Figure 610 Fragility curves for a tall structure on a saturated sand deposit Comparisonbetween results for variable soil and equivalent uniform soil in terms of (a)average settlements (b) differential settlements

(see Popescu et al 2005b for more details) and (2) corresponding uniformsoils

As can be easily observed in Figure 610 the fragility curves calculatedfor variable soil are shifted to the left compared to the ones for uniformsoil indicating that ndash for a given seismic intensity ndash there is a significantlyhigher probability of exceeding the response threshold when soil variabilityis accounted for than for an assumed corresponding uniform soil For exam-ple for a seismic ground motion with Arias Intensity of 1 ms (or PGA ofabout 025 g) a 62 exceedance probability of a 5 cm differential settle-ment is estimated for spatially variable soils versus only a 22 exceedanceprobability for corresponding uniform soils (Figure 610b)

The behavior at very large seismic intensities is very interesting As faras average settlements are concerned (Figure 610a) both fragility curvesconverge to the same probability (approximately unity) indicating that thedegree of liquefaction of the soil below structure is about the same for bothuniform and variable soils This is in agreement with the results presentedin Figures 69a and 69b for ground level soil In contrast the two fragilitycurves for differential settlements in Figure 610b are still far apart at verylarge Arias Intensities This suggets that ndash even if the computed degree ofliquefaction is about the same at the end of the seismic event for both uniformand variable soils ndash the initiation of liquefaction in variable soils takes placein isolated patches that strongly affect subsequent local soil deformationsand therefore differential settlements of foundations This is believed to bedue to lateral migration of pore water from loose soil zones that liquefy firsttowards denser soil pockets (as discussed in Section 644) leading eventuallyto local settlements that cannot be captured in an analysis assuming uniformsoil properties

Soils under static and dynamic loads 251

654 Static bearing capacity of shallow foundations onrandom heterogeneous soil

The effects of various probabilistic characteristics of soil variability on theBC of shallow foundations were studied by Popescu et al (2005c) in aparametric study involving the CV the PDF and the horizontal correla-tion distance of the random field modeling the spatial variability of soilstrength The layout of the foundation under consideration is the one shownin Figure 65 and the main assumptions made are mentioned in Section642 The finite element analyses involved in the MCS scheme were per-formed using ABAQUSStandard (Hibbitt et al 1998) Some of the resultsare presented in Figure 611 in terms of fragility curves expressing theprobability of BC failure of the strip foundation on randomly heteroge-neous soil as a function of the bearing pressure For comparison withresults of corresponding deterministic analyses the bearing pressures q inFigure 611 are normalized with respect to the ultimate BC obtained for

04 06 08 1 120

02

04

06

08

1

prob

abili

ty o

f BC

failu

re

Reference case CV=40 θHB=1Gamma distribution

Beta distribution

CV=10

normalized bearing pressure (qqudet)

qq u

det =

082

PF=5051

054

068 094

PF=50

qHB=4

Figure 611 Fragility curves illustrating the probability of bearing capacity failure of astrip foundation of width B on randomly heterogeneous soil as a functionof the bearing pressure q normalized with respect to the ultimate bearingcapacity qdet

u corresponding to uniform soil with undrained shear strengthcu = 100 kPa

252 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

the corresponding uniform soil qdetu Each fragility curve represents the

results of MCS involving 100 realizations of the spatial variability of soilstrength generated using a set of probabilistic characteristics describing soilvariability One of the sets ndash corresponding to CV = 40 θHB = 1 andgamma-distributed soil strength ndash is labeled ldquoreference caserdquo The other threecases shown in the figure are obtained by varying one parameter of the refer-ence case at a time It is mentioned that the method for building the fragilitycurves shown in Figure 611 is different from the traditional procedure usingBernoulli trials (eg Shinozuka et al 2000 Deodatis et al 2000) and isdescribed in detail in Popescu et al (2005c)

Figure 611 indicates that the two most important probabilistic character-istics of inherent soil heterogeneity ndash with respect to their effects on BCfailure ndash are the coefficient of variation (CV) and marginal PDF of soilstrength Both the CV and the PDF control the amount of loose pocketsin the soil mass Figure 612 demonstrates that the symmetrical beta distri-bution used in this study has a thicker left tail than the gamma distributionthat is positively skewed Therefore for the same CV the soil with beta-distributed shear strength exhibits a larger amount of soft pockets resultingin lower BC than the soil with gamma-distributed shear strength For therange considered in this study it was found that the horizontal correlationdistance θH does not affect significantly the average ultimate BC Howeverit appears that θH has a significant effect on the slope of the fragility curvesThe explanation of this behavior is believed to be related to averaging of the

0 50 100 150 200 250 300

Shear strength cu(kPa)

Mean value

cuav=100kPa

Beta

Gamma

Figure 612 The two models selected for the marginal PDF of the undrained shear strengthof soil Both PDFs have the same mean value (cav

u = 100 kPa) The coefficientof variation used for this plot is CV = 40 for both PDFs (after Popescu et al2005c)

Soils under static and dynamic loads 253

shear strength over the length of the failure surface larger horizontal corre-lation distances tend to diminish the averaging effect on the bearing capacityfailure mechanism and therefore yield larger variability of the results interms of ultimate BC

An equivalent undrained shear strength cu for uniform soils can be inferredfrom the fragility curves presented in Figure 611 in the following way Basedon the linear relation between ultimate BC and cu it is possible to define anapproximate equivalent uniform cu from the normalized pressures qqdet

ucorresponding to a probability of BC failure PF = 50 For example forthe type of soils and BVP analyzed in this example the equivalent uniformcu for the reference case is cEQ

u = 082cavu = 82 kPa (refer to Figure 611 and

remember that cavu = 100 kPa) Similar cEQ

u values can be inferred for theother cases illustrated in Figure 611 as listed in Table 61

Instead of an equivalent uniform cu corresponding to a failure probabil-ity of 50 most modern codes use nominal values of material strengthcorresponding to much lower failure probabilities (usually 5 or 10) Thedetermination of such nominal values for the BC of soil ndash denoted by quN ndashis demonstrated graphically in Figure 611 for a probability of BC fail-ure PF = 5 The resulting normalized nominal values are displayed inTable 61 Regarding the earlier discussion about the effect of the horizontalcorrelation distance θH Table 61 indicates that even if θH has little effect onthe average behavior of spatially variable soils (less than 4 change in cEQ

uwhen θHB varies from 1 to 4) it affects significantly the resulting nominalvalues (21 change in quN when θHB varies from 1 to 4)

More results such as those presented in Figure 611 and Table 61 includ-ing the effects of soil deformability on the BC and also the effects ofvarious probabilistic characteristics on differential settlements are presentedin Popescu et al (2005c)

66 Conclusions

Some distinctive features of spatially variable random heterogeneous soil andthe main geomechanical aspects governing its behavior under load have been

Table 61 Normalized equivalent uniform undrained shear strength and normalizednominal values for the cases illustrated in Figure 611

CV = 40 θHB = 1gamma PDF(reference case)

CV = 10θHB = 1gamma PDF

CV = 40θHB = 1beta PDF

CV = 40θHB = 4gamma PDF

cEQu cav

u 082 098 071 079

quNqdetu 068 094 051 054

254 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

analyzed and discussed for two types of soil mechanics problems seismicallyinduced soil liquefaction and bearing capacity of shallow foundations Forthe case of liquefaction it was shown that pore water pressures build up firstin the loose soil areas and then due to pressure gradients water is ldquoinjectedrdquointo neighboring dense sand In this way the strong pockets are softened andthe overall liquefaction resistance is reduced becoming considerably lowerthan that of the corresponding uniform soil For the case of geotechnicalproblems involving presence of a failure surface the spatial variability ofsoil properties can change dramatically the failure mechanism from the wellknown theoretical result obtained for uniform soils (eg the Prandtl ndash Reisnersolution for bearing capacity) to a failure surface that seeks the weakest pathfollowing predominantly the weak zones (pockets) in the soil mass Such asurface will have a more or less irregular shape and could differ significantlyfrom one possible realization of the spatial distribution of soil strength toanother

It was demonstrated that random soil heterogeneity has similar overalleffects (specifically inducing lower mechanical resistance) on phenomenathat are fundamentally different limit equilibrium type of failures involvinga large volume of soil ndash for BC and micro-scale pore pressure build-upmechanisms ndash for liquefaction Moreover for both phenomena addressedhere the loose zones (pockets) in the soil mass have a crucial and criticaleffect on the failure mechanisms (eg for soil liquefaction they are the initialsources of EPWP build-up while for BC the failure surface seeks the weakestpath) It also appears that random soil heterogeneity has a more pronouncedeffect for phenomena governed by highly nonlinear laws

An outcome of major importance of both experimental investigations andnumerical simulations discussed in this chapter is that the inherent spatialvariability of soil properties affects the fundamental nature of soil behav-ior leading to lower failure loads as compared to those for correspondinguniform soils Therefore besides variability in the computed response spa-tial variability of soil properties will also produce a response (eg failuremechanism or liquefaction pattern) that is in general different from thetheoretical response of the corresponding uniform soil On the other handother uncertainties considered in reliability analyses such as random mea-surement errors and transformation errors only induce variability in thecomputed response Consequently it is imperative that the effects of inherentsoil heterogeneity be estimated separately from those of other uncertaintiesand only after that combined for reliability assessments

For both problems discussed here it was found that the CV and marginalPDF of soil strength are the two most important parameters of spatial vari-ability in reducing liquefaction resistance or bearing capacity and producingsubstantial differential settlements This is due to the fact that both CV andPDF (more specifically the shape of its left tail) control the amount of loosepockets in the soil mass For the range of correlation distances considered

Soils under static and dynamic loads 255

in this study the horizontal correlation distance was found to have only aminor influence on overall liquefaction resistance or bearing capacity It wasfound however to significantly affect the variability of the response andtherefore the low fractiles and the nominal values

References

American Society for Testing and Materials (1989) Standard test method for deepquasi-static cone and friction-cone penetration tests of soil (D3441ndash86) In AnnualBook of Standards Vol 408 ASTM Philadelphia pp 414ndash19

Arias A (1970) A measure of earthquake intensity In Seismic Design for NuclearPower Plants Ed R J Hansen MIT Press Cambridge MA pp 438ndash83

Brenner C E (1991) Stochastic finite element methods literature review Techni-cal Report 35ndash91 Institute of Engineering Mechanics University of InnsbruckAustria

Brzakala W and Pula W (1996) A probabilistic analysis of foundation settlementsComputers and Geotechnics 18(4) 291ndash309

Budiman J S Mohammadi J and Bandi S (1995) Effect of large inclusionson liquefaction of sand In Proceedings Conference on Geotechnical Engr DivGeotechnical Special Publication 56 ASCE pp 48ndash63

Chakrabortty P Jafari-Mehrabadi A and Popescu R (2004) Effects of low per-meability soil layers on seismic stability of submarine slopes In Proceedings of the57th Canadian Geotechnical Conference Quebec City PQ on CD-ROM

Cherubini C (2000) Reliability evaluation of shallow foundation bearing capacityon cprime φprime soils Canadian Geotechnical Journal 37 264ndash9

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental EngineeringASCE 130(10) 985ndash1003

DeGroot D J and Baecher G B (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering ASCE 119(1) 147ndash66

Deodatis G (1989) Stochastic FEM sensitivity analysis of nonlinear dynamicproblems Probabilistic Engineering Mechanics 4(3) 135ndash41

Deodatis G (1996) Simulation of ergodic multi-variate stochastic processes Journalof Engineering Mechanics ASCE 122(8) 778ndash87

Deodatis G and Micaletti R (2001) Simulation of highly skewed non-Gaussian stochastic processes Journal of Engineering Mechanics ASCE 127(12)1284ndash95

Deodatis G Saxena V and Shinozuka M (2000) Effect of spatial variability ofground motion on bridge fragility curves In Proceedings of the 8th ASCE SpecialtyConference on Probabilistic Mechanics and Structural Reliability University ofNotre Dame IL on CD-ROM

Der Kiureghian A and Ke J B (1988) The stochastic finite element method instructural reliability Probabilistic Engineering Mechanics 3(2) 83ndash91

Elishakoff I (1983) Probabilistic Methods in the Theory of Structures WilleyElkateb T Chalaturnyk R and Robertson P K (2003) Simplified geostatistical

analysis of earthquake-induced ground response at the Wildlife Site CaliforniaUSA Canadian Geotechnical Journal 40 16ndash35

256 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

El-Ramly H Morgenstern N R and Cruden D (2002) Probabilistic slope stabilityanalysis for practice Canadian Geotechnical Journal 39 665ndash83

El-Ramly H Morgenstern N R and Cruden D (2005) Probabilistic assessmentof a cut slope in residual soil Geotechnique 55(2) 77ndash84

Fenton G A (1999a) Estimation for stochastic soil models Journal of Geotechnicaland Geoenvironmental Engineering 125(6) 470ndash85

Fenton G A (1999b) Random field modeling of CPT data Journal of Geotechnicaland Geoenvironmental Engineering 125(6) 486ndash98

Fenton G A and Griffiths D V (2003) Bearing capacity prediction of spatiallyrandom c ndash φ soils Canadian Geotechnical Journal 40 54ndash65

Fenton G A and VanMarcke E H (1998) Spatial variation in liquefaction riskGeotechnique 48(6) 819ndash31

Focht J A and Focht III J A (2001) Factor of safety and reliability in geotech-nical engineering (Discussion) Journal of Geotechnical and GeoenvironmentalEngineering ASCE 127(8) 704ndash6

Ghosh B and Madabhushi S P G (2003) Effects of localized soil inhomogeneityin modifying seismic soilndashstructure interaction In Proceeding of the 16th ASCEEngineering Mechanics Conference Seattle WA July

Griffths D V and Fenton G A (2004) Probabilistic slope stability analysis by finiteelements Journal of Geotechnical and Geoenvironmental Engineering ASCE130(5) 507ndash18

Griffths D V Fenton G A and Manoharan N (2002) Bearing capacity of roughrigid strip footing on cohesive soil probabilistic study Journal of Geotechnicaland Geoenvironmental Engineering ASCE 128(9) 743ndash55

Grigoriu M (1995) Applied non-Gaussian Processes Prentice-Hall EnglewoodCliffs NJ

Grigoriu M (1998) Simulation of stationary non-Gaussian translation processesJournal of Engineering Mechanics ASCE 124(2) 121ndash6

Gulf Canada Resources Inc (1984) Frontier development Molikpaq Tarsiutdelineation 1984ndash85 season Technical Report 84F012

Gurley K and Kareem A (1998) Simulation of correlated non-Gaussian pres-sure fields Meccanica-International the Journal of the Italian Association ofTheoretical and Applied Mechanics 33 309ndash17

Hibbitt Karlsson amp Sorensen Inc (1998) ABAQUS version 58 ndash TheoryManual Hibbitt Karlsson amp Sorensen Inc Pawtucket RI

Hicks M A and Onisiphorou C (2005) Stochastic evaluation of static liquefactionin a predominantly dilative sand fill Geotechnique 55(2) 123ndash33

Huoy L Breysse D and Denis A (2005) Influence of soil heterogeneity on loadredistribution and settlement of a hyperstatic three-support frame Geotechnique55(2) 163ndash70

Jaksa M B (2007) Modeling the natural variability of over-consolidated clay inAdelaide South Australia In Characterisation and Natural Properties of Nat-ural Soils Eds Tan Phoon Hight and Leroueil Taylor amp Francis Londonpp 2721ndash51

Jefferies M G and Davies M P (1993) Use of CPTu to estimate equivalent SPTN60 Geotechnical Testing Journal 16(4) 458ndash68

Konrad J-M and Dubeau S (2002) Cyclic strength of stratified soil samplesIn Proceedings of the 55th Canadian Geotechnical Conference Ground and

Soils under static and dynamic loads 257

Water Theory to Practice Canadian Geotechnical Society Alliston ON Octoberpp 89ndash94

Koutsourelakis S Prevost J H and Deodatis G (2002) Risk assessment of aninteracting structurendashsoil system due to liquefaction Earthquake Engineering andStructural Dynamics 31 851ndash79

Lacasse S and Nadim F (1996) Uncertainties in characteristic soil properties InProceedings Uncertainty in the Geologic Environment From Theory to PracticeASCE New York NY pp 49ndash75

Lumb P (1966) The variability of natural soils Canadian Geotechnical Journal3 74ndash97

Marcuson W F III (1978) Definition of terms related to liquefaction Journal ofthe Geotechnical Engineering Division ASCE 104(9) 1197ndash200

Masters F and Gurley K (2003) Non-Gaussian simulation CDF map-based spec-tral correction Journal of Engineering Mechanics ASCE 129(12) 1418ndash1428

Nobahar A and Popescu R (2000) Spatial variability of soil properties ndash effects onfoundation design In Proceedings of the 53rd Canadian Geotechnical ConferenceMontreal Queacutebec 2 pp 1139ndash44

Paice G M Griffiths G V and Fenton G A (1996) Finite element modeling ofsettlement on spatially random soil Journal of Geotechnical Engineering ASCE122 777ndash80

Phoon K-K (2006) Modeling and simulation of stochastic data In ProceedingsGeoCongress 2006 Geotechnical Engineering in the Information Technology Ageon CD-ROM

Phoon K-K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanadian Geotechnical Journal 36 612ndash24

Phoon K-K and Kulhawy F H (2001) Characterization of geotechnical vari-ability and Evaluation of geotechnical property variability (Reply) CanadianGeotechnical Journal 38 214ndash15

Phoon K K Huang H W and Quek S T (2005) Simulation of strongly non-Gaussian processes using KarhunenndashLoeve expansion Probabilistic EngineeringMechanics 20(2) 188ndash98

Popescu R (1995) Stochastic variability of soil properties data analysis digital sim-ulation effects on system behavior PhD Thesis Princeton University PrincetonNJ httpceeprincetonedusimradupapersphd

Popescu R (2004) Bearing capacity prediction of spatially random c ndash φ soils(Discussion) Canadian Geotechnical Journal 41 366ndash7

Popescu R Chakrabortty P and Prevost J H (2005b) Fragility curvesfor tall structures on stochastically variable soil In Proceedings of the 9thInternational Conference on Structural Safety and Reliability (ICOSSAR)Eds G Augusti G I Schueller and M Ciampoli Millpress Rotterdampp 977ndash84

Popescu R Deodatis R and Nobahar A (2005c) Effects of soil heterogeneity onbearing capacity Probabilistic Engineering Mechanics 20(4) 324ndash41

Popescu R Deodatis G and Prevost J H (1998b) Simulation of non-Gaussianhomogeneous stochastic vector fields Probabilistic Engineering Mechanics13(1) 1ndash13

Popescu R Prevost J H and Deodatis G (1996) Influence of spatial variabil-ity of soil properties on seismically induced soil liquefaction In Proceedings

258 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

Uncertainty in the Geologic Environment From Theory to Practice ASCEMadison Wisconsin pp 1098ndash112

Popescu R Prevost J H and Deodatis G (1997) Effects of spatial variability onsoil liquefaction some design recommendations Geotechnique 47(5) 1019ndash36

Popescu R Prevost J H and Deodatis G (1998a) Spatial variability of soil proper-ties two case studies In Geotechnical Earthquake Engineering and Soil DynamicsGeotechnical Special Publication No 75 ASCE Seattle pp 568ndash79

Popescu R Prevost J H and Deodatis G (1998c) Characteristic percentileof soil strength for dynamic analyses In Geotechnical Earthquake Engineer-ing and Soil Dynamics Geotechnical Special Publication No 75 ASCE Seattlepp 1461ndash71

Popescu R Prevost J H and Deodatis G (2005a) 3D effects in seismic liquefactionof stochastically variable soil deposits Geotechnique 55(1) 21ndash32

Popescu R Prevost J H Deodatis G and Chakrabortty P (2006) Dynamics ofporous media with applications to soil liquefaction Soil Dynamics and EarthquakeEngineering 26(6ndash7) 648ndash65

Prevost J H (1985) A simple plasticity theory for frictional cohesionless soils SoilDynamics and Earthquake Engineering 4(1) 9ndash17

Prevost J H (2002) DYNAFLOW ndash A nonlinear transient finite element analysisprogram Version 02 Technical Report Department of Civil and Environmen-tal Engineering Princeton University Princeton NJ httpwwwprincetonedusimdynaflow

Przewlocki J (2000) Two-dimensional random field of mechanical propertiesJournal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)373ndash7

Puig B Poirion F and Soize C (2002) Non-Gaussian simulation using hermitepolynomial expansion convergences and algorithms Probabilistic EngineeringMechanics 17(3) 253ndash64

Rackwitz R (2000) Reviewing probabilistic soil modeling Computers andGeotechnics 26 199ndash223

Robertson P K and Wride C E (1998) Evaluating cyclic liquefaction potentialusing the cone penetration test Canadian Geotechnical Journal 35(3) 442ndash59

Sakamoto S and Ghanem R (2002) Polynomial chaos decomposition for the simu-lation of non-Gaussian non-stationary stochastic processes Journal of EngineeringMechanics ASCE 128(2) 190ndash201

Schmertmann J H (1978) Use the SPT to measure soil properties ndash Yes buthellipIn Dynamic Geotechnical Testing (STP 654) ASTM Philadelphia pp 341ndash355

Shinozuka M and Dasgupta G (1986) Stochastic finite element methods in dynam-ics In Proceedings of the 3rd Conference on Dynamic Response of StructuresASCE UCLA CA pp 44ndash54

Shinozuka M and Deodatis G (1988) Simulation of multi-dimensional Gaussianstochastic fields by spectral representation Applied Mechanics Reviews ASME49(1) 29ndash53

Shinozuka M and Deodatis G (1996) Response variability of stochastic finiteelement systems Journal Engineering Mechanics ASCE 114(3) 499ndash519

Shinozuka M Feng M Q Lee J and Naganuma T (2000) Statistical analysisof fragility curves Journal of Engineering Mechanics ASCE 126(12) 1224ndash31

Soils under static and dynamic loads 259

Tang W H (1984) Principles of probabilistic characterization of soil propertiesIn Probabilistic Characterization of Soil Properties Bridge Between Theory andPractice ASCE Atlanta pp 74ndash89

VanMarcke E H (1977) Probabilistic modeling of soil profiles Journal ofGeotechnical Engineering Division ASCE 109(5) 1203ndash14

VanMarcke E H (1983) Random Fields Analysis and Synthesis The MIT PressCambridge MA

VanMarcke E H (1989) Stochastic finite elements and experimental measure-ments In International Conference on Computational Methods and ExperimentalMeasurements Capri Italy pp 137ndash155 Springer

Yamazaki F and Shinozuka M (1988) Digital generation of non-Gaussianstochastic fields Journal of Engineering Mechanics ASCE 114(7) 1183ndash97

Chapter 7

Stochastic finite elementmethods in geotechnicalengineering

Bruno Sudret and Marc Berveiller

71 Introduction

Soil and rock masses naturally present heterogeneity at various scales ofdescription This heterogeneity may be of two kinds

bull the soil properties can be considered piecewise homogeneous onceregions (eg layers) have been identified

bull no specific regions can be identified meaning that the spatial variabilityof the properties is smooth

In both cases the use of deterministic values for representing the soil char-acteristics is poor since it ignores the natural randomness of the mediumAlternatively this randomness may be modeled properly using probabilitytheory

In the first of the two cases identified above the material properties may bemodeled in each region as random variables whose distribution (and possiblymutual correlation) have to be specified In the second case the introductionof random fields is necessary Probabilistic soil modeling is a long-term storysee for example VanMarcke (1977) DeGroot and Baecher (1993) Fenton(1999ab) Rackwitz (2000) Popescu et al (2005)

Usually soil characteristics are investigated in order to feed models ofgeotechnical structures that are in project Examples of such structures aredams embankments pile or raft foundations tunnels etc The design thenconsists in choosing characterictics of the structure (dimensions materialproperties) so that it fulfills some requirements (eg retain water support abuilding etc) under a given set of environmental actions that we will callldquoloadingrdquo The design is carried out practically by satisfying some design cri-teria which usually apply onto model response quantities (eg displacementssettlements strains stresses etc) The conservatism of the design accordingto codes of practice is ensured first by introducing safety coefficients andsecond by using penalized values of the model parameters In this approachthe natural spatial variability of the soil is completely hidden

Stochastic finite element methods 261

From another point of view when the uncertainties and variability ofthe soil properties have been identified methods that allow propagationof these uncertainties throughout the model have to be used Classicallythe methods can be classified according to the type of information on the(random) response quantities they provide

bull The perturbation method allows computation of the mean value andvariance of the mechanical response of the system (Baecher and Ingra1981 Phoon et al 1990) This gives a feeling on the central part of theresponse probability density function (PDF)

bull Structural reliability methods allow investigation of the tails of theresponse PDF by computing the probability of exceedance of a pre-scribed threshold (Ditlevsen and Madsen 1996) Among these methodsFOSM (first-order second-moment methods) have been used in geotech-nical engineering (Phoon et al 1990 Mellah et al 2000 Eloseily et al2002) FORMSORM and importance sampling are applied less in thiscontext and have proven successful in engineering mechanics both inacademia and more recently in industry

bull Stochastic finite element (SFE) methods named after the pioneeringwork by Ghanem and Spanos (1991) aim at representing the completeresponse PDF in an intrinsic way This is done by expanding the response(which after proper discretization of the problem is a random vectorof unknown joint PDF) onto a particular basis of the space of randomvectors of finite variance called the polynomial chaos (PC) Applica-tions to geotechnical problems can be found in Ghanem and Brzkala(1996) Sudret and Der Kiureghian (2000) Ghiocel and Ghanem (2002)Clouteau and Lafargue (2003) Sudret et al (2004 2006) Berveiller et al(2006)

In the following we will concentrate on this last class of methods Indeedonce the coefficients of the expansion have been computed a straightfor-ward post-processing of these quantities gives the statistical moments of theresponse under consideration the probability of exceeding a threshold orthe full PDF

The chapter is organized as follows Section 72 presents methods forrepresenting random fields that are applicable for describing the spatial vari-ability of soils Section 73 presents the principles of polynomial chaos expan-sions for representing both the (random) model response and possibly thenon-Gaussian input Section 74 and (respectively 75) reviews the so-calledintrusive (respectively non-intrusive) solving scheme in stochastic finite ele-ment problems Section 76 is devoted to the practical post-processing ofpolynomial chaos expansions Finally Section 77 presents some applicationexamples

262 Bruno Sudret and Marc Berveiller

72 Representation of spatial variability

721 Basics of probability theory and notation

Probability theory gives a sound mathematical framework to the repre-sention of uncertainty and randomness When a random phenomenon isobserved the set of all possible outcomes defines the sample space denotedby An event E is defined as a subset of containing outcomes θ isin The set of events defines the σ -algebra F associated with A probabil-ity measure allows one to associate numbers to events ie their probabilityof occurrence Finally the probability space constructed by means of thesenotions is denoted by ( F P)

A real random variable X is a mapping X ( F P)minusrarrR For continuousrandom variables the PDF and cumulative distribution function (CDF) aredenoted by fX(x) and FX(x) respectively

FX(x) = P(X le x) fX(x) = dFX(x)dx

(71)

The mathematical expectation will be denoted by E[middot] The mean varianceand nth moment of X are

micro equiv E[X] =int infin

minusinfinxfX(x)dx (72)

σ 2 = E[(X minusmicro)2

]=int infin

minusinfin(x minusmicro)2 fX(x)dx (73)

microprimen = E

[Xn]= int infin

minusinfinxn fX(x)dx (74)

A random vector X is a collection of random variables whose probabilisticdescription is contained in its joint PDF denoted by fX(x) The covari-ance of two random variables X and Y (eg two components of a randomvector) is

Cov[X Y] = E[(X minusmicroX)(Y minusmicroY )

](75)

Introducing the joint distribution fXY (x y) of these variables Equation (75)can be rewritten as

Cov[X Y] =int infin

minusinfin

int infin

minusinfin(x minusmicroX)(y minusmicroY ) fXY (x y)dxdy (76)

The vectorial space of real random variables with finite second moment(E[X2]lt infin) is denoted by L2 ( F P) The expectation operator defines

Stochastic finite element methods 263

an inner product on this space

〈X Y〉 = E[XY] (77)

This allows in particuler definition of orthogonal random variables (whentheir inner product is zero)

722 Random fields

A unidimensional random field H(x θ ) is a collection of random variablesassociated with a continuous index x isin Ω sub Rn where θ isin is the coordi-nate in the outcome space Using this notation H(x θo) denotes a particularrealization of the field (ie a usual function mapping Ω into R) whereasH(xo θ ) is the random variable associated with point xo Gaussian ran-dom fields are of practical interest because they are completely describedby a mean function micro(x) a variance function σ 2(x) and an autocovariancefunction CHH(x xprime)

CHH(x xprime) = Cov[H(x) H(xprime)

](78)

Alternatively the correlation structure of the field may be prescribed throughthe autocorrelation coefficient function ρ(x xprime) defined as

ρ(x xprime) = CHH(x xprime)σ (x)σ (xprime)

(79)

Random fields are non-numerable infinite sets of correlated random vari-ables which is computationally intractable Discretizing the random fieldH(x) consists in approximating it by H(x) which is defined by means of afinite set of random variables χi i = 1 n gathered in a random vectordenoted by χ

H(x θ )Discretizationminusrarr H(x θ ) = G[x χ (θ )] (710)

Several methods have been developed since the 1980s to carry out this tasksuch as the spatial average method (VanMarcke and Grigoriu 1983) themidpoint method (Der Kiureghian and Ke 1988) and the shape functionmethod (W Liu et al 1986ab) A comprehensive review and compari-son of these methods is presented in Li and Der Kiureghian (1993) Theseearly methods are relatively inefficient in the sense that a large numberof random variables is required to achieve a good approximation of thefield

More efficient approaches for discretization of random fields using seriesexpansion methods have been introduced in the past 15 years includ-ing the KarhunenndashLoegraveve Expansion (KL) (Ghanem and Spanos 1991)

264 Bruno Sudret and Marc Berveiller

the Expansion Optimal Linear Estimation (EOLE) method (Li and DerKiureghian 1993) and the Orthogonal Series Expansion (OSE) (Zhang andEllingwood 1994) Reviews of these methods have been presented in Sudretand Der Kiureghian (2000) see also Grigoriu (2006) The KL and EOLEmethods are now briefly presented

723 KarhunenndashLoegraveve expansion

Let us consider a Gaussian random field H(x) defined by its mean valuemicro(x) and autocovariance function CHH(x xprime) = σ (x)σ (xprime)ρ(x xprime) TheKarhunenndashLoegraveve expansion of H(x) reads

H(x θ ) = micro(x) +infinsum

i=1

radicλi ξi(θ )ϕi(x) (711)

where ξi(θ ) i = 1 are zero-mean orthogonal variables andλi ϕi(x)

are solutions of the eigenvalue problemint

Ω

CHH(x xprime)ϕi(xprime)dΩxprime = λi ϕi(x) (712)

Equation (712) is a Fredholm integral equation Since kernel CHH(middot middot) isan autocovariance function it is bounded symmetric and positive definiteThus the set of ϕi forms a complete orthogonal basis The set of eigenvalues(spectrum) is moreover real positive and numerable In a sense Equation(711) corresponds to a separation of the space and randomness variables inH(x θ )

The KarhunenndashLoegraveve expansion possesses other interesting properties(Ghanem and Spanos 1991)

bull It is possible to order the eigenvalues λi in a descending series convergingto zero Truncating the ordered series (711) after the Mth term givesthe KL approximated field

H(x θ ) = micro(x) +Msum

i=1

radicλi ξi(θ )ϕi(x) (713)

bull The covariance eigenfunction basis ϕi(x) is optimal in the sense thatthe mean square error (integrated over Ω) resulting from a truncationafter the Mth term is minimized (with respect to the value it would takewhen any other complete basis hi(x) is chosen)

bull The set of random variables appearing in (711) is orthonormal if andonly if the basis functions hi(x) and the constants λi are solutions ofthe eigenvalue problem (712)

Stochastic finite element methods 265

bull As the random field is Gaussian the set of ξi are independent standardnormal variables Furthermore it can be shown (Loegraveve 1977) that theKarhunenndashLoegraveve expansion of Gaussian fields is almost surely conver-gent For non-Gaussian fields the KL expansion also exists howeverthe random variables appearing in the series are of unknown law andmay be correlated (Phoon et al 2002b 2005 Li et al 2007)

bull From Equation (713) the error variance obtained when truncating theexpansion after M terms turns out to be after basic algebra

Var[H(x) minus H(x)

]= σ 2(x) minus

Msumi=1

λi ϕ2i (x)

= Var[H(x)] minus Var[H(x)

]ge 0 (714)

The right-hand side of the above equation is always positive because itis the variance of some quantity This means that the KarhunenndashLoegraveveexpansion always underrepresents the true variance of the field Theaccuracy of the truncated expansion has been investigated in details inHuang et al (2001)

Equation (712) can be solved analytically only for few autocovari-ance functions and geometries of Ω Detailed closed form solutions fortriangular and exponential covariance functions for one-dimensionalhomogeneous fields can be found in Ghanem and Spanos (1991)Otherwise a numerical solution to the eigenvalue problem (712) canbe obtained (same reference chapter 2) Wavelet techniques have beenrecently applied for this purpose in Phoon et al (2002a) leading to afairly efficient approximation scheme

724 The EOLE method

The expansion optimal linear estimation method (EOLE) was proposed byLi and Der Kiureghian (1993) It is based on the pointwise regression ofthe original random field with respect to selected values of the field and acompaction of the data by spectral analysis

Let us consider a Gaussian random field as defined above and a grid ofpoints x1 xN in the domain Ω Let us denote by χ the random vectorH(x1) H(xN) By construction χ is a Gaussian vector whose mean valuemicroχ and covariance matrix χ χ read

microiχ = micro(xi) (715)(

χ χ

)ij

= Cov[H(xi) H(xj)

]= σ (xi)σ (xj)ρ(xi xj) (716)

266 Bruno Sudret and Marc Berveiller

The optimal linear estimation (OLE) of random variable H(x) onto therandom vector χ reads

H(x) asymp micro(x) +primeHχ (x) middot minus1

χ χ middot(χ minusmicroχ

)(717)

where ()prime denotes the transposed matrix and Hχ (x) is a vector whosecomponents are given by

jHχ

(x) = Cov[H(x)χj

]= Cov

[H(x) H(xj)

](718)

Let us now consider the spectral decomposition of the covariancematrix χχ

χχ φi = λi φi i = 1 N (719)

This allows one to linearly transform the original vector χ

χ (θ ) = microχ +Nsum

i=1

radicλi ξi(θ )φi (720)

where ξi i = 1 N are independent standard normal variables Substitut-ing for (720) in (717) and using (719) yields the EOLE representation ofthe field

H(x θ ) = micro(x) +Nsum

i=1

ξi(θ )radicλi

φiTH(x)χ (721)

As in the KarhunenndashLoegraveve expansion the series can be truncated after r le Nterms the eigenvalues λi being sorted first in descending order The varianceof the error for EOLE is

Var[H(x) minus H(x)

]= σ 2(x) minus

rsumi=1

1λi

(φT

i H(x)χ

)2(722)

As in KL the second term in the above equation is identical to the varianceof H(x) Thus EOLE also always underrepresents the true variance Due tothe form of (722) the error decreases monotonically with r the minimalerror being obtained when no truncation is made (r = N) This allows one todefine automatically the cut-off value r for a given tolerance in the varianceerror

Stochastic finite element methods 267

73 Polynomial chaos expansions

731 Expansion of the model response

Having recognized that the input parameters such as the soil propertiescan be modeled as random fields (which are discretized using standardnormal random variables) it is clear that the response of the system isa nonlinear function of these variables After a discretization procedure(eg finite element or finite difference scheme) the response may be con-sidered as a random vector S whose probabilistic properties are yet to bedetermined

Due to the above representation of the input it is possible to expand theresponse S onto the so-called polynomial chaos basis which is a basis of thespace of random variables with finite variance (Malliavin 1997)

S =infinsum

j=0

Sj Ψj

(ξn

infinn=1

)(723)

In this expression the Ψjrsquos are the multivariate Hermite polynomials definedby means of the ξnrsquos This basis is orthogonal with respect to the Gaussianmeasure ie the expectation of products of two different such polynomialsis zero (see details in Appendix A)

Computationnally speaking the input parameters are represented usingM independent standard normal variables see Equations (713) and (721)Considering all M-dimensional Hermite polynomials of degree not exceedingp the response may be approximated as follows

S asympPminus1sumj=0

Sj Ψj (ξ ) ξ = ξ1 ξM (724)

The number of unknown (vector) coefficients in this summation is

P =(

M + pp

)= (M + p)

M p (725)

The practical construction of a polynomial chaos of order M and degree pis described in Appendix A The problem is now recast as computing theexpansion coefficients Sj j = 0 P minus 1 Two classes of methods arepresented below in Sections 74 and 75

732 Representation of non-Gaussian input

In Section 72 the representation of the spatial variability through Gaussianrandom fields has been shown It is important to note that many soil

268 Bruno Sudret and Marc Berveiller

properties should not be modeled as Gaussian random variables or fieldsFor instance the Poisson ratio is a bounded quantity whereas Gaussianvariables are defined on R Parameters such as the Youngrsquos modulus orthe cohesion are positive in nature modeling them as Gaussian intro-duces an approximation that should be monitored carefully Indeed whena large dispersion of the parameter is considered choosing a Gaussianrepresentation can easily lead to negative realizations of the parameterwhich have no physical meaning (lognormal variables or fields are oftenappropriate)

As a consequence if the parameter under consideration is modeled bya non-Gaussian random field it is not possible to expand it as a linearexpression in standard normal variables as in Equations (713) and (721)Easy-to-define non-Gaussian random fields H(x) are obtained by translationof a Gaussian field N(x) using a nonlinear transform h()

H(x) = h(N(x)) (726)

The discretization of this kind of field is straightforward the nonlinear trans-form h() is directly applied to the discretized underlying Gaussian field N(x)(see eg Ghanem 1999 for lognormal fields)

H(x) = h(N(x)) (727)

From another point of view the description of the spatial variability ofparameters is in some cases beyond the scope of the analysis For instancesoil properties may be considered homogeneous in some domains Theseparameters are not well known though and it may be relevant to modelthem as (usually non-Gaussian) random variables

It is possible to transform any continuous random variable with finite vari-ance into a standard normal variable using the iso-probabilistic transformdenoting by FX(x) (respectively (x)) the CDF of X (respectively a standardnormal variable ξ ) the direct and inverse transform read

ξ = minus1 FX(x) X = Fminus1X (ξ ) (728)

If the input parameters are modeled by a random vector with independentcomponents it is possible to represent it using a standard normal randomvector of the same size by applying the above transform componentwiseIf the input random vector has a prescribed joint PDF it is generally not pos-sible to transform it exactly into a standard normal random vector Howeverwhen only marginal PDF and correlations are known an approximate repre-sentation may be obtained by the Nataf transform (Liu and Der Kiureghian1986)

As a conclusion the input parameters of the model which do ordo not exhibit spatial variability may always be represented after some

Stochastic finite element methods 269

discretization process mapping or combination thereof as functionals ofstandard normal random variables

bull for non-Gaussian independent random variables see Equation (728)bull for Gaussian random fields see Equations(713) and (721)bull for non-Gaussian random fields obtained by translation see

Equation (727)

Note that Equation (728) is an exact representation whereas the fielddiscretization techniques provide only approximations (which converge tothe original field if the number of standard normal variables tends toinfinity)

In the sequel we consider that the discretized input fields and non Gaussianrandom variables are represented through a set of independent standardnormal variables ξ of size M and we denote by X(ξ ) the functional thatyields the original variables and fields

74 Intrusive SFE method for static problems

The historical SFE approach consists in computing the polynomial chaoscoefficients of the vector of nodal displacements U(θ ) (Equation (724)) Itis based on the minimization of the residual in the balance equation in theGalerkin sense (Ghanem and Spanos 1991) To illustrate this method letus consider a linear mechanical problem whose finite element discretizationleads to the following linear system (in the deterministic case)

K middot U = F (729)

Let us denote by Nddl the number of degrees of freedom of the structure iethe size of the above linear system If the material parameters are describedby random variables and fields the stiffness matrix K in the above equa-tion becomes random Similarly the load vector F may be random Thesequantities can be expanded onto the polynomial chaos basis

K =infinsum

j=0

KjΨj (730)

F =infinsum

j=0

FjΨj (731)

In these equations Kj are deterministic matrices whose complete descriptioncan be found elsewhere (eg Ghanem and Spanos (1991) in the case when theinput Youngrsquos modulus is a random field and Sudret et al (2004) when theYoungrsquos modulus and the Poisson ratio are non-Gaussian random variables)

270 Bruno Sudret and Marc Berveiller

In the same manner Fj are deterministic load vectors obtained from the data(Sudret et al 2004 Sudret et al 2006)

As a consequence the vector of nodal displacements U is random and maybe represented on the same basis

U =infinsum

j=0

UjΨj (732)

When the three expansions (730)ndash(732) are truncated after P terms andsubstituted for in Equation (729) the residual εP in the stochastic balanceequation reads

εP =(

Pminus1sumi=0

KiΨi

)middot⎛⎝Pminus1sum

j=0

UjΨj

⎞⎠minus

Pminus1sumj=0

FjΨj (733)

Coefficients U0 UPminus1 are obtained by minimizing the residual using aGalerkin technique This minimization is equivalent to requiring the residualbe orthogonal to the subspace of L2(FP) spanned by ΨjPminus1

j=0

E[εPΨk

]= 0 k = 0 P minus 1 (734)

After some algebra this leads to the following linear system whose size isequal to Nddl times P

⎛⎜⎜⎜⎝

K00 middot middot middot K0Pminus1K10 middot middot middot K1Pminus1

KPminus10 middot middot middot KPminus1Pminus1

⎞⎟⎟⎟⎠ middot

⎛⎜⎜⎜⎝

U0U1

UPminus1

⎞⎟⎟⎟⎠=

⎛⎜⎜⎜⎝

F0F1

FPminus1

⎞⎟⎟⎟⎠ (735)

where Kjk =Pminus1sumi=0

dijk Ki and dijk = E[ΨiΨjΨk]Once the system has been solved the coefficients Uj may be post-processed

in order to represent the response PDF (eg by Monte Carlo simulation)to compute the mean value standard deviation and higher moments or toevaluate the probability of exceeding a given threshold The post-processingtechniques are detailed in Section 76 It is important to note already thatthe set of Ujrsquos contains all the probabilistic information on the responsemeaning that post-processing is carried out without additional computationon the mechanical model

Stochastic finite element methods 271

The above approach is easy to apply when the mechanical model is linearAlthough nonlinear problems have been recently addressed (Matthies andKeese 2005 Nouy 2007) their treatment is still not completely matureMoreover this approach naturally yields the expansion of the basic responsequantities (such as the nodal displacements in mechanics) When derivedquantities such as strains or stresses are of interest additional work (andapproximations) is needed Note that in the case of non-Gaussian inputrandom variables expansion of these variables onto the PC basis is neededin order to apply the method which introduces an approximation of theinput Finally the implementation of the historical method as described inthis section has to be carried out for each class of problem this is why it hasbeen qualified as intrusive in the literature All these reasons have led to thedevelopment of so-called non-intrusive methods that in some sense providean answer to the above drawbacks

75 Non-intrusive SFE methods

751 Introduction

Let us consider a scalar response quantity S of the model under considerationeg a nodal displacement strain or stress component in a finite elementmodel

S = h(X) (736)

Contrary to Section 74 each response quantity of interest is directlyexpanded onto the polynomial chaos as follows

S =infinsum

j=0

Sj Ψj (737)

The P-term approximation reads

S =Pminus1sumj=0

Sj Ψj (738)

Two methods are now proposed to compute the coefficients in this expansionfrom a series of deterministic finite element analysis

752 Projection method

The projection method is based on the orthogonality of the polynomial chaos(Le Maicirctre et al 2002 Ghiocel and Ghanem 2002) By pre-multiplying

272 Bruno Sudret and Marc Berveiller

Equation (738) by Ψi and taking the expectation of both members it comes

E[SΨj

]= E

[ infinsumi=0

Sj Ψi Ψj

](739)

Due to the orthogonality of the basis E[Ψi Ψj

]= 0 for any i = j Thus

Sj =E[SΨj

]E[Ψ 2

j

] (740)

In this expression the denominator is known analytically (see Appendix A)and the numerator may be cast as a multidimensional integral

E[SΨj

]=int

RMh(X(ξ ))Ψj(ξ )ϕM(ξ )dξ (741)

where ϕM is the M-dimensional multinormal PDF and where the dependencyof S in ξ through the iso-probabilistic transform of the input parameters X(ξ )has been given for the sake of clarity

This integral may be computed by crude Monte Carlo simulation (Field2002) or Latin Hypercube Sampling (Le Maicirctre et al 2002) Howeverthe number of samples required in this case should be large enough say10000ndash100000 to obtain a sufficient accuracy In cases when the responseS is obtained by a computationally demanding finite element model thisapproach is practically not applicable Alternatively the use of quasi-randomnumbers instead of Monte Carlo (Niederreiter 1992) simulation has beenrecently investigated in Sudret et al (2007) and appears promising

An alternative approach presented in Berveiller et al (2004) and Matthiesand Keese (2005) is the use of a Gaussian quadrature scheme to evaluatethe integral Equation (741) is computed as a weighted summation of theintegrands evaluated at selected points (the so-called integration points)

E[SΨj

]asymp

Ksumi1=1

middot middot middotKsum

iM=1

ωi1 ωiM

h(X(ξi1

ξiM

))Ψj

(ξi1

ξiM

)(742)

In this expression the integration points ξij 1 le i1 le middotmiddot middot le iM le K and

weights ωij 1 le i1 le middotmiddot middot le iM le K in each dimension are computed using

the theory of orthogonal polynomials with respect to the Gaussian measureFor a Kth order scheme the integration points are the roots of the Kth orderHermite polynomial (Abramowitz and Stegun 1970)

Stochastic finite element methods 273

The proper order of the integration scheme K is selected as follows ifthe response S in Equation (737) was polynomial of order p (ie Sj = 0 forj ge P) the terms in the integral (741) would be of degree less than or equalto 2p Thus an integration scheme of order K = p + 1 would give the exactvalue of the expansion coefficients We take this as a rule in the general casewhere the result now is only an approximation of the true value of Sj

As seen from Equations (740)(742) the projection method allows one tocompute the expansion coefficients from selected evaluations of the modelThus the method is qualified as non-intrusive since the deterministic com-putation scheme (ie a finite element code) is used without any additionalimplementation or modification

Note that in finite element analysis the response is usually a vector (eg ofnodal displacements nodal stresses etc) The above derivations are strictlyvalid for a vector response S the expectation in Equation (742) beingcomputed component by component

753 Regression method

The regression method is another approach for computing the responseexpansion coefficients It is nothing but the regression of the exact solu-tion S with respect to the polynomial chaos basis Ψj(ξ ) j = 1 P minus 1Let us assume the following expression for a scalar response quantity S

S = h(X) = S(ξ ) + ε S(ξ ) =Pminus1sumj=0

Sj Ψj(ξ ) (743)

where the residual ε is supposed to be a zero-mean random variable andS = Sj j = 0 P minus 1 are the unknown coefficients The minimizationof the variance of the residual with respect to the unknown coefficientsleads to

S = ArgminE[(h(X (ξ )) minus S(ξ ))2

](744)

In order to solve Equation (744) we choose a set of Q regression points inthe standard normal space say ξ1 ξQ From these points the isoprob-abilistic transform (728) gives a set of Q realizations of the input vector Xsay x1 xQ The mean-square minimization (744) leads to solve thefollowing problem

S = Argmin1Q

Qsumi=1

⎧⎨⎩h(xi) minus

Pminus1sumj=0

Sj Ψj(ξi)

⎫⎬⎭

2

(745)

274 Bruno Sudret and Marc Berveiller

Denoting by Ψ the matrix whose coefficients are given by Ψ ij = Ψj(ξi) i =

1 Q j = 0 Pminus1 and by Sex the vector containing the exact responsevalues computed by the model Sex = h(xi) i = 1 Q the solution toEquation (745) reads

S = (Ψ T middotΨ )minus1 middotΨ T middotSex (746)

The regression approach detailed above is comparable with the so-calledresponse surface method used in many domains of science and engineeringIn this context the set of x1 xQ is the so-called experimental design InEquation (746) Ψ T middotΨ is the information matrix Computationally speak-ing it may be ill-conditioned Thus a particular solver such as the SingularValue Decomposition method should be employed (Press et al 2001)

It is now necessary to specify the choice of the experimental design Start-ing from the early work by Isukapalli (1999) it has been shown in Berveiller(2005) and Sudret (2005) that an efficient design can be built from the rootsof the Hermite polynomials as follows

bull If p denotes the maximal degree of the polynomials in the truncated PCexpansion then the p + 1 roots of the Hermite polynomial of degreep + 1 (denoted by Hep+1) are computed say r1 rp+1

bull From this set M-uplets are built using all possible combinations of theroots rk = (ri1

riM) 1 le i1 le middotmiddot middot le iM le p+1 k = 1 (p+1)M

bull The Q points in the experimental design ξ1 ξQ are selected amongthe r jrsquos by retaining those which are closest to the origin of the spaceie those with the smallest norm or equivalently those leading to thelargest values of the PDF ϕM(ξ j)

To choose the size Q of the experimental design the following empiricalrule was proposed by Berveiller (2005) based on a large number of numericalexperiments

Q = (M minus 1)P (747)

A slightly more efficient rule leading to a smaller value of Q has beenrecently proposed by Sudret (2006 2008) based on the invertibility of theinformation matrix

76 Post-processing of the SFE results

761 Representation of response PDF

Once the coefficients Sj of the expansion of a response quantity areknown the polynomial approximation can be simulated using Monte

Stochastic finite element methods 275

Carlo simulation A sample of standard normal random vector is generatedsay ξ (1) ξ (n) Then the PDF can be plotted using a histogram rep-resentation or better kernel smoothing techniques (Wand and Jones1995)

762 Computation of the statistical moments

From Equation (738) due to the orthogonality of the polynomial chaosbasis it is easy to see that the mean and variance of S is given by

E[S] = S0 (748)

Var[S] = σ 2S =

Pminus1sumj=1

S2j E[Ψ 2

j

](749)

where the expectation of Ψ 2j is given in Appendix A Moments of higher

order are obtained in a similar manner Namely the skewness and kurtosiscoefficients of response variable S (denoted by δS and κS respectively) areobtained as follows

δS equiv 1

σ 3S

E[(S minus E[S])3

]= 1

σ 3S

Pminus1sumi=1

Pminus1sumj=1

Pminus1sumk=1

E[ΨiΨjΨk]Si Sj Sk (750)

κS equiv 1

σ 4S

E[(S minus E[S])4

]= 1

σ 4S

Pminus1sumi=1

Pminus1sumj=1

Pminus1sumk=1

Pminus1suml=1

E[ΨiΨjΨkΨl]Si Sj Sk Sl (751)

Here again expectation of products of three (respectively four) Ψjrsquos areknown analytically see for example Sudret et al (2006)

763 Sensitivity analysis selection of important variables

The problem of selecting the most ldquoimportantrdquo input variables of a modelis usually known as sensitivity analysis In a probabilistic context meth-ods of global sensitivity analysis aim at quantifying which input variable(or combination of input variables) influences most the response variabil-ity A state-of-the-art of such techniques is available in Saltelli et al (2000)They include regression-based methods such as the computation of standard-ized regression coefficients (SRC) or partial correlation coefficients (PCC)and variance-based methods also called ANOVA techniques for ldquoANalysisOf VAriancerdquo In this respect the Sobolrsquo indices (Sobolrsquo 1993 Sobolrsquo andKucherenko 2005) are known as the most efficient tool to find out theimportant variables of a model

The computation of Sobolrsquo indices is traditionnally carried out by MonteCarlo simulation (Saltelli et al 2000) which may be computationally

276 Bruno Sudret and Marc Berveiller

unaffordable in case of time-consuming models In the context of stochasticfinite element methods it has been recently shown in Sudret (2006 2008)that the Sobolrsquo indices can be derived analytically from the coefficients of thepolynomial chaos expansion of the response once the latter have been com-puted by one of the techniques detailed in Sections 74 and 75 For instancethe first order Sobolrsquo indices δi i = 1 M which quantify what fractionof the response variance is due to each input variable i = 1 M

δi = VarXi

[E[S|Xi

]]Var[S]

(752)

can be evaluated from the coefficients of the PC expansion (Equation (738))as follows

δPCi =

sumαisinIi

S2α E[Ψ 2

α

]σ 2

S (753)

In this equation σ 2S is the variance of the model response computed from

the PC coefficients (Equation (749)) and the summation set (defined usingthe multi-index notation detailed in Appendix) reads

Ii =α αi gt 0αj =i = 0

(754)

Higher-order Sobolrsquo indices which correspond to interactions of theinput parameters can also be computed using this approach see Sudret(2006) for a detailed presentation and an application to geotechnicalengineering

764 Reliability analysis

Structural reliability analysis aims at computing the probability of failure of amechanical system with respect to a prescribed failure criterion by account-ing for uncertainties arising in the model description (geometry materialproperties) or the environment (loading) It is a general theory whose devel-opment began in the mid 1970s The research on this field is still active ndash seeRackwitz (2001) for a review

Surprisingly the link between structural reliability and the stochastic finiteelement methods based on polynomial chaos expansions is relatively new(Sudret and Der Kiureghian 2000 2002 Berveiller 2005) For the sake ofcompleteness three essential techniques for solving reliability problems arereviewed in this section Then their application together with (a) a deter-minitic finite element model and (b) a PC expansion of the model responseis detailed

Stochastic finite element methods 277

Problem statement

Let us denote by X = X1X2 XM

the set of random variables describing

the randomness in the geometry material properties and loading This setalso includes the variables used in the discretization of random fields if anyThe failure criterion under consideration is mathematically represented by alimit state function g(X) defined in the space of parameters as follows

bull g(X) gt 0 defines the safe state of the structurebull g(X) le 0 defines the failure statebull g(X) = 0 defines the limit state surface

Denoting by fX(x) the joint PDF of random vector X the probability offailure of the structure is

Pf =int

g(x)le0

fX(x) dx (755)

In all but academic cases this integral cannot be computed analyticallyIndeed the failure domain is often defined by means of response quantities(eg displacements strains stresses etc) which are computed by means ofcomputer codes (eg finite element code) in industrial applications mean-ing that the failure domain is implicitly defined as a function of X Thusnumerical methods have to be employed

Monte Carlo simulation

Monte Carlo simulation (MCS) is a universal method for evaluating integralssuch as Equation (755) Denoting by 1[g(x)le0](x) the indicator function ofthe failure domain (ie the function that takes the value 0 in the safe domainand 1 in the failure domain) Equation (755) rewrites

Pf =int

RM

1[g(x)le0](x) fX(x) dx = E[1[g(x)le0](x)

](756)

where E[] denotes the mathematical expectation Practically Equation(756) can be evaluated by simulating Nsim realizations of the random

vector X sayX (1) X(Nsim)

For each sample g

(X(i))

is evaluated

An estimation of Pf is given by the empirical mean

Pf = 1Nsim

Nsimsumi=1

1[g(x)le0](X(i)) = Nfail

Nsim(757)

278 Bruno Sudret and Marc Berveiller

where Nfail denotes the number of samples that are in the failure domainAs mentioned above MCS is applicable whatever the complexity of thedeterministic model However the number of samples Nsim required to get anaccurate estimation of Pf may be dissuasive especially when the value of Pf issmall Indeed if the order of magnitude of Pf is about 10minusk a total numberNsim asymp 410k+2 is necessary to get accurate results when using Equation(757) This number corresponds approximately to a coefficient of variationCV equal to 5 for the estimator Pf Thus crude MCS is not applicablewhen small values of Pf are sought andor when the CPU cost of each runof the model is non-negligible

FORM method

The first-order reliability method (FORM) has been introduced to get anapproximation of the probability of failure at a low cost (in terms of numberof evaluations of the limit state function)

The first step consists in recasting the problem in the standard nor-mal space by using a iso-probabilistic transformation X rarr ξ = T (X) TheRosenblatt or Nataf transformations may be used for this purpose ThusEquation (756) rewrites

Pf =int

g(x)le0

fX(x) dx =int

g(Tminus1(ξ ))le0

ϕM(ξ ) dξ (758)

where ϕM (ξ ) stands for the standard multinormal PDF

ϕM(ξ ) = 1(radic2π)n exp

(minus1

2

(ξ2

1 +middotmiddot middot+ ξ2M

))(759)

This PDF is maximal at the origin and decreases exponentially with ξ2Thus the points that contribute at most to the integral in Equation (758)are those of the failure domain that are closest to the origin of the space

The second step in FORM thus consists in determining the so-called designpoint ie the point of the failure domain closest to the origin in the stan-dard normal space This point Plowast is obtained by solving an optimizationproblem

Plowast = ξlowast = Argminξ2 g

(Tminus1(ξ )

)le 0

(760)

Several algorithms are available to solve the above optimisation problemeg the AbdondashRackwitz (Abdo and Rackwitz 1990) or the SQP (sequential

Stochastic finite element methods 279

quadratic programming) algorithm The corresponding reliability index isdefined as

β = sign[g(Tminus1(0))

]middot∥∥ξlowast∥∥ (761)

It corresponds to the algebraic distance of the design point to the origincounted as positive if the origin is in the safe domain or negative in theother case

The third step of FORM consists in replacing the failure domain by thehalf space HS(Plowast) defined by means of the hyperplane which is tangent tothe limit state surface at the design point (see Figure 71) This leads to

Pf =int

g(Tminus1(ξ ))le0

ϕM(ξ ) dξ asympint

HS(Plowast)

ϕM(ξ ) dξ (762)

The latter integral can be evaluated in a closed form and gives the firstorder approximation of the probability of failure

Pf asymp Pf FORM = (minusβ) (763)

where (x) denotes the standard normal CDF The unit normal vectorα = ξlowastβ allows definition of the sensitivity of the reliability index withrespect to each variable Precisely the squared components α2

i of α (whichsum to one) are a measure of the importance of each variable in the computedreliability index

g(ξ)=0

Failure Domain

x2

x2 P

x1 x1

HS(P)

β

α

Figure 71 Principle of the first-order reliability method (FORM)

280 Bruno Sudret and Marc Berveiller

Importance sampling

FORM as described above gives an approximation of the probability offailure without any measure of its accuracy contrary to Monte Carlo sim-ulation which provides an estimator of Pf together with the coefficient ofvariation thereof Importance sampling (IS) is a technique that allows tocombine both approaches First the expression of the probability of failureis modified as follows

Pf =int

RMIDf

(X(ξ ))ϕM (ξ )dξ (764)

where IDf(X(ξ )) is the indicator function of the failure domain Let us intro-

duce the sampling density (ξ ) in the above equation which may be anyvalid M-dimensional PDF

Pf =int

RMIDf

(X(ξ ))ϕM (ξ ) (ξ )

(ξ )dξ = E

[IDf

(X(ξ ))ϕM (ξ ) (ξ )

](765)

where E [] denotes the expectation with respect to the sampling density (ξ )To smartly apply IS after a FORM analysis the following sampling densityis chosen

(ξ ) = (2π )minusM2 exp(

minus12

∥∥ξ minus ξlowast∥∥2)

(766)

This allows one to concentrate the sampling around the design point ξlowastThen the following estimator of the probability of failure is computed

Pf IS = 1Nsim

Nsimsumi=1

1Df

(X(ξ (i))

)ϕM

(ξ (i))

(ξ (i)) (767)

which may be rewritten as

Pf IS = exp[minusβ22]Nsim

Nsimsumi=1

1Df

(ξ (i))

exp[minusξ (i) middot ξlowast] (768)

As in any simulation method the coefficient of variation CV of this estimatorcan be monitored all along the simulation Thus the process can be stoppedas soon as a small value of CV say less than 5 is obtained As the samplesare concentrated around the design point a limited number of samples say100ndash1000 is necessary to obtain this accuracy whatever the value of theprobability of failure

Stochastic finite element methods 281

765 Reliability methods coupled with FESFE models

The reliability methods (MCS FORM and IS) described in the section aboveare general ie not limited to stochastic finite element methods They canactually be applied whatever the nature of the model may it be analyticalor algorithmic

bull When the model is a finite element model a coupling between the reli-ability algorithm and the finite element code is necessary Each time thealgorithm requires the evaluation of the limit state function the finiteelement code is called with the current set of input parameters Then thelimit state function is evaluated This technique is called direct couplingin the next section dealing with application examples

bull When an SFE model has been computed first the response is approxi-mately represented as a polynomial series in standard normal randomvariables (Equation (737)) This is an analytical function that can nowbe used together with any of the reliability methods mentioned above

In the next section several examples are presented In each case when areliability problem is addressed the direct coupling and the post-processingof a PC expansion are compared

77 Application examples

The application examples presented in the sequel have been originally pub-lished elsewhere namely in Sudret and Der Kiureghian (2000 2002) for thefirst example Berveiller et al (2006) for the second example and Berveilleret al (2004) for the third example

771 Example 1 Foundation problem ndash spatialvariability

Description of the deterministic problem

Consider an elastic soil layer of thickness t lying on a rigid substratumA superstructure to be founded on this soil mass is idealized as a uniformpressure P applied over a length 2B of the free surface (see Figure 72) Thesoil is modeled as an elastic linear isotropic material A plane strain analysisis carried out

Due to the symmetry half of the structure is modeled by finite elementsStrictly speaking there is no symmetry in the system when random fields ofmaterial properties are introduced However it is believed that this simpli-fication does not significantly influence the results The parameters selectedfor the deterministic model are listed in Table 71

282 Bruno Sudret and Marc Berveiller

Table 71 Example 1 ndash Parameters of the deterministic model

Parameter Symbol Value

Soil layer thickness t 30 mFoundation width 2B 10 mApplied pressure P 02 MPaSoil Youngrsquos modulus E 50 MPaSoil Poissonrsquos ratio ν 03Mesh width L 60 m

A

2B

tE n

Figure 72 Settlement of a foundation ndash problem definition

(a) Mesh (b) Deformed Shape

Figure 73 Finite element mesh and deformed shape for mean values of the parameters bya deterministic analysis

A refined mesh was first used to obtain the ldquoexactrdquo maximum displacementunder the foundation (point A in Figure 72) Less-refined meshes were thentried in order to design a mesh with as few elements as possible that yieldedno more than 1 error in the computed maximum settlement The meshdisplayed in Figure 73(a) was eventually chosen It contains 99 nodes and80 elements The maximum settlement computed with this mesh is equalto 542 cm

Stochastic finite element methods 283

Description of the probabilistic data

The assessment of the serviceability of the foundation described in theabove paragraph is now investigated under the assumption that the Youngrsquosmodulus of the soil mass is spatially varying

The Youngrsquos modulus of the soil is considered to vary only in the verticaldirection so that it is modeled as a one-dimensional random field along thedepth This is a reasonable model for a layered soil medium The field isassumed to be lognormal and homogeneous Its second-moment propertiesare considered to be the mean microE = 50 MPa the coefficient of variationδE = σEmicroE = 02 The autocorrelation coefficient function of the underlyingGaussian field (see Equation (726)) is ρEE(z zprime) = exp(minus|z minus zprime| ) where zis the depth coordinate and = 30 m is the correlation length

The accuracy of the discretization of the underlying Gaussian field N(x)depends on the number of terms M retained in the expansion For eachvalue of M a global indicator of the accuracy of the discretization ε iscomputed from

ε = 1|Ω|intΩ

Var[N(x) minus N(x)

]Var[N(x)]

dΩ (769)

A relative accuracy in the variance of 12 (respectively 8 6) is obtainedwhen using M = 2 (respectively 3 4) terms in the KL expansion of N(x)Of course these values are closely related to the parameters defining the ran-dom field particularly the correlation length As is comparable here to thesize of the domain Ω an accurate discretization is obtained using few terms

Reliability analysis

The limit state function is defined in terms of the maximum settlement uA atthe center of the foundation

g(ξ ) = u0 minus uA(ξ ) (770)

where u0 is an admissible threshold initially set equal to 10 cm and ξ is thevector used for the random field discretization

Table 72 reports the results of the reliability analysis carried out either bydirect coupling between the finite element model and the FORM algorithm(column 2) or by the application of FORM after solving the SFE problem(column 6 for various values of p) Both results have been validated usingimportance sampling (columns 3 and 7 respectively) In the direct cou-pling approach 1000 samples (corresponding to 1000 deterministic FE runs)were used leading to a coefficient of variation of the simulation less than 6In the SFE approach the polynomial chaos expansion of the response is used

284 Bruno Sudret and Marc Berveiller

Table 72 Example 1 ndash Reliability index β ndash Influence of the orders of expansion M and p(u0 = 10cm)

M βFORMdirect β IS

direct p P βFORMSFE β IS

SFE

2 3452 3433 2 6 3617 36133 10 3474 3467

3 3447 3421 2 10 3606 35973 20 3461 3461

4 3447 3449 2 15 3603 35923 35 3458 3459

for importance sampling around the design point obtained by FORM (ie noadditional finite element run is required) and thus 50000 samples can beused leading to a coefficient of variation of the simulation less than 1

It appears that the solution is not much sensitive to the order of expansionof the input field (when comparing the results for M = 2 with respect to thoseobtained for M = 4) This can be understood easily by the fact that the maxi-mum settlement of the foundation is related to the global (ie homogenized)behavior of the soil mass Modeling in a refined manner the spatial variabil-ity of the stiffness of the soil mass by adding terms in the KL expansion doesnot significantly influence the results

In contrary it appears that a PC expansion of third degree (p = 3) isrequired in order to get a satisfactory accuracy on the reliability index

Parametric study

A comprehensive comparison of the two approaches is presented in Sudretand Der Kiureghian (2000) where the influences of various parameters areinvestigated Selected results are reported in this section More precisely theaccuracy of the SFE method combined with FORM is investigated whenvarying the value of the admissible settlement from 6 to 20 cm whichleads to an increasing reliability index A two-term (M = 2) KL expan-sion of the underlying Gaussian field is used The results are reported inTable 73 Column 2 shows the values obtained by direct coupling betweenFORM and the deterministic finite element model Column 4 shows the val-ues obtained using FORM after the SFE solution of the problem using anintrusive approach

The results in Table 73 show that the ldquoSFE+FORMrdquo procedure obviouslyconverges to the direct coupling results when p is increased It appears that athird-order expansion is accurate enough to predict reliability indices up to 5For larger values of β a fourth-order expansion should be used

Note that a single SFE analysis is carried out to get the reliability indicesassociated with the various values of the threshold u0 (once p is chosen)

Stochastic finite element methods 285

Table 73 Example 1 ndash Influence of the thresholdin the limit state function

u0 (cm) βdirect p βSFE

2 04776 0473 3 0488

4 04882 2195

8 2152 3 21654 21662 3617

10 3452 3 34744 34672 4858

12 4514 3 45594 45342 6494

15 5810 3 59184 58462 8830

20 7480 3 77374 7561

In contrary a FORM analysis has to be restarted for each value of u0when direct coupling is used As a conclusion if a single value of β (andrelated Pf asymp (minusβ)) is of interest direct coupling using FORM is proba-bly the most efficient method When the evolution of β with respect to athreshold is investigated the ldquoSFE+FORMrdquo approach may become moreefficient

772 Example 2 Foundation problem ndash non Gaussianvariables

Deterministic problem statement

Let us consider now an elastic soil mass made of two layers of differentisotropic linear elastic materials lying on a rigid substratum A foundationon this soil mass is modeled by a uniform pressure P1 applied over a length2B1 = 10 m of the free surface An additional load P2 is applied over a length2B2 = 5 m (Figure 74)

Due to the symmetry half of the structure is modeled by finite element(Figure 74) The mesh comprises 80 QUAD4 elements as in the previoussection The finite element code used in this analysis is the open source codeCode_Aster1 The geometry is considered as deterministic The elastic mate-rial properties of both layers and the applied loads are modeled by random

286 Bruno Sudret and Marc Berveiller

A

2B1 = 10m

t1 = 775m

t2 = 2225m E2 n2

E1 n1

P1

P2

2B2 = 5m

Figure 74 Example 2 Foundation on a two-layer soil mass

Table 74 Example 2 Two-layer soil layer mass ndash Parameters of the model

Parameter Notation Type of PDF Mean value Coef ofvariation

Upper layer soil thickness t1 Deterministic 775 m ndashLower layer soil thickness t2 Deterministic 2225 m ndashUpper layer Youngrsquos modulus E1 Lognormal 50 MPa 20Lower layer Youngrsquos modulus E2 Lognormal 100 MPa 20Upper layer Poisson ratio ν1 Uniform 03 15Lower layer Poisson ratio ν2 Uniform 03 15Load 1 P1 Gamma 02 MPa 20 Load 2 P2 Weibull 04 MPa 20

variables whose PDF are specified in Table 74 All six random variables aresupposed to be independent

Again the model response under consideration is the maximum verti-cal displacement at point A (Figure 74) The finite element model is thusconsidered as an algorithmic function h() that computes the vertical nodaldisplacement uA as a function of the six input parameters

uA = h(E1E2ν1ν2P1P2) (771)

Reliability analysis

The serviceability of this foundation on a layered soil mass vis-agrave-vis anadmissible settlement is studied Again two stategies are compared

bull A direct coupling between the finite element model and the probabilisticcode PROBAN (Det Norske Veritas 2000) The limit state function

Stochastic finite element methods 287

given in Equation (770) is rewritten in this case as

g(X ) = u0 minush(E1 E2 ν1 ν2 P1 P2) (772)

where u0 is the admissible settlement The failure probability is com-puted using FORM analysis followed by importance sampling Onethousand samples are used in IS allowing a coefficient of variation ofthe simulation less than 5

bull A SFE analysis using the regression method is carried out leading to anapproximation of the maximal vertical settlement

uA =Psum

j=0

ujΨj(ξk6k=1) (773)

For this purpose the six input variables E1 E2 ν1 ν2 P1 P2 are firsttransformed into a six-dimensional standard normal gaussian vectorξ equiv ξk6

k=1 Then a third-order (p = 3) PC expansion of the response

is performed which requires the computation of P =(

6 + 33

)= 84

coefficients An approximate limit state function is then considered

g(X) = u0 minusPsum

j=0

ujΨj(ξk6k=1) (774)

Then FORM analysis followed by importance sampling is applied (onethousand samples coefficient of variation less than 1 for the simula-tion) Note that in this case FORM as well as IS are performed usingthe analytical limit state function Equation (774) This computationis almost costless compared to the computation of the PC expansioncoefficients ujPminus1

j=0 in Equation (773)

Table 75 shows the probability of failure obtained by direct couplingand by SFEregression using various numbers of points in the experimentaldesign (see Section 753) Figure 75 shows the evolution of the ratio betweenthe logarithm of the probability of failure (divided by the logarithm of theconverged probability of failure) versus the number of regression points forseveral values of the maximum admissible settlement u0 Accurate results areobtained when using 420 regression points or more for different values of thefailure probability (from 10minus1 to 10minus4) When taking less than 420 pointsresults are inaccurate When taking more than 420 points the accuracy isnot improved Thus this number seems to be the best compromise betweenaccuracy and efficiency Note that it corresponds to 5times84 points as pointedout in Equation (747)

288 Bruno Sudret and Marc Berveiller

Table 75 Example 2 Foundation on a two-layered soil ndash probability of failure Pf

Thresholdu0 (cm)

Directcoupling

Non-intrusive SFEregression approach

84 pts 168 pts 336 pts 420 pts 4096 pts

12 30910minus1 16210minus1 27110minus1 33110minus1 32310minus1 33210minus1

15 68310minus2 67710minus2 69010minus2 84310minus2 67310minus2 69310minus2

20 21310minus3 ndash 99510minus5 82210minus4 20110minus3 19810minus3

22 46110minus4 ndash 74710minus7 13110minus4 38010minus4 42410minus4

Number of FE runsrequired

84 168 336 420 4096

09

1

11

12

13

14

15

16

17

18

19

0 200 400 600 800 1000Number of points

12 cm15 cm20 cm22 cm

Figure 75 Example 2 Evolution of the logarithm of the failure probability divided by theconverged value vs the number of regression points

773 Example 3 Deep tunnel problem

Deterministic problem statement and probabilistic model

Let us consider a deep tunnel in an elastic isotropic homogeneous soil massLet us consider a homogeneous initial stress field The coefficient of earth

pressure at rest is defined as K0 = σ0xx

σ0yy

Parameters describing geometry

Stochastic finite element methods 289

material properties and loads are given in Table 76 The analysis is carriedout under plane strain conditions Due to the symmetry of the problemonly a quarter of the problem is modeled by finite element using appropri-ate boundary conditions (Figure 76) The mesh contains 462 nodes and420 4-node linear elements which allow a 14-accuracy evaluation of theradial displacement of the tunnel wall compared to a reference solution

Moment analysis

One is interested in the radial displacement (convergence) of the tunnelwall ie the vertical displacement of point E denoted by uE The valueum

E obtained for the mean values of the random parameters (see Table 76)is 624 mm A third-order (p = 3) PC expansion of this nodal displace-

ment is computed This requires P =(

4 + 33

)= 35 coefficients Various SFE

Table 76 Example 3 ndash Parameters of the model

Parameter Notation Type Mean Coef of Var

Tunnel depth L Deterministic 20 m ndashTunnel radius R Deterministic 1 m ndashVertical initial stress minusσ 0

yy Lognormal 02 MPa 30Coefficient of earth

pressure at restK0 Lognormal 05 10

Youngrsquos modulus E Lognormal 50 MPa 20Poisson ratio ν Uniform [01ndash03] 02 29

E n

o

L

L

BA

CD

E

x

yR

dy = 0

dx = 0

dx = 0dy = 0

dx = 0dy= 0

sYY0

sXX0

Figure 76 Scheme of the tunnel Mesh of the tunnel

290 Bruno Sudret and Marc Berveiller

methods are applied to solve the SFE problem namely the intrusive method(Section 74) and the non intrusive regression method (Section 75) usingvarious experimental designs The statistical moments of uE (reduced meanvalue E[uE]um

E coefficient of variation skewness and kurtosis coefficients)are reported in Table 77

The reference solution is obtained by Monte Carlo simulation of thedeterministic finite element model using 30000 samples (column 2) Thecoefficient of variation of this simulation is 025 The regression methodgives good results when there are at least 105 regression points (note thatthis corresponds again to Equation (747)) These results are slightly betterthan those obtained by the intrusive approach (column 3) especially for theskewness and kurtosis coefficients This is due to the fact that input variablesare expanded (ie approximated) onto the PC basis when applying the intru-sive approach while they are exactly represented through the isoprobabilistictransform in the non intrusive approaches

Reliability analysis

Let us now consider the reliability of the tunnel with respect to an admissibleradial displacement u0 The deterministic finite element model is consideredas an algorithmic function h() that computes the radial nodal displacementuE as a function of the four input parameters

uE = h(Eνσ 0

yyK0

)(775)

Two solving stategies are compared

bull A direct coupling between the finite element model PROBAN The limitstate function reads in this case

g(X) = u0 minush(Eνσ 0

yyK0

)(776)

Table 77 Example 3 ndash Moments of the radial displacement at point E

ReferenceMonte Carlo

IntrusiveSFE (p = 3)

Non-intrusive SFERegression

35 pts 70 pts 105 pts 256 pts

uEumE 1017 1031 1311 1021 1019 1018

Coeff of var 0426 0427 1157 0431 0431 0433Skewness minus1182 minus0807 minus0919 minus1133 minus1134 minus1179Kurtosis 5670 4209 13410 5312 5334 5460Number of FE runs required minus 35 70 105 256

Stochastic finite element methods 291

where u0 is the admissible radial displacement The failure probabilityis computed using FORM analysis followed by importance samplingOne thousand samples are used in IS allowing a coefficient of variationof the simulation less than 5

bull A SFE analysis using the regression method is carried out leading to anapproximation of the radial displacement

uE =Psum

j=0

ujΨj(ξk4k=1) (777)

and the associated limit state function reads

g() = u0 minusPsum

j=0

ujΨj(ξk4k=1) (778)

The generalized reliability indices β = minusminus1(Pf IS) associated to limitstate functions (776) and (778) for various values of u0 are reported inTable 78

The above results show that at least 105 points of regression should be usedwhen a third-order PC expansion is used Additional points do not improvethe accuracy of the results The intrusive and non-intrusive approaches givevery similar results They are close to the direct coupling results when theobtained reliability index is not too large For larger values the third-orderPC expansion may not be accurate enough to solve the reliability problemAnyway the non-intrusive approach (which does not introduce any approxi-mation of the input variables) is slightly more accurate than the non intrusivemethod as explained in Example 2

Table 78 Example 3 Generalized reliability indices β = minusminus1(Pf IS) vs admissibleradial displacement

Thresholdu0 (cm)

Direct coupling Intrusive SFE(p = 3P = 35)

Non-intrusive SFEregression

35 pts 70 pts 105 pts 256 pts

7 0427 0251 minus0072 0227 0413 04058 0759 0571 0038 0631 0734 07529 1046 1006 0215 1034 0994 1034

10 1309 1309 0215 1350 1327 128612 1766 1920 0538 1977 1769 174715 2328 2907 0857 2766 2346 232217 2627 3425 1004 3222 2663 265320 3342 4213 1244 3823 3114 3192

292 Bruno Sudret and Marc Berveiller

78 Conclusion

Modeling soil material properties properly is of crucial importance ingeotechnical engineering The natural heterogeneity of soil can be fruitfullymodeled using probability theory

bull If an accurate description of the spatial variability is required randomfields may be employed Their use in engineering problems requires theirdiscretization Two efficient methods have been presented for this pur-pose namely the KarhunenndashLoegraveve expansion and the EOLE methodThese methods should be better known both by researchers and engi-neers since they provide a much better accuracy than older methodssuch as point discretization or local averaging techniques

bull If an homogenized behavior of the soil is sufficient with respect to thegeotechnical problem under consideration the soil characteristics maybe modeled as random variables that are usually non Gaussian

In both cases identification of the parameters of the probabilistic model isnecessary This is beyond the scope of this chapter

Various methods have been reviewed that predict the impact of input ran-dom parameters onto the response of the geotechnical model Attention hasbeen focused on a class of stochastic finite element methods based on poly-nomial chaos expansions It has been shown how the input variablesfieldsshould be first represented using functions of standard normal variables

Two classes of methods for computing the expansion coefficients havebeen presented namely the intrusive and non-intrusive methods The histor-ical intrusive approach is well-suited to solve linear problems It has beenextended to some particular non linear problems but proves delicate to applyin these cases In contrast the projection and regression methods are easyto apply whatever the physics since they make use only of the deterministicmodel as available in the finite element code Several runs of the model forselected values of the input parameters are required The computed responsesare processed in order to get the PC expansion coefficients of the responseNote that the implementation of these non-intrusive methods is done onceand for all and can be applied thereafter with any finite element software athand and more generally with any model (possibly analytical) However thenon-intrusive methods may become computationnally expensive when thenumber of input variables is large which may be the case when discretizedrandom fields are considered

Based on a large number of application examples the authors suggest theuse of second-order (p = 2) PC expansions for estimating mean and standarddeviation of response quantities When reliability problems are consideredat least a third-order expansion is necessary to catch the true shape of theresponse PDF tail

Stochastic finite element methods 293

Acknowledgments

The work of the first author on spatial variability and stochastic finiteelement methods has been initiated during his post-doctoral stay in theresearch group of Professor Armen Der Kiureghian (Departement of Civiland Environmental Engineering University of California at Berkeley) whois gratefully acknowledged The work on non-intrusive methods (as well asmost application examples presented here) is part of the PhD thesis of thesecond author This work was co-supervised by Professor Maurice Lemaire(Institut Franccedilais de Meacutecanique Avanceacutee Clermont-Ferrand France) whois gratefully acknowledged

Appendix A

A1 Hermite polynomials

The Hermite polynomials Hen(x) are solutions of the following differentialequation

yprimeprime minus xyprime + ny = 0 n isin N (779)

They may be generated in practice by the following recurrence relationship

He0(x) = 1 (780)

Hen+1(x) = xHen(x) minus nHenminus1(x) (781)

They are orthogonal with respect to the Gaussian probability measure

int infin

minusinfinHem(x)Hen(x)ϕ(x)dx = nδmn (782)

where ϕ(x) = 1radic

2π eminusx22 is the standard normal PDF If ξ is a standardnormal random variable the following relationship holds

E[Hem(ξ )Hen(ξ )

]= nδmn (783)

The first three Hermite polynomials are

He1(x) = x He2(x) = x2 minus 1 He3(x) = x3 minus 3x (784)

A2 Construction of the polynomial chaos

The Hermite polynomial chaos of order M and degree p is the set of multi-variate polynomials obtained by products of univariate polynomials so that

294 Bruno Sudret and Marc Berveiller

the maximal degree is less than or equal to p Let us define the followinginteger sequence α

α = αi i = 1 M αi ge 0Msum

i=1

αi le p (785)

The multivariate polynomial Ψα is defined by

Ψα(x1 xM) =Mprod

i=1

Heαi(xi) (786)

The number of such polynomials of degree not exceeding p is

P = (M + p)Mp (787)

An original algorithm to determine the set of αrsquos is detailed in Sudret and DerKiureghian (2000) Let Z be a standard normal random vector of size M Itis clear that

E[Ψα(Z)Ψβ (Z)

]=

Mprodi=1

E[Heαi

(Zi)Heβi(Zi)]

= δαβ

Mprodi=1

E[He2

αi(Zi)](788)

The latter equation shows that the polynomial chaos basis is orthogonal

Note

1 This is an open source finite element code developed by Electriciteacute de France RampDDivision see httpwwwcode-asterorg

References

Abdo T and Rackwitz R (1990) A new β-point algorithm for large timeinvariant and time-variant reliability problems In A Der Kiureghian and P Thoft-Christensen Editors Reliability and Optimization of Structural Systems rsquo90 Proc3rd WG 75 IFIP Conf Berkeley 26ndash28 March 1990 Springer Verlag Berlin (1991)pp 1ndash12

Abramowitz M and Stegun I A Eds (1970) Handbook of Mathematical FunctionsDover Publications Inc New York

Baecher G-B and Ingra T-S (1981) Stochastic finite element method in set-tlement predictions Journal of the Geotechnical Engineering Division ASCE107(4) 449ndash63

Stochastic finite element methods 295

Berveiller M (2005) Stochastic finite elements intrusive and non intrusive methodsfor reliability analysis PhD thesis Universiteacute Blaise Pascal Clermont-Ferrand

Berveiller M Sudret B and Lemaire M (2004) Presentation of two methodsfor computing the response coefficients in stochastic finite element analysis InProceedings of the 9th ASCE Specialty Conference on Probabilistic Mechanicsand Structural Reliability Albuquerque USA

Berveiller M Sudret B and Lemaire M (2006) Stochastic finite element a nonintrusive approach by regression Revue Europeacuteenne de Meacutecanique Numeacuterique15(1ndash3) 81ndash92

Choi S Grandhi R Canfield R and Pettit C (2004) Polynomial chaos expansionwith latin hypercube sampling for estimating response variability AIAA Journal45 1191ndash8

Clouteau D and Lafargue R (2003) An iterative solver for stochastic soil-structureinteraction In Computational Stochastic Mechanics (CSM-4) Eds P Spanos andG Deodatis Millpress Rotterdam pp 119ndash24

DeGroot D and Baecher G (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering ASCE 119(1) 147ndash66

Der Kiureghian A and Ke J-B (1988) The stochastic finite element method instructural reliability Probabilistic Engineering Mechanics 3(2) 83ndash91

Det Norske Veritas (2000) PROBAN userrsquos manual V43Ditlevsen O and Madsen H (1996) Structural Reliability Methods John Wiley

and Sons ChichesterEloseily K Ayyub B and Patev R (2002) Reliability assessment of pile groups in

sands Journal of Structural Engineering ASCE 128(10) 1346ndash53Fenton G-A (1999a) Estimation for stochastic soil models Journal of Geotechnical

Engineering ASCE 125(6) 470ndash85Fenton G-A (1999b) Random field modeling of CPT data Journal of Geotechnical

Engineering ASCE 125(6) 486ndash98Field R (2002) Numerical methods to estimate the coefficients of the polyno-

mial chaos expansion In Proceedings of the 15th ASCE Engineering MechanicsConference

Ghanem R (1999) The nonlinear Gaussian spectrum of log-normal stochasticprocesses and variables Journal of Applied Mechanic ASME 66 964ndash73

Ghanem R and Brzkala V (1996) Stochastic finite element analysis of randomlylayered media Journal of Engineering Mechanics 122(4) 361ndash9

Ghanem R and Spanos P (1991) Stochastic Finite Elements ndash A Spectral ApproachSpringer New York

Ghiocel D and Ghanem R (2002) Stochastic finite element analysis of seis-mic soil-structure interaction Journal of Engineering Mechanics (ASCE) 12866ndash77

Grigoriu M (2006) Evaluation of KarhunenndashLoegraveve spectral and sampling rep-resentations for stochastic processes Journal of Engineering Mechanics (ASCE)132(2) 179ndash89

Huang S Quek S and Phoon K (2001) Convergence study of the truncatedKarhunenndashLoegraveve expansion for simulation of stochastic processes InternationalJournal of Numerical Methods in Engineering 52(9) 1029ndash43

Isukapalli S S (1999) Uncertainty Analysis of transport-transportation modelsPhD thesis The State University of New Jersey NJ

296 Bruno Sudret and Marc Berveiller

Le Maicirctre O Reagen M Najm H Ghanem R and Knio D (2002) A stochasticprojection method for fluid flow-II Random Process Journal of ComputationalPhysics 181 9ndash44

Li C and Der Kiureghian A (1993) Optimal discretization of random fieldsJournal of Engineering Mechanics 119(6) 1136ndash54

Li L Phoon K and Quek S (2007) Simulation of non-translation processes usingnon Gaussian KarhunenndashLoegraveve expansion Computers and Structures 85(5ndash6)pp 264ndash276

Liu P-L and Der Kiureghian A (1986) Multivariate distribution models withprescribed marginals and covariances Probabilistic Engineering Mechanics 1(2)105ndash12

Liu W Belytschko T and Mani A (1986a) Probabilistic finite elements fornon linear structural dynamics Computer Methods in Applied Mechanics andEngineering 56 61ndash86

Liu W Belytschko T and Mani A (1986b) Random field finite elementsInternational Journal of Numerical Methods in Engineering 23(10) 1831ndash45

Loegraveve M (1977) Probability Theory Springer New-YorkMalliavin P (1997) Stochastic Analysis Springer BerlinMatthies H and Keese A (2005) Galerkin methods for linear and nonlinear elliptic

stochastic partial differential equations Computer Methods in Applied Mechanicsand Engineering 194 1295ndash331

Mellah R Auvinet G and Masrouri F (2000) Stochastic finite elementmethod applied to non-linear analysis of embankments Probabilistic EngineeringMechanics 15(3) 251ndash9

Niederreiter H (1992) Random Number Generation and Quasi-Monte CarloMethods Society for Industrial and Applied Mathematics Philadelphia PA

Nouy A (2007) A generalized spectral decomposition technique to solve a classof linear stochastic partial differential equations Computer Methods in AppliedMechanics and Engineering 196(45ndash48) 4521ndash37

Phoon K Huang H and Quek S (2005) Simulation of strongly non Gaussianprocesses using Karhunen-Loegraveve expansion Probabilistic Engineering Mechanics20(2) 188ndash98

Phoon K Huang S and Quek S (2002a) Implementation of KarhunenndashLoegraveveexpansion for simulation using a waveletndashGalerkin scheme Probabilistic Engi-neering Mechanics 17(3) 293ndash303

Phoon K Huang S and Quek S (2002b) Simulation of second-order processesusing KarhunenndashLoegraveve expansion Computers and Structures 80(12) 1049ndash60

Phoon K Quek S Chow Y and Lee S (1990) Reliability analysis of pilesettlements Journal of Geotechnical Engineering ASCE 116(11) 1717ndash35

Popescu R Deodatis G and Nobahar A (2005) Effects of random heterogene-ity of soil properties on bearing capacity Probabilistic Engineering Mechanics20 324ndash41

Press W Vetterling W Teukolsky S-A and Flannery B-P (2001) NumericalRecipes Cambridge University Press Cambridge

Rackwitz R (2000) Reviewing probabilistic soil modeling Computers andGeotechnics 26 199ndash223

Rackwitz R (2001) Reliability analysis ndash a review and some perspectives StructuralSafety 23 365ndash95

Stochastic finite element methods 297

Saltelli A Chan K and Scott E Eds (2000) Sensitivity Analysis John Wiley andSons New York

Sobolrsquo I (1993) Sensitivity estimates for nonlinear mathematical models Mathe-matical Modeling and Computational Experiment 1 407ndash14

Sobolrsquo I and Kucherenko S (2005) Global sensitivity indices for nonlinearmathematical models Review Wilmott Magazine 1 56ndash61

Sudret B (2005) Des eacuteleacutements finis stochastiques spectraux aux surfaces de reacuteponsestochastiques une approche unifieacutee In Proceedings 17egraveme Congregraves Franccedilais deMeacutecanique ndash Troyes (in French)

Sudret B (2006) Global sensitivity analysis using polynomial chaos expansionsIn Procedings of the 5th International Conference on Comparative StochasticMechanics (CSM5) Eds P Spanos and G Deodatis Rhodos

Sudret B (2008) Global sensitivity analysis using polynomial chaos expansionsReliability Engineering and System Safety In press

Sudret B Berveiller M and Lemaire M (2004) A stochastic finite element methodin linear mechanics Comptes Rendus Meacutecanique 332 531ndash7 (in French)

Sudret B Berveiller M and Lemaire M (2006) A stochastic finite element pro-cedure for moment and reliability analysis Revue Europeacuteenne de MeacutecaniqueNumeacuterique 15(7ndash8) 1819ndash35

Sudret B Blatman G and Berveiller M (2007) Quasi random numbers in stochas-tic finite element analysis ndash application to global sensitivity analysis In Proceedingsof the 10th International Conference on Applications of Statistics and Probabilityin Civil Engineering (ICASP10) Tokyo

Sudret B and Der Kiureghian A (2000) Stochastic finite elements and reliabilitya state-of-the-art report Technical Report n˚ UCBSEMM-200008 University ofCalifornia Berkeley

Sudret B and Der Kiureghian A (2002) Comparison of finite element reliabilitymethods Probabilistic Engineering Mechanics 17 337ndash48

VanMarcke E (1977) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103(GT11) 1227ndash46

VanMarcke E-H and Grigoriu M (1983) Stochastic finite element analysis ofsimple beams Journal of Engineering Mechanics ASCE 109(5) 1203ndash14

Wand M and Jones M (1995) Kernel Smoothing Chapman and Hall LondonZhang J and Ellingwood B (1994) Orthogonal series expansions of random

fields in reliability analysis Journal of Engineering Mechanics ASCE 120(12)2660ndash77

Chapter 8

Eurocode 7 andreliability-based design

Trevor L L Orr and Denys Breysse

81 Introduction

This chapter outlines how reliability-based geotechnical design is being intro-duced in Europe through the adoption of Eurocode 7 the new Europeanstandard for geotechnical design Eurocode 7 is based on the limit statedesign concept with partial factors and characteristic values This chaptertraces the development of Eurocode 7 it explains how the overall relia-bility of geotechnical structures is ensured in Eurocode 7 it shows howthe limit state concept and partial factor method are implemented inEurocode 7 for geotechnical designs it explains how characteristic val-ues are selected and design values obtained it presents the partial factorsgiven in Eurocode 7 to obtain the appropriate levels of reliability and itexamines the use of probability-based reliability methods such as first-order reliability method (FORM) analyses and Monte Carlo simulations forgeotechnical designs and the use of these methods for calibrating the par-tial factor values An example is presented of a spread foundation designedto Eurocode 7 using the partial factor method and this is followed bysome examples of the use of probabilistic methods to investigate howuncertainty in the parameter values affects the reliability of geotechnicaldesigns

82 Eurocode program

In the 1975 the Commission of the European Community (CEC) decidedto initiate work on the preparation of a program of harmonized codesof practice known as the Eurocodes for the design of structures Thepurpose and intended benefits of this program were that by providingcommon design criteria it would remove the barriers to trade due to theexistence of different codes of practice in the member states of what was

Eurocode 7 and RBD 299

then the European Economic Community (EEC) and is now the EuropeanUnion (EU) The Eurocodes would also serve as reference documents forfulfilling the requirements for mechanical resistance stability and resis-tance to fire including aspects of durability and economy specified in theConstruction Products Directive which have been adopted in each EUmember statersquos building regulations A further objective of the Eurocodesis to improve the competitiveness of the European construction industryinternationally

The set of Eurocodes consists of 10 codes which are European stan-dards ie Europaumlische Norms (ENs) The first Eurocode EN 1990 sets outthe basis of design adopted in the set of Eurocodes the second EN 1991provides the loads on structures referred to as actions the codes EN 1992 ndashEN 1997 and EN 1999 provide the rules for designs involving the differentmaterials and the code EN 1998 provides the rules for seismic design ofstructures Part 1 of EN 1997 called Eurocode 7 and referred to as thisthroughout this chapter provides the general rules for geotechnical designAs explained in the next section EN 1997 is published by CEN (ComiteacuteEacuteuropeacuteen de Normalisation or European Commitee for Standardization) asare the other Eurocodes

83 Development of Eurocode 7

The development of Eurocode 7 started in 1980 when Professor KevinNash Secretary General of the International Society for Soil Mechanics andFoundation Engineering invited Niels Krebs Ovesen to form a committeeconsisting of representatives from eight of the nine EEC member states atthat time which were Belgium Denmark France Germany Ireland ItalyNetherlands and the UK (there was no representative from Luxembourg andGreece joined the committee from the first meeting in 1981 when it joinedthe EEC) to prepare for the CEC a draft model limit state design code forEurocode 7 which was published in 1987 Subsequently the work on allthe Eurocodes was transferred from the CEC to CEN and the pre-standardversion of Eurocode 7 which was based on partial material factors waspublished in 1994 as ENV 1997ndash1 Eurocode 7 Geotechnical design Part 1General rules Then taking account of comments received on the ENV ver-sion and including partial resistance factors as well as partial material factorsthe full standard version of Eurocode 7 ndash Part 1 EN 1997ndash1 was publishedby CEN in November 2004 Since each member state is responsible for thesafety levels of structures within its jurisdiction there was as specified inGuidance Document Paper L (EC 2002) a two-year period following pub-lication of the EN ie until November 2006 for each country to preparea National Annex giving the values of the partial factors and other safetyelements so that Eurocode 7 could be used for geotechnical designs in thatcountry

300 Trevor L L Orr and Denys Breysse

84 Eurocode design requirements and the limitstate design concept

The basic Eurocode design requirements given in EN 1990 are thata structure shall be designed and executed in such a way that it willduring its intended life with appropriate degrees of reliability and in aneconomical way

bull sustain all actions and influences likely to occur during execution anduse and

bull remain fit for the use for which it is required

To achieve these basic design requirements the limit state design concept isadopted in all the Eurocodes and hence in Eurocode 7 The limit state designconcept involves ensuring that for each design situation the occurrence ofall limit states is sufficiently unlikely where a limit state is a state beyondwhich the structure no longer fulfills the design criteria The limit states thatare considered are ultimate limit states (ULSs) and serviceability limit states(SLSs) which are defined as follows

1 Ultimate limit states are those situations involving safety such as thecollapse of a structure or other forms of failure including excessivedeformation in the ground prior to failure causing failure in the sup-ported structure or where there is a risk of danger to people or severeeconomic loss Ultimate limit states have a low probability of occurrencefor well-designed structures as noted in Section 812

2 Serviceability limit states correspond to those conditions beyond whichthe specified requirements of the structure or structural element are nolonger met Examples include excessive deformations settlements vibra-tions and local damage of the structure in normal use under workingloads such that it ceases to function as intended Serviceability limitstates have a higher probability of occurrence than ultimate limit statesas noted in Section 812

The calculation models used in geotechnical designs to check that theoccurrence of a limit state is sufficiently unlikely should describe the behav-ior of the ground at the limit state under consideration Thus separateand different calculations should be carried out to check the ultimate andserviceability limit states In practice however it is often known from expe-rience which limit state will govern the design and hence having designedfor this limit state the avoidance of the other limit states may be veri-fied by a control check ULS calculations will normally involve analyzinga failure mechanism and using ground strength properties while SLS calcu-lations will normally involve a deformation analysis and ground stiffness or

Eurocode 7 and RBD 301

compressibility properties The particular limit states to be considered in thecase of common geotechnical design situations are listed in the appropriatesections of Eurocode 7

Geotechnical design differs from structural design since it involves anatural material (soil) which is variable and whose properties need tobe determined from geotechnical investigations rather than a manufac-tured material (such as concrete or steel) which is made to meet certainspecifications Hence in geotechnical design the geotechnical investiga-tions to identify and characterize the relevant ground mass and determinethe characteristic values of the ground properties are an important partof the design process This is reflected in the fact that Eurocode 7 hastwo parts Part 1 ndash General rules and Part 2 ndash Ground investigation andtesting

85 Reliability-based designs and Eurocode designmethods

As noted in the previous section the aim of designs to the Eurocodes isto provide structures with appropriate degrees of reliability thus designsto the Eurocodes are reliability-based designs (RBDs) Reliability is definedin EN 1990 as the ability of a structure or a structural member to fulfillthe specified requirements including the design working life for which ithas been designed and it is noted that reliability is usually expressed inprobabilistic terms EN 1990 states that the required reliability for a struc-ture shall be achieved by designing in accordance with the appropriateEurocodes and by the use of appropriate execution and quality managementmeasures

EN 1990 allows for different levels of reliability which take account of

bull the cause andor mode of obtaining a limit state (ie failure)bull the possible consequences of failure in terms of risk to life injury or

potential economic lossbull public aversion to failure andbull the expense and procedures necessary to reduce the risk of failure

Examples of the use of different levels of reliability are the adoption of ahigh level of reliability in a situation where a structure poses a high risk tohuman life or where the economic social or environmental consequencesof failure are great as in the case of a nuclear power station and a low levelof reliability where the risk to human life is low and where the economicsocial and environmental consequences of failure are small or negligible asin the case of a farm building

In geotechnical designs the required level of reliability is obtained inpart through ensuring that the appropriate design measures and control

302 Trevor L L Orr and Denys Breysse

checks are applied to all aspects and at all stages of the design process fromconception through the ground investigation design calculations and con-struction to maintenance For this purpose Eurocode 7 provides lists ofrelevant items and aspects to be considered at each stage Also a risk assess-ment system known as the Geotechnical Categories is provided to takeaccount of the different levels of complexity and risk in geotechnical designsThis system is discussed in Section 86

According to EN 1990 designs to ensure that the occurrence of limit stateis sufficiently unlikely may be carried out using either of the following designmethods

bull the partial factor method orbull an alternative method based directly on probabilistic methods

The partial factor method is the normal Eurocode design method and is themethod presented in Eurocode 7 This method involves applying appropri-ate partial factor values specified in the National Annexes to Eurocode 7 tostatistically based characteristic parameter values to obtain the design val-ues for use in relevant calculation models to check that a structure has therequired probability that neither an ultimate nor a serviceability limit statewill be exceeded during a specified reference period This design methodis referred to as a semi-probabilistic reliability method in EN 1990 asexplained in Section 812 The selection of characteristic values and theappropriate partial factor values to achieve the required reliability level arediscussed in Sections 88ndash811 As explained in Section 812 there is no pro-cedure in Eurocode 7 to allow for reliability differentiation by modifyingthe specified partial factor values and hence the calculated reliability levelin order to take account of the consequences of failure Instead reliabilitydifferentiation may be achieved through using the system of GeotechnicalCategories

Eurocode 7 does not provide any guidance on the direct use of fully proba-bilistic reliability methods for geotechnical design However EN 1990 statesthat the information provided in Annex C of EN 1990 which includes guid-ance on the reliability index β value may be used as a basis for probabilisticdesign methods

86 Geotechnical risk and GeotechnicalCategories

In Eurocode 7 three Geotechnical Categories referred to as Geotechni-cal Categories 1 2 and 3 have been introduced to take account of thedifferent levels of complexity of a geotechnical design and to establishthe minimum requirements for the extent and content of the geotechnicalinvestigations calculations and construction control checks to achieve the

Eurocode 7 and RBD 303

required reliability The factors affecting the complexity of a geotechnicaldesign include

1 the nature and size of the structure2 the conditions with regard to the surroundings for example neighboring

structures utilities traffic etc3 the ground conditions4 the groundwater situation5 regional seismicity and6 the influence of the environment eg hydrology surface water subsi-

dence seasonal changes of moisture

The use of the Geotechnical Categories is not a code requirement asthey are presented as an application rule rather than a principle andso are optional rather than mandatory The advantage of the Geotech-nical Categories is that they provide a framework for categorizing thedifferent levels of risk in a geotechnical design and for selecting appro-priate levels of reliability to account for the different levels of riskGeotechnical risk is a function of two factors the geotechnical haz-ards (ie dangers) and the vulnerability of people and the structureto specific hazards With regard to the design complexity factors listedabove factors 1 and 2 (the structure and its surroundings) relate tovulnerability and factors 3ndash6 (ground conditions groundwater seismic-ity and environment) are geotechnical hazards The various levels ofcomplexity of these factors in relation to the different GeotechnicalCategories and the associated geotechnical risks are shown in Table 81taken from Orr and Farrell (1999) It is the geotechnical designerrsquosresponsibility to ensure through applying the Eurocode 7 requirementsappropriately and by the use of appropriate execution and qualitymanagement measures that structures have the required reliability iehave sufficient safety against failure as a result of any of the potentialhazards

In geotechnical designs to Eurocode 7 the distinction between theGeotechnical Categories lies in the degree of expertise required and inthe nature and extent of the geotechnical investigations and calculationsto be carried out as shown in Table 82 taken from Orr and Farrell(1999) Some examples of structures in each Geotechnical Category arealso shown in this table As noted in the previous section Eurocode 7does not provide for any differentiation in the calculated reliability forthe different Geotechnical Categories by allowing variation in the par-tial factors values Instead while satisfying the basic design requirementsand using the specified partial factor values the required reliability isachieved in the higher Geotechnical Categories by greater attention tothe quality of the geotechnical investigations the design calculations the

Table 81 Geotechnical Categories related to geotechnical hazards and vulnerability levels

Factors to beconsidered

Geotechnical Categories

GC1 GC2 GC3

Geotechnicalhazards

Low Moderate High

Groundconditions

Known fromcomparableexperience to bestraightforwardNot involving softloose orcompressible soilloose fill orsloping ground

Ground conditionsand propertiescan be determinedfrom routineinvestigations andtests

Unusual orexceptionally difficultground conditionsrequiring non-routineinvestigations andtests

Groundwatersituation

No excavationsbelow water tableexcept whereexperienceindicates this willnot causeproblems

No risk of damagewithout priorwarning tostructures due togroundwaterlowering or drainageNo exceptionalwater tightnessrequirements

High groundwaterpressures andexceptionalgroundwaterconditions egmulti-layered stratawith variablepermeability

Regionalseismicity

Areas with no orvery lowearthquake hazard

Moderate earthquakehazard whereseismic design code(EC8) may be used

Areas of highearthquake hazard

Influence of theenvironment

Negligible risk ofproblems due tosurface watersubsidencehazardouschemicals etc

Environmentalfactors covered byroutine designmethods

Complex or difficultenvironmental factorsrequiring specialdesign methods

Vulnerability Low Moderate HighNature and

size of thestructure andits elements

Small and relativelysimple structuresor constructionInsensitivestructures inseismic areas

Conventional typesof structures withno abnormal risks

Very large or unusualstructures andstructures involvingabnormal risks Verysensitive structures inseismic areas

Surroundings Negligible risk ofdamage to orfrom neighboringstructures orservices andnegligible riskfor life

Possible risk ofdamage toneighboringstructures orservices due forexampleto excavationsor piling

High risk of damage toneighboringstructures or services

Geotechnical risk Low Moderate High

Eurocode 7 and RBD 305

monitoring during construction and the maintenance of the completedstructure

The reliability of a geotechnical design is influenced by the expertise ofthe designer No specific guidelines are given in Eurocode 7 with regard tothe level of expertise required by designers for particular Geotechnical Cate-gories except that as stated in both EN 1990 and EN 1997ndash1 it is assumedthat structures are designed by appropriately qualified and experienced per-sonnel Table 82 provides an indication of the levels of expertise required bythose involved in the different Geotechnical Categories The main featuresof the different Geotechnical Categories are summarized in the followingparagraphs

861 Geotechnical Category 1

Geotechnical Category 1 includes only small and relatively simple struc-tures for which the basic design requirements may be satisfied on the basisof experience and qualitative geotechnical investigations and where there isnegligible risk for property and life due to the ground or loading conditionsGeotechnical Category 1 designs involve empirical procedures to ensure therequired reliability without the use of any probability-based analyses ordesign methods Apart from the examples listed in Table 82 Eurocode 7does not and without local knowledge cannot provide detailed guidanceor specific requirements for Geotechnical Category 1 these must be foundelsewhere for example in local building regulations national guidancedocuments and textbooks According to Eurocode 7 the design of Geotech-nical Category 1 structures requires someone with appropriate comparableexperience

862 Geotechnical Category 2

Geotechnical Category 2 includes conventional types of structures and foun-dations with no abnormal risk or unusual or exceptionally difficult groundor loading conditions Structures in Geotechnical Category 2 require quan-titative geotechnical data and analyses to ensure that the basic requirementswill be satisfied and require a suitably qualified person normally a civilengineer with appropriate geotechnical knowledge and experience Routineprocedures may be used for field and laboratory testing and for designand construction The partial factor method presented in Eurocode 7 is themethod normally used for Geotechnical Category 2 designs

863 Geotechnical Category 3

Geotechnical Category 3 includes structures or parts of structures thatdo not fall within the limits of Geotechnical Categories 1 or 2

Table 82 Investigations designs and structural types related to Geotechnical Categories

Geotechnical Categories

GC1 GC2 GC3

Expertiserequired

Person withappropriatecomparableexperience

Experienced qualifiedperson

Experiencedgeotechnicalspecialist

Geotechnicalinvestigations

Qualitativeinvestigationsincluding trial pits

Routineinvestigationsinvolving boringsfield andlaboratory tests

Additional moresophisticatedinvestigations andlaboratory tests

Designprocedures

Prescriptivemeasures andsimplified designprocedures egdesign bearingpressures basedon experience orpublishedpresumed bearingpressures Stabilityor deformationcalculations maynot be necessary

Routine calculationsfor stability anddeformationsbased on designproceduresin EC7

More sophisticatedanalyses

Examples ofstructures bull Simple 1 and 2

storey structuresand agriculturalbuildings havingmaximum designcolumn load of250 kN andmaximum designwall load of100 kNm

bull Retaining walls andexcavationsupports whereground leveldifference doesnot exceed 2 m

bull Small excavationsfor drainage andpipes

Conventionalbull Spread and pile

foundationsbull Walls and other

retainingstructures

bull Bridge piers andabutments

bull Embankments andearthworks

bull Ground anchorsand other supportsystems

bull Tunnels in hardnon-fractured rock

bull Very largebuildings

bull Large bridgesbull Deep excavationsbull Embankments on

soft groundbull Tunnels in soft or

highly permeableground

Eurocode 7 and RBD 307

Geotechnical Category 3 includes very large or unusual structures struc-tures involving abnormal risks or unusual or exceptionally difficult groundor loading conditions and structures in highly seismic areas Geotechni-cal Category 3 structures will require the involvement of a specialist suchas a geotechnical engineer While the requirements in Eurocode 7 are theminimum requirements for Geotechnical Category 3 Eurocode 7 does notprovide any special requirements for Geotechnical Category 3 Eurocode 7states that Geotechnical Category 3 normally includes alternative provisionsand rules to those given in Eurocode 7 Hence in order to take account of theabnormal risk associated with Geotechnical Category 3 structures it wouldbe appropriate to use probability-based reliability analyses when designingthese to Eurocode 7

864 Classification into a particular GeotechnicalCategory

If the Geotechnical Categories are used the preliminary classification ofa structure into a particular category is normally performed prior to anyinvestigation or calculation being carried out However this classificationmay need to be changed during or following the investigation or designas additional information becomes available Also when using this sys-tem all parts of a structure do not have to be treated according to thehighest Geotechnical Category Only some parts of a structure may needto be classified in a higher category and only those parts will need to betreated differently for example with regard to the level of investigation orthe degree of sophistication of the design A higher Geotechnical Categorymay be used to achieve a higher level of reliability or a more economicaldesign

87 The partial factor method and geotechnicaldesign calculations

Geotechnical design calculations involve the following components

bull imposed loads or displacements referred to as actions F in theEurocodes

bull properties of soil rock and other materials X or ground resistances Rbull geometrical data abull partial factors γ or some other safety elementsbull action effects E for example resulting forces or calculated settlementsbull limiting or acceptable values C of deformations crack widths vibra-

tions etc andbull a calculation model describing the limit state under consideration

308 Trevor L L Orr and Denys Breysse

ULS design calculations in accordance with Eurocode 7 involve ensuringthat the design effect of the actions or action effect Ed does not exceed thedesign resistance Rd where the subscript d indicates a design value

Ed le Rd (81)

while SLS calculations involve ensuring that the design action effect Ed (egsettlement) is less than the limiting value of the deformation of the structureCd at the SLS

Ed le Cd (82)

In geotechnical design the action effects and resistances may be functionsof loads and material strengths as well as being functions of geometricalparameters For example in the design of a cantilever retaining wall againstsliding due to the frictional nature of soil both the action effect E whichis the earth pressure force on the retaining wall and the sliding resistanceR are functions of the applied loads as well as being functions of the soilstrength and the height of the wall ie in symbolic form they are EFXaand RFXa

When using the partial factor method in a ULS calculation and assum-ing the geometrical parameters are not factored the Ed and Rd may beobtained either by applying partial action and partial material factors γFand γM to representative loads Frep and characteristic soil strengths Xk orelse partial action effect and partial resistance factors γE and γR may beapplied to the action effects and the resistances calculated using unfactoredrepresentative loads and characteristic soil strengths This is indicated in thefollowing equations where the subscripts rep and k indicate representativeand characteristic values respectively

Ed = E[γFFrepXkγMad] (83)

and Rd = R[γFFrepXkγMad] (84)

or Ed = γEE[FrepXkad] (85)

and Rd = R[FrepXkad]γR (86)

The difference between the Design Approaches presented in Section 811 iswhether Equations (83) and (84) or Equations (85) and (86) are usedThe definition of representative and characteristic values and how they areselected and the choice of partial factor values to obtain Ed and Rd arediscussed in the following sections

Eurocode 7 and RBD 309

88 Characteristic and representative valuesof actions

EN 1990 defines the characteristic value of an action Fk as its main repre-sentative value and its value is specified as a mean value an upper or lowervalue or a nominal value EN 1990 states that the variability of perma-nent loads G may be neglected if G does not vary significantly during thedesign working life of the structure and its coefficient of variation V is smallGk should then be taken equal to the mean value EN 1990 notes that thevalue of V for Gk can be in the range 005 to 010 depending on the typeof structure For variable loads Q the characteristic value Qk may eitherbe an upper value with an intended probability of not being exceeded ora nominal value when a statistical distribution is not known In the caseof climatic loads a probability of 002 is quoted in EN 1990 for referenceperiod of 1 year corresponding to a return period of 50 years for the char-acteristic value No V value for variable loads is given in EN 1990 but atypical value would be 015 which is the value used in the example in Section814 If a load F is a normally distributed random variable with a meanvalue micro(F) standard deviation σ (F) and coefficient of variation V(F) thecharacteristic value corresponding to the 95 fractile is given by

Fk = micro(F) + 1645σ (F) = microF(1 + 1645V(F)) (87)

The representative values of actions Frep are obtained from the equation

Frep = ψFk (88)

where ψ = either 10 or ψ0 ψ1 or ψ2The factors ψ0 ψ1 or ψ2 are factors that are applied to the characteristic

values of the actions to obtain the representative values for different designsituations The factor ψ0 is the combination factor used to obtain the funda-mental combination ψ0Qk of a variable action for persistent and transientdesign situations and is applied only to the non-leading variable actions Thefactor ψ0 is chosen so that the probability that the effects caused by the com-bination will be exceeded is approximately the same as the probability of theeffects caused by the characteristic value of an individual action The factorψ1 is used to obtain the frequent value ψ1Qk of a variable action and thefactor ψ2 is used to obtain the quasi-permanent value ψ2Qk of a variableaction for accidental or seismic design situations The factor ψ1 is chosenso that either the total time within the reference period during which it isexceeded is only a small given part of the reference period or so that theprobability of it being exceeded is limited to a given value while the factorψ2 is the value determined so that the total period of time for which it willbe exceeded is a large fraction of the reference period Both ψ1 and ψ2 areapplied to all the variable actions

310 Trevor L L Orr and Denys Breysse

The equations for combining the actions for different design situationsusing the factors ψ0 ψ1 and ψ2 and the values of these factors in thecase of imposed snow and wind loads on buildings are given in EN 1990An example of a ψ value is ψ0 = 07 for an imposed load on a buildingthat is not a load in a storage area Because the different variable loads areoften much less significant than the permanent loads in geotechnical designscompared with the significance of the variable loads to the permanent loadsin many structural designs (for example comparing the permanent and vari-able loads in the designs of a slope and a roof truss) the combination factorsψ0 are used much less in geotechnical design than in structural designConsequently in geotechnical design the representative actions are oftenequal to the characteristic actions and hence Fk is normally used for Frep indesign calculations

89 Characteristic values of material properties

EN 1990 defines the characteristic value of a material or product propertyXk as ldquothe value having a prescribed probability of not being attained in ahypothetical unlimited test seriesrdquo In structural design this value generallycorresponds to a specified fractile of the assumed statistical distribution ofthe particular property of the material or product Where a low value ofa material property is unfavorable EN 1990 states that the characteristicvalue should be defined as the 5 fractile For this situation and assuminga normal distribution the characteristic value is given by the equation

Xk = micro(X) minus 1645σ (X) = micro(X)(1 minus 1645V(X)) (89)

where micro(X) is the mean value σ (X) is the standard deviation and V(X) isthe coefficient of variation of the unlimited test series X and the coefficient1645 provides the 5 fractile of the test results

The above definition of the characteristic value and Equation (89) areapplicable in the case of test results that are normally distributed and whenthe volume of material in the actual structural element being designed issimilar to the volume in the test element This is normally the situation instructural design when for example the failure of a beam is being analyzedand the strength is based on the results of tests on concrete cubes Howeverin geotechnical design the volume of soil involved in a geotechnical failureis usually much greater than the volume of soil involved in a single test theconsequence of this is examined in the following paragraphs

In geotechnical design Equation (89) may not be applicable because thedistribution of soil properties may not be normal and if V(X) is large thecalculated Xk value may violate a physical lower bound for example a crit-ical state value of φprime while if V(X) is greater than 06(= 11645) then thecalculated Xk value is negative This problem with Equation (89) does not

Eurocode 7 and RBD 311

occur in the case of structural materials such as steel for which the V(X) val-ues are normally about 01 normally distributed and do not have physicallower bounds

As noted above Equation (89) is not normally applicable in geotechni-cal design because the volume of soil involved in a failure is usually muchlarger than the volume of soil involved in a single field test or in a teston a laboratory specimen Since soil even homogeneous soil is inherentlyvariable the value affecting the occurrence of a limit state is the meanie average value over the relevant slip surface or volume of ground nota locally measured low value Hence in geotechnical designs the charac-teristic strength is the 5 fractile of the mean strengths measured alongthe slip surface or in the volume of soil affecting the occurrence of thatlimit state which is not normally the 5 fractile of the test results How-ever a particular situation when Equation (89) is applicable in geotechnicaldesign is when centrifuge tests are used to obtain the mean soil strengthof the soil on the entire failure surface because then the characteristicvalue is the 5 fractile of the soil strengths obtained from the centrifugetests

How the volume of soil involved in a failure affects the characteristicvalue is demonstrated by considering the failures of a slope and a foun-dation The strength value governing the stability of the slope is the meanvalue of a large volume of soil involving the entire slip surface while thefailure mechanism of a spread foundation involves a much smaller volumeof ground and if the spread foundation is in a weak zone of the sameground as in the slope the mean value governing the stability of the foun-dation will be lower than the mean value governing the stability of theslope Hence the characteristic value chosen to design the spread founda-tion should be a more cautious (ie a lower) estimate of the mean valuecorresponding to a local mean value than the less cautious estimate of themean value corresponding to a global mean value chosen to design theslope in the same soil Refined slope stability analyses have shown thatthe existence of weaker zones can change the shape of slip surface cross-ing these zones Thus careful attention needs to be paid to the influence ofweaker zones even if some simplified practical methods have been proposedto account for their influence when deriving characteristic values (Breysseet al 1999)

A further difference between geotechnical and structural design is that nor-mally only a very limited number of test results are available in geotechnicaldesign Arising from this the mean and standard deviation values obtainedfrom the test results may not be the same as the mean and standard deviationvalues of the soil volume affecting the occurrence of the limit state

Because of the nature of soil and soil tests leading to the problems outlinedabove when using the EN 1990 definition of the characteristic value for soilproperties the definition of the characteristic value of a soil property is given

312 Trevor L L Orr and Denys Breysse

in Eurocode 7 as ldquoa cautious estimate of the value affecting the occurrenceof the limit staterdquo There is no mention in the Eurocode 7 definition of theprescribed probability or the unlimited test series that are in the EN 1990definition Each of the terms ldquocautiousrdquo ldquoestimaterdquo and ldquovalue affecting theoccurrence of the limit staterdquo in the Eurocode 7 definition of the characteristicvalue is important and how they should be interpreted is explained in thefollowing paragraphs

The characteristic value is an estimated value and the selection of thisvalue from the very limited number of test results available in geotechni-cal designs involves taking account of spatial variability and experience ofthe soil and hence using judgment and caution The problem in selectingthe characteristic value of a ground property is deciding how cautious theestimate of the mean value should be In an application rule to explain thecharacteristic value in geotechnical design Eurocode 7 states that ldquoif sta-tistical methods are used the characteristic value should be derived suchthat the calculated probability of a worst value governing the occurrence ofthe limit state under consideration is not greater than 5rdquo It also statesthat such methods should differentiate between local and regional samplingand should allow the use of a priori knowledge regarding the variabilityof ground properties However Eurocode 7 provides no guidance for thedesigner on how this ldquocautious estimaterdquo should be chosen instead it reliesmainly on the designerrsquos professional expertise and relevant experience ofthe ground conditions

The following equation for the characteristic value corresponding to a95 confidence level that the actual mean value is greater than this valueis given by

Xk = m(X) minus (tradic

N)s(X) (810)

where m(X) and s(X) are the mean and the standard deviation of the sampleof test values and t is known as the Student t value (Student 1908) the valueof which depends on the actual number N of test values considered and onthe required confidence level

The following simpler equation for the characteristic value which hasbeen found to be useful in practice has been proposed by Schneider (1997)

Xk = m(X) minus 05s(X) (811)

The selection of the characteristic value is illustrated by the followingexample Ten undrained triaxial tests were carried out on samples obtainedat various depths from different boreholes through soil in which 10 m longpiles are to be founded and the measured undrained shear strength cu valuesare plotted in Figure 81 It is assumed that the measured cu values are thecorrect values at the locations where the samples were taken so that there is

Eurocode 7 and RBD 313

0

2

4

6

8

10

12

14

20 40 60 80 1000

Undrained shear strength cu kPa

Dep

th b

elow

gro

und

leve

l (m

)

Test values

Mean shaft and toe values

Eqn 811 char valuesover shaft and toe

Eqn 810 char valuesover shaft and toe

5 fractile char values overshaft and toe (Eqn 89)

Design values based onEqn 811

Figure 81 Test results and characteristic values obtained using different equations

no bias or variation in the measured values due to the testing method Themeans of the 7 measured values along the shaft and the 4 measured values inthe zone 1 m above and 2 m below the toe were calculated as m(cus) = 61 kPaand m(cut) = 50 kPa respectively and these values are plotted in Figure 81The standard deviations of the measured cu values along the shaft and aroundthe toe were calculated as s(cus) = 143 kPa and s(cut) = 66 kPa givingcoefficients of variation V(cus) = 023 and V(cut) = 013

If Equation (89) is used and the means and standard deviations of thetwo sets of test results are assumed to be the same as the means andstandard deviations of all the soil along the shaft and around the toethe characteristic cu along the shaft is cusk = m(cus) minus 1645 s(cus) =610minus1645times143 = 375 kPa while the characteristic cu around the toe iscutk = m(cut) minus 164 s(cut) = 500 minus 164 times 66 = 392 kPa These resultsare clearly incorrect as they are both less than the lowest measured valuesand the calculated characteristic value around the toe is greater than thecharacteristic value along the shaft They provide the 5 fractiles of the cuvalues measured on the relatively small volume of soil in the samples testedand not the 5 estimates of the mean cu values of all the soil over the eachfailure zone

Using Equation (810) and choosing t =1943 for N =7 the characteristiccu value along the shaft is

cusk =m(cus)minus(1943radic

N)s(cus)=610minus(1943radic

7)143=505 kPa

314 Trevor L L Orr and Denys Breysse

and choosing t = 2353 for N = 4 the characteristic cu value around thetoe is

cut k = m(cut) minus (2353radic

N)s(cut) = 500 minus (2353radic

4)66 = 423 kPa

Using Schneiderrsquos Equation (811) the characteristic cu value along theshaft is

cusk = m(cus) minus 05s(cus) = 610 minus 05 times 143 = 539 kPa

and the characteristic cu value around the toe is

cutk = m(cut) minus 05s(cut) = 500 minus 05 times 66 = 467 kPa

These characteristic values are all plotted in Figure 81 and show that forthe test results in this example the values obtained using Equations (810)and (811) are similar with the values obtained using Equation (810) beingslightly more conservative than those obtained using Equation (811) Fromthe results obtained in this example it can be seen that the characteristicvalue of a ground property is similar to the value that has conven-tionally been selected for use in geotechnical designs as noted by Orr(1994)

When only very few test results are available interpretation of the char-acteristic value using the classical statistical approach outlined above is notpossible However using prior knowledge in the form of local or regionalexperience of the particular soil conditions the characteristic value may beestimated using a Bayesian statistical procedure as shown for example byOvesen and Denver (1994) and Cherubini and Orr (1999) An example ofprior knowledge is an estimate of coefficient of variation of the strength fora particular soil deposit Typical ranges of V for soil parameters are givenin Table 83 The value V = 010 for the coefficient of variation of tanφprime isused in the example in Section 815

Table 83 Typical range of V for soil parameters

Soil parameter Typical range of V values RecommendedV value if limitedtest resultsavailable

tanφprime 005ndash015 010cprime 020ndash040 040cu 020ndash040 030mv 020ndash040 040γ (weight density) 001ndash010 0

Eurocode 7 and RBD 315

Duncan (2000) has proposed a practical rule to help the engineer in assess-ing V This rule is based on the ldquo3-sigma assumptionrdquo which is equivalentto stating that nearly all the values of a random property lie within the[Xmin Xmax] interval where Xmin and Xmax are equal to Xmean minus 3σ andXmean +3σ respectively Thus if the engineer is able to estimate the ldquoworstrdquovalue Xmin and the ldquobestrdquo value Xmax that X can take then σ can be cal-culated as σ = [Xmax minus Xmin]6 and Equation (89) used to determine theXk value However Duncan added that ldquoa conscious effort should be madeto make the range between Xmin and Xmax as wide as seemingly possibleor even wider to overcome the natural tendency to make the range toosmallrdquo

810 Characteristic values of pile resistances andξ values

When the compressive resistance of a pile is measured in a series of staticpile load tests these tests provide the values of the measured mean com-pressive resistance Rcm controlling the settlement and ultimate resistanceof the pile at the locations of the test piles In this situation and whenthe structure does not have the capacity to transfer load from ldquoweakrdquo toldquostrongrdquo piles Eurocode 7 states that the characteristic compressive pileresistance Rck should be obtained by applying the correlation factors ξ inTable 84 to the mean and lowest Rcm values so as to satisfy the followingequation

Rck = Min (Rcm)mean

ξ1

(Rcm)min

ξ2

(812)

Using Equation (812) and the ξ values in Table 84 it is found that whenonly one static pile load test is carried out the characteristic pile resistanceis the measured resistance divided by 14 whereas if five or more tests arecarried out the characteristic resistance is equal to lowest measured valueWhen pile load tests are carried out the tests measure the mean strengthof the soil over the relevant volume of ground affecting the failure of thepile Hence when assessing the characteristic value from pile load tests the

Table 84 Correlation factors to derive characteristic values from static pile loadtests

ξ for nlowast = 1 2 3 4 ge 5

ξ1 14 13 12 11 10ξ2 14 12 105 10 10

lowastn = number of tested piles

316 Trevor L L Orr and Denys Breysse

ξ factors take account of the variability of the soil and the uncertainty in thesoil strength across the test site

811 Design Approaches and partial factors

For ULSs involving failure in the ground referred to as GEO ultimatelimit states Eurocode 7 provides three Design Approaches termed DesignApproaches 1 2 and 3 (DA1 DA2 and DA3) and recommended valuesof the partial factors γF = γE γM and γR for each Design Approachthat are applied to the representative or characteristic values of theactions soil parameters and resistances as appropriate in accordance withEquations (83)ndash(86) to obtain the design values of these parameters Thesets of recommended partial factor values are summarized in Table 85 Thistable shows that there are two sets of partial factors for Design Approach 1Combination 1 (DA1C1) when the γF values are greater than unity and

Table 85 Eurocode 7 partial factors on actions soil parameters and resistances forGEOSTR ultimate limit states in persistent and transient design situations

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Partial factors onactions (γF) orthe effects ofactions (γE)

Set A1 A2 A1 A1Structural

actions

A2Geotechnical

actions

Permanentunfavorableaction

γG 135 10 135 135 10

Permanentfavorable action

γG 10 10 10 10 10

Variableunfavorableaction

γQ 15 13 15 15 13

Variable favorableaction

γQ 0 0 0 0 0

Accidental action γA 10 10 10 10 10

Partial factors forsoil parameters(γM)

Set M1 M2 M1 M2

Angle of shearingresistance (thisfactor is appliedto tanφprime)

γtanφprime 10 125 10 125

Continued

Table 85 Contrsquod

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Effectivecohesion cprime

γcprime 10 125 10 125

Undrained shearstrength cu

γcu 10 14 10 14

Unconfinedstrength qu

γqu 10 14 10 14

Weight density ofground γ

γγ

10 10 10 10

Partial resistancefactors (γR)

Spreadfoundationsretainingstructures andslopes

Set R1 R1 R2 R3

Bearing resistance γRv 10 10 14 10Sliding resistance

incl slopesγRh 10 10 11 10

Earth resistanc γRh 10 10 14 10

Pile foundations ndashdriven piles

Set R1 R4 R2 R3

Base resistance γb 10 13 11 10Shaft resistance

(compression)γs 10 13 11 10

Total combined(compression)

γst 10 13 11 10

Shaft in tension γt 125 16 115 11

Pile foundations ndashbored piles

Set R1 R4 R2 R3

Base resistance γRe 125 16 115 10Shaft resistance

(compression)γb 10 13 11 10

Total combined(compression)

γs 115 15 11 10

Shaft in tension γt 125 16 115 11

Continued

318 Trevor L L Orr and Denys Breysse

Table 85 Contrsquod

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Pile foundations ndashCFA piles

Set R1 R4 R2 R3

Base resistance γRe 11 145 11 10Shaft resistance

(compression)γb 10 13 11 10

Total combined(compression)

γs 11 14 11 10

Shaft in tension γt 125 16 115 11

Prestressedanchorages

Set R1 R4 R2 R3

Temporaryresistance

γat 11 11 11 10

Permanentresistance

γap 11 11 11 10

lowastIn the design of piles set M1 is used for calculating the resistance of piles or anchors

the γM and γR values are equal to unity except for the design of piles andanchors and Combination 2 (DA1C2) when the γF value on permanentactions and the γR values are equal to unity and the γM values are greaterthan unity again except for the design of piles and anchorages when theγM values are equal to unity and the γR values are greater than unity InDA2 the γF and γR values are greater than unity while the γM values areequal to unity DA3 is similar to DA1C2 except that a separate set of γFfactors is provided for structural loads and the γR values for the design of acompression pile are all equal to unity so that this Design Approach shouldnot be used to design a compression pile using the compressive resistancesobtained from pile load tests

When on the resistance side partial material factors γM greater than unityare applied to soil parameter values the design is referred to as a materi-als factor approach (MFA) whereas if partial resistance factors γR greaterthan unity are applied to resistances the design is referred to as a resistancefactor approach (RFA) Thus DA1C2 and DA3 are both materials factorapproaches while DA2 is a resistance factor approach

The recommended values of the partial factors given in Eurocode 7 forthe three Design Approaches are based on experience from a number ofcountries in Europe and have been chosen to give designs similar to thoseobtained using existing design standards The fact that Eurocode 7 has threeDesign Approaches with different sets of partial factors means that use of

Eurocode 7 and RBD 319

the different Design Approaches produces designs with different overallfactors of safety as shown by the results of the spread foundation exam-ple in Section 813 presented in Table 87 and hence different reliabilitylevels

In the case of SLS calculations the design values of Ed and Cd inEquation (82) are equal to the characteristic values since Eurocode 7 statesthat the partial factors for SLS design are normally taken as being equal to 10

8111 Model uncertainty

It should be noted that as well as accounting for uncertainty in the loadsmaterial properties and the resistances the partial factors γF = γE γM and γRalso account for uncertainty in the calculation models for the actions and theresistances The influence of model uncertainty on the reliability of designsis examined in Section 818

812 Calibration of partial factors andlevels of reliability

Figure 82 which is an adaptation of a figure in EN 1990 presents anoverview of the various methods available for calibrating the numericalvalues of the γ factors for use with the limit state design equations in theEurocodes According to this figure the factors may be calibrated using eitherof two procedures

Deterministic methodsProcedure 1

Historical methodsEmpirical methods

Probabilistic methodsProcedure 2

Full probabilistic(Level III)

FORM(Level II)

Calibration Calibration Calibration

Semi-probabilisticmethods(Level I)

Partial factordesign

Method c

Method bMethod a

Figure 82 Overview of the use of reliability methods in the Eurocodes (from EN 1990)

320 Trevor L L Orr and Denys Breysse

bull Procedure 1 On the basis of calibration with long experience oftraditional design This is the deterministic method referred to asMethod a in Figure 82 which includes historical and empiricalmethods

bull Procedure 2 On the basis of a statistical evaluation of experimental dataand field observations which should be carried out within the frame-work of a probabilistic reliability theory Two probabilistic methods areshown in Figure 82 Method b which is a full probabilistic or Level IIImethod involving for example Monte Carlo simulations and Method cthe first-order reliability method (FORM) or Level II method In theLevel II method rather than considering failure as a continuous func-tion as in the Level III method the reliability is checked by determiningthe safety index β or probability of failure Pf at only a certain point onthe failure surface

As pointed out by Gulvanessian et al (2002) the term ldquosemi-probabilisticmethods (Level I)rdquo used in Figure 82 which is taken from EN 1990 is notnow generally accepted and may be confusing as it is not defined in EN 1990It relates to the partial factor design method used in the Eurocodes wherethe required target reliability is achieved by applying specified partial factorvalues to the characteristic values of the basic variables

Commenting on the different calibration design methods EN 1990 statesthat calibration of the Eurocodes and selection of the γ values has beenbased primarily on Method a ie on calibration to a long experience ofbuilding tradition The reason for this is to ensure that the safety levels ofstructures designed to the Eurocodes are acceptable and similar to existingdesigns and because Method c which involves full probabilistic analysesis seldom used to calibrate design codes due to the lack of statistical dataHowever EN 1990 states that Method c is used for further developmentof the Eurocodes Examples of this in the case of Eurocode 7 include therecent publication of some reliability analyses to calibrate the γ values andto assess the reliability of serviceability limit state designs eg Honjo et al(2005)

The partial factor values chosen in the ENV version of Eurocode 7 wereoriginally based on the partial factor values in the Danish Code of Practicefor Foundation Engineering (DGI 1978) because when work started onEurocode 7 in 1981 Denmark was the only western European country thathad a limit state geotechnical design code with partial factors It is inter-esting to note that Brinch Hansen (1956) of Denmark was the first to theuse the words ldquolimit designrdquo in a geotechnical context He was also thefirst to link the limit design concept closely to the concept of partial fac-tors and to introduce these two concepts in Danish foundation engineeringpractice (Ovesen 1995) before they were adopted in structural design inEurope

Eurocode 7 and RBD 321

Table 86 Target reliability index β and target probability of failure pf values

Limit state Target Reliability Index β Target Probability of Failure Pf

1 year 50 years 1 year 50 years

ULS 47 38 1 times 10minus6 72 times 10minus5

SLS 29 15 2 times 10minus3 67 times 10minus2

EN 1990 states that if Procedure 2 or a combination of Procedures 1and 2 is used to calibrate the partial factors then for ULS designs theγ values should be chosen such that the reliability levels for representativestructures are as close as possible to the target reliability index β valuegiven in Table 86 which is 38 for 50 years corresponding to the lowfailure rate or low probability of failure Pf = 72 times 10minus5 For SLS designsthe target β value is 15 corresponding to a higher probability of failurePf = 67 times 10minus2 than for ULS designs The ULS and SLS β values are 47and 29 for 1 year corresponding to Pf values of 1 times 10minus6 and 2 times 10minus3respectively It should be noted that the β values of 38 and 47 provide thesame reliability the only difference being the different lengths of the refer-ence periods Structures with higher reliability levels may be uneconomicalwhile those with lower reliability levels may be unsafe Examples of the useof reliability analyses in geotechnical designs are provided in Sections 814and 816

As mentioned by Gulvanessian et al (2002) the estimated reliabilityindices depend on many factors including the type of geotechnical structurethe loading and the ground conditions and their variability and conse-quently they have a wide scatter as shown in Section 817 Also the resultsof reliability analyses depend on the assumed theoretical models used todescribe the variables and the limit state as shown by the example inSection 818 Furthermore as noted in EN 1990 the actual frequency offailure is significantly dependent on human error which is not consid-ered in partial factor design nor are many unforeseen conditions suchas an unexpected nearby excavation and hence the estimated β valuedoes not necessarily provide an indication of the actual frequency offailure

EN 1990 provides for reliability differentiation through the introduc-tion of three consequences classes high medium and low with differentβ values to take account of the consequences of failure or malfunction of thestructure The partial factors in Eurocode 7 correspond to the medium conse-quences class In Eurocode 7 rather than modifying the partial factor valuesreliability differentiation is achieved through the use of the GeotechnicalCategories described in Section 86

322 Trevor L L Orr and Denys Breysse

813 Design of a spread foundation using thepartial factor method

The square pad foundation shown in Figure 83 based on Orr (2005) ischosen as an example of a spread foundation designed to Eurocode 7 usingthe partial factor method For the ULS design the consequence of usingthe different Design Approaches is investigated The foundation supports acharacteristic permanent vertical central load Gk of 900 kN which includesthe self weight of the foundation plus a characteristic variable vertical cen-tral load Qk of 600 kN and is founded at a depth of 08 m in soil withcuk = 200 kPa cprime

k = 0 φprimek = 35 and mvk = 0015m2MN The width of

the foundation is required to support the given loads without the settlementof the foundation exceeding 25 mm assuming the groundwater cannot riseabove the foundation level

The ULS design is carried out by checking that the requirement Ed le Rdis satisfied The design action effect Ed is equal to the design loadFd = γGGk + γQQk and obtained using the partial factors given in Table 85for Design Approaches 1 2 and 3 as appropriate For drained conditionsthe design resistance Rd is calculated using the following equations for thebearing resistance and bearing factors for drained conditions obtained fromAnnex D of Eurocode 7

r = RA = (12)Bγ primeNγ sγ + qprimeNqsq (813)

where

Nq = eπ tanφprimetan(45 +φprime2)

Nγ = 2(Nq minus 1)tanφprime

sq = 1 + (BL)sinφprime

sγ = 1 minus 03(BL)

Gk = 900kN Qk = 600kN

GWLB =

d = 08 m

Soil Stiff tillcuk = 200 kPack = 0 kPaφk = 35degγ= 22 kNm3

mvk = 0015 m2MN

Maximum allowable settlement = 25mm

Figure 83 Square pad foundation design example

Eurocode 7 and RBD 323

Table 87 Design foundation widths actions bearing resistances and overall factors ofsafety for pad foundation design example for drained conditions

B (m) Fk (kN) Rk (kN) Fd = Rd OFS = RkFk

DA1C1 (134)lowast 1500 21150 21150 (141)lowastDA1C2 171 1500 34749 16800 232DA2 156 1500 29610 21150 197DA3 190 1500 43726 21150 292

lowast Since B for DA1C1 is less than B for DA1C2 the B for DA1C1 is not the DA1 design width

and applying the partial factors for Design Approaches 1 2 and 3 given inTable 85 either to tanφprime

k or to the characteristic resistance Rk calculatedusing the φprime

k value as appropriate The calculated ULS design widths B forthe different Design Approaches for drained conditions are presented inTable 87 together with the design resistances Rd which are equal to thedesign loads Fd For Design Approach 1 DA1C2 is the relevant combina-tion as the design width of 171 m is greater than the DA1C1 design widthof 134 m For undrained conditions the DA1C2 design width is 137 mcompared to 171 m for drained conditions hence the design is controlledby the drained conditions

The overall factors of safety OFS using each Design Approach obtainedby dividing Rk by Fk are also given in Table 87 The OFS values in Table 87show that for the φprime value and design conditions in this example DesignApproach 3 is the most conservative giving the highest OFS value of 292Design Approach 2 is the least conservative giving the lowest OFS value of197 and Design Approach 1 gives an OFS value of 232 which is betweenthese values The relationships between the characteristic and the designvalues of the resistances and the total loads for each Design Approach areplotted in Figure 84 The lines joining the characteristic and design valuesshow how for each Design Approach starting from the same total charac-teristic load Fk = Gk +Qk = 900+600 = 1500 kN the partial factors causethe design resistances to decrease andor the design loads to increase to reachthe design values where Rd = Fd Since this problem is linear with respectto the total load when the ratio between the permanent and variable loadsis constant and when φprime is constant lines of constant OFS correspond-ing to lines through the characteristic values for each Design Approachplot as straight lines on Figure 84 These lines show the different safetylevels and hence the different reliability levels obtained using the differ-ent Design Approaches for this square pad foundation design exampleIt should be noted that the design values given in Table 87 and plottedin Figure 84 are for ULS design and do not take account of the settlementof the foundation

324 Trevor L L Orr and Denys Breysse

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000

Resistance R (kN)

Tot

al fo

unda

tion

load

(G

+ Q

) kN

Design values

DA3DA1C1

DA2

Characteristic(unfactored)

values

Failure criterionRd = Fd

Safe region

OFS = (141)

OFS = 197

OFS = 232

OFS = 292

DA1C2

Figure 84 OFS values using the different Design Approaches for spread foundationexample and relationship between characteristic and design values

The settlement needs be checked by carrying out an SLS design calcula-tion to ensure that the settlement of the foundation due to the characteristicloading Ed does not exceed the allowable settlement Cd of 25 mm ieEd le Cd The SLS loads are calculated using the characteristic loads sincethe SLS partial factors are equal to unity Two settlement components areconsidered the immediate settlement si and the consolidation settlement scThe immediate settlement is evaluated using elasticity theory and the follow-ing equation which has the form of that given in Annex F2 of Eurocode 7

si = p(1 minus ν2u)Bf Eu (814)

where p is the SLS net bearing pressure induced at the base level of thefoundation B is the foundation width Eu is the undrained Youngrsquos modulusνu is undrained Poissonrsquos ratio (equal to 05) and f is a settlement coefficientwhose value depends on the nature of the foundation and its stiffness (forthe settlement at the center of a flexible square foundation in this example

Eurocode 7 and RBD 325

f = 112 is used) For the stiff till it is assumed that Eu = 750cu = 750times200 =150 MPa The consolidation settlement is calculated by dividing the groundbelow the foundation into layers and summing the settlement of each layerusing the following equation

sc = micromvhσ primei = micromvhαip (815)

where mv is the coefficient of volume compressibility = 0015m2MN(assumed constant in each layer for the relevant stress level) h is thethickness of the layer σi

prime is the increment in vertical effective stressin the ith layer due to p and is given by αip where αi is the coeffi-cient for the increase in vertical effective stress in the ith layer obtainedfrom the Fadum chart and micro is the settlement reduction coefficient toaccount for the fact that the consolidation settlement is not one-dimensional(Craig 2004) and its value is chosen as 055 for the square foundation onstiff soil

The SLS is checked for the DA1C2 design foundation width B = 171 mThe SLS bearing pressure is

p = (Gk + Qk)A = (900 + 600)1712 = 5130 kPa

The immediate settlement

si = p(1 minus ν2u)Bf Eu = 5130 times (1 minus 052)171 times 112150 = 49 mm

To calculate the consolidation settlement the soil below foundation isdivided into four layers 10 m thick The increases in vertical stressσ1

prime = αip at the centers of the layers due to the foundation are estimatedfrom the αi factors which are 090 040 018 and 01 giving stress increasesof 463 206 93 and 51 kPa Hence the consolidation settlement

sc =micromvhαip=055times0015times10times(090+040+018+010)5146

=055times0015times10times158times5146=61 mm

Total settlement s = si + sc = 49 + 61 = 110 mm lt 25 mmTherefore the SLS requirement is satisfied for the DA1C2 foundation

width of 171 m

814 First-order reliability method (FORM)analyses

In EN 1990 a performance function g which is referred to as the safetymargin SM in this section is defined as

g = SM = R minus E (816)

where R is the resistance and E is the action effect SM is used in LevelII FORM analyses (see Figure 82) to investigate the reliability of a design

326 Trevor L L Orr and Denys Breysse

Assuming E R and SM are all random variables with Gaussian distributionsthe reliability index is given by

β = micro(SM)σ (SM) (817)

where micro(SM) is the mean value and σ (SM) is the standard deviation of SMgiven by

micro(SM) = micro(R) minusmicro(E) (818)

σ (SM) =radicσ (R)2 +σ (E)2 (819)

The probability of failure is the probability that SM lt 0 and is obtainedfrom

Pf = P[SM lt 0] = P[R lt E] = (minusβ) = 1 minus(β) (820)

where denotes the standardized normal cumulative distribution functionThe example chosen to illustrate the use of the FORM analysis is

the design for undrained conditions of the square foundation shown inFigure 83 Performing a FORM analysis for drained conditions is morecomplex due to the nonlinear expression for the drained bearing resistancefactors Nq and Nγ (see Section 813) The action effect and the bearingresistance for undrained conditions are

E = Fu = G + Q (821)

R = Ru = B2(Nccusc + γD) = B2((π + 2)12cu + γD) (822)

If these variables are assumed to be independent then the means andstandard deviations of Fu and Ru are

micro(Fu)= micro (G) +micro (Q) (823)

σ(Fu)=radicσ (G)2 +σ (Q)2 (824)

micro(Ru) = B2((π + 2)12micro(cu) + γD) (825)

σ (Ru) = B2((π + 2)12)σ (cu) (826)

In order to ensure the relevance of the comparisons between the partial factordesign method and the FORM analysis the means of all the three randomvariables are selected so that the characteristic values are equal to the givencharacteristic values of Gk = 900 kN Qk = 600 kN and cuk = 200 kPa Themean values of these parameters needed in the FORM analysis to determine

Eurocode 7 and RBD 327

Table 88 Parameter values for reliability analyses of the spread foundation example

Coefficient of variation V Mean micro Standard deviation σ

ActionsG 0 900 kN 0 kNQ 015 4587 kN 688 kNSoil parameterstanφprime 010 0737 (equiv 364˚) 0074 (equiv +minus 3˚)cu 030 2353 kPa 706 kPamv 040 001875 m2MN 00075 m2MN

the reliability are calculated from these characteristic values as follows usingthe appropriate V values given in Table 88

bull for the permanent load in this example it is assumed that V(G) = 0in accordance with EN 1990 as noted in Section 88 so that the meanpermanent load is equal to the characteristic value since reorganizingEquation (87) gives

micro(G) = Gk(1 + 1645V(G)) = Gk = 900 kN (827)

bull for the variable load it is assumed to be normally distributed as notedin Section 88 so that for an occurrence probability of 002 or 2 themultiplication factor in Equation (89) is 2054 rather than 1654 for5 and hence for V(Q) = 015 the mean value of the variable load isgiven by

micro(Qm) = Qk(1 + 2054V(Q)) (828)

micro(Q) = 600(1 + 2054 times 015) = 4587 kN

bull for material parameters using Equation (89) by Schneider (1997)

micro(X) = Xk(1 minus 05V(X)) (829)

micro(cu) = cuk(1 minus 05 times V(X)) = 200(1 minus 05 times 030) = 2353 kPa

Although not required for the undrained FORM analysis of the squarefoundation the mean values of tanφprime and mv which are used in the MonteCarlo simulations in Section 816 are also calculated using Equation (829)and are given in Table 88 together with the standard deviation valuescalculated by multiplying the mean and V values Hence assumingGaussian distributions and substituting the mean G Q and cu values inEquations (823) (825) and (818) for micro(Fu) and micro(Ru) and micro(SM) and

328 Trevor L L Orr and Denys Breysse

using the DA1C1 foundation width of B = 137 m reported in Section 813for undrained conditions gives

micro(Fu) = micro(G) +micro(Q) = 9000 + 4587 = 13587 kN

micro(Ru) = B2((π + 2)12micro(cu) + γD)

= 1372((π + 2)12 times 2353 + 22 times 08) = 27578 kN

micro(SM) = micro(Ru) minusmicro(Fu) = 27578 minus 13587 = 13991 kN

Substituting the standard deviation values for G Q and cu in Equations(824) (826) and (819) for σ (Fu) σ (Ru) and σ (SM) gives

σ(Fu)=radicσ (G)2 +σ (Q)2 =

radic02 + 6882 = 688 kN

σ (Ru) = B2(π + 2)12σ (cu) = 1372(π + 2)12 times 706 = 7629 kN

σ (SM) =radicσ(Ru)2 +σ

(Fu)2 =

radic76292 + 6882 = 7660 kN

Hence substituting the values for micro(SM) and σ (SM) in Equation (817) thereliability index is

β = micro(SM)σ (SM) = 139917660 = 183

and from Equation (820) the probability of failure is

PfULS = (minusβ) = (minus183) = 0034 = 34 times 10minus2

The calculated β value of 183 for undrained conditions for a foundationwidth of 137 m is much less than the target ULS β value of 38 and hencethe calculated probability of failure of 34 times 10minus2 is much greater than thetarget value of 72 times 10minus5

It has been seen in Table 87 that the design width for drained conditionsis B = 171 m for DA1C2 and hence this width controls the design It istherefore appropriate to check the reliability of this design foundation widthfor drained conditions Substituting B = 171 m in Equations (825) and(826) gives β = 230 and PfULS = 106 times 10minus2 Although this foundationwidth has a greater reliability both values are still far from and do notsatisfy the target values The reliability indices calculated in this examplemay indirectly demonstrate that in general geotechnical design equationsare usually conservative ie because of model errors they underpredict thereliability index and overpredict the probability of failure The low calculatedreliability indices shown above can arise because the conservative modelerrors are not included The reliability index is also affected by uncertaintiesin the calculation model as discussed in Section 818 However Phoon and

Eurocode 7 and RBD 329

Kulhawy (2005) found that the mean model factor for undrained bearingcapacity at the pile tip is close to unity If this model factor is applicable tospread foundations it will not account for the low β values reported above

The overall factor of safety OFS as defined in Section 813 is obtainedby dividing the characteristic resistance by the characteristic load ForB = 171 m Rk = 1712((π + 2) times 12 times 200 + 22 times 08) = 36598 kN andFk = 900 + 600 = 1500 kN so that OFS = RkFk = 241821500 = 244Thus using the combination of the recommended partial factors of 14 oncu and 13 on Q for undrained conditions with B = 171 m gives the OFSvalue of 244 which is similar in magnitude to the safety factor often used intraditional foundation design whereas the calculated probability of failureof 106 times 10minus2 for this design is far from negligible

815 Effect of variability in cu on design safety andreliability of a foundation in undrainedconditions

To investigate how the variability of the ground properties affects the designsafety and reliability of a foundation in undrained conditions the influenceof the variability of cu on MR where MR is ratio of the mean resistance tothe mean load micro(Ru)micro(Fu) is first examined It should be noted that theMR value is not the traditional factor of safety used in deterministic designswhich is normally a cautious estimate of the resistance divided by the loadnor is it the overall factor of safety for Eurocode 7 designs defined in Section813 as OFS = RkFk The influence of the variability of cu on MR is investi-gated by varying V(cu) in the range 0 to 040 As in the previous calculationsthe characteristic values of the variables G Q and cu are kept equal to thegiven characteristic values so that the following analyses are consistent withthe previous partial factor design and the FORM analysis An increase inV(cu) causes micro(Ru) and hence MR to increase as shown by the graphs ofMR against V(cu) in Figure 85 for the two foundation sizes B = 137 mand 171 m The increase in MR with increase in V(cu) is due to the greaterdifference between the characteristic and mean cu values as the V(cu) valueincreases For undrained conditions with B = 137 m and V(cu) = 03 theratio MR = micro(Ru)micro(Fu) = 2757813587 = 203 whereas for V(cu) = 0when micro(Ru) = Rk then MR = Rkmicro(Fu) = 2349113587 = 173

The fact that MR increases as V(cu) increases demonstrates the problemwith using MR which is based on the mean R value as the safety factorand not taking account of the variability of cu because it is not logical thatthe safety factor should increase as V(cu) increases This is confirmed bythe graphs of β and PfULS plotted against V(cu) in Figures 86 and 87which show as expected that for a given foundation size the reliabilityindex β decreases and the ULS probability of failure of the foundationPfULS increases as V(cu) increases This problem with MR is overcome ifthe safety factor is calculated using a cautious estimate of the mean R value

330 Trevor L L Orr and Denys Breysse

1

15

2

25

3

35

000 010 020 030 040

B=171 m

B=137 m

V(cu)

OFS = 161 (B=137 m)

OFS = 244 (B=171m)

MR

Figure 85 Change in mean resistance mean load ratio MR with change in V(cu)

00

05

10

15

20

25

30

35

40

45

50

040302010

B=171 m

B=137m

V(cu)

b

Figure 86 Change in reliability index β with change in V(cu)

that takes account of the variability of cu for example using the OFS andthe Eurocode 7 characteristic value as defined in Eurocode 7 rather than themean R value For undrained conditions with B = 171 m OFS = 244 ascalculated above while for B=139 m OFS = 161 However these OFS val-ues are constant as V(cu) increases as shown in Figure 85 and thus the OFSvalue provides no information on how the reliability of a design changes asV(cu) changes The fact that the OFS value does not provide any informationon how the reliability of a design changes as V(cu) changes demonstrates theimportance of and hence the interest in reliability modeling

Eurocode 7 and RBD 331

10E-07

10E-06

10E-05

10E-04

10E-03

10E-02

10E-01

10E+0000 01 02 03 04

B=171 m

B=137 m

V(cu)

PfULS

Figure 87 Change in probability of failure Pf with change in V(cu)

The graphs in Figures 86 and 87 show that for a given foundation sizethe reliability index β decreases and the ULS probability of failure of thefoundation for undrained conditions PfUL increases as V(cu) increases Thetarget reliability index β = 38 and the corresponding target probability offailure PfULS = 72times10minus5 are only achieved if V(cu) does not exceed 017for B = 171 m the design width required for drained conditions whichexceeds that for undrained conditions Since this V(cu) value is less than thelowest V(cu) value of 020 typically encountered in practice as shown bythe range of V(cu) in Table 83 this demonstrates that undrained designs toEurocode 7 such as this spread foundation have a calculated ULS probabil-ity of failure greater than the target value Choosing the mean V(cu) value of030 from Table 83 for this example with B = 171 m gives β = 230 withPfULS = 106 times 10minus2

816 Monte Carlo analysis of the spreadfoundation example

Since a FORM analysis is an approximate analytical method for calculat-ing the reliability of a structure an alternative and better method is to usea Monte Carlo analysis which involves carrying out a large number ofsimulations using the full range of combinations of the variable param-eters to assess the reliability of a structure In this section the spreadfoundation example in Section 813 is analyzed using Monte Carlo simu-lations to estimate the reliability level of the design solution obtained bythe Eurocode 7 partial factor design method As noted in Section 812 and

332 Trevor L L Orr and Denys Breysse

shown in Table 86 the target reliability levels in EN 1990 correspond to β

values equal to 38 at the ULS and 15 at the SLS respectively The designfoundation width B to satisfy the ULS using DA1C2 was found to be171 m (see Table 87) and this B value also satisfies the SLS requirementof the foundation settlement not exceeding a limiting value Cd of 25 mm

8161 Simulations treating both loads and the three soilparameters as random variables

Considering the example in Section 813 five parameters can be consideredas being random variables two on the ldquoloading siderdquo G and Q and three onthe ldquoresistance siderdquo tanφprime cu and mv The foundation breadth B depthd and the soil weight density γ are considered to be constants For thesake of simplicity all of the random variables are assumed to have normaldistributions with coefficients of variation V within the normal rangesfor these parameters (see the V values for loads in Section 88 and for soilparameters in Table 83) Since the parameter values given in the exampleare the characteristic values the mean values for the reference case need tobe calculated as in the case of the FORM analyses in Section 814 usingthe appropriate V values and Equations (827)ndash(829) The mean values andstandard deviations were calculated in Section 814 using these equationsfor the five random parameters and are given in Table 88

When using Monte Carlo simulations a fixed number of simulations ischosen (typically N = 104 or 105 and then for each simulation a set ofrandom variables is generated and the ULS and SLS conditions are checkedIt can happen that during the generation process with normally distributedvariables when V is high some negative parameter values can be generatedHowever these values have no physical meaning When this occurs they aretaken to be zero and the process is continued It has been checked that thesevalues have no significant influence on the estimated failure probability Anexample of a distribution generated by this process is the cumulative distribu-tion of φprime values in Figure 88 for the random variable tanφprime plotted aroundthe mean value of φprime = 364 Since in Eurocode 7 the ULS criterion to besatisfied is Fd le Rd it is checked in the ULS simulations that the calculatedaction F[G Q] does not exceed the calculated resistance R[B tan φprime] Thenumber of occurrences for which F gtR is counted (NFULS) and the estimatedULS failure probability PfULS is the ratio of NFULS to N

PfULS = NFULSN (830)

Similarly since the SLS criterion to be satisfied in Eurocode 7 is Ed le Cdin the SLS simulations it is checked that the action effect E[B G Q cu mv]is less than the limiting value of the deformation of the structure at the SLSCd The number of occurrences for which EgtCd is counted (NFSLS) and the

Eurocode 7 and RBD 333

0010203040506070809

1

20 25 30 35 40 45 50

fprime (deg)

F (fprime)

364

Figure 88 Cumulative distribution of generated φprime values

Table 89 Estimated and target Pf and β values for spread foundation example

Probability of failure Pf Reliability Index β

Estimated Target Estimated Target

ULS 16 times 10minus3 72 times 10minus5 296 38SLS 49 times 10minus3 67 times 10minus2 258 15

estimated SLS failure probability PfSLS is the ratio of NFSLS to N

PfSLS = NfSLSN (831)

The estimated Pf values from the ULS and SLS simulations are 16 times 10minus3

and 49 times 10minus3 respectively corresponding to estimated β values of296 and 258 These values are presented in Table 89 together with thetarget values These values show that the target reliability is not satisfiedat the ultimate limit state but is satisfied at the serviceability limit statesince βULS = 296 is less than the ULS target value of 38 and βSLS = 258is greater than the SLS target value of 15 This is similar to the resultobtained in Section 814 when using the FORM analyses to calculate thePf value for undrained conditions where for B = 171 m and V(cu) = 03PfULS = 969times10minus3 corresponding to β = 234 The influence of variationsin the V values for the soil parameter on the Pf value obtained from theMonte Carlo analyses is examined in Section 816

A limitation of the Monte Carlo simulations is that the calculated fail-ure probabilities obtained at the end of the simulations depend not only onthe variability of the soil properties which are focused on here but also

334 Trevor L L Orr and Denys Breysse

on the random distribution of the loading This prevents too general con-clusions being drawn about the exact safety level or β value correspondingto a Eurocode design since these values change if different assumptions aremade regarding the loading It is more efficient and more realistic to ana-lyze the possible ranges of these values and to investigate the sensitivity ofthe design to changes in the assumptions about the loading For examplea value of V = 005 has been chosen for the permanent load when accordingto EN 1990 a constant value equal to the mean value could be chosen as analternative (see Section 88)

Another limitation concerning the Monte Carlo simulations arises fromthe assumption of the statistical independence of the three soil propertiesThis assumption is not normally valid in practice since soil with a highcompressibility is more likely to have a low modulus It is of course possibleto perform simulations assuming some dependency but such simulationsneed additional assumptions concerning the degree of correlation betweenthe variables hence more data and the generality of the conclusions willbe lost It should however be pointed out that assuming the variables areindependent usually underestimates the probabilities of unfavorable eventsand hence is not a conservative assumption

817 Effect of soil parameter variability on thereliability of a foundation in drainedconditions

To investigate how the variability of the soil parameters affects the relia-bility of a drained foundation design and a settlement analysis simulationsare carried out based on reference coefficients of variation Vref for the fiverandom variables equal to the V values given in Table 88 that were used forthe FORM analyses The sensitivity of the calculated probability of failureis investigated for variations in the V values of the three soil parametersaround Vref in the range Vmin to Vmax shown in Table 810 All the prob-ability estimates are obtained with at least N = 40000 simulations thisnumber being doubled in the case of small probabilities All computationsare carried out for the square foundation with B = 171 m resulting from thepartial factor design using DA1C2 For each random variable the mean andstandard deviations of the Gaussian distribution are chosen so that usingEquation 829 they are consistent with the given characteristic value as

Table 810 Range of variation of V values for the three soil parameters

Vmin Vref Vmax

tanφprime 005 010 021cu (kPa) 015 030 045mv (m2kN) 020 040 060

Eurocode 7 and RBD 335

explained in Section 814 The consequence of this is that as V increases themean and standard deviation have both to increase to keep the characteristicvalue constant and equal to the given value

The influence of the V value for each soil parameter on the reliability of thespread foundation is investigated by varying the V value for one parameterwhile keeping the V values for the other parameters constant The resultspresented are

bull the influence of V(tanφprime) on the estimated PfULS and the estimated βULSin Figures 89 and 810

bull the influence of V(mv) on the estimated PfSLS and the estimated βSLS inFigures 811 and 812

10E-05

10E-04

10E-03

10E-02

10E-01

10E+000 005 01 015 02 025

target probability at ULS

PfULS

V(tanfprime)Vref

Figure 89 Influence of V(tanφprime) on PfULS and comparison with ULS target value

(Pf = 72 times 10minus5)

0

05

1

15

2

25

3

35

4

45

0 02502015010005

βULS

target reliability index = 38

V(tanfprime)Vref

Figure 810 Influence of V(tanφprime) on βULS and comparison with ULS target value

336 Trevor L L Orr and Denys Breysse

10E-05

10E-04

10E-03

10E-02

10E-01

10E+0001 02 03 04 05 06 07

PfSLS

Vref V(mv)

target probability at SLS

Figure 811 Influence of V(mv) on PfSLS and comparison with SLS target value

(Pf = 67 times 10minus2)

0

05

1

15

2

25

3

35

4

01 02 03 04 05 06 07

bSLS

target reliability index = 15

Vref V(mv)

Figure 812 Influence of V(mv) on βSLS and comparison with SLS target value

When the estimated βULS values in Figure 810 are examined it is foundthat the target level is not satisfied since for Vref(tanφprime) = 01 βULS is 295which gives a probability of failure of about 10minus3 instead of the targetvalue of 72 times 10minus5 The graph of Pf in Figure 89 shows that the targetprobability level can only be achieved if the coefficient of variability of tanφprimeis less than 008

The influence of V(cu) on the estimated βSLS has not been investigateddirectly only in combination with the influence of V(mv) on βSLS The reasonis because the variation in cu has less effect on the calculated settlementsthan the variation in mv In fact the immediate settlement estimated from

Eurocode 7 and RBD 337

Eu = 750cu is only 49110 = 45 of the total deterministic settlementcalculated in Section 813 and cu is less variable than mv Hence thevariability of mv would normally be the main source of SLS uncertaintyHowever the situation is complicated by the fact that the immediatesettlement is proportional to 1cu which is very large if cu is near zero (or evennegative if a negative value is generated for cu) for simulations when V(cu) isvery large This results from the assumption of a normal distribution for cuand a practical solution has been obtained by rejecting any simulated valuelower than 10 kPa Another way to calculate the settlement would have beento consider a log-normal distribution of the parameters In all simulationsthe coefficients of variation of both cu and mv were varied simultaneouslywith V(cu) = 075V(mv) This should be borne in mind when interpretingthe graphs of the estimated PfSLS and βSLS values plotted against V(mv) inFigures 811 and 812 These show that the target PfSLS and βSLS values areachieved in all cases even with V(mv) = 06 and that when the variabili-ties are at their maximum ie when V(mv) = 06 and V(cu) = 045 thenPfSLS = 515 times 10minus2 and βSLS = 163

818 Influence of model errors on the reliabilitylevels

In this section other components of safety and the homogeneity of safety andcode calibration are considered The simulations considered in this sectioncorrespond to those using the reference set of coefficients of variation inTable 810 which give βSLS = 258 and βULS = 296 presented in Table 89and discussed in Section 816

As noted in Section 811 the partial factors in Eurocode 7 account foruncertainties in the models for the loads and resistances as well as foruncertainties in the loads material properties and resistances themselvesConsider for example the model (Equation (815)) used to calculate theconsolidation settlement of the foundation example in Section 813 Untilnow the way the settlement was calculated has not been examined and thecalculation model has been assumed to be correct In fact the consolidationsettlement has been calculated as the sum of the settlements of a limited num-ber of layers below the foundation the stress in each layer being deducedfrom the Fadum chart If there are i layers summing the coefficients of influ-ence αi for each layer gives a global coefficient α = 158 for the foundationsettlement Also the reduction coefficient micro has been taken equal to 055thus the product αmicro is equal to 158 times 055 = 087

Thus the consolidation settlement calculation model has some uncertain-ties for instance because of the influence of the layers at greater depths andbecause micro is an empirical coefficient whose value is chosen more on the basisof ldquoexperiencerdquo than by relying on a reliable theoretical model For instanceif the chosen micro value is changed from 055 to 075 in a first step and then to

338 Trevor L L Orr and Denys Breysse

10 in a second step the βSLS value obtained from the simulations reducesfrom 258 to 216 in the first step and to 127 in the second step this lastvalue being well below the target βSLS value

On the other hand if the maximum allowable settlement is only 20 mminstead of 25 mm and if α and micro are unchanged then βSLS decreases from258 to 206 These two examples illustrate the very high sensitivity of thereliability index to the values of the coefficients used in the model to calculatethe settlement and to the value given or chosen for the limiting settlementFor the foundation example considered above this sensitivity is larger thanthat due to a reasonable change in the coefficients of variation of the soilproperties

819 Uniformity and consistency of reliability

As noted in Section 84 the aim of Eurocode 7 is to provide geotechnicaldesigns with the appropriate degrees of reliability This means that the cho-sen target probability of failure should take account of the mode of failurethe consequences of failure and the public aversion to failure It thus canvary from one type of structure to another but it should also be as uniformas possible for all similar components within a given geotechnical categoryfor example for all spread foundations for all piles and for all slopes Itshould also be uniform (consistent) for different sizes of a given type ofstructure As pointed out in Section 812 this is achieved for ULS designsby choosing γ values such that the reliability levels for representative struc-tures are as close as possible to the specified target reliability index βULSvalue of 38

To achieve the required reliability level the partial factors need to becalibrated As part of this process the influence of the size of the founda-tion on the probability of failure is investigated The simplest way to carryout this is to change the magnitude of the loading while keeping all theother parameters constant Thus a scalar multiplying factor λ is intro-duced and the safety level of foundations designed to support the loadingλ(Gk + Qk) is analyzed for drained conditions as the load factor λ variesFor nine λ values in the range 04ndash20 the design foundation width B isdetermined for DA1C2 and constant (Gk +Qk) and soil parameter valuesThe resulting graphs of foundation size B and characteristic bearing pres-sure pk increase monotonically as functions of λ as shown by the curveson Figure 813

The characteristic bearing pressure pk increases with load factor λ sincethe characteristic load λ(Gk + Qk) increases at a rate greater than B2 Thisoccurs because B increases at a smaller rate than the load factor as can beseen from the graph of B against λ in Figure 813 Examination of the graphsin Figure 813 shows that variation of λ in the range 04ndash20 results in Bvarying in the range 113ndash233 m and pk varying in the range 472ndash553 kPa

Eurocode 7 and RBD 339

0

05

1

15

2

25

3

Load factor λ

Fou

ndat

ion

wid

th B

(m

)

0025201510050

1000

2000

3000

4000

5000

6000

Cha

ract

eris

tic b

earin

g pr

essu

re

pk(k

Pa)

B

pk

Figure 813 Variation in foundation width B (m) and characteristic bearing pressure pwith load factor λ

0

05

1

15

2

25

3

35

0 025 05 075 1 125 15 175 2 225 25

ULS

SLS

β

λ

Figure 814 Graphs showing variation in reliability index β with load factor λ for ULSand SLS

The variation in pk corresponds to a variation between minus8 and +8around the 514 kPa value obtained when λ = 1

Monte Carlo simulations performed for the same nine λ values as aboveand using the V values in Table 88 for the five load and soil parameterstreated as random variables result in estimates of the reliability indicesfor the ULS and SLS for each λ value Graphs of the estimated βULS andβSLS values obtained from the simulations are plotted in Figure 814 Thesegraphs show that βULS remains constant as λ varies and remains slightly

340 Trevor L L Orr and Denys Breysse

below 30 for all λ values Thus for this example and this loading sit-uation the Eurocode 7 semi-probabilistic approach provides designs thatare consistent with regard to the reliability level at the ULS (any smallvariations in the graph are only due to statistical noise) However exam-ining the graph of βSLS against λ in Figure 814 it can be seen that as Bincreases βSLS decreases and hence PfSLS increases which demonstratesthat the Eurocode 7 semi-probabilistic approach does not provide SLSdesigns with a consistent reliability level In this example the reliability leveldecreases as λ increases although for λ less than 25 the βSLS values obtainedremain above the target level of 15

The reliability level of geotechnical designs depends on the characteristicsoil property value chosen which as noted in Section 89 is a cautious esti-mate of the mean value governing the occurrence of the limit state It was alsonoted that the failure of a large foundation involving a large slip surface or alarge volume of ground will be governed by a higher characteristic strengthvalue than the value governing the failure of a small foundation involvinga small volume of ground Consequently depending on the uniformity ofthe ground with depth the ULS reliability level may actually increase as thefoundation size increases rather than remaining constant as indicated by theβULS graph in Figure 814 The correct modeling of the change in both βULSand βSLS as the load factor and hence foundation size is increased needs amore refined model of ground properties than that used in the present model

If the ground property is considered as a random variable it has also tobe described as having a spatial correlation ie the property at a given pointis only slightly different from that at the neighboring points The spatialcorrelation can be measured in the field (Bolle 1994 Jaksa 1995 Aleacuten1998 Moussouteguy et al 2002) and modeled via geostatistics and ran-dom field theory Using this theory the ground property is considered as aspatially distributed random variable the modeling of which in the simplercases requires the long-range variance which is identical to the V parameterdiscussed above and a correlation length (or fluctuation scale) lc whichdescribes how the spatial correlation decreases with distance

Thus the effects of ground variability on the response of structures andbuildings resting on the ground can be predicted However it is not possi-ble to summarize how ground variability or uncertainty in the geotechnicaldata affects the response such as the settlement of structures and build-ings since the spatial variation can induce soilndashstructure interaction patternsthat are specific to particular ground conditions and structures A theoreticalinvestigation by Breysse et al (2005) has shown that the response of a struc-ture is governed by a dimensional ratio obtained by dividing a characteristicdimension of the structure for instance the distance between the founda-tion supports or a pipe length by the soil correlation length Eurocode 7requires the designer to consider the effects of soil variability However soilndashstructure interaction analyses to take account of the effects of soil variability

Eurocode 7 and RBD 341

are not a part of routine geotechnical designs ie Geotechnical Category 2Situations where the soil variability needs to be included in the analysesare Geotechnical Category 3 and hence no specific guidance is provided inEurocode 7 for such situations since they often involve nonlinear constitu-tive laws for the materials An example of such a situation is where the effectsof soil variability on the reliability of a piled raft have been analyzed andconfirmed by Bauduin (2003) and Niandou and Breysse (2005) Howeverproviding details of these analyses is beyond the scope of this chapter

820 Conclusions

This chapter explains the reliability basis for Eurocode 7 the new Europeanstandard for geotechnical design Geotechnical designs to Eurocode 7 aimto achieve the required reliability for a particular geotechnical structure bytaking into account all the relevant factors at all stages of the design byconsidering all the relevant limit states and by the use of appropriate designcalculations For ultimate limit state design situations the required levelof reliability is achieved by applying appropriate partial factors to char-acteristic values of the loads and the soil parameters or resistances Forserviceability limit state design situations the required level of reliabilityis achieved by using characteristic loads and soil parameter values with allthe partial factor values equal to unity The ultimate limit state partial fac-tors used in design calculations to Eurocode 7 are selected on the basis oflong experience and may be calibrated using reliability analyses Whetherpartial factors are applied to the soil parameters or the resistances has givenrise to three Design Approaches with different sets of partial factors whichresult in different designs and hence different reliabilities depending on theDesign Approach adopted The reliability of geotechnical designs has beenshown to be significantly dependent on the variability of the soil parame-ters and on the assumptions in the calculation model Traditional factorsof safety do not take account of the variability of the soil and hence can-not provide a reliable assessment of the probability of failure or actualsafety

The advantage of reliability analyses is that they take account of thevariability of the parameters involved in a geotechnical design and sopresent a consistent analytical framework linking the variability to the tar-get probability of failure and hence providing a unifying framework betweengeotechnical and structural designs (Phoon et al 2003) It has been shownthat due to uncertainty in the calculation models and model errors relia-bility analyses tend to overpredict the probability of geotechnical designsReliability analyses are particularly appropriate for investigating and eval-uating the probability of failure in the case of complex design situations orhighly variable ground conditions and hence offer a more rational basis forgeotechnical design decisions in such situations

342 Trevor L L Orr and Denys Breysse

References

Aleacuten C (1998) On probability in geotechnics Random calculation models exempli-fied on slope stability analysis and groundndashsuperstructure interaction PhD thesisChalmers University Sweden

Bauduin C (2003) Assessment of model factors and reliability index for ULS designof pile foundations In Proceedings Conference BAP IV Deep Foundation onBored amp Auger Piles Ghent 2003 Ed W F Van Impe Millpress Rotterdampp 119ndash35

Bolle A (1994) How to manage the spatial variability of natural soils InProbabilities and Materials Ed D Breysse Kluwer Dordrecht pp 505ndash16

Breysse D Kouassi P and Poulain D (1999) Influence of the variability ofcompacted soils on slope stability of embankments In Proceedings InternationalConference on the Application of Statistics and Probability ICASP 8 Sydney EdsR E Melchers and M G Stewart Balkema Rotterdam 1 pp 367ndash73

Breysse D Niandou H Elachachi S M and Houy L (2005) Generic approachof soilndashstructure interaction considering the effects of soil heterogeneity Geotech-nique LV(2) pp 143ndash50

Brinch Hansen J (1956) Limit design and safety factors in soil mechanics (inDanish with an English summary) In Bulletin No 1 Danish Geotechnical InstituteCopenhagen

Cherubini C and Orr T L L (1999) Considerations on the applicability ofsemi-probabilistic Bayesian methods to geotechnical design In Proceedings XXConvegno Nazionale di Geotechnica Parma Associatione Geotecnica Italianapp 421ndash6

Craig R F (2004) Craigrsquos Soil Mechanics Taylor amp Francis LondonDGI (1978) Danish Code of Practise for Foundation Engineering Danish Geotech-

nical Institute CopenhagenDuncan J M (2000) Factors of safety and reliability in geotechnical engineer-

ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

EC (2002) Guidance Paper L ndash Application and Use of Eurocodes EuropeanCommission Brussels

Gulvanessian H Calgaro J-A and Holickyacute M (2002) Designersrsquo Guide to EN1990 ndash Eurocode 1990 Basis of Structural Design Thomas Telford London

Honjo Y Yoshida I Hyodo J and Paikowski S (2005) Reliability analyses forserviceability and the problem of code calibration In Proceedings InternationalWorkshop on the Evaluation of Eurocode 7 Trinity College Dublin pp 241ndash9

Jaksa M B (1995) The influence of spatial variability on the geotechnical designproperties of a stiff overconsolidated clay PhD thesis University of AdelaideAustralia

Moussouteguy N Breysse D and Chassagne P (2002) Decrease of geotechnicaluncertainties via a better knowledge of the soilrsquos heterogeneity Revue Franccedilaisede Geacutenie Civil 3 343ndash54

Niandou H and Breysse D (2005) Consequences of soil variability and soilndashstructure interaction on the reliability of a piled raft In Proceedings of Interna-tional Conference on Statistics Safety and Reliability ICOSSARrsquo05 Rome EdsG Augusti G I Schueumlller and M Ciampoli Millpress Rotterdam pp 917ndash24

Eurocode 7 and RBD 343

Orr T L L (1994) Probabilistic characterization of Irish till properties In Risk andReliability in Ground Engineering Thomas Telford London pp 126ndash33

Orr T L L (2005) Design examples for the Eurocode 7 Workshop In Proceedingsof the International Workshop on the Evaluation of Eurocode 7 Trinity CollegeDublin pp 67ndash74

Orr T L L and Farrell E R (1999) Geotechnical Design to Eurocode 7 SpringerLondon

Ovesen N K (1995) Eurocode 7 A European code of practise for geotechnicaldesign In Proceedings International Symposium on Limit State Design in Geotech-nical Engineering ISLSD 93 Copenhagen Danish Geotechnical Institute 3pp 691ndash710

Ovesen N K and Denver H (1994) Assessment of characteristic values of soilparameters for design In Proceedings XIII International Conference on SoilMechanics and Foundation Engineering New Delhi Balkema 1 437ndash60

Phoon K K and Kulhawy F H (2005) Characterization of model uncertainties fordrilled shafts under undrained axial loading In Contemporary Issues in Founda-tion Engineering (GSP 131) Eds J B Anderson K K Phoon E Smith J E LoehrASCE Reston

Phoon K K Becker D E Kulhawy F H Honjo Y Ovesen N K and Lo S R(2003) Why consider reliability analysis for geotechnical limit state design InProceedings International Workshop on Limit State Design in Geotechnical Engi-neering Practise Eds K K Phoon Y Honjo and D Gilbert World ScientificPublishing Company Singapore pp 1ndash17

Schneider H R (1997) Definition and determination of characteristic soil prop-erties In Proceedings XII International Conference on Soil Mechanics andGeotechnical Engineering Hamburg Balkema Rotterdam pp 4 2271ndash4

Student (1908) The probable error of a mean Biometrica 6 1ndash25

Chapter 9

Serviceability limit statereliability-based design

Kok-Kwang Phoon and Fred H Kulhawy

91 Introduction

In foundation design the serviceability limit state often is the governingcriterion particularly for large-diameter piles and shallow foundationsIn addition it is widely accepted that foundation movements are difficultto predict accurately Most analytical attempts have met with only limitedsuccess because they did not incorporate all of the important factors suchas the in-situ stress state soil behavior soilndashfoundation interface charac-teristics and construction effects (Kulhawy 1994) A survey of some ofthese analytical models showed that the uncertainties involved in applyingeach model and evaluating the input parameters were substantial (Callananand Kulhawy 1985) Ideally the ultimate limit state (ULS) and the service-ability limit state (SLS) should be checked using the same reliability-baseddesign (RBD) principle The magnitude of uncertainties and the target reli-ability level for SLS are different from those of ULS but these differenceswill be addressed consistently and rigorously using reliability-calibrateddeformation factors (analog of resistance factors)

One of the first systematic studies reporting the development of RBDcharts for settlement of single piles and pile groups was conducted byPhoon and co-workers (Phoon et al 1990 Quek et al 1991 1992)A more comprehensive implementation that considered both ULS andSLS within the same RBD framework was presented in EPRI ReportTR-105000 (Phoon et al 1995) SLS deformation factors (analog of resis-tance factors) were derived by considering the uncertainties underlyingnonlinear loadndashdisplacement relationships explicitly in reliability calibra-tions Nevertheless the ULS still received most of the attention in laterdevelopments although interest in applying RBD for the SLS appears tobe growing in the recent literature (eg Fenton et al 2005 Misra andRoberts 2005 Zhang and Ng 2005 Zhang and Xu 2005 Paikowsky and

Serviceability limit state reliability-based design 345

Lu 2006) A wider survey is given elsewhere (Zhang and Phoon 2006)A special session on ldquoServiceability Issues in Reliability-Based Geotechni-cal Designrdquo was organized under the Risk Assessment and ManagementTrack at Geo-Denver 2007 the ASCE Geo-Institute annual conferenceheld in February 2007 in Denver Colorado Four papers were presentedon excavation-induced ground movements (Boone 2007 Finno 2007Hsiao et al 2007 Schuster et al 2007) and two papers were pre-sented on foundation settlements (Roberts et al 2007 Zhang and Ng2007)

The reliability of a foundation at the ULS is given by the probability ofthe capacity being less than the applied load If a consistent load test inter-pretation procedure is used then each measured loadndashdisplacement curvewill produce a single ldquocapacityrdquo The ratio of this measured capacity to apredicted capacity is called a bias factor or a model factor A lognormaldistribution usually provides an adequate fit to the range of model factorsfound in a load test database (Phoon and Kulhawy 2005a b) It is natu-ral to follow the same approach for the SLS The capacity is replaced byan allowable capacity that depends on the allowable displacement (Phoonet al 1995 Paikowsky and Lu 2006) The distribution of the SLS bias ormodel factor can be established from a load test database in the same wayThe chief drawback is that the distribution of this SLS model factor has tobe re-evaluated when a different allowable displacement is prescribed It istempting to argue that the loadndashdisplacement behavior is linear at the SLSand subsequently the distribution of the model factor for a given allow-able displacement can be extrapolated to other allowable displacements bysimple scaling However for reliability analysis the applied load follows aprobability distribution and it is possible for the loadndashdisplacement behav-ior to be nonlinear at the upper tail of the distribution (corresponding tohigh loads) Finally it may be more realistic to model the allowable dis-placement as a probability distribution given that it is affected by manyinteracting factors such as the type and size of the structure the propertiesof the structural materials and the underlying soils and the rate and uni-formity of the movement The establishment of allowable displacements isgiven elsewhere (Skempton and MacDonald 1956 Bjerrum 1963 Grantet al 1974 Burland and Wroth 1975 Burland et al 1977 Wahls 1981Moulton 1985 Boone 2001)

This chapter employs a probabilistic hyperbolic model to performreliability-based design checks at the SLS The nonlinear features of theloadndashdisplacement curve are captured by a two-parameter hyperbolic curve-fitting Equation The uncertainty in the entire loadndashdisplacement curve isrepresented by a relatively simple bivariate random vector containing thehyperbolic parameters as its components It is not necessary to evaluatea new model factor and its accompanying distribution for each allowable

346 Kok-Kwang Phoon and Fred H Kulhawy

displacement It is straightforward to introduce the allowable displacementas a random variable for reliability analysis To accommodate this additionalsource of uncertainty using the standard model factor approach it is neces-sary to determine a conditional probability distribution of the model factor asa continuous function of the allowable displacement However the approachproposed in this chapter is significantly simpler Detailed calculation steps forsimulation and first-order reliability analysis are illustrated using EXCELtradeto demonstrate this simplicity It is shown that a random vector contain-ing two curve-fitting parameters is the simplest probabilistic model forcharacterizing the uncertainty in a nonlinear loadndashdisplacement curve andshould be the recommended first-order approach for SLS reliability-baseddesign

92 Probabilistic hyperbolic model

The basic idea behind the probabilistic hyperbolic model is to (a) reduce themeasured loadndashdisplacement curves into two parameters using a hyperbolicfit (b) normalize the resulting hyperbolic curves using an interpreted failureload to reduce the data scatter and (c) model the remaining scatter usingan appropriate random vector (possibly correlated andor non-normal) forthe curve-fitting hyperbolic parameters There are three advantages in thisempirical approach First this approach can be applied easily in practicebecause it is simple and does not require input parameters that are diffi-cult to obtain However the uncertainties in the stiffness parameters areknown to be much larger than the uncertainties in the strength parameters(Phoon and Kulhawy 1999) Second the predictions are realistic becausethe approach is based directly on load test results Third the statistics ofthe model can be obtained easily as described below The first criterion isdesirable but not necessary for RBD The next two criteria however arecrucial for RBD If the average model bias is not addressed different mod-els will produce different reliability for the same design scenario as a resultof varying built-in conservatism which makes reliability calculations fairlymeaningless The need for robust model statistics should be evident becauseno proper reliability analysis can be performed without these data

921 Example Augered Cast-in-Place (ACIP) pile underaxial compression

Phoon et al (2006) reported an application of this empirical approach toan ACIP pile load test database The ACIP pile is formed by drilling acontinuous flight auger into the ground and upon reaching the requireddepth pumping sandndashcement grout or concrete down the hollow stem

Serviceability limit state reliability-based design 347

as the auger is withdrawn steadily The side of the augered hole is sup-ported by the soil-filled auger and therefore no temporary casing or slurryis needed After reaching the ground surface a reinforcing steel cage ifrequired is inserted by vibrating it into the fresh grout The databasecompiled by Chen (1998) and Kulhawy and Chen (2005) included casehistories from 31 sites with 56 field load tests conducted mostly in sandycohesionless soils The diameter (B) depth (D) and DB of the pilesranged from 030 to 061 m 18 to 261 m and 51 to 682 respec-tively The associated geotechnical data typically are restricted to thesoil profile depth of ground water table and standard penetration test(SPT) results The range of effective stress friction angle is 32ndash47 degreesand the range of horizontal soil stress coefficient is 06ndash24 (estab-lished by correlations) The loadndashdisplacement curves are summarized inFigure 91a

The normalized hyperbolic curve considered in their study is expressed as

QQSTC

= ya + by

(91)

in which Q = applied load QSTC = failure load or capacity interpretedusing the slope tangent method a and b = curve-fitting parameters andy = pile butt displacement Note that the curve-fitting parameters are phys-ically meaningful with the reciprocals of a and b equal to the initial slopeand asymptotic value respectively (Figure 92) The slope tangent methoddefines the failure load at the intersection of the loadndashdisplacement curvewith the initial slope (not elastic slope) line offset by (015 + B120) inchesin which B = shaft diameter in inches Phoon et al (2006) observed that

0 0 5 10 15 20 25 3010 20 30 40 50 60 70 80Displacement (mm)

0

500

1000

1500

2000

Load

Q (

kN)

(a) Displacement (mm)

00

10

20

30

QQ

ST

C

DB gt 20

(b)

c30-1

Figure 91 (a) Loadndashdisplacement curves for ACIP piles in compression and (b) fitted hyper-bolic curves normalized by slope tangent failure load (QSTC) for DB gt 20 FromPhoon et al 2006 Reproduced with the permission of the American Society ofCivil Engineers

348 Kok-Kwang Phoon and Fred H Kulhawy

00

04

08

12

16

0 20 40 60 80y (mm)

QQ

ST

C

1b = 1613

slope = 1a = 0194 mmminus1

QQSTC = y(515 + 062y)

Figure 92 Definition of hyperbolic curve-fitting parameters

the normalized loadndashdisplacement curves for long piles (DB gt 20) clustertogether quite nicely (Figure 91b) Case 30-1 is a clear outlier within thisgroup and is deleted from subsequent analyses There are 40 load tests in thisgroup of ldquolongrdquo piles with diameter (B) depth (D) and DB ranging from030 to 061 m 75 to 261 m and 210 to 682 respectively The range ofeffective stress friction angle is 32ndash43 degrees and the range of horizontalsoil stress coefficient is 06ndash21

The curve-fitting Equation is empirical and other functional forms canbe considered as shown in Table 91 However the important criterionis to apply a curve-fitting Equation that produces the least scatter in thenormalized loadndashdisplacement curves

922 Model statistics

Each continuous loadndashdisplacement curve can be reduced to two curve-fitting parameters as illustrated in Table 92 for ACIP piles Using the datagiven in the last two columns of this table one can construct an appropriatebivariate random vector that can reproduce the scatter in the normalized loadover the full range of displacements The following steps are recommended

1 Validate that the variations in the hyperbolic parameters are indeedldquorandomrdquo

2 Fit the marginal distributions of a and b using a parametric form(eg lognormal distribution) or a non-parametric form (eg Hermitepolynomial)

Serviceability limit state reliability-based design 349

Table 91 Curve-fitting Equations for loadndashdisplacement curves from various foundations

Foundation type Loading mode Curve-fitting Equation Reference source

Spreadfoundation

Uplift QQm = y(a+by) Phoon et al 19952003 2007Phoon andKulhawy 2002b

Drilled shaft Uplift QQm = y(a + by) Phoon et al 1995Phoon andKulhawy 2002a

Compression(undrained)

QQm = (yB)[a + b(yB)] Phoon et al 1995

Compression(drained)

QQm = a(yB)b Phoon et al 1995

Lateral-moment

QQm = (yD)[a + (yD)] Phoon et al 1995Kulhawy andPhoon 2004

Augeredcast-in-place(ACIP) pile

Compression(drained)

QQm = y(a + by) Phoon et al 2006

Pressure-injectedfooting

Uplift(drained)

QQm = y(a + by) Phoon et al 2007

Interpreted failure capacity (Qm) tangent intersection method for spread foundations L1ndashL2 methodfor drilled shafts under uplift and compression hyperbolic capacity for drilled shafts under lateral-moment loading slope tangent method for ACIP piles and pressure-injected footings

3 Calculate the productndashmoment correlation between a and b using

ρab =

nsumi=1

(ai minus a

)(bi minus b

)radic

nsumi=1

(ai minus a

)2 nsumi=1

(bi minus b

)2(92)

in which (ai bi) denotes a pair of a and b values along one row inTable 92 n = sample size (eg n = 40 in Table 92) and a and b = thefollowing sample means

a =

nsumi=1

ai

n

b =

nsumi=1

bi

n(93)

Table 92 Hyperbolic curve-fitting parameters for ACIP piles under axial compression[modified from load test data reported by Chen (1998)]

Case no B (m) D (m) DB SPT-N QSTC (kN) a (mm) b

c1 041 192 473 184 19571 8382 0499c2 041 149 368 234 17570 10033 0382c3 041 101 248 391 17614 3571 0679c4 030 107 350 166 3670 4296 0615c5-1 041 177 435 214 20817 7311 0445c5-2 041 198 488 146 19037 5707 0591c6 041 137 338 146 7295 3687 0679c7 030 118 387 116 6850 2738 0701c8-1 036 120 339 237 10720 3428 0723c9 036 104 292 184 16547 7559 0461c10 041 91 225 290 14678 5029 0582c11 036 119 334 316 16547 2528 0744c12 041 131 323 153 11031 5260 0533c13 036 116 326 358 16057 4585 0632c14 041 118 290 43 8896 2413 0770c15 036 122 343 216 24909 15646 0328c16-1 041 134 330 270 10675 4572 0607c16-2 041 134 330 267 10764 7376 0613c17 041 90 221 142 5516 2047 0682c18-1 036 139 390 344 33805 12548 0380c19-1 036 192 540 271 13344 5829 0570c19-2 036 242 682 210 11832 6419 0559c19-3 041 261 641 200 12276 4907 0690c19-4 041 186 458 224 21528 4426 0692c20 052 166 319 304 37363 1312 0840c21 036 78 219 165 8896 8128 0510c22-1 061 200 328 114 17792 10297 0640c22-2 041 149 368 77 9519 4865 0487c22-3 041 199 490 100 11743 5599 0517c23-1 036 75 210 227 13166 4511 0622c24-1 041 95 234 117 11832 3142 0501c24-2 041 95 234 117 11743 3353 0513c24-3 041 95 234 215 10052 4104 0367c24-4 041 90 221 80 8629 4928 0430c25-1 061 175 287 188 10675 1006 0780c25-2 061 175 287 188 12632 1237 0923c25-3 061 160 263 182 10764 1352 0932c25-4 061 200 328 184 11120 4128 1050c25-5 061 175 287 188 15479 4862 0748c27-3 046 121 264 144 6405 3054 0792

Depth-averaged SPT-N value along shaft

Serviceability limit state reliability-based design 351

For the hyperbolic parameters in Table 92 the productndashmomentcorrelation is minus0673

4 Construct a bivariate probability distribution for a and b using thetranslation model

Note that the construction of a bivariate probability distribution for aand b is not an academic exercise in probability theory Each pair ofa and b values within one row of Table 92 is associated with a singleloadndashdisplacement curve [see Equation (91)] Therefore one should expecta and b to be correlated as a result of this curve-fitting exercise Thisis similar to the well-known negative correlation between cohesion andfriction angle A linear fit to a nonlinear MohrndashCoulomb failure enve-lope will create a small intercept (cohesion) when the slope (frictionangle) is large or vice versa If one adopts the expedient assumptionthat a and b are statistically independent random variables then thecolumn of a values can be shuffled independently of the column of bvalues In physical terms this implies that the initial slopes of the loadndashdisplacement curves can be varied independently of their asymptotic valuesThis observation is not supported by measured load test data as shownin Section 933 In addition there is no necessity to make this simplify-ing assumption from a calculation viewpoint Correlated a and b valuescan be calculated with relative ease as illustrated below using the datafrom Table 92

93 Statistical analyses

931 Randomness of the hyperbolic parameters

In the literature it is common to conclude that a parameter is a randomvariable by presenting a histogram However it is rarely emphasized thatparameters exhibiting variations are not necessarily ldquorandomrdquo A randomvariable is an appropriate probabilistic model only if the variations arenot explainable by deterministic variations in the database for examplefoundation geometry or soil property Figure 93 shows that the hyperbolicparameters do not exhibit obvious trends with DB or the average SPT-Nvalue

932 Marginal distributions of a and b

The lognormal distribution provides an adequate fit to both hyperbolicparameters as shown in Figure 94 (p-values from the AndersonndashDarlinggoodness-of-fit test pAD are larger than 005) Note that SD is the stan-dard deviation COV is the coefficient of variation and n is the samplesize Because of lognormality a and b can be expressed in terms of standard

0

4

8

12

0 10 20 30 40

SPT-N

a pa

ram

eter

(m

m)

0

4

8

12

10 20 30 40 50

DBa

para

met

er (

mm

)

0

04

08

12

0 10 20 30 40 50

SPT-N

b pa

ram

eter

0

04

08

12

10 20 30 40 50

DB

b pa

ram

eter

Figure 93 Hyperbolic parameters versus DB and SPT-N for ACIP piles

0 5 10 15 20a parameter (mm)

0

01

02

03

Rel

ativ

e fr

eque

ncy

Mean = 515 mmSD = 307 mmCOV = 060

n = 40pAD = 0183

00 05 10 15 20b parameter

0

01

02

03

Rel

ativ

e fr

eque

ncy

Mean = 062SD = 016COV = 026

n = 40pAD = 0887

Figure 94 Marginal distributions of hyperbolic parameters for ACIP piles From Phoonet al 2006 Reproduced with the permission of the American Society of CivilEngineers

Serviceability limit state reliability-based design 353

normal variables X1 and X2 as follows

a = exp(ξ1X1 +λ1)

b = exp(ξ2X2 +λ2) (94)

The parameters λ1 and ξ1 are the equivalent normal mean and equivalentnormal standard deviation of a respectively as given by

ξ1 =radic

ln(1 + s2

am2a

)= 0551

λ1 = ln(ma) minus 05ξ21 = 1488 (95)

in which the mean and standard deviation of a are ma = 515 mm andsa = 307 mm respectively The parameters λ2 and ξ2 can be calculated fromthe mean and standard deviation of b in the same way and are given byλ2 = minus0511 and ξ2 = 0257

There is no reason to assume that a lognormal probability distribution willalways provide a good fit for the hyperbolic parameters A general but simpleapproach based on Hermite polynomials can be used to fit any empiricaldistributions of a and b

a = a10H0(X1) + a11H1(X1) + a12H2(X1) + a13H3(X1)

+ a14H4(X1) + a15H5(X1) +middotmiddot middot (96)

b = a20H0(X2) + a21H1(X2) + a22H2(X2) + a23H3(X2)

+ a24H4(X2) + a25H5(X2) +middotmiddot middot

in which the Hermite polynomials Hj() are given by

H0(X) = 1

H1(X) = X (97)

Hk+1(X) = X Hk(X) minus k Hkminus1(X)

The advantages of using a Hermite expansion are (a) Equation (96) isdirectly applicable for reliability analysis (Phoon and Honjo 2005 Phoonet al 2005) (b) a short expansion typically six terms is sufficient and(c) Hermite polynomials can be calculated efficiently using the recursion for-mula shown in the last row of Equation (97) The main effort is to calculatethe Hermite coefficients in Equation (96) Figure 95 shows that the entirecalculation can be done with relative ease using EXCEL Figure 96 comparesa and b values simulated using the lognormal distribution and the Hermiteexpansion with the measured values in Table 92

354 Kok-Kwang Phoon and Fred H Kulhawy

Column

A Sort values of ldquoardquo using ldquoData gt Sort gt Ascendingrdquo Let f be a 40times1 vector containing values in column A B Rank the values in column A in ascending order eg B4 = RANK(A4$A$4$A$431) C Calculate empirical cumulative distribution F(a) using (rank)(sample size +1) eg C4 = B441 D Calculate standard normal variate using Φminus1[F(a)] eg D4 = NORMSINV(C4)FK Calculate Hermite polynomials using recursion eg F4 = 1 G4 = D4 H4 = G4lowast$D4-(H$3-1)lowastF4 M Let H be a 40times6 matrix containing values in column F to K Calculate the Hermite coefficients in column M (denoted by a 6times1 vector a1) by HTH a1 = HTf The EXCEL array formula is =MMULT(MINVERSE(MMULT(TRANSPOSE(F4K43)F4K43)) MMULT(TRANSPOSE(F4K43)A4A43))

EXCEL functions

Figure 95 Calculation of Hermite coefficients for a using EXCEL

Hyperbolic parameters for uplift loadndashdisplacement curves associatedwith spread foundations drilled shafts and pressure-injected footings areshown in Figure 97 Although lognormal distributions can be fitted tothe histograms (dashed line) they are not quite appropriate for the bparameters describing the asymptotic uplift loadndashdisplacement behavior ofspread foundations and drilled shafts (p-values from the AndersonndashDarlinggoodness-of-fit test pAD are smaller than 005)

933 Correlation between a and b

The random vector (a b) is described completely by a bivariate probabil-ity distribution The assumption of statistical independence commonly isadopted in the geotechnical engineering literature for expediency The prac-tical advantage is that the random components can be described completely

Serviceability limit state reliability-based design 355

a parameter (mm)

Per

cent

1614121086420

100

80

60

40

20

0

MeasuredHermiteLognormal

b parameter

Per

cent

150125100075050

100

80

60

40

20

0

MeasuredHermiteLognormal

Figure 96 Simulation of hyperbolic parameters using lognormal distribution and Hermiteexpansion

by marginal or univariate probability distributions such as those shown inFigures 94 and 97 Another advantage is that numerous distributions aregiven in standard texts and the data on hand usually can be fitted to oneof these classical distributions The disadvantage is that this assumption canproduce unrealistic scatter plots between a and b if the data are stronglycorrelated For hyperbolic parameters it appears that strong correlationsare the norm rather than the exception (Figure 98)

It is rarely emphasized that the multivariate probability distributions (evenbivariate ones) underlying random vectors are very difficult to constructtheoretically to estimate empirically and to simulate numerically A cur-sory review of basic probability texts reveals that non-normal distributionsusually are presented in the univariate form It is not possible to generalize

356 Kok-Kwang Phoon and Fred H Kulhawy

Hyperbolic parameters Foundation

ba (mm)

Spreadfoundation

0 4 8 12 16 20

a parameter (mm)

0

01

02

03

04R

elat

ive

freq

uenc

y

Mean = 713 mmSD = 466 mmCOV = 065

n = 85pAD = 0032

28

02 04 06 08 10 12 14

b parameter

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 075SD = 014COV = 018

n = 85pAD lt0005

Drilled shaft

a parameter (mm)

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 134 mmSD = 073 mmCOV = 054

n = 48pAD = 0887

b parameter

0

02

04

06

08

Rel

ativ

e fr

eque

ncy

Mean = 089SD = 0063COV = 007

n = 48pAD lt 0005

Pressure-injectedfooting

0 1 2 3 4 5

0 1 2 3 4 5

a parameter (mm)

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 138 mmSD = 095 mmCOV = 068

n = 25pAD =0772

02 04 06 08 10 12 14

02 04 06 08 10 12 14

b parameter

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 077SD = 021COV = 027

n = 25pAD = 0662

Figure 97 Marginal distributions of hyperbolic parameters for uplift loadndashdisplacementcurves associated with spread foundations drilled shafts and pressure-injectedfootings From Phoon et al 2007 Reproduced with the permission of theAmerican Society of Civil Engineers

these univariate formulae to higher dimensions unless the random variablesare independent This assumption is overly restrictive in reliability analysiswhere input parameters can be correlated by physics (eg sliding resistanceincreases with normal load undrained shear strength increases with loadingrate etc) or by curve-fit (eg cohesion and friction angle in a linear MohrndashCoulomb failure envelope hyperbolic parameters from loadndashdisplacementcurves) A full discussion of these points is given elsewhere (Phoon 2004a2006)

Serviceability limit state reliability-based design 357

0

4

8

12

16

20

02 04 06 08 10 12 14

b parameter

a pa

ram

eter

(m

m)

Augered cast-in-place pile(compression)

0

4

8

12

16

20

b parametera

para

met

er (

mm

)

claysand

Spread foundation (uplift)

No tests = 40a Mean = 515 mm SD = 307 mm COV = 060b Mean = 062 SD = 016 COV = 026Correlation = -067

No tests = 85a Mean = 713 mm SD = 466 mm COV = 065b Mean = 075 SD = 014 COV = 018Correlation = -024

0

1

2

3

4

5

02 04 06 08 10 12 14b parameter

a pa

ram

eter

(m

m)

claysand

Drilled shaft (uplift)

0

1

2

3

4

5

02 04 06 08 10 12 14

02 04 06 08 10 12 14

b parameter

a pa

ram

eter

(m

m)

Pressure-injected footing(uplift)

No tests = 48a Mean = 134 mm SD = 073 mm COV = 054b Mean = 089 SD = 0063 COV = 007Correlation = -059

No tests = 25a Mean = 138 mm SD = 095 mm COV = 068b Mean = 077 SD = 021 COV = 027Correlation = -073

Figure 98 Correlation between hyperbolic parameters From Phoon et al 2006 2007Reproduced with the permission of the American Society of Civil Engineers

94 Bivariate probability distribution functions

941 Translation model

For bivariate or higher-dimensional probability distributions there are fewpractical choices available other than the multivariate normal distributionIn the bivariate normal case the entire dependency relationship between bothrandom components can be described completely by a productndashmoment cor-relation coefficient [Equation (92)] The practical importance of the normaldistribution is reinforced further because usually there are insufficient data tocompute reliable dependency information beyond the correlation coefficient

It is expected that a bivariate probability model for the hyperbolicparameters will be constructed using a suitably correlated normal model

358 Kok-Kwang Phoon and Fred H Kulhawy

The procedure for this is illustrated using an example of hyperbolicparameters following lognormal distributions

1 Simulate uncorrelated standard normal random variables Z1 and Z2(available in EXCEL under ldquoTools gt Data Analysis gt Random NumberGenerationrdquo)

2 Transform Z1 and Z2 into X1 (mean = 0 standard deviation = 1) andX2 (mean = 0 standard deviation = 1) with correlation ρX1X2

by

X1 = Z1

X2 = Z1ρX1X2+ Z2

radic1 minusρ2

X1X2(98)

3 Transform X1 and X2 to lognormal a and b using Equation (94)

Note that the correlation between a and b (ρab) as given below is not thesame as the correlation between X1 and X2 (ρX1X2

)

ρab = exp(ξ1ξ2ρX1X2) minus 1radic

[exp(ξ21 ) minus 1][exp(ξ2

2 ) minus 1](99)

Therefore one needs to back-calculate ρX1X2from ρab ( = minus067) using

Equation (99) before computing Equation (98) The main effort in the con-struction of a translation-based probability model is calculating the equiv-alent normal correlation For the lognormal case a closed-form solutionexists [Equation (99)] Unfortunately there are no convenient closed-formsolutions for the general case One advantage of using Hermite polynomialsto fit the empirical marginal distributions of a and b [Equation (96)] is thata simple power series solution can be used to address this problem

ρab =

infinsumk=1

ka1ka2kρkX1X2radicradicradicradic( infinsum

k=1ka2

1k

)(infinsum

k=1ka2

2k

) (910)

The bivariate lognormal model based on ρX1X2= minus08 [calculated from

Equation (99) with ρab = minus07] produces a scatter of simulated a andb values that is in reasonable agreement with that exhibited by the mea-sured values (Figure 99a) In contrast adopting the assumption of statisticalindependence between a and b corresponding to ρX1X2

= 0 produces theunrealistic scatter plot shown in Figure 99b in which there are too many

Serviceability limit state reliability-based design 359

0

4

8

12

16

20

02 04 06 08 10 12 02 04 06 08 10 12

b parameter

a pa

ram

eter

(m

m)

MeasuredLognormal

(a)0

4

8

12

16

20

b parameter

a pa

ram

eter

(m

m)

MeasuredLognormal

(b)

Figure 99 Simulated hyperbolic parameters for ACIP piles (a) ρX1X2= minus08 and

(b) ρX1X2= 0

simulated points above the observed scatter as highlighted by the dashedoval Note that the marginal distributions for a and b in Figures 99a andb are almost identical (not shown) The physical difference produced bycorrelated and uncorrelated hyperbolic parameters can be seen in the simu-lated loadndashdisplacement curves as well (Figures 910bndashd) These simulatedcurves are produced easily by substituting simulated pairs of a and b intoEquation (91)

942 Rank model

By definition the productndashmoment correlation ρab is bounded between ndash1and 1 One of the key limitations of a translation model is that it is notpossible to simulate the full range of possible productndashmoment correlationsbetween a and b (Phoon 2004a 2006) Based on the equivalent normalstandard deviations of a and b for ACIP piles (ξ1 = 0551 and ξ2 = 0257) itis easy to verify that Equation (99) with ρX1X2

=minus1 will result in a minimumvalue of ρab = minus085 The full relationship between translation non-normalcorrelation and equivalent normal correlation is shown in Figure 911 Moreexamples are given in Phoon (2006) as reproduced in Figure 912 (noteρab = ρY1Y2

)If a translation-based probability model is inadequate a simple alternate

approach is to match the rank correlation as follows

1 Convert a and b to their respective ranks Note that these ranks arecalculated based on ascending values of a and b ie the smallest valueis assigned a rank value equal to 1

2 Calculate the rank correlation using the same Equation as the productndashmoment correlation [Equation (92)] except the rank values are used in

360 Kok-Kwang Phoon and Fred H Kulhawy

0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80Displacement (mm)

00

10

20

30

QQ

ST

C

00

10

20

30

QQ

ST

C

00

10

20

30Q

QS

TC

(a)

Displacement (mm)

0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80Displacement (mm) Displacement (mm)

(b)

00

10

20

30

QQ

ST

C

(c) (d)

Figure 910 Normalized hyperbolic curves for ACIP piles (a) measured (b) ρX1X2= minus08

(c) ρX1X2= minus04 and (d) ρX1X2

= 0

place of the actual a and b values The rank correlation for Table 92is ndash071

3 Simulate correlated standard normal random variables usingEquation (98) with ρX1X2

equal to the rank correlation computed above4 Simulate independent non-normal random variables following any

desired marginal probability distributions5 Re-order the realizations of the non-normal random variables such that

they follow the rank structure underlying the correlated standard normalrandom variables

The simulation procedure for hyperbolic parameters of ACIP piles is illus-trated in Figure 913 Note that the simulated a and b values in columnsM and N will follow the rank structure (columns J and K) of the corre-lated standard normal random variables (columns H and I) because theexponential transform (converting normal to lognormal) is a monotonicincreasing function The theoretical basis underlying the rank model is dif-ferent from the translation model and is explained by Phoon et al (2004)

Serviceability limit state reliability-based design 361

minus10

minus05

00

05

10

minus10 minus05 00 05 10

Normal correlation rX1X2

Non

-nor

mal

cor

rela

tion

ra

b

-067

minus078

Figure 911 Relationship between translation non-normal correlation and equivalentnormal correlation

and Phoon (2004b) It suffices to note that there is no unique method of con-structing correlated non-normal random variables because the correlationcoefficient (productndashmoment or rank) does not provide a full description ofthe association between two non-normal variables

95 Serviceability limit state reliability-baseddesign

This section presents the practical application of the probabilistic hyper-bolic model for serviceability limit state (SLS) reliability-based design TheSLS occurs when the foundation displacement (y) is equal to the allowabledisplacement (ya) The foundation has exceeded serviceability if ygt ya Con-versely the foundation is satisfactory if y lt ya These three situations can bedescribed concisely by the following performance function

P = y minus ya = y(Q) minus ya (911)

An alternate performance function is

P = Qa minus Q = Qa(ya) minus Q (912)

Figure 914 illustrates the uncertainties associated with these performancefunctions In Figure 914a the applied load Q is assumed to be deterministic

362 Kok-Kwang Phoon and Fred H Kulhawy

minus1 minus05 0 05 1minus1

minus05

0

05

1

r Y1Y

2

minus1

minus05

0

05

1

r Y1Y

2

(a) x1 = 03 x2 = 03

minus1 minus05 0 05 1rX1X2

rX1X2

(b) x1= 1 x2 = 1

minus1 minus05 0 05 1minus1

minus05

0

05

1

rX1X2rX1X2

r Y1Y

2

(c) x1 = 1 x2 = 03

minus1 minus05 0 05 1minus1

minus05

0

05

1

r Y1Y

2

(d) x1 = 2 x2 = 03

Figure 912 Some relationships between observed correlation (ρY1Y2) and correlation of

underlying equivalent Gaussian random variables (ρX1X2) From Phoon 2006

Reproduced with the permission of the American Society of Civil Engineers

to simplify the visualization It is clear that the displacement follows a distri-bution even if Q is deterministic because the loadndashdisplacement curve y(Q)is uncertain The allowable displacement may follow a distribution as wellIn Figure 914b the allowable displacement is assumed to be determinis-tic In this alternate version of the performance function the allowable loadQa follows a distribution even if ya is deterministic because of the uncer-tainty in the loadndashdisplacement curve The effect of a random load Q andthe possibility of upper tail values falling on the nonlinear portion of theloadndashdisplacement curve are illustrated in this figure

Serviceability limit state reliability-based design 363

Column

A Measured ldquoardquo values from Table 92 B Measured ldquobrdquo values from Table 92 C Rank the values in column A in ascending order eg C3 = RANK(A3$A$3$A$421) D Rank the values in column B in ascending order eg D3 = RANK(B3$B$3$B$421)F G Simulate two columns of independent standard normal random variables using Tools gt Data Analysis gt Random Number Generation gt Number of Variables = 2 Number of Random Numbers = 40 Distribution = Normal Mean = 0 Standard deviation = 1 Output Range = $F$3 H Set X1 = Z1 eg H3 = F3

I Set X2 = Z1rX1X2 + Z2(1 ndash rX1X2

2)05 with rX1X2 = rank correlation between columns C

and D eg I3 = F3(ndash071)+G3SQRT(1ndash071^2) M Simulated ldquoardquo values eg M3 = EXP(H30551+1488) N Simulated ldquobrdquo values eg N3 = EXP(I30257ndash0511)

EXCEL functions

Figure 913 Simulation of rank correlated a and b values for ACIP piles using EXCEL

The basic objective of RBD is to ensure that the probability of failure of acomponent does not exceed an acceptable threshold level For ULS or SLSthis objective can be stated using the performance function as follows

pf = Prob(P lt 0) le pT (913)

in which Prob(middot) = probability of an event pf = probability of failure andpT = acceptable target probability of failure A more convenient alternativeto the probability of failure is the reliability index (β) which is defined as

β = minusΦminus1(pf ) (914)

in which Φminus1(middot) = inverse standard normal cumulative function

364 Kok-Kwang Phoon and Fred H Kulhawy

00

04

08

12

16

y (mm)

Random allowabledisplacement

Deterministicapplied load

Random displacement caused by uncertainty inloadndashdisplacement curve

(a)

00

04

08

12

16

0 20 40 60 80

0 20 40 60 80

y (mm)

QQ

ST

CQ

QS

TC

Deterministic allowabledisplacement

Random applied load

Random allowable loadcaused by uncertainty in loadndashdisplacement curve

(b)

Figure 914 Serviceability limit state reliability-based design (a) deterministic applied loadand (b) deterministic allowable displacement

951 First-order reliability method (FORM)

The probability of failure given by Equation (913) can be calculated eas-ily using the first-order reliability method (FORM) once the probabilistichyperbolic model is established

pf = Prob(Qa lt Q

)= Prob(

ya

a + byaQm lt Q

)(915)

The interpreted capacity Qm allowable displacement ya and applied load Qare assumed to be lognormally distributed with for example coefficients ofvariation (COV) = 05 06 and 02 respectively The COV of Qm is related

Serviceability limit state reliability-based design 365

to the capacity prediction method and the quality of the geotechnical dataPhoon et al (2006) reported a COV of 05 associated with the predictionof compression capacity of ACIP piles using the Meyerhof method TheCOV of ya is based on a value of 0583 reported by Zhang and Ng (2005)for buildings on deep foundations The COV of Q = 02 is appropriatefor live load (Paikowsky 2002) The hyperbolic parameters are negativelycorrelated with an equivalent normal correlation of ndash08 and are lognormallydistributed with statistics given in Figure 94 The mean of ya is assumed tobe 25 mm Equation (915) can be re-written to highlight the mean factor ofsafety (FS) as follows

pf = Prob

(ya

a + byalt

mQQlowast

mQmQlowast

m

)= Prob

(ya

a + byalt

1FS

QlowastQlowast

m

)(916)

in which mQmand mQ = mean of Qm and Q FS = mQm

mQ and Qlowastm and

Qlowast = unit-mean lognormal random variables with COV = 05 and 02 Notethat FS refers to the ultimate limit state It is equivalent to the global factorof safety adopted in current practice

Figure 915 illustrates that the entire FORM calculation can be performedwith relative ease using EXCEL The most tedious step in FORM is the searchfor the most probable failure point which requires a nonlinear optimizerLow and Tang (1997 2004) recommended using the SOLVER function inEXCEL to perform this tedious step However their proposal to performoptimization in the physical random variable space is not recommendedbecause the means of these random variables can differ by orders of magni-tudes For example the mean b = 062 for the ACIP pile database while themean of Qm can be on the order of 1000 kN In Figure 915 the SOLVERis invoked by changing cells $B$14$B$18 which are random variables instandard normal space (ie uncorrelated zero-mean unit variance compo-nents) An extensive discussion of reliability analysis using EXCEL is givenelsewhere (Phoon 2004a)

For a typical FS = 3 the SLS reliability index for this problem is 221corresponding to a probability of failure of 00134

952 Serviceability limit state model factor approach

The allowable load can be evaluated from the interpreted capacity using aSLS model factor (MS) as follows

Qa = ya

a + byaQm = MSQm (917)

366 Kok-Kwang Phoon and Fred H Kulhawy

Cell

E14E18 E14=B14 E15=B14$K$6+B15SQRT(1-$K$6^2) E16=B16 E17=B17 E18=B18B22B26 B22=EXP(E14H6+G6) B23=EXP(E15H7+G7) etcD22 B24(B24B23+B22)-B26B25D3D25 = SQRT(MMULT(TRANSPOSE(B14B18)B14B18))D28 = NORMSDIST(-D25) Solver Set Target Cell $D$25 Equal To Min By Changing Cells $B$14$B$18 Subject to the Constraints $D$22 lt= 0

EXCEL functions

Figure 915 EXCEL implementation of first-order reliability method for serviceability limitstate

If MS follows a lognormal distribution then a closed-form solution existsfor Equation (915)

β =

ln

⎡⎢⎣(

mMsmQm

mQ

)radicradicradicradicradic 1 + COV2Q(

1 + COV2Ms

)(1 + COV2

Qm

)⎤⎥⎦

radicln[(

1 + COV2Ms

)(1 + COV2

Qm

)(1 + COV2

Q

)] (918)

This procedure is similar to the bias or model factor approach used in ULSRBD However the distribution of MS has to be evaluated for each allowabledisplacement Figure 916 shows two distributions of MS determined by

Serviceability limit state reliability-based design 367

05 10 15 20 25

QQSTC

05 10 15 20 25

QQSTC

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

yallow = 15 mm

Mean = 106SD = 014COV = 014

n = 1000pAD lt 0005

yallow = 25 mmMean = 123

SD = 016COV = 013

n = 1000pAD lt 0005

Figure 916 Simulated distributions of SLS model factors (QQSTC ) for ACIP piles (allow-able displacement ya assumed to be deterministic)

substituting simulated values of a and b into ya(a + bya) with ya = 15 mmand 25 mm In contrast the foundation capacity is a single number (not acurve) and only one model factor distribution is needed

Based on first-order second-moment analysis the mean (mMs) and COV

(COVMs) of MS can be estimated as

mMsasymp ya

ma + mbya(919)

COVMsasympradic

s2a + y2

as2b + 2yaρabsasb

ma + mbya(920)

For ya = 25 mm the mean and COV of MS are estimated to be 121and 0145 which compare favorably with the simulated statistics shownin Figure 916 (mean = 123 and COV = 013)

For the example discussed in Section 951 the reliability index calculatedusing Equation (918) is 229 and the corresponding probability of failureis 00112 The correct FORM solution is β = 238 (pf = 00086) For adeterministic ya = 15 mm Equation (918) gives β = 200 (pf = 00227)while FORM gives β = 213 (pf = 00166) These small differences areto be expected given that the histograms shown in Figure 916 are notlognormally distributed despite visual agreement The null hypothesis oflognormality is rejected because the p-values from the Anderson-Darlinggoodness-of-fit test (pAD) are significantly smaller than 005 In our opin-ion one practical disadvantage of the SLS model factor approach is thatMS can not be evaluated easily from a load test database if ya followsa distribution Given the simplicity of the FORM calculation shown inFigure 915 it is preferable to use the probabilistic hyperbolic model

368 Kok-Kwang Phoon and Fred H Kulhawy

for SLS reliability analysis The distributions of the hyperbolic param-eters do not depend on the allowable displacement and there are nopractical difficulties in incorporating a random ya for SLS reliabilityanalysis

953 Effect of nonlinearity

It is tempting to argue that the loadndashdisplacement behavior is linear at theSLS However for reliability analysis the applied load follows a probabilitydistribution and it is possible for the loadndashdisplacement behavior to be non-linear at the upper tail of the distribution This behavior is illustrated clearlyin Figure 914b The probabilistic hyperbolic model can be simplified to a lin-ear model by setting b = 0 (physically this is equivalent to an infinitely largeasymptotic limit) For the example discussed in Section 951 the FORMsolution under this linear assumption is β = 276 (pf = 000288) Note thatthe solution is very unconservative because the probability of failure is about47 times smaller than that calculated for the correct nonlinear hyperbolicmodel If the mean allowable displacement is reduced to 15 mm and themean factor of safety is increased to 10 then the effect of nonlinearity is lesspronounced For the linear versus nonlinear case β = 350 (pf = 0000231)versus β = 336 (pf = 0000385) respectively

954 Effect of correlation

The effect of correlated hyperbolic parameters on the loadndashdisplacementcurves is shown in Figure 910 For the example discussed in Section 951the FORM solution for uncorrelated hyperbolic parameters is β = 205(pf = 00201) The probability of failure is 15 times larger than that calcu-lated for the correlated case For a more extreme case of FS = 10 and meanya = 50 mm the FORM solution for uncorrelated hyperbolic parametersis β = 442 (pf = 499 times 10minus6) Now the probability of failure is signifi-cantly more conservative It is 33 times larger than that calculated for thecorrelated case given by β = 467 (pf = 149 times 10minus6) Note that assumingzero correlation between the hyperbolic parameters does not always producethe most conservative (or largest) probability of failure If the hyperbolicparameters were found to be positively correlated perhaps with a weakequivalent normal correlation of 05 the FORM solution would be β = 425(pf = 106times10minus5) The probability of failure for the uncorrelated case is now21 times smaller than that calculated for the correlated case

The effect of a negative correlation is to reduce the COV of the SLS modelfactor as shown by Equation (920) A smaller COVMs leads to a higher reli-ability index as shown by Equation (918) The reverse is true for positivelycorrelated hyperbolic parameters

Serviceability limit state reliability-based design 369

955 Uncertainty in allowable displacement

The allowable displacement is assumed to be deterministic in the major-ity of the previous studies For the example discussed in Section 951the FORM solution for a deterministic allowable displacement is β = 238(pf = 000864) The probability of failure is 16 times smaller than that cal-culated for a random ya with COV = 06 For a more extreme case of FS = 10and mean ya = 50 mm the FORM solutions for deterministic and randomya are β = 469(pf = 136 times 10minus6) and β = 467(pf = 149 times 10minus6) respec-tively It appears that the effect of uncertainty in the allowable displacementis comparable to or smaller than the effect of correlation in the hyperbolicparameters

956 Target reliability index for serviceability limit state

The Eurocode design basis BS EN19902002 (British Standards Institute2002) specifies the safety serviceability and durability requirements fordesign and describes the basis for design verification It is intended to beused in conjunction with nine other Eurocodes that govern both struc-tural and geotechnical design and it specifies a one-year target reliabilityindex of 29 for SLS Phoon et al (1995) recommended a target reliabil-ity index of 26 for transmission line (and similar) structure foundationsbased on extensive reliability calibrations with existing designs for differentfoundation types loading modes and drainage conditions Zhang and Xu(2005) analyzed the settlement of 149 driven steel H-piles in Hong Kongand calculated a reliability index of 246 based on a distribution of mea-sured settlements at the service load (mean = 199 mm and COV = 023)and a distribution of allowable settlement for buildings (mean = 96 mm andCOV = 0583)

Reliability indices corresponding to different mean factors of safety anddifferent allowable displacements can be calculated based on the EXCELmethod described in Figure 915 The results for ACIP piles are calculatedusing the statistics given in Figure 94 and are presented in Figure 917It can be seen that a target reliability index = 26 is not achievable at amean FS = 3 for a mean allowable displacement of 25 mm However it isachievable for a mean FS larger than about 4 Note that the 50-year returnperiod load Q50 asymp 15 mQ for a lognormal distribution with COV = 02Therefore a mean FS = 4 is equivalent to a nominal FS = mQm

Q50 = 27A nominal FS of 3 is probably closer to prevailing practice than a meanFS = 3

It is also unclear if the allowable displacement prescribed in practice reallyis a mean value or some lower bound value For a lognormal distributionwith a COV = 06 a mean allowable displacement of 50 mm produces amean less one standard deviation value of 25 mm Using this interpretation a

370 Kok-Kwang Phoon and Fred H Kulhawy

1

2

3

4

5

1 2 3 4 5Mean factor of safety = mQmmQ

Rel

iabi

lity

inde

x

5025

15

mya

(mm)

EN1990EPRI

ab

515 mm062

mQm

06026

05

mean COV

02mQ

mya

Hyperbolic parameters

Capacity Qm

Applied load Q

Allowable displacement ya 06

Figure 917 Relationship between reliability index and mean factor of safety for ACIP piles

target reliability index of 26 is achievable at a mean FS about 3 or a nominalFS about 2

Overall the EPRI target reliability index for SLS is consistent withfoundation designs resulting from a traditional global FS between 2 and 3

96 Simplified reliability-based design

Existing implementations of RBD are based on a simplified approach thatinvolves the use of multi-factor formats for checking designs Explicit reli-ability calculations such as those presented in Section 95 are not donecommonly in current routine design Simplified RBD checks can be calibratedusing the following general procedure (Phoon et al 1995)

1 Select realistic performance functions for the ULS andor the SLS2 Assign probability models for each basic design parameter that are

commensurate with available statistical support3 Conduct a parametric study on the variation of the reliability level with

respect to each parameter in the design problem using the FORM toidentify appropriate calibration domains

4 Determine the range of reliability levels implicit in existing designs5 Adjust the resistance factors (for ULS) or deformation factors (for SLS)

in the RBD Equations until a consistent target reliability level is achievedwithin each calibration domain

Serviceability limit state reliability-based design 371

The calibration of simplified RBD checks was discussed by Phoon andKulhawy (2002a) for drilled shafts subjected to drained uplift by Phoonet al (2003) for spread foundations subjected to uplift and by Kulhawy andPhoon (2004) for drilled shafts subjected to undrained lateral-moment load-ing This section presents an example of SLS design of drilled shafts subjectedto undrained uplift (Phoon et al 1995) The corresponding ULS design wasgiven by Phoon et al (1995 2000)

961 Performance function for serviceability limit state

The uplift capacity of drilled shafts is governed by the following verticalequilibrium Equation

Qu = Qsu + Qtu + W (921)

in which Qu = uplift capacity Qsu = side resistance Qtu = tip resistanceand W = weight of foundation For undrained loading the side resistancecan be calculated as follows

Qsu = πBα

int D

0su(z)dz (922)

in which B = shaft diameter D = shaft depth α = adhesion factorsu = undrained shear strength and z = depth The α factor is calibratedfor a specific su test type For su determined from CIUC (consolidated-isotropically undrained compression) tests the corresponding adhesionfactor was determined by the following regression Equation

α = 031 + 017(su

pa) + ε (923)

in which pa = atmospheric stress asymp 100 kNm2 and ε = normal randomvariable with mean = 0 and standard deviation = 01 resulting from linearregression analysis The undrained shear strength (su) was modeled as alognormal random variable with mean (msu) = 25 minus 200 kNm2 and COV(COVsu

) = 10minus70 It has been well-established for a long time (eg Lumb1966) that there is no such thing as a ldquotypicalrdquo COV for soil parametersbecause the degree of uncertainty depends on the site conditions degreeof equipment and procedural control quality of the correlation model andother factors If geotechnical RBD codes do not consider this issue explicitlythen practicing engineers can not adapt the code design to their specificset of local circumstances such as site conditions measurement techniquescorrelation models standards of practice etc If code provisions do not allowthese adaptations then local ldquoad-hocrdquo procedures are likely to develop that

372 Kok-Kwang Phoon and Fred H Kulhawy

basically defeat the purpose of the code in the first place This situation isnot desirable A good code with proper guidelines is preferable

The tip resistance under undrained loading conditions develops fromsuction forces and was estimated by

Qtu = (minusu minus ui)Atip (924)

in which ui = initial pore water stress at the foundation tip or base u =change in pore water stress caused by undrained loading and Atip = tipor base area Note that no suction force develops unless ndashu exceeds uiTip suction is difficult to predict accurately because it is sensitive to thedrainage conditions in the vicinity of the tip In the absence of an accuratepredictive model and statistical data it was assumed that the tip suction stressis uniformly distributed between zero and one atmosphere [Note that manydesigners conservatively choose to disregard the undrained tip resistance inroutine design]

For the loadndashdisplacement curve it was found that the simple hyperbolicmodel resulted in the least scatter in the normalized curves (Table 91) Thevalues of a and b corresponding to each loadndashdisplacement curve were deter-mined by using the displacement at 50 of the failure load (y50) and thedisplacement at failure (yf ) as follows

a = y50yf

yf minus y50(925a)

b = yf minus 2y50

yf minus y50(925b)

Forty-two loadndashdisplacement curves were analyzed using Equations 925(a and b) The database is given in Table 93 and the statistics of a and bare summarized in Table 94 It can be seen that the effect of soil type issmall and therefore the statistics calculated using all of the data were usedIn this earlier study a and b were assumed to be independent lognormalrandom variables for convenience This assumption is conservative as notedin Section 954 The mean and one standard deviation loadndashdisplacementcurves are plotted in Figure 918 along with the data envelopes

Following Equation (912) the performance function at SLS is

P = Qua minus F = ya

a + byaQu minus F (926)

in which F = weather-related load effect for transmission line structure foun-dations = kV2 (Task Committee on Structural Loadings of ASCE 1991)V = Gumbel random variable with COV = 30 and k = deterministicconstant The probability distributions assigned to each design parameterare summarized in Table 95

Table 93 Hyperbolic curve-fitting parameters for drilled shafts under uplift (Phoon et al1995)

Casea Soil typeb Qcu (kN) Dd (m) Be (m) yf

50 (mm) ygf (mm) ah (mm) bh

51 C 320 317 063 048 1270 050 09681 C 311 457 061 090 1270 096 092125 C 311 457 061 102 1270 110 091151 C 285 244 061 134 671 168 075161 C 338 244 091 061 732 066 09125NE C 80 198 052 091 1270 098 09238A2 C 222 305 061 081 1270 087 09338A3 C 498 610 061 091 1270 098 092401 C 285 366 061 084 1270 090 093402 C 338 366 061 074 1270 079 094561 C 934 1006 035 250 2500 278 089562 C 552 680 035 125 1270 139 0895617 C 667 750 030 062 1270 066 0955623 C 667 649 045 062 1270 066 095631 C 2225 1158 152 140 1270 158 087644 C 1050 366 152 080 1270 085 093691 C 1388 2033 091 173 635 238 062731 C 338 280 061 081 1158 087 092736 C 507 369 076 122 1016 138 086141 S 231 244 091 037 1270 038 097181 S 445 305 091 135 1615 148 091201 S 409 305 091 134 2438 142 094411 S 365 305 091 033 508 035 093441 S 534 853 046 122 1270 135 0894610 S 25 137 034 115 1270 127 0904711 S 71 244 037 077 1270 082 0934812 S 163 366 037 154 1270 175 08649E S 296 427 041 115 1270 127 090511 S 525 366 091 061 1270 064 095621 S 890 640 107 049 635 053 092622 S 890 640 107 098 1400 105 092703 S 1557 610 061 102 1270 110 091951 S 338 1219 053 200 2624 216 092952 S 347 1219 038 312 1999 370 081992 S 1086 305 122 066 419 078 081993 S 1255 366 122 056 762 061 092211 S + C 454 1524 041 091 1270 098 092361 S + C 712 1036 041 191 1270 224 082421 S + C 1629 2134 046 134 1270 149 088501 S + C 100 290 030 147 820 178 078502 S + C 400 381 030 254 2311 285 088961 S + C 667 1219 053 133 1270 149 088

aCase number in Kulhawy et al (1983)bS = sand C = clay S + C = sand and claycFailure load determined by L1ndashL2 method (Hirany and Kulhawy 1988)dFoundation deptheFoundation diameterf Displacement at 50 of failure loadgDisplacement at failure loadhCurve-fitted parameters for normalized hyperbolic model based on Equation (925)

374 Kok-Kwang Phoon and Fred H Kulhawy

Table 94 Statistics of a and b for uplift loadndashdisplacement curve From Phoon andKulhawy 2002a Reproduced with the permission of the American Society of CivilEngineers

Soil type No of tests a b

Mean (mm) COV Mean COV

Clay 19 116 052 089 009Sand 17 122 067 091 005Mixed 6 180 036 086 006All 42 127 056 089 007

20

15

10

Nor

mal

ized

load

FQ

u

05

00 10 20

Displacement y (mm)

Data envelopes +- 1 Standard deviation

30

Mean equationFQu = y(127 + 089y)

40 50

Figure 918 Normalized uplift loadndashdisplacement curve for drilled shafts From Phoon andKulhawy 2002a Reproduced with the permission of the American Society ofCivil Engineers

962 Parametric study

To conduct a realistic parametric study typical values of the design param-eters should be used Herein the emphasis was on those used in the electricutility industry which sponsored the basic research study The drilled shaftdiameter (B) was taken as 1ndash3 m and the depth to diameter ratio (DB) wastaken as 3ndash10 The typical range of the mean wind speed (mV ) was back-calculated from the 50-year return period wind speed (v50) given in Table 95

Serviceability limit state reliability-based design 375

Table 95 Summary of probability distributions for design parameters

Parameter Description Distribution Mean COV

V Wind speed Gumbel 30ndash50a ms 030a Curve-fitting parameter

[Equation (924a)]Lognormal 127 mm 056

b Curve-fitting parameter[Equation (924b)]

Lognormal 089 007

ε Regression error associatedwith adhesion factormodel [(Equation (923)]

Normal 0 01

su Undrained shear strength Lognormal 25ndash200 kNm2 01ndash07Qtu Undrained tip suction stress Uniform 0ndash1 atmosb ndash

a50-year return period wind speed data from Task Committee on Structural Loadings (1991)bRange of uniform distribution

and the COV of V (COVV = 03) as follows

v50 = mV (1 + 259 COVV) (927)

The constant of proportionality k that was used in calculating the founda-tion load was determined by the following serviceability limit state designcheck

F50 = kv250 = Quan (928)

in which Quan = nominal allowable uplift capacity calculated using

Quan = ya

ma + mbyaQun (929)

in which ma = mean of a = 127 mm mb = mean of b = 089 and ya =allowable displacement = 25 mm The nominal uplift capacity (Qun) isgiven by

Qun = Qsun + Qtun + W (930)

in which Qsun = nominal uplift side resistance and Qtun = nominal uplift tipresistance The nominal uplift side resistance was calculated as follows

Qsun = πBDαnmsu (931)

αn = 031 + 017(msu

pa

) (932)

376 Kok-Kwang Phoon and Fred H Kulhawy

in which αn = nominal adhesion factor The nominal tip resistance (Qtun)was evaluated using

Qtun = ( WAtip

minus ui)Atip (933)

The variations of the probability of failure (pf ) with respect to the 50-yearreturn period wind speed allowable displacement limit foundation geome-try and the statistics of the undrained shear strength were given by Phoonet al (1995) It was found that pf is affected significantly by the mean andCOV of the undrained shear strength (msu and COVsu) Therefore the fol-lowing domains were judged to be appropriate for the calibration of thesimplified RBD checks

1 msu = 25ndash50 kNm2 (medium clay) 50ndash100 kNm2 (stiff clay) and100ndash200 kNm2 (very stiff clay)

2 COVsu = 10ndash30 30ndash50 and 50ndash70

For the other less-influential design parameters calibration can be per-formed over the entire range of typical values Note that the COVs of mostmanufactured structural materials fall between 10 and 30 For exam-ple Reacutethaacuteti (1988) citing the 1965 specification of the American ConcreteInstitute noted that the quality of concrete can be evaluated as follows

COV lt 10 excellent

COV = 10minus15 good

COV = 15minus20 satisfactory

COV gt 20 bad

The COVs of natural geomaterials usually are much larger and do not fallwithin a narrow range (Phoon and Kulhawy 1999)

963 Load and Resistance Factor Design (LRFD)

The following LRFD format was chosen for reliability calibration

F50 = ΨuQuan (934)

in which Ψu = deformation factor The SLS deformation factors werecalibrated using the following general procedure (Phoon et al 1995)

1 Partition the parameter space into several smaller domains following theapproach discussed in Section 962 For example the combination of

Serviceability limit state reliability-based design 377

msu = 25ndash50 kNm2 and COVsu = 30ndash50 is one possible calibrationdomain The reason for partitioning is to achieve greater uniformity inreliability over the full range of design parameters

2 Select a set of representative points from each domain For examplemsu = 30 kNm2 and COVsu = 40 constitutes one calibration pointIdeally the set of representative points should capture the full range ofvariation in the reliability level over the whole domain

3 Determine an acceptable foundation design for each calibration pointand evaluate the reliability index for each design Foundation design isperformed using a simplified RBD check [eg Equation (934)] and a setof trial resistance or deformation factors

4 Adjust the resistance or deformation factors until the reliability indicesfor the representative designs are as close to the target level (eg 32 forULS and 26 for SLS) as possible in a statistical sense

The results of this reliability calibration exercise are shown in Table 96A comparison between Figure 919a [existing design check based onEquation (928)] and Figure 919b [LRFD design check based onEquation (934)] illustrates the improvement in the uniformity of the reliabil-ity levels It is interesting to note that deformation factors can be calibratedfor broad ranges of soil property variability (eg COV of su = 10ndash3030ndash50 50ndash70) without compromising on the uniformity of reliabilityachieved Experienced engineers should be able to choose the appropriatedata category even with limited statistical data supplemented with first-order guidelines (Phoon and Kulhawy 1999) and reasoned engineeringjudgment

Table 96 Undrained uplift deformation factors for drilled shaftsdesigned using F50 = ΨuQuan (Phoon et al 1995)

Clay Undrained shear strength u

Mean (kNm2) COV ()

Medium 25ndash50 10ndash30 06530ndash50 06350ndash70 062

Stiff 50ndash100 10ndash30 06430ndash50 06150ndash70 058

Very stiff 100ndash200 10ndash30 06130ndash50 05750ndash70 052

Note Target reliability index = 26

378 Kok-Kwang Phoon and Fred H Kulhawy

(a) 10-1

10-2

10-3

Failu

re p

roba

bilit

y p

f

10-4

(b) 10-1

10-2

10-3

Failu

re p

roba

bilit

y p

f

10-4

0 10 20 30

Diameter = 2m

Depthdiameter = 6

Diameter = 2m

Depthdiameter = 6

mean supa = 05

mean supa = 10

mean supa = 05

mean supa = 10

F50 = Quan

ya = 25 mm

F50 = Ψu Quan

ya = 25 mm

40

COV su ()

COV su ()

50 60 70 80 90

0 10 20 30 40 50 60 70 80 90

Figure 919 Performance of SLS design checks for drilled shafts subjected to undraineduplift (a) existing and (b) reliability-calibrated LRFD (Phoon et al 1995)

Two key conclusions based on wider calibration studies (Phoon et al1995) are noteworthy

1 One resistance or deformation factor is insufficient to achieve a uniformtarget reliability across the full range of geotechnical COVs

Serviceability limit state reliability-based design 379

Table 97 Ranges of soil property variability for reliabilitycalibration in geotechnical engineering

Geotechnical parameter Property variability COV ()

Undrained shear strength Lowa 10ndash30Mediumb 30ndash50Highc 50ndash70

Effective stress friction angle Lowa 5ndash10Mediumb 10ndash15Highc 15ndash20

Horizontal stress coefficient Lowa 30ndash50Mediumb 50ndash70Highc 70ndash90

atypical of good quality direct lab or field measurementsbtypical of indirect correlations with good field data except for the standard

penetration test (SPT)ctypical of indirect correlations with SPT field data and with strictly empirical

correlations

2 In terms of COV three ranges of soil property variability (low mediumand high) are sufficient to achieve reasonably uniform reliability levelsfor simplified RBD checks The appropriate ranges depend on thegeotechnical parameter as shown in Table 97 Note that the cate-gories for effective stress friction angle are similar to those specifiedfor concrete

97 Conclusions

This chapter discussed the application of a probabilistic hyperbolic modelfor performing reliability-based design checks at the serviceability limit stateThe uncertainty in the entire loadndashdisplacement curve is represented by a rel-atively simple bivariate random vector containing the hyperbolic parametersas its components The steps required to construct a realistic random vectorare illustrated using forty measured pairs of hyperbolic parameters for ACIPpiles It is important to highlight that these hyperbolic parameters typicallyexhibit a strong negative correlation A translation-based lognormal modelor a more general translation-based Hermite model is needed to capture thiscorrelation aspect correctly The common assumption of statistical indepen-dence can circumvent the additional complexity associated with a translationmodel because the bivariate probability distribution reduces to two signif-icantly simpler univariate distributions in this special case However thescatter in the measured loadndashdisplacement curves can not be reproducedproperly by simulation under this assumption

380 Kok-Kwang Phoon and Fred H Kulhawy

The nonlinear feature of the loadndashdisplacement curve is captured by thehyperbolic curve-fitting Equation This nonlinear feature is crucial in reli-ability analysis because the applied load follows a probability distributionand it is possible for the loadndashdisplacement behavior to be nonlinear at theupper tail of the distribution Reliability analysis for the more general caseof a random allowable displacement can be evaluated easily using an imple-mentation of the first-order reliability method in EXCEL The proposedapproach is more general and convenient to use than the bias or modelfactor approach because the distributions of the hyperbolic parameters donot depend on the allowable displacement The random vector contain-ing two curve-fitting parameters is the simplest probabilistic model forcharacterizing the uncertainty in a nonlinear loadndashdisplacement curve andshould be the recommended first-order approach for SLS reliability-baseddesign

Simplified RBD checks for SLS and ULS can be calibrated within a sin-gle consistent framework using the probabilistic hyperbolic model Becauseof the broad range of geotechnical COVs one resistance or deformationfactor is insufficient to achieve a uniform target reliability level (the keyobjective of RBD) In terms of COV three ranges of soil property variabil-ity (low medium and high) are more realistic for reliability calibration ingeotechnical engineering Numerical values of these ranges vary as a func-tion of the influential geotechnical parameter(s) Use of these ranges allowsfor achieving a more uniform target reliability level

References

Bjerrum L (1963) Allowable settlement of structures In Proceedings of the3rd European Conference on Soil Mechanics and Foundation Engineering (3)Wiesbaden pp 135ndash7

Boone S J (2001) Assessing construction and settlement-induced building damagea return to fundamental principles In Proceedings Underground ConstructionInstitution of Mining and Metallurgy London pp 559ndash70

Boone S J (2007) Assessing risks of construction-induced building damage forlarge underground projects In Probabilistic Applications in Geotechnical Engi-neering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

British Standards Institute (2002) Eurocode Basis of structural design BS EN19902002 London

Burland J B and Wroth C P (1975) Settlement of buildings and associ-ated damage Building Research Establishment Current Paper Building ResearchEstablishment Watford

Burland J B Broms B B and DeMello V F B (1977) Behavior of founda-tions and structures state of the art report In Proceedings of the 9th Interna-tional Conference on Soil Mechanics and Foundation Engineering (2) Tokyopp 495ndash546

Serviceability limit state reliability-based design 381

Callanan J F and Kulhawy F H (1985) Evaluation of procedures for predictingfoundation uplift movements Report ELndash4107 Electric Power Research InstitutePalo Alto

Chen J-R (1998) Case history evaluation of axial behavior of augered cast-in-placepiles and pressure-injected footings MS thesis Cornell University Ithaca NY

Fenton G A Griffiths D V and Cavers W (2005) Resistance factors forsettlement design Canadian Geotechnical Journal 42(5) 1422ndash36

Finno R J (2007) Predicting damage to buildings from excavation induced groundmovements In Probabilistic Applications in Geotechnical Engineering (GSP 170)Eds K K Phoon G A Fenton E F Glynn C H Juang D V Griffiths T FWolff and L M Zhang ASCE Reston (CDROM)

Grant R Christian J T and VanMarcke E H (1974) Differential settlement ofbuildings Journal of Geotechnical Engineering ASCE 100(9) 973ndash91

Hirany A and Kulhawy F H (1988) Conduct and interpretation of load tests ondrilled shaft foundations detailed guidelines Report EL-5915(1) Electric PowerResearch Institute Palo Alto

Hsiao E C L Juang C H Kung G T C and Schuster M (2007) Reliabil-ity analysis and updating of excavation-induced ground settlement for buildingserviceability evaluation In Probabilistic Applications in Geotechnical Engineer-ing (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Kulhawy F H (1994) Some observations on modeling in foundation engineer-ing In Proceeding of the 8th International Conference on Computer Methodsand Advances in Geomechanics (1) Eds H J Siriwardane and M M ZamanA A Balkema Rotterdam pp 209ndash14

Kulhawy F H and Chen J-R (2005) Axial compression behavior of augered cast-in-place (ACIP) piles in cohesionless soils In Advances in Designing and TestingDeep Foundations (GSP 129) Eds C Vipulanandan and F C Townsend ASCEReston pp 275ndash289 [Also in Advances in Deep Foundations (GSP 132) EdsC Vipulanandan and F C Townsend ASCE Reston 13 p]

Kulhawy F H and Phoon K K (2004) Reliability-based design of drilled shaftsunder undrained lateral-moment loading In Geotechnical Engineering for Trans-portation Projects (GSP 126) Eds M K Yegian and E Kavazanjian ASCEReston pp 665ndash76

Kulhawy F H OrsquoRourke T D Stewart J P and Beech J F (1983) Transmissionline structure foundations for uplift-compression loading load test summariesReport EL-3160 Electric Power Research Institute Palo Alto

Low B K and Tang W H (1997) Efficient reliability evaluation using spreadsheetJournal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Lumb P (1966) Variability of natural soils Canadian Geotechnical Journal 3(2)74ndash97

Misra A and Roberts L A (2005) Probabilistic axial load displacement relation-ships for drilled shafts In Contemporary Issues in Foundation Engineering (GSP131) Eds J B Anderson K K Phoon E Smith and J E Loehr ASCE Reston(CDROM)

382 Kok-Kwang Phoon and Fred H Kulhawy

Moulton L K (1985) Tolerable movement criteria for highway bridges ReportFHWARD-85107 Federal Highway Administration Washington DC

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep founda-tions In Proceedings of the International Workshop on Foundation Design Codesand Soil Investigation in View of International Harmonization and PerformanceBased Design Tokyo A A Balkema Lisse pp 59ndash94

Paikowsky S G and Lu Y (2006) Establishing serviceability limit state in design ofbridge foundations In Foundation Analysis and Design Innovative Methods (GSP153) Eds R L Parsons L M Zhang W D Guo K K Phoon and M YangASCE Reston pp 49ndash58

Phoon K K (1995) Reliability-based design of foundations for transmission linestructures PhD thesis Cornell University Ithaca NY

Phoon K K (2004a) General non-Gaussian probability models for first-order reli-ability method (FORM) a state-of-the-art report ICG Report 2004-2-4 (NGIReport 20031091-4) International Centre for Geohazards Oslo

Phoon K K (2004b) Application of fractile correlations and copulas to non-Gaussian random vectors In Proceedings of the 2nd International ASRANet(Network for Integrating Structural Safety Risk and Reliability) ColloquiumBarcelona (CDROM)

Phoon K K (2006) Modeling and simulation of stochastic data In Geo-Congress 2006 Geotechnical Engineering in the Information Technology AgeEds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K and Honjo Y (2005) Geotechnical reliability analyses towards devel-opment of some user-friendly tools In Proceedings of the 16th InternationalConference on Soil Mechanics and Geotechnical Engineering Osaka (CDROM)

Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanadian Geotechnical Journal 36(4) 612ndash24

Phoon K K and Kulhawy F H (2002a) Drilled shaft design for transmission linestructures using LRFD and MRFD In Deep Foundations 2002 (GSP116) EdsM W OrsquoNeill and F C Townsend ASCE Reston pp 1006ndash17

Phoon K K and Kulhawy F H (2002b) EPRI study on LRFD and MRFDfor transmission line structure foundations In Proceedings International Work-shop on Foundation Design Codes and Soil Investigation in View of Interna-tional Harmonization and Performance Based Design Tokyo Balkema Lissepp 253ndash61

Phoon K K and Kulhawy F H (2005a) Characterization of model uncertaintiesfor laterally loaded rigid drilled shafts Geotechnique 55(1) 45ndash54

Phoon K K and Kulhawy F H (2005b) Characterization of model uncertaintiesfor drilled shafts under undrained axial loading In Contemporary Issues in Foun-dation Engineering (GSP 131) Eds J B Anderson K K Phoon E Smith andJ E Loehr ASCE Reston 13 p

Phoon K K Chen J-R and Kulhawy F H (2006) Characterization ofmodel uncertainties for augered cast-in-place (ACIP) piles under axial compres-sion In Foundation Analysis and Design Innovative Methods (GSP 153) EdsR L Parsons L M Zhang W D Guo K K Phoon and M Yang ASCE Restonpp 82ndash9

Serviceability limit state reliability-based design 383

Phoon K K Chen J-R and Kulhawy F H (2007) Probabilistic hyperbolic modelsfor foundation uplift movements In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Phoon K K Kulhawy F H and Grigoriu M D (1995) Reliability-based designof foundations for transmission line structures Report TR-105000 EPRI PaloAlto

Phoon K K Kulhawy F H and Grigoriu M D (2000) Reliability-based designfor transmission line structure foundations Computers and Geotechnics 26(3ndash4)169ndash85

Phoon K K Kulhawy F H and Grigoriu M D (2003) Multiple resistancefactor design (MRFD) for spread foundations Journal of Geotechnical andGeoenvironmental Engineering ASCE 129(9) 807ndash18

Phoon K K Nadim F and Lacasse S (2005) First-order reliability methodusing Hermite polynomials In Proceedings of the 9th International Conferenceon Structural Safety and Reliability Rome (CDROM)

Phoon K K Quek S T Chow Y K and Lee S L (1990) Reliability analysis ofpile settlement Journal of Geotechnical Engineering ASCE 116(11) 1717ndash35

Phoon K K Quek S T and Huang H W (2004) Simulation of non-Gaussianprocesses using fractile correlation Probabilistic Engineering Mechanics 19(4)287ndash92

Quek S T Chow Y K and Phoon K K (1992) Further contributions toreliability-based pile settlement analysis Journal of Geotechnical EngineeringASCE 118(5) 726ndash42

Quek S T Phoon K K and Chow Y K (1991) Pile group settlement a proba-bilistic approach International Journal of Numerical and Analytical Methods inGeomechanics 15(11) 817ndash32

Reacutethaacuteti L (1988) Probabilistic Solutions in Geotechnics Elsevier New YorkRoberts L A Misra A and Levorson S M (2007) Probabilistic design methodol-

ogy for differential settlement of deep foundations In Probabilistic Applications inGeotechnical Engineering (GSP 170) Eds K K Phoon G A Fenton E F GlynnC H Juang D V Griffiths T F Wolff amp L M Zhang ASCE Reston(CDROM)

Schuster M J Juang C H Roth M J S Hsiao E C L and Kung G T C (2007)Serviceability limit state for probabilistic characterization of excavation-inducedbuilding damage In Probabilistic Applications in Geotechnical Engineering (GSP170) Eds K K Phoon G A Fenton E F Glynn C H Juang D V GriffithsT F Wolff and L M Zhang ASCE Reston (CDROM)

Skempton A W and MacDonald D H (1956) The allowable settlement ofbuildings Proceedings of the Institution of Civil Engineers Part 3(5) 727ndash68

Task Committee on Structural Loadings (1991) Guidelines for Electrical Transmis-sion Line Structural Loading Manual and Report on Engineering Practice 74ASCE New York

Wahls H E (1981) Tolerable settlement of buildings Journal of GeotechnicalEngineering Division ASCE 107(GT11) 1489ndash504

Zhang L M and Ng A M Y (2005) Probabilistic limiting tolerable displacementsfor serviceability limit state design of foundations Geotechnique 55(2) 151ndash61

384 Kok-Kwang Phoon and Fred H Kulhawy

Zhang L M and Ng A M Y (2007) Limiting tolerable settlement and angulardistortion for building foundations In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Zhang L M and Phoon K K (2006) Serviceability considerations in reliability-based foundation design In Foundation Analysis and Design Innovative Methods(GSP 153) Eds R L Parsons L M Zhang W D Guo K K Phoon and M YangASCE Reston pp 127ndash36

Zhang L M and Xu Y (2005) Settlement of building foundations based onfield pile load tests In Proceedings of the 16th International Conference on SoilMechanics and Geotechnical Engineering Osaka (CDROM)

Chapter 10

Reliability verification usingpile load tests

Limin Zhang

101 Introduction

Proof pile load tests are an important means to cope with uncertainties in thedesign and construction of pile foundations In this chapter the term loadtest refers to a static load test following specifications such as ASTM D1143BS8004 (BSI 1986) ISSMFE (1985) and PNAP 66 (Buildings Department2002) Load tests serve several purposes Some of their functions include ver-ification of design parameters (this is especially important if the geotechnicaldata are uncertain) establishment of the effects of construction methods onfoundation capacities and provision of data for the improvement of designmethodologies in use and for research purposes (Passe et al 1997 Zhangand Tang 2002) In addition to these functions savings may be derived fromload tests (Hannigan et al 1997 Passe et al 1997) With load tests lowerfactors of safety (FOS) or higher-strength parameters may be used Hencethere are cost savings even when no changes in the design are made after theload tests For example the US Army Corps of Engineers (USACE) (1993)the American Association of State Highway and Transportation Officials(AASHTO) (1997) and Geotechnical Engineering Office (2006) recommendthe use of different FOSs depending on whether load tests are carried out ornot to verify the design The FOSs recommended by the USACE are shownin Table 101 The FOS under the usual load combination may be reducedfrom 30 for designs based on theoretical or empirical predictions to 20for designs based on the same predictions that are verified by proof loadtests

Engineers have used proof pile tests in the allowable stress design (ASD) asfollows The piles are sized using a design method suitable for the soil condi-tions and the construction procedure for the project considered In designingthe piles a FOS of 20 is used when proof load tests are utilized Preliminarypiles or early test piles are then constructed necessary design modificationsmade based on construction control and proof tests on the test piles orsome working piles conducted If the test piles do not reach a prescribed fail-ure criterion at the maximum test load (ie twice the design load) then the

386 Limin Zhang

Table 101 Factor of safety for pile capacity (after USACE 1993)

Method of determining capacity Loading condition Minimum factor of safety

Compression Tension

Theoretical or empirical Usual 20 20prediction to be verified Unusual 15 15by pile load test Extreme 115 115

Theoretical or empirical Usual 25 30prediction to be verified Unusual 19 225by pile driving analyzer Extreme 14 17

Theoretical or empirical Usual 30 30prediction not verified Unusual 225 225by load test Extreme 17 17

design is validated otherwise the design load for the piles must be reducedor additional piles or pile lengths must be installed

In a reliability-based design (RBD) the value of proof load tests can be fur-ther maximized In a RBD incorporating the Bayesian approach (eg Ang andTang 2007) the same load test results reveal more information Predictedpile capacity using theoretical or empirical methods can be very uncertainResults from load tests not only suggest a more realistic pile capacity valuebut also greatly reduce the uncertainty of the pile capacity since the errorassociated with load-test measurements is much smaller than that associatedwith predictions In other words the reliability of a design for a test site canbe updated by synthesizing existing knowledge of pile design and site-specificinformation from load tests

For verification of the reliability of designs and calibration of new theoriesit is preferable that load tests be conducted to failure Load tests are oftenused as proof tests however the maximum test load is usually specified astwice the anticipated design load (ASTM D 1143 Buildings Department2002) unless failure occurs first In many cases the pile is not brought tofailure at the maximum test load Ng et al (2001a) analyzed more than 30cases of static load tests on large-diameter bored piles in Hong Kong Theyfound that the Davisson failure criterion was reached in less than one half ofthe cases and in no case was the 10-diameter settlement criterion suggestedby ISSMFE (1985) and BSI (1986) reached In the Hong Kong Driven PileDatabase developed recently (Zhang et al 2006b) only a small portion ofthe 312 static load tests on steel H-piles were conducted to failure as specifiedby the Davisson criterion

Kay (1976) Baecher and Rackwitz (1982) Lacasse and Goulois (1989)and Zhang and Tang (2002) investigated updating the reliability of pilesusing load tests conducted to failure Zhang (2004) studied how the majority

Reliability verification 387

of proof tests not conducted to failure increases the reliability of pilesand how the conventional proof test method can be modified to allow for agreater value in the RBD framework

This chapter consists of three parts A systematic method to incorpo-rate the results of proof load tests into foundation design is first describedSecond illustrative acceptance criteria for piles based on proof load testsare proposed for use in the RBD Finally modifications to the conventionalproof test procedures are suggested so that the value derived from the prooftests can be maximized Reliability and factor of safety are used together inthis chapter since they provide complementary measures of an acceptabledesign (Duncan 2000) as well as better understanding of the effect of prooftests on the costs and reliability of pile foundations This chapter will focuson ultimate limits design Discussion on serviceability limit states is beyondthe scope of the chapter

102 Within-site variability of pile capacity

In addition to uncertainties with site investigation laboratory testing andprediction models the values of capacity of supposedly identical piles withinone site also vary Suppose several ldquoidenticalrdquo test piles are constructedat a seemingly uniform site and are load-tested following an ldquoidenticalrdquoprocedure The measured values of the ultimate capacity of the piles wouldusually be different due to the so-called ldquowithin-siterdquo variability follow-ing Baecher and Rackwitz (1982) For example Evangelista et al (1977)tested 22 ldquoidenticalrdquo bored piles in a sandndashgravel site The piles were ldquoallrdquo08 m in diameter and 20 m in length and construction of the piles wasassisted with bentonite slurry The load tests revealed that the coefficient ofvariation (COV) of the settlement of these ldquoidenticalrdquo piles at the intendedworking load was 021 and the COV of the applied loads at the meansettlement corresponding to the intended load was 013

Evangelista et al (1977) and Zhang and Tang (2002) described severalsources of the within-site variability of the pile capacity inherent variabil-ity of properties of the soil in the influence zone of each pile constructioneffects variability of pile geometry (length and diameter) variability of prop-erties of the pile concrete and soil disturbance caused by pile driving andafterward set-up The effects of both construction and set-up are worth aparticular mention because they can introduce many mechanisms that causelarge scatter of data One example of construction effects is the influence ofdrilling fluids on the capacity of drilled shafts According to OrsquoNeill andReese (1999) improper handling of bentonite slurry alone could reduce thebeta factor for pile shaft resistance from a common range of 04ndash12 to below01 Set-up effects refer to the phenomenon that the pile capacity increaseswith time following pile installation Chow et al (1998) Shek et al (2006)and many others revealed that the capacity of driven piles in sand can increase

388 Limin Zhang

from a few percent to over 200 after the end of initial driving Such effectsmake the pile capacity from a load test a ldquonominalrdquo value

The variability of properties of the soil and the pile concrete is affected bythe space over which the properties are estimated (Fenton 1999) The issueof spatial variability of soils is the subject of interest in several chapters of thisbook Note that after including various effects the variability of the soils at asite may not be the same as the variability of the pile capacity at the same siteDasaka and Zhang (2006) investigated the spatial variability of a weatheredground using random field theory and geostatistics The scale of fluctuationof the depth of competent rock (moderately decomposed rock) beneath abuilding block is approximately 30 m whereas the scale of fluctuation ofthe as-build depth of the piles that provide the same nominal capacity isonly approximately 10 m

Table 102 lists the values of the coefficient of variation (COV) of thecapacity of driven piles from load tests in eight sites These values rangefrom 012 to 028 Note that the variability reported in the table is among testpiles in one site the variability among production piles and among differentsites may be larger (Baecher and Rackwitz 1982) In this chapter a meanvalue of COV = 020 is adopted for analysis This assumes that the standarddeviation value is proportional to the mean pile capacity

Kay (1976) noted that the possible values of the pile capacity within asite favor a log-normal distribution This may be tested with the data from16 load tests in a site in southern Italy reported by Evangelista et al (1977)

Table 102 Within-site viability of the capacity of driven piles in eight sites

Site Numberof piles

Diameter(m)

Length(m)

Soil COV References

AshdodIsrael

12 ndash ndash Sand 022 Kay 1976

BremerhavenGermany

9 ndash ndash Sand 028 Kay 1976

San FranciscoUSA

5 ndash ndash Sand andclay

027 Kay 1976

Southern Italy 12 040 80 Sand andgravel

025 Evangelistaet al 1977

Southern Italy 4 040 120 Sand andgravel

012 Evangelistaet al 1977

Southern Italy 17 052 180 Sand andgravel

019 Evangelistaet al 1977

Southern Italy 3 036 73 Sand andgravel

012 Evangelistaet al 1977

Southern Italy 4 046 70 Sand andgravel

014 Evangelistaet al 1977

Southern Italy 16 050 150 Clay sandand gravel

020 Evangelistaet al 1977

Reliability verification 389

00

02

04

06

08

10

0 500 1000 1500 2000Pile capacity (kN)

Cu

mu

lati

ve d

istr

ibu

tio

n

Measurements

Theoretical normal

Theoretical log-normal

Maximumdeviation

5 criticalvalue

Normal 026 032Log-normal 022 032

Figure 101 Observed and theoretical distributions of within-site pile capacity

Figure 101 shows the measured and assumed normal and log-normal distri-butions of the pile capacity in the site Using the KolmogorovndashSmirnov testneither the normal nor the log-normal theoretical curves appear to fit theobserved cumulative curve very well However it is acceptable to adopt alog-normal distribution for mathematical convenience since the maximumdeviation in Figure 101 is still smaller than the 5 critical value The tailpart of the cumulative distribution of the pile capacity is of more interestto designers since the pile capacity in that region is smaller In Figure 101it can be seen that the assumed log-normal distribution underestimates thepile capacity at a particular cumulative percentage near the tail Thereforethe assumed distribution will lead to results on the conservative side

From the definition the within-site variability is inherent in a particulargeological setting and a geotechnical construction procedure at a specificsite The within-site variability represents the minimum variability for aconstruction procedure at a site which cannot be reduced using load tests

103 Updating pile capacity with proof load tests

1031 Proof load tests that pass

For proof load tests that are not carried out to failure the pile capacityvalues are not known although they are greater than the maximum testloads Define the variate x as the ratio of the measured pile capacity tothe predicted pile capacity (called the ldquobearing capacity ratiordquo hereafter)At a particular site x can be assumed to follow a log-normal distribution(Whitman 1984 Barker et al 1991) with the mean and standard deviation

390 Limin Zhang

of x being micro and σ and those of ln(x) being η and ξ where σ or ξ describes thewithin-site variability of the pile capacity Suppose the specified maximumtest load corresponds to a value of x = xT For example if the maximumtest load is twice the design load and a FOS of 20 is used then the value ofxT is 10 From the log-normal probability density function the probabilitythat the test pile does not fail at the maximum test load (ie x gt xT) is

p(x ge xT) =infinint

xT

1radic2πξx

exp

minus1

2

(ln(x) minusη

ξ

)2

dx (101)

Noting that y = (ln(x) minusη)ξ Equation (101) becomes

p(x ge xT) =infinint

ln(xT)minusη

ξ

1radic2π

exp(

minus12

y2)

dy = 1 minus

(ln(xT) minusη

ξ

)

=

(minus ln(xT) minusη

ξ

)(102)

where is the cumulative distribution function of the standard normaldistribution Suppose that n proof tests are conducted and none of the testpiles fails at xT If the standard deviation ξ of ln(x) is known but its meanmicro or η is a variable then the probability that all of the n test piles do notfail at xT is

L(micro) =nprod

i=1

pX(x ge xT)∣∣∣micro = n

(minus ln(xT) minusη

ξ

)(103)

L(micro) is also called the ldquolikelihood functionrdquo of micro Given L(micro) the updateddistribution of the mean of the bearing capacity ratio is (eg Ang andTang 2007)

f primeprime(micro) = kn(

minus ln(xT) minusη

ξ

)f prime(micro) (104)

where f prime(micro) is the prior distribution of micro which can be constructed basedon the empirical log-normal distribution of x and the within-site variabilityinformation (Zhang and Tang 2002) and k is a normalizing constant

k =⎡⎣ infinintminusinfin

n(

minus ln(xT) minusη

ξ

)f prime(micro)dmicro

⎤⎦

minus1

(105)

Reliability verification 391

Given an empirical distribution N(microXσX) the prior distribution can alsobe assumed as a normal distribution with the following parameters

microprime = microX (106)

σ prime =radicσ 2

X minusσ 2 (107)

The updated distribution of the bearing capacity ratio x is thus (eg Angand Tang 2007)

fX(x) =infinint

minusinfinfX(x |micro )f primeprime(micro)dmicro (108)

where fX(x |micro ) is the distribution of x given the distribution of its mean Thisdistribution is assumed to be log-normal as mentioned earlier

1032 Proof load tests that do not pass

More generally suppose only r out of n test piles do not fail at x = xT Theprobability that this event occurs now becomes

L(micro) =(

nr

)[p(x ge xT)

∣∣∣micro ]r [1 minus p(x ge xT)∣∣∣micro ](nminusr)

(109)

and the normalizing constant k is

k=⎡⎣ infinintminusinfin

(nr

)[p(xgexT)

∣∣∣micro ]r[1minusp(xgexT)∣∣∣micro ](nminusr)

f prime(micro)dmicro

⎤⎦

minus1

(1010)

Accordingly the updated distribution of the mean of the bearing capacityratio is

f primeprime(micro) = k(

nr

)[p(x ge xT)

∣∣∣micro ]r [1 minus p(x ge xT)∣∣∣micro ](nminusr)

f prime(micro) (1011)

Equation (1011) has two special cases When all tests pass (ie r = n)Equation (1011) reduces to Equation (104) If none of the tests passes(ie r = 0) a dispersive posterior distribution will result The updated distri-bution of the bearing capacity ratio can be obtained using Equation (108)

1033 Proof load tests conducted to failure

Let us now consider the proof load tests carried out to failure ie caseswhere the pile capacity values are known To start with the log-normally

392 Limin Zhang

distributed pile capacity assumed previously is now transformed into anormal variate If the test outcome is a set of n observed values representinga random sample following a normal distribution with a known standarddeviation σ and a mean of x then the distribution of the pile capacity canbe updated directly using Bayesian sampling theory Given a known priornormal distribution Nmicro(microprimeσ prime) the posterior density function of the bearingcapacity ratio f primeprime

X(x) is also normal (Kay 1976 Zhang and Tang 2002 Angand Tang 2007)

fXprimeprime (x) = NX(microXprimeprime σXprimeprime ) (1012)

where

microXprimeprime =

x(σ prime)2 +microprime(σ2

n

)(σ prime)2 +

(σ2

n

) (1013)

σXprimeprime = σ

radicradicradicradicradic1 +(σ prime2n

)(σ prime)2 +

(σ2

n

) (1014)

1034 Multiple types of tests

In many cases multiple test methods are used for construction quality assur-ance at a single site The construction control of driven H-piles in Hong Kong(Chu et al 2001) and the seven-step construction control process of Bell et al(2002) are two examples In the Hong Kong practice both dynamic formulaand static analysis are adopted for determining the pile length Early testpiles or preliminary test piles are required at the early stage of constructionto verify the construction workmanship and design parameters In additionup to 10 of working piles may be tested using high-strain dynamic testseg using a Pile Driving Analyzer (PDA) and one-half of these PDA testsshould be analyzed by a wave equation analysis such as the CAse Pile WaveAnalysis Program (CAPWAP) Finally at the end of construction 1 ofworking piles should be proof-tested using static loading tests With theseconstruction control measures in mind a low factor of safety is commonlyused Zhang et al (2006a) described the piling practice in the language ofBayesian updating A preliminary pile length is first set based primarily on adynamic formula The pile performance is then verified by PDA tests duringthe final setting tests The combination of the dynamic formula (prior) andthe PDA tests will result in a posterior distribution Taking the posteriordistribution after the PDA tests as a prior the pile performance is furtherupdated based on the outcome of the CAPWAP analyses and finally updatedbased on the outcome of the proof load tests This exercise can be repeatedif more indirect or direct verification tests are involved

Reliability verification 393

104 Reliability of piles verified by proof tests

1041 Calculation of reliability index

In a reliability-based design (RBD) the ldquosafetyrdquo of a pile can be describedby a reliability index β To be consistent with current efforts in code devel-opment such as AASHTOrsquos LRFD Bridge Design Specifications (Barkeret al 1991 AASHTO 1997 Withiam et al 2001 Paikowsky 2002) or theNational Building Code of Canada (NRC 1995 Becker 1996a b) the first-order reliability method (FORM) is used for calculating the reliability indexIf both resistance and load effects are log-normal variates then the reliabilityindex for a linear performance function can be written as (Whitman 1984)

β =ln

⎛⎝R

Q

radicradicradicradic1 + COV2Q

1 + COV2R

⎞⎠

radicln[(1 + COV2

R)(1 + COV2Q)]

(1015)

where Q and R = the mean values of load effect and resistance respectivelyCOVQ and COVR = the coefficients of variation (COV) for the load effectand resistance respectively If the only load effects to be considered are deadand live loads Barker et al (1991) Becker (1996b) and Withiam et al(2001) have shown that

R

Q=

λRFOS(

QD

QL+ 1)

λQDQD

QL+λQL

(1016)

and

COV2Q = COV2

QD + COV2QL (1017)

where QD and QL = the nominal values of dead and live loads respectivelyλRλQD and λQL = the bias factors for the resistance dead load and liveload respectively with the bias factor referring to the ratio of the mean valueto the nominal value COVQD and COVQL = the coefficients of variationfor the dead load and live load respectively and FOS = the factor of safetyin the traditional allowable stress design

If the load statistics are prescribed Equations (1015) and (1016) indicatethat the reliability of a pile foundation designed with a FOS is a function ofλR and COVR As will be shown later if a few load tests are conducted ata site the values of λR and COVR associated with the design analysis can

394 Limin Zhang

be updated using the load test results If the results are favorable then theupdated COVR will decrease but the updated λR will increase The reliabilitylevel or the β value will therefore increase Conversely if a target reliabilityβT is specified the FOS required to achieve the βT can be calculated usingEquations (1015)ndash(1017) and the costs of construction can be reduced ifthe load test results are favorable

1042 Example design based on an SPT method andverified by proof tests

For illustration purposes a standard penetration test (SPT) method proposedby Meyerhof (1976) for pile design is considered This method uses the aver-age corrected SPT blow count near the pile toe to estimate the toe resistanceand the average uncorrected blow count along the pile shaft to estimate theshaft resistance According to statistical studies conducted by Orchant et al(1988) the bias factor and COV of the pile capacity from the SPT methodare λR = 130 and COVR = 050 respectively The within-site variability ofthe pile capacity is assumed to be COV = 020 (Zhang and Tang 2002)which is smaller than the COVR of the design analysis This is because theCOVR of the prediction includes more sources of errors such as model errorsand differences in construction effects between the site where the model isapplied and the sites from which the information was extracted to formulatethe model (Zhang et al 2004)

Calculations for updating the probability distribution can be conductedusing an Excel spreadsheet Figure 102 shows the empirical distribution ofthe bearing capacity ratio x based on the given λR and COVR values andthe updated distributions after verification by proof tests The translatedmean η and standard deviation ξ of ln(x) calculated based on the givenλR and COVR are used to define the empirical log-normal distributionIn Figure 102(a) all proof tests are positive (ie the test piles do not failat twice the design load) the mean value of the updated pile capacity afterverification by the proof tests increases with the number of tests while theCOV value decreases Specifically the updated mean increases from 130for the empirical distribution to 174 after the design has been verified bythree positive tests In Figure 102(b) the cases in which no test one test twotests and all three tests are positive are considered As expected the updatedmean decreases significantly when the number of positive tests decreases Theupdated mean and COV values with different outcomes from the proof testsare summarized in Table 103 The updated distributions in Figure 102may be approximated by the log-normal distribution for the convenience ofreliability calculations using Equation (1015) The log-normal distributionfits the cases with some non-positive tests very well For the cases in whichall tests are either positive or negative the log-normal distribution slightlyexaggerates the scatter of the distributions as shown in Figure 102(b)

Reliability verification 395

00

05

10

15

20

00 05 10 15 20 25 30 35 40Bearing capacity ratio x

Pro

bab

ility

den

sity

1 positive test

2 positive tests

3 positive tests

5 positive tests

Empirical distribution

00

05

10

15

20

00 05 10 15 20 25 30 35 40

Bearing capacity ratio x

Pro

bab

ility

den

sity

3 positive out of 3 tests

3 out of 3 approx log-normal

2 positive out of 3 tests

1 positive out of 3 tests

none positive out of 3 tests

Empirical distribution

(a)

(b)

Figure 102 Distributions of the pile capacity ratio after verification by (a) proof tests thatpass at twice the design load and (b) proof tests in which some piles fail

The use of the log-normal distribution is therefore slightly on the con-servative side Because of the differences in the distributions updated byproof tests of different outcomes the COV values in Table 103 do notchange in a descending or ascending manner as the number of positive testsdecreases

The following typical load statistics in the LRFD Bridge Design Specifi-cations (AASHTO 1997) are adopted for illustrative reliability analysesλQD = 108 λQL = 115 COVQD = 013 and COVQL = 018 Thedead-to-live load ratio QDQL is structure-specific Investigations by

396 Limin Zhang

Table 103 Updated mean and COV of the bearing capacity ratio based on SPT(Meyerhof 1976) and verified by proof tests

Total numberof tests

Number ofpositive tests

Mean COV at FOS = 20

4 4 177 038 2673 119 024 2382 104 024 2001 091 024 1570 069 030 060

3 3 174 038 2582 114 025 2211 096 025 1700 072 031 067

2 2 168 039 2451 106 026 1910 075 032 077

1 1 159 041 2230 082 034 095

Barker et al (1991) and McVay et al (2000) show that β is relatively insen-sitive to this ratio For the SPT method considered the calculated β valuesare 192 and 190 for distinct QDQL values of 369 (75 m span length) and158 (27 m span length) respectively if a FOS of 25 is used The differ-ence between the two β values is indeed small In the following analyses thelarger value of QDQL = 369 which corresponds to a structure on whichthe dead loads dominate is adopted This QDQL value was used by Barkeret al (1991) and Zhang and Tang (2002) and would lead to factors of safetyon the conservative side

Figure 103 shows the reliability index β values for single piles designedwith a FOS of 20 and verified by several proof tests Each of the curves inthis figure represents the reliability of the piles when they have been verifiedby n proof tests out of which r tests are positive [see the likelihood functionEquation (109)] The β value corresponding to the empirical distribution(see Figure 102) and a FOS of 20 is 149 If one conventional proof test isconducted to verify the design and the test is positive then the β value willbe updated to 223 The updated β value will continue to increase if moreproof tests are conducted and if all the tests are positive In the cases whennot all tests are positive the reliability of the piles will decrease with thenumber of tests that are not positive For instance the β value of the pilesverified by three positive tests is 258 If one two or all of the three test pilesfail the β values decrease to 221 170 and 067 respectively If multipleproof tests are conducted the target reliability marked by the shaded zone

Reliability verification 397

00

05

10

15

20

25

30

0 1 2 3 4 5

Number of positive tests

Rel

iab

ility

ind

ex b

All tests positiveNumber of positive tests out of 4 testsNumber of positive tests out of 3 testsNumber of positive tests out of 2 testsNumber of positive tests out of 1 test

Target reliability zone βTS =17ndash25

Figure 103 Reliability of single driven piles designed with a FOS of 20 and verified byconventional proof tests of different outcomes

can still be satisfied even if some of the tests are not positive Issues on targetreliability will be discussed later in this chapter

The FOS that will result in a sufficient level of reliability is of interest toengineers Figure 104 shows the calculated FOS values required to achievea β value of 20 for the pile foundation verified by proof tests with differentoutcomes It can be seen that a FOS of 20 is sufficient for piles that areverified by one or more consecutive positive tests or by three or four testsin which no more than one test is not positive However larger FOS valuesshould be used if the only proof test or one out of two tests is not positive

The updating effects of the majority of the proof tests conducted to twicethe design load and the tests in which the piles have a mean measured capac-ity of twice the design load are different Table 104 compares the updatedstatistics of the two types of tests For the proof tests that are not con-ducted to failure the updated mean bearing capacity ratio and β valuessignificantly increase with the number of tests For the tests that are con-ducted to failure at x = 10 the updated mean value will approach 10 andthe updated COV value will approach the assumed within-site COV valueof 020 as expected when the number of tests is sufficiently large This isbecause test outcomes carry a larger weight than that of the prior informationas described in Equations (1013) and (1014)

398 Limin Zhang

00

05

10

15

20

25

30

35

0 1 2 3 4 5

Number of positive tests

Req

uir

ed f

acto

r o

f sa

fety

All tests positive

Number of positive tests out of 4 tests

Number of positive tests out of 3 tests

Number of positive tests out of 2 tests

Number of positive tests out of 1 test

Factor of safety commonlyused for design verified byconventional load tests

Figure 104 Factor of safety for single driven piles that are required to achieve a targetreliability index of 20 after verification by proof load tests

Table 104 Comparison of the updating effects of proof tests that pass at xT = 10 andtests in which the test piles fail at a mean bearing capacity ratio of xT = 10

Numberof tests

Tests that pass at xT = 10 Tests that fail at xT = 10

Mean COV Mean COV

0 130 050 149 130 050 1491 159 041 223 106 027 1892 168 039 245 104 024 1983 174 038 258 104 023 2024 177 038 267 103 022 2045 180 037 274 103 022 205

10 189 036 294 102 021 209

NoteMean and COV of the bearing capacity ratio x

1043 Effect of accuracy of design methods

The example in the previous section focuses on a single design methodOne may ask if the observations from the example would apply to otherdesign methods Table 105 presents statistics of a number of commonlyused methods for design and construction of driven piles (Zhang et al 2001)

Tabl

e10

5R

elia

bilit

yst

atis

tics

ofsi

ngle

driv

enpi

les

(aft

erZ

hang

etal

20

01)

Met

hod

cate

gory

Pred

ictio

nm

etho

dN

umbe

rof

case

sBi

asfa

ctor

λR

Coef

ficie

ntof

varia

tion

COV R

ASD

fact

orof

safe

tyFO

S

Relia

bilit

yin

dex

Re

fere

nces

Dyn

amic

met

hods

CA

PWA

P(E

OD

Flo

rida

data

base

)44

160

035

25

311

McV

ayet

al(

2000

)

with

mea

sure

men

tsC

APW

AP

(BO

RF

lori

dada

taba

se)

791

260

352

52

55M

cVay

etal

(20

00)

(Con

stru

ctio

nst

age)

CA

PWA

P(E

OD

nat

iona

lda

taba

se)

125

163

049

25

239

Paik

owsk

yan

dSt

ener

sen

(200

0)

CA

PWA

P(B

OR

nat

iona

lda

taba

se)

162

116

034

25

238

Paik

owsk

yan

dSt

ener

sen

(200

0)

Ener

gyap

proa

ch(E

OD

Fl

orid

ada

taba

se)

271

110

342

52

29M

cVay

etal

(20

00)

Ener

gyap

proa

ch(B

OR

Fl

orid

ada

taba

se)

720

840

363

252

11M

cVay

etal

(20

00)

Ener

gyap

proa

ch(E

OD

na

tiona

ldat

abas

e)12

81

080

402

51

94Pa

ikow

sky

and

Sten

erse

n(2

000)

Ener

gyap

proa

ch(B

OR

na

tiona

ldat

abas

e)15

30

790

373

251

92Pa

ikow

sky

and

Sten

erse

n(2

000)

Stat

icm

etho

dsA

lpha

met

hod

clay

type

Indash

110

021

25

308

Sidi

(198

5)B

arke

ret

al(

1991

)(D

esig

nst

age)

Alp

ham

etho

dcl

ayty

peII

ndash2

340

572

52

72Si

di(1

985)

Bar

ker

etal

(19

91)

Beta

met

hod

ndash1

030

212

52

82Si

di(1

985)

Bar

ker

etal

(19

91)

CPT

met

hod

ndash1

030

362

51

98O

rcha

ntet

al(

1988

)Ba

rker

etal

(19

91)

Schm

ertm

ann

(197

8)La

mbd

am

etho

dcl

ayty

peI

ndash1

020

412

51

74Si

di(1

985)

Bar

ker

etal

(19

91)

Mey

erho

frsquosSP

Tm

etho

dndash

130

050

25

192

Orc

hant

etal

(19

88)

Bark

eret

al

(199

1)

Not

eT

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a

400 Limin Zhang

In this table failure of piles is defined by the Davisson criterion For eachdesign method in the table the driven pile cases are put together regard-less of ground conditions or types of pile response (ie end-bearing orfloating) Ideally the cases should be organized into several subsets accord-ing to their ground conditions and types of pile response The statisticsin the table are therefore only intended to be used to illustrate the pro-posed methodology The bias factors of the methods in the table varyfrom 079 to 234 and the COV values vary from 021 to 057 Indeedthe ASD approach results in designs with levels of safety that are ratheruneven from one method to another (ie β = 174ndash311) If a FOS of 20is used for all these methods and each design analysis is verified by twopositive proof tests conducted to twice the design load (ie xT gt 10)the statistics of these methods can be updated as shown in Table 106The updated bias factors are greater but the updated COVR values aresmaller than those in Table 105 The updated β values of all thesemethods fall into a narrow range (ie β = 222ndash289) with a mean ofapproximately 25

Now consider the case when a FOS of 20 is applied to all these methods inTable 105 and the design is verified by two proof tests conducted to failureat an average load of twice the design load (ie xT = 10) The correspondingupdated statistics using the Bayesian sampling theory [Equations (1012)ndash(1014)] are also shown in Table 106 Both the updated bias factors (λR =097ndash112) and the updated COVR values (COVR = 021ndash024) fall intonarrow bands Accordingly the updated β values also fall into a narrowband (ie β = 178ndash231) with a mean of approximately 20

The results in Table 106 indicate that the safety level of a design veri-fied by proof tests is less influenced by the accuracy of the design methodThis is logical in the context of Bayesian statistical theory in which theinformation of the empirical distribution will play a smaller role when moremeasured data at the site become available This is consistent with foun-dation engineering practice In the past reliable designs had been achievedby subjecting design analyses of varying accuracies to proof tests and otherquality control measures (Hannigan et al 1997 OrsquoNeill and Reese 1999)This also shows the effectiveness of the observational method (Peck 1969)with which uncertainties can be managed and acceptable safety levels canbe maintained by acquiring additional information during constructionHowever the importance of the accuracy of a design method in sizing the pileshould be emphasized A more accurate design method has a smaller COVRand utilizes a larger percentage of the actual pile capacity (McVay et al2000) Hence the required safety level can be achieved more economicallyIt should also be noted that the within-site COV is an important parameterIf the within-site COV at the site is larger than 02 as assumed in thischapter the updated reliability will be lower than the results presented inTable 106

Tabl

e10

6R

elia

bilit

yst

atis

tics

ofsi

ngle

driv

enpi

les

desi

gned

usin

ga

FOS

of2

0an

dve

rifie

dby

two

load

test

s

Pred

ictio

nm

etho

dVe

rified

bytw

opo

sitive

test

sx T

gt1

0Ve

rified

bytw

ote

sts

with

am

ean

x T=

10

Bias

fact

orλ

R

Coef

ficie

ntof

varia

tion

COV R

Relia

bilit

yin

dex

Bi

asfa

ctor

λR

Coef

ficie

ntof

varia

tion

COV R

Relia

bilit

yin

dex

CA

PWA

P(E

OD

Flo

rida

data

base

)1

710

322

891

120

242

21C

APW

AP

(BO

RF

lori

dada

taba

se)

146

030

262

107

024

207

CA

PWA

P(E

OD

nat

iona

ldat

abas

e)1

890

422

581

070

242

05C

APW

AP

(BO

Rn

atio

nald

atab

ase)

138

029

254

105

024

202

Ener

gyap

proa

ch(E

OD

Flo

rida

data

base

)1

340

282

501

040

241

99En

ergy

appr

oach

(BO

RF

lori

dada

taba

se)

122

028

226

098

024

181

Ener

gyap

proa

ch(E

OD

nat

iona

ldat

abas

e)1

420

312

461

030

241

95En

ergy

appr

oach

(BO

Rn

atio

nald

atab

ase)

120

028

222

097

024

178

Alp

ham

etho

dcl

ayty

peI

112

021

240

109

021

231

Alp

ham

etho

dcl

ayty

peII

254

052

269

109

024

209

Beta

met

hod

107

021

222

103

021

210

CPT

met

hod

133

029

243

102

024

193

Lam

bda

met

hod

clay

type

I1

400

322

421

020

241

92M

eyer

hofrsquos

SPT

met

hod

168

039

245

104

024

198

Not

e(1

)T

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a(2

)x T

=Rat

ioof

the

max

imum

test

load

toth

epr

edic

ted

pile

capa

city

402 Limin Zhang

105 Acceptance criterion based on proof loadtests

1051 General principle

In the conventional allowable stress design approach acceptance of a designusing proof tests is based on whether the test piles fail before they are loadedto twice the design load Presumably the level of safety of the designs thatare verified by ldquosatisfactoryrdquo proof tests is not uniform In a reliability-baseddesign a general criterion for accepting piles should be that the pile foun-dation as a whole after verification by the proof tests meets the prescribedtarget reliability levels for both ultimate limit states and serviceability limitstates Discussion on serviceability limit states is beyond the scope of thischapter

In order to define acceptance conditions ldquofailurerdquo of piles has to be definedfirst Many failure criteria exist in the literature Hirany and Kulhawy (1989)Ng et al (2001b) and Zhang and Tang (2001) conducted extensive reviewof failure criteria for piles and drilled shafts and discussed the bias in theaxial capacity arising from failure criteria A particular failure criterion maybe applicable only to certain deep foundation types and ground conditionsIn discussing a particular design method a failure criterion should alwaysbe specified explicitly In the examples presented in this chapter only drivenpiles are involved and only the Davisson failure criterion is used These exam-ples are for illustrative purposes Suitable failure criteria should be selectedin establishing acceptance conditions for a particular job

The next step in defining acceptance conditions is to select the target reli-ability of the pile foundation The target reliability of the pile foundationshould match that of the superstructure and of other types of foundationsMeyerhof (1970) Barker et al (1991) Becker (1996a) OrsquoNeill and Reese(1999) Phoon et al (2000) Zhang et al (2001) Paikowsky (2002) and oth-ers have studied the target reliability of foundations based on calibration ofthe ASD practice A target reliability index βT between 30 and 35 appearsto be suitable for pile foundations

The reliability of a pile group can be significantly higher than that of singlepiles (Tang and Gilbert 1993 Bea et al 1999 Zhang et al 2001) Sinceproof tests are mostly conducted on single piles it is necessary to find a targetreliability for single piles that matches the target reliability of the founda-tion system Based on calibration of the ASD practice Wu et al (1989)Tang (1989) and Barker et al (1991) found a range of reliability indexvalues between 14 and 31 and Barker et al (1991) ASSHTO (1997) andWithiam et al (2001) recommended target reliability index values of βTS =20ndash25 for single driven piles and βTS = 25ndash30 for single drilled shaftsZhang et al (2001) attributed the increased reliability of pile foundationsystems to group effects and system effects They calculated βTS values for

Reliability verification 403

single driven piles to achieve a βT value of 30 For pile groups larger thanfour piles the calibrated βTS values are mostly in the range of 20ndash28 ifno system effects are considered The βTS values decrease to 17ndash25 whena system factor of 125 is considered and further to 15ndash20 when a largersystem factor of 15 is considered For a structure supported by four or fewerfar-apart piles the pile group effect and system effect may not be depend-able (Zhang et al 2001 Paikowsky 2002) The target reliability of singlepiles should therefore be the same as that of the foundation system sayβTS = 30

1052 Reliability-based acceptance criteria

In the ASD approach it is logical to use a maximum test load of twicethe design load since a FOS of 20 is commonly used for designs verifiedby proof load tests Table 106 shows that the ASD approach using twoproof tests conducted to twice the design load can indeed lead to a uniformtarget reliability level around β = 25 regardless of the accuracy of the designmethod Even if the test piles fail at an average load of twice the design loada uniform reliability level around β = 20 can still be obtained Comparedwith recommended target reliability indices the reliability of the currentASD practice for large pile groups may be considered sufficient In the RBDapproach the target reliability is a more direct acceptance indicator FromEquations (1015) and (1016) a target reliability can be achieved by manycombinations of the proof test parameters such as the number of tests thetest load the FOS and the test outcomes Thus the use of other FOS valuesand test loads is equally feasible

Analyses with the SPT method (Meyerhof 1976) are again conductedto illustrate the effects of the load test parameters Figure 105 shows theupdated reliability index of piles designed using a FOS of 20 and verified byvarious numbers of tests conducted to 10ndash30 times the design load If theproof tests were carried out to the design load (k = 10) the tests would havea negligible effect on the reliability of the piles hence the effectiveness ofthe tests is minimal This is because the probability that the piles do not failat the design load is 96 based on the empirical distribution and the prooftests only prove an event of little uncertainty If the test load were set at15 times (k = 15) the design load the effectiveness of the proof tests is stilllimited since a βT value of 25 cannot be verified with a reasonable numberof proof tests The test load of twice (k = 20) the design load provides abetter solution when the design is verified by one or two positive prooftests the updated β values will be in the range of 17ndash25 which may beconsidered acceptable for large pile systems If the test load is set at threetimes (k = 30) the design load then a high target reliability of βT = 30can be verified by one or two successful proof tests Figure 106 furtherdefines suitable maximum test loads that are needed to achieve specific target

00

05

10

15

20

25

30

35

40

0 1 2 3 4 5

Number of positive tests

Rel

iab

ility

ind

ex β

k= 10k = 15k = 20k = 25k = 30

Target reliability zone βTS = 17-25 for large pile groups

Target reliability βTS = 30for non-redundant piles

Figure 105 Reliability index of single driven piles designed with a FOS of 20 and verifiedby proof tests conducted to various maximum test loads (in k times the designload)

00

10

20

30

40

15 20 25 30 35 40

Rat

io o

f m

axim

um

tes

t lo

ad t

o d

esig

n lo

ad

1 positive proof test2 positive proof tests

3 positive proof tests

5 positive proof tests

Target reliabilityzone βTS = 17ndash25 forlarge pile groups

Target reliability βTS = 30for non-redundant piles

Target reliability index b

Figure 106 Maximum test load that is required to verify the SPT design method using aFOS of 20

Reliability verification 405

reliability values using a limited number of proof tests A maximum test loadof 25 times or three times the design load is necessary for non-redundantpile groups where group effects and system effects are not presentotherwise the required target reliability of βT = 30 cannot be verified by areasonable number of conventional proof tests conducted only to twice thedesign load

Figure 107 shows the FOS values needed to achieve a β value of 25 Asthe verification test load increases the required FOS decreases In particularif two proof tests are conducted to 15 times (xT = 15) the predicted pilecapacity a βT of 25 can be achieved with a FOS of approximately 15compared with a FOS of 20 if a test load equal to the predicted pile capacity(xT = 10) is adopted

For codification purposes the effects of proof test loads on the reliability ofvarious design methods should be studied and carefully collected databasesare needed to characterize the variability of the design methods The analysisof the design methods in Table 105 is for illustrating the methodology onlyLimitations with the reliability statistics in the table have been pointed outearlier Table 107 presents the FOS values required to achieve βT values of20 25 and 30 when the design is verified by two proof tests conductedto different test loads Given a proof test load the FOS values required for

00

05

10

15

20

25

30

35

40

0 2 3 4 5

Number of positive tests

Req

uir

ed f

acto

r o

f sa

fety

xT = 075

xT = 050

xT = 125

xT = 150

xT = 10

Figure 107 Factor of safety that is required to achieve a target reliability level of βT = 25after verification by proof tests conducted to various maximum test loads

Tabl

e10

7Fa

ctor

sof

safe

tyre

quir

edto

achi

eve

targ

etre

liabi

lity

inde

xva

lues

ofβ

T=

20

βT

=2

5an

T=

30

Pred

ictio

nm

etho

dVe

rified

bytw

ote

sts

with

x Tgt

10

Verifi

edby

two

test

sw

ithx T

gt1

25Ve

rified

bytw

ote

sts

with

x Tgt

15

T

=2

0

T=

25

T

=3

0

T=

20

T

=2

5

T=

30

T

=2

0

T=

25

T

=3

0

CA

PWA

P(E

OD

Flo

rida

data

base

)1

421

722

081

271

521

831

131

351

62

CA

PWA

P(B

OR

Flo

rida

data

base

)1

591

912

291

391

651

971

231

461

73

CA

PWA

P(E

OD

nat

iona

lda

taba

se)

153

193

242

131

162

201

113

139

171

CA

PWA

P(B

OR

nat

iona

lda

taba

se)

165

197

236

143

170

202

126

150

178

Ener

gyap

proa

ch(E

OD

Fl

orid

ada

taba

se)

168

200

239

145

173

205

129

152

180

Ener

gyap

proa

ch(B

OR

Fl

orid

ada

taba

se)

183

218

259

155

184

219

136

161

190

Ener

gyap

proa

ch(E

OD

na

tiona

ldat

abas

e)1

682

032

451

431

712

051

241

481

77

Ener

gyap

proa

ch(B

OR

na

tiona

ldat

abas

e)1

852

202

621

571

862

211

371

621

91

Alp

ham

etho

dcl

ayty

peI

177

206

239

174

202

234

170

197

229

Alp

ham

etho

dcl

ayty

peII

138

181

236

122

157

203

107

136

174

Beta

met

hod

187

218

254

181

210

245

175

203

237

CPT

met

hod

171

205

245

147

175

208

129

153

181

Lam

bda

met

hod

clay

type

I1

712

062

491

441

732

071

251

491

78M

eyer

hofrsquos

SPT

met

hod

164

204

254

137

169

207

118

143

175

Mea

nva

lue

167

201

243

146

175

210

130

155

185

Not

e(1

)T

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a(2

)x T

=R

atio

ofth

em

axim

umte

stlo

adto

the

pred

icte

dpi

leca

paci

ty

Reliability verification 407

Table 108 Illustrative values of recommended factor of safety and maximum test load

T = 20 T = 25 T = 30

xT FOS k xT FOS k xT FOS k

100 170 17 100 200 20 100 245 25125 150 19 125 175 22 125 210 27150 130 20 150 155 24 150 185 28

Note(1)Based on the reliability statistics in Table 105 for driven piles(2) Pile failure is defined by the Davisson criterion(3) Two proof tests are assumed and the outcomes are assumed to be positive(4) xT = ratio of the maximum test load to the predicted pile capacity(5) FOS = rounded factor of safety(6) k = recommended ratio of the maximum test load to the design load (rounded values)

the design methods to achieve a prescribed target reliability level are ratheruniform Thus the mean of the FOS values for all the methods offers asuitable FOS for design Table 108 summarizes the rounded average FOSvalues that could be used with proof tests based on the limited reliabilitystatistics in Table 105 Rounded values of the ratio of the maximum testload to the design load k which is the multiplication of FOS by xT are alsoshown in Table 108

Table 108 provides illustrative criteria for the design and acceptance ofdriven pile foundations using proof tests The criteria may be implementedin the following steps

1 calculate the design load and choose a target reliability say βT = 252 size the piles using a FOS (eg 175) bearing in mind that proof load

tests will be conducted to verify the design3 construct a few trial piles4 proof test the trial piles to the required test load (eg 22 times the design

load)5 accept the design if the proof tests are positive

It can be argued that proof load tests should be conducted to a large load(eg xT = 150 or 28 times the design load in the case of FOS = 185and βT = 30) First this is required for verifying the reliability of non-redundant pile foundations in which the group effects and system effectsare not present Second the large test load warrants the use of a lowerFOS (eg smaller than 20) and considerable savings since the extra costsfor testing a pile to a larger load are relatively small Third more test pileswill fail at a larger test load (ie 71 of test piles may fail at xT = 15based on the empirical distribution in Figure 102) and hence the tests will

408 Limin Zhang

have greater research value for improvement of practice Malone (1992) hasmade a similar suggestion in studying the performance of piles in weatheredgranite

Because of the differences in the acceptance criteria adopted in the ASDand RBD approaches some ldquoacceptable proof testsrdquo in the ASD may becomeunacceptable in the RBD For example the reliability of a column sup-ported by a single large diameter bored pile may not be sufficient becausethe β value of the pile can still be smaller than βT = 30 even if the pilehas been proof-tested to twice the design load On the other hand someldquounsatisfactoryrdquo piles in the ASD may turn out to be satisfactory in theRBD provided that the design analysis and the outcome of the proof teststogether result in sufficient system reliability For example the requirementof βT = 20 can be satisfied when the test load is somewhat smaller thantwice the design load (see Table 108) or when one out of four tests fails (seeFigure 103)

106 Summary

It has been shown that pile load tests are an integral part of the design andconstruction process of pile foundations Pile load tests can be used as ameans for verifying reliability and reducing costs Whether a load test iscarried out to failure or not the test outcome can be used to ensure thatthe acceptance reliability is met using the methods described in this chapterThus contributions of load tests can be included in a design in a systematicmanner Although various analysis methods could arrive at considerablydifferent designs the reliability of the designs associated with these analysismethods is rather uniform if the designs adopt the same FOS of 20 and areverified by consecutive positive proof tests

In the reliability-based design the acceptance criterion is proposed to bethat the pile foundation as a whole after verification by the proof testsmeets the specified target reliability levels for both ultimate limit states andserviceability limit states This is different from the conventional allowablestress design in which a test pile will not be accepted if it fails before it hasbeen loaded to twice the design load Since the reliability of a single pile is notthe same as the reliability of a pile foundation system the target reliabilityfor acceptance purposes should be structure-specific

If the maximum test load were only around the design load little infor-mation from the test could be derived The use of a maximum test load ofone times the predicted pile capacity is generally acceptable for large pilegroup foundations However a larger test load of 15 times the predictedpile capacity is recommended First it is needed for verifying the reliability ofthe pile foundation in which group effects and system effects are not presentSecond it warrants the use of a smaller FOS and hence savings in pile foun-dation costs Third the percentage of test piles that may fail at 15 times

Reliability verification 409

the predicted pile capacity is larger The tests will thus have greater researchvalue for further improvement of practice

Acknowledgment

The research described in this chapter was substantially supported by theResearch Grants Council of the Hong Kong Special Administrative Region(Project Nos HKUST603502E and HKUST612603E)

References

AASHTO (1997) LRFD Bridge Design Specifications American Association of StateHighway and Transportation Officials Washington DC

Ang A H-S and Tang W H (2007) Probability Concepts in Engineering-Emphasis on Applications to Civil and Environmental Engineering 2nd editionJohn Wiley amp Sons New York

Baecher G R and Rackwitz R (1982) Factors of safety and pile load testsInternational Journal for Numerical and Analytical Methods in Geomechanics6 409ndash24

Barker R M Duncan J M Rojiani K B Ooi P S K Tan C K and Kim S G(1991) Manuals for the Design of Bridge Foundations NCHRP Report 343Transportation Research Board National Research Council Washington DC

Bea R G Jin Z Valle C and Ramos R (1999) Evaluation of reliabilityof platform pile foundations Journal of Geotechnical and GeoenvironmentalEngineering ASCE 125 696ndash704

Becker D E (1996a) Eighteenth Canadian geotechnical colloquium limit statesdesign for foundations Part I An overview of the foundation design processCanadian Geotechnical Journal 33 956ndash83

Becker D E (1996b) Eighteenth Canadian geotechnical colloquium limit statesdesign for foundations Part II Development for the national building code ofCanada Canadian Geotechnical Journal 33 984ndash1007

Bell K R Davie J R Clemente J L and Likins G (2002) Proven success fordriven pile foundations In Proceedings of the International Deep FoundationsCongress Geotechnical Special Publication No 116 Eds M W OrsquoNeill andF C Townsend ASCE Reston VA pp 1029ndash37

BSI (1986) BS 8004 British Standard Code of Practice for Foundations BritishStandards Institution London

Buildings Department (2002) Pile foundations-practice notes for authorizedpersons and registered structural engineers PNAP66 Buildings DepartmentHong Kong

Chow F C Jardine R J Brucy F and Nauroy J F (1998) Effect of time onthe capacity of pipe piles in dense marine sand Journal of Geotechnical andGeoenvironmental Engineering ASCE 124(3) 254ndash64

Chu R P K Hing W W Tang A and Woo A (2001) Review and way for-ward of foundation construction requirements from major client organizationsIn New Development in Foundation Construction The Hong Kong Institution ofEngineers Hong Kong

410 Limin Zhang

Dasaka S M and Zhang L M (2006) Evaluation of spatial variability of weath-ered rock for pile design In Proceedings of International Symposium on NewGeneration Design Codes for Geotechnical Engineering Practice 2ndash3 Nov 2006Taipei World Scientific Singapore (CDROM)

Duncan J M (2000) Factors of safety and reliability in geotechnical engineer-ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126307ndash16

Evangelista A Pellegrino A and Viggiani C (1977) Variability among piles ofthe same foundation In Proceedings of the 9th International Conference on SoilMechanics and Foundation Engineering Tokyo pp 493ndash500

Fenton G A (1999) Estimation for stochastic soil models Journal of GeotechnicalEngineering ASCE 125 470ndash85

Geotechnical Engineering Office (2006) Foundation Design and Construction GEOPublication 12006 Geotechnical Engineering Office Hong Kong

Hannigan P J Goble G G Thendean G Likins G E and Rausche F(1997) Design and Construction of Driven Pile Foundations Workshop ManualVol 1 Publication No FHWA-HI-97-014 Federal Highway AdministrationWashington DC

Hirany A and Kulhawy F H (1989) Interpretation of load tests on drilled shafts ndashPart 1 Axial compression In Foundation Engineering Current Principles andPractices Vol 2 Ed F H Kulhawy ASCE New York pp 1132ndash49

ISSMFE Subcommittee on Field and Laboratory Testing (1985) Axial pile loadingtest ndash Part 1 Static loading Geotechnical Testing Journal 8 79ndash89

Kay J N (1976) Safety factor evaluation for single piles in sand Journal ofGeotechnical Engineering ASCE 102 1093ndash108

Lacasse S and Goulois A (1989) Uncertainty in API parameters for predictionsof axial capacity of driven piles in sand In Proceedings of the 21st OffshoreTechnology Conference Houston TX pp 353ndash8

Malone A W (1992) Piling in tropic weathered granite In Proceedings ofInternational Conference on Geotechnical Engineering Malaysia TechnologicalUniversity Johor Bahru pp 411ndash57

McVay M C Birgisson B Zhang L M Perez A and Putcha S (2000) Loadand resistance factor design (LRFD) for driven piles using dynamic methods ndash aFlorida perspective Geotechnical Testing Journal ASTM 23 55ndash66

Meyerhof G G (1970) Safety factors in soil mechanics Canadian GeotechnicalJournal 7 349ndash55

Meyerhof G G (1976) Bearing capacity and settlement of pile foundations Journalof Geotechnical Engineering ASCE 102 195ndash228

Ng C W W Li J H M and Yau T L Y (2001a) Behaviour of large diameterfloating bored piles in saprolitic soils Soils and Foundations 41 37ndash52

Ng C W W Yau T L Y Li J H M and Tang W H (2001b) New failureload criterion for large diameter bored piles in weathered geomaterials Journal ofGeotechnical and Geoenvironmental Engineering ASCE 127 488ndash98

NRC (1995) National Building Code of Canada National Research CouncilCanada Ottawa

OrsquoNeill M W and Reese L C (1999) Drilled Shafts Construction Proce-dures and Design Methods Publication No FHWA-IF-99-025 Federal HighwayAdministration Washington DC

Reliability verification 411

Orchant C J Kulhawy F H and Trautmann C H (1988) Reliability-basedfoundation design for transmission line structures Vol 2 Critical evaluation ofin-situ test methods EL-5507 Final Report Electrical Power Institute Palo Alto

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep foun-dations In Foundation Design Codes ndash Proceedings IWS Kamakura 2002 EdsY Honjo O Kusakabe K Matsui M Kouda and G Pokharel A A BalkemaRotterdam pp 59ndash94

Paikowsky S G and Stenersen K L (2000) The performance of the dynamicmethods their controlling parameters and deep foundation specifications InProceedings of the Sixth International Conference on the Application of Stress-Wave Theory to Piles Eds S Niyama and J Beim A A Balkema Rotterdampp 281-304

Passe P Knight B and Lai P (1997) Load tests In FDOT Geotechnical NewsVol 7 Ed P Passe Florida Department of Transportation Tallahassee

Peck R B (1969) Advantages and limitations of the observational method in appliedsoil mechanics Geotechnique 19 171ndash87

Phoon K K Kulhawy F H and Grigoriu M D (2000) Reliability-based design fortransmission line structure foundations Computers and Geotechnics 26 169ndash85

Schmertmann J H (1978) Guidelines for cone penetration test performanceand design Report No FHWA-TS-78-209 Federal Highway AdministrationWashington DC

Shek M P Zhang L M and Pang H W (2006) Setup effect in long piles inweathered soils Geotechnical Engineering Proceedings of the Institution of CivilEngineers 159(GE3) 145ndash52

Sidi I D (1985) Probabilistic prediction of friction pile capacities PhD thesisUniversity of Illinois Urbana-Champaign

Tang W H (1989) Uncertainties in offshore axial pile capacity In Geotechni-cal Special Publication No 27 Vol 2 Ed F H Kulhawy ASCE New Yorkpp 833ndash47

Tang W H and Gilbert R B (1993) Case study of offshore pile system reliabilityIn Proceedings 25th Offshore Technology Conference Houston TX pp 677ndash86

US Army Corps of Engineers (1993) Design of Pile Foundations ASCE PressNew York

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110 145ndash88

Withiam J L Voytko E P Barker R M Duncan J M Kelly B C Musser S Cand Elias V (2001) Load and resistance factor design (LRFD) for highway bridgesubstructures Report No FHWA HI-98-032 Federal Highway AdministrationWashington DC

Wu T H Tang W H Sangrey D A and Baecher G B (1989) Reliability ofoffshore foundations ndash state of the art Journal of Geotechnical Engineering ASCE115 157ndash78

Zhang L M (2004) Reliability verification using proof pile load tests Journal ofGeotechnical and Geoenvironmental Engineering ASCE 130 1203ndash13

Zhang L M and Tang W H (2001) Bias in axial capacity of single bored piles aris-ing from failure criteria In Proceedings ICOSSARrsquo2001 International Associationfor Structural Safety and Reliability Eds R Crotis G Schuller and M ShinozukaA A Balkema Rotterdam (CDROM)

412 Limin Zhang

Zhang L M and Tang W H (2002) Use of load tests for reducing pilelength In Geotechnical Special Publication No 116 Eds M W OrsquoNeill andF C Townsend ASCE Reston pp 993ndash1005

Zhang L M Li D Q and Tang W H (2006a) Level of construction control andsafety of driven piles Soils and Foundations 46(4) 415ndash25

Zhang L M Shek M P Pang W H and Pang C F (2006b) Knowledge-based pile design using a comprehensive database Geotechnical EngineeringProceedings of the Institution of Civil Engineers 159(GE3) 177ndash85

Zhang L M Tang W H and Ng C W W (2001) Reliability of axially loadeddriven pile groups Journal of Geotechnical and Geoenvironmental EngineeringASCE 127 1051ndash60

Zhang L M Tang W H Zhang L L and Zheng J G (2004) Reducing uncer-tainty of prediction from empirical correlations Journal of Geotechnical andGeoenvironmental Engineering ASCE 130 526ndash34

Chapter 11

Reliability analysis of slopes

Tien H Wu

111 Introduction

Slope stability is an old and familiar problem in geotechnical engineeringSince the early beginnings impressive advances have been made and a wealthof experience has been collected by the profession The overall performanceof the general methodology has been unquestionably successful Neverthe-less the design of slopes remains a challenge particularly with new andemerging problems As with all geotechnical projects a design is based oninputs primarily loads and site characteristics The latter includes the subsoilprofile soil moisture conditions and soil properties Since all of the above areestimated from incomplete data and cannot be determined precisely for thelifetime of the structure an uncertainty is associated with each componentTo account for this the traditional approach is to apply a safety factorderived largely from experience This approach has worked well but diffi-culty is encountered where experience is inadequate or nonexistent Thenreliability-based design (RBD) offers the option of evaluating the uncertain-ties and estimating their effects on safety The origin of RBD can be tracedto partial safety factors Taylor (1948) explained the need to account fordifferent uncertainties about the cohesional and frictional components ofsoil strength and Lumb (1970) used the standard deviation to represent theuncertainty and expressed the partial safety factors in terms of the standarddeviations Partial safety factors were incorporated into design practice byDanish engineers in the 1960s (Hansen 1965)

This chapter reviews reliability methods as applied to slope stability anduncertainties in loads pore pressure strength and analysis Emphasis isdirected toward the simple methods the relevant input data and their appli-cations The use of the first-order second-moment method is illustratedby several detailed examples since the relations between the variables areexplicit and readily understood Advance methods are reviewed and thereader is referred to the relevant articles for details

414 Tien H Wu

112 Methods of stability and reliability analysis

Three types of analysis may be used for deterministic prediction of failurelimit equilibrium analysis displacement analysis for seismic loads and finiteelement analysis Reliability analysis can be applied to all three types

1121 Probability of failure

Reliability is commonly expressed as a probability of failure Pf or areliability index β defined as

Pf = 1 minusΦ[β] (111a)

If Fs has normal distribution

Pf = 1 minusΦ

[Fs minus 1σ (Fs)

] β = Fs minus 1

σ (Fs)(111b)

and if Fs has log-normal distribution

Pf = 1 minusΦ

[lnFs minus 05∆(Fs)

2

∆(Fs)

] β = lnFs minus 05∆(Fs)

2

∆(Fs)(111c)

Φ(β) = 1radic2π

int β

minusinfine(minus 1

2 z2)dz (111d)

Fs = safety factor and the overbar denotes the mean value σ () = standarddeviation ∆ = () coefficient of variation (COV) and ∆(Fs) = σ (Fs)FsEquation (111c) is approximate and should be limited to ∆lt 05

The term Fs minus1 Equation (111b) or ln Fs minus05∆(Fs)2 Equation (111c)

denotes the distance between the mean safety factor and Fs = 1 or failureThe term σ (Fs) or ∆(Fs) which represents the uncertainty about Fs isreflected by the spread of the probability density function (pdf) f (Fs)Figure 111 shows the pdf for normal distribution Curves 1 and 2 repre-sent two designs with the same mean safety factor Fs but with σ (Fs) = 04and 12 respectively The shaded area under each curve is Pf It can be seenthat Pf for design 2 is several times greater than for design 1 because of thegreater uncertainty The reliability index β in Equations (111b) and (111c)is a convenient expression that normalizes the safety factor with respect toits uncertainty Two designs with the same β will have the same Pf Bothnormal and ln-normal distributions have been used to represent soil proper-ties and safety factors The ln-normal has the advantage that it does not havenegative values However except for very large values of β the differencebetween the two distributions is not large

Reliability analysis of slopes 415

045

040

035

030

025

f(F

s)

Fs

020

015

010

005

0minus2 minus1 0

(1) σ = 04

(2) σ = 12

1 2 3 4 5 6 7

Figure 111 Probability distribution of Fs

While the mathematical rules for computation of probability are wellestablished the meaning of probability is not well defined It can be inter-preted as the relative frequency of occurrence of an event or a degree ofbelief The general notion is that it denotes the ldquochancerdquo that an event willoccur The reader should refer to Baecher and Christian (2003) for a thor-ough treatment of this issue What constitutes an acceptable Pf dependson the consequence or damage that would result from a failure This isa decision-making problem and is described by Gilbert in Chapter 5 Thecurrent approach in Load Resistance Factor Design (LRFD) is to choose aldquotargetrdquo β by calibration against current practice (Rojiani et al 1991)

1122 Limit equilibrium method

The method of slices (Fellenius 1936) with various modifications is widelyused for stability analysis Figure 112 shows the forces on a slice for a verylong slope failure or plane strain The general equations for the safety factorare (Fredlund and Krahn 1977)

Fsm = Mr

Mo=sum cprimelr + (P minus ul)r tanφprimesum

Wx minussumPf +sumkWe (112a)

Fsf = Fr

Fo=sum

cprimel cosα+sum (P minus ul) tanφprime cosαsumP sinα+sumkW

(112b)

416 Tien H Wu

x

e

W

kW

Xi+1

Ei

XiEi+1

Sm

P

I

f

r

α

Figure 112 Forces on failure surface

where subscripts m and f denote equilibrium with respect to moment andforce Mo Mr = driving and resisting moments Fo Fr = horizontal driv-ing force and resistance W = weight of slice P = normal force on baseof slice whose length is l r f = moment arms of the shear and normalforces on base of slice k = seismic coefficient e = moment arm of inertiaforce α = angle of inclination of base The expression for P depends on theassumption used to render the problem statically determinate The summa-tion in Equations (112) is taken over all the slices For unsaturated soilsthe pore pressure consists of the poreair pressure ua and the porewater pres-sure uw The equations for Fsm and Fsf are given in Fredlund and Rahardjo(1993)

The first-order second moment method (FOSM) is the simplest methodto calculate the mean and variance of Fs For a function Y = g(X1 Xn)in which X1 Xn = random variables the mean and variance are

Y = g[X1 Xn)] (113a)

V(Y) =sum

i

(partgpartXi

)2

V(Xi) + sum

i

sumj

(partgpartXi

)(partgpartXj

)Cov(XiXj)

= ρijσ (Xi)σ (Xj) (113b)

where partgpartXi = sensitivity of g to Xi Cov (Xi Xj) = covariance of Xi Xj ρij =correlation coefficient Given the mean and variance of the input variables Xi

Reliability analysis of slopes 417

to Equations (112) Equations (113) are used to calculate Fs and V(Fs)The distribution of Y is usually unknown except for very simple forms ofthe function g If the normal or ln-normal distribution is assumed β and Pfcan be calculated via Equations (111)

In many geotechnical engineering problems the driving moment is welldefined so that its uncertainty is small and may be ignored Then only V(Mr)and V(Fr) need to be evaluated Let the random variables be cprime tanφprime and uApplication of Equation (113b) to the numerator in Equation (112a) yields

V(Mr) = r2l2V(cprime) +[r2(P minus ul)2]V(tanϕprime)

+r2l(P minus ul)ρcφ σ (cprime)σ (tanϕprime) +[minusrl tanϕprime]2V(u) (114a)

If a total stress analysis is performed for undrained loading or φ = 0

V[Mr] = r2l2V(c) (114b)

Tang et al (1976) and Yucemen and Tang (1975) provide examples ofapplication of FOSM to total stress analysis and effective stress analysis ofslopes Some of the simplifications that have been used include the assump-tion of independence between Xi and Xj and use of the Fs for the criticalcircle determined by limit equilibrium analysis to calculate Pf

Example 1

The slope failure in Chicago described by Tang et al (1976) and Ireland(1954) Figure 113 is used to illustrate the relationship between the meansafety factor Fs the uncertainty about Fs and the failure probability Pf

SandUpper ClayMiddle ClayLower Clay

5

0

-5

Ele

v m

-10Critical slip surfacefor new slope (3)

Original Slope

3 deg

2 deg

1 deg

Critical slip surfacefor original slope (2) Observed slip surface (1)

New slope21

31

0 200su kPa

Figure 113 Slope failure in Chicago (adapted from Ireland (1954) and Tang et al (1976))su = undrained shear strength

418 Tien H Wu

Table 111 Reliability index and failure probabilities Chicago slope

Slope Slip surface Fs ∆(Mr) Ln-normal Normal

β Pf β Pf

Existing Observed 118 018 082 020 084 021Critical 086 018 minus092 082 minus010 082

Redesigned Critical 129 016 162 005 149 007

The mean safety factor calculated with the means of the undrained shearstrengths is 118 for the observed slip surface The uncertainty about Fscomes from the uncertainty about the resisting moment expressed as∆(MR)which in turn comes from the uncertainty about the shear strength ofthe soil and inaccuracies in the stability analysis The total uncertainty is∆(MR) = ∆(Fs) = 018 Equation (112c) which assumes the ln-normal dis-tribution for Fs gives β = 082 Pf = 02 (Table 111) For the critical slipsurface determined by limit equilibrium analysis similar calculations yieldβ = minus092 and Pf = 082 A redesigned slope with Fs = 129 has β = 162and Pf = 005 For comparison the values of β and Pf calculated with thenormal distribution are also shown in Table 111 The evaluation of theuncertainties and ∆(MR) is described in Example 3

With respect to the difference between the critical failure surface (2) andthe observed failure surface (1) it should be noted that all of the soil sampleswere taken outside the slide area (Ireland 1954) Hence the strengths alongthe failure surfaces remain questionable In probabilistic terms the perti-nent question now becomes given that failure has or has not occurredwhat is the shear strength This issue is addressed in Section 1137 (see alsoChristian 2004)

Example 2

During wet seasons slope failures on hillsides with a shallow soil coverand landfill covers may occur with seepage parallel to the ground surface(Soong and Koerner 1996 Wu and Abdel-Latif 2000) The failure surfaceapproaches a plane Figure 114 and Equations (112) reduce to

Fsf = Fr

Fo= Fsm = (P minus ul) tanϕprime cosα

P sinα= (γh minus γwhw)tanϕprime cosα

γhsinα

(115)

with P = γhcosα ul = γwhw cosα γ = unit weight of soil γw = unit weightof water and h and hw are as shown in Figure 114 It is assumed that γ is

Reliability analysis of slopes 419

h

α

hw

I

Figure 114 Infinite slope

the same above and below the water table and k = 0 The major hazardcomes from the rise in the water level hw Procedures for estimating hw aredescribed in Section 1124

For the present example consider a slope with h = 06 m α = 22 cprime = 0ϕprime = 30 Assume that the annual maximum of hw has a mean and standarddeviation equal to hw = 012 m and σ (hw) = 0075 m Substituting the meanvalues into Equation (115) gives FR = 05 kN and Fs = 128 FollowingEquation (113b) the variance of FR is

V(FR) = [minusγw cosα tanϕprime]2V(hw) +[(γh minus γwhw)cosα]2V(tanϕprime)(116a)

If V(tanϕ) = 0

V(FR) = [γw cos22 tan30]2[0075]2 = 00014kN2 (116b)

∆(FR) = radic0001405 = 0075 (116c)

Then β = 325 Pf = 0001 according to Equations (111c) and (111d) Thisis the annual failure probability

For a design life of 20 years the failure probability may be found withthe binomial distribution The probability of x occurrences in n independenttrials with a probability of occurrence p per trial is

P(X = x) = nx(n minus x)p

x(1 minus p)nminusx (117)

With p = 0001 and n = 20 x = 0 P(X = 0) = 099 which is the probabilityof no failure The probability of failure is 1 minus 099 = 001 To illustrate the

420 Tien H Wu

importance of hw let σ (hw) = 003 m Similar calculations lead to β = 85and Pf asymp 0

Consider next the additional uncertainty due to uncertainty about tan ϕprimeUsing σ (tanϕprime) = 002 in Equation (116a) with σ (hw) = 0075 m gives

V(FR) = [(06γ minus 012γw)cos22]2(002)2

+[γw cos22 tan30]2[0075]2

= 00017kN2 (118)

The annual failure probability becomes 00013 Note that this cannot be usedin Equation (117) to compute the failure probability in 20 years becausetanϕprime in successive years are not independent trials

While FOSM is simple to use it has shortcomings which include theassumption of linearity in Equations (113) It is also not invariant withrespect to the format used for safety Baecher and Christian (2003) givesome examples The HasoferndashLind method also called the first-order relia-bility method (FORM) is an improvement over FOSM It uses dimensionlessvariables

Xlowasti = Xi minus Xiσ [Xi] (119a)

and reliability is measured by the distance of the design point from the failurestate

g(Xlowast1 X

lowasti ) = 0 (119b)

which gives the combination of X1 Xn that will result in failure For limitequilibrium analysis g = Fs minus 1 = 0 Figure 115 shows g as a function ofXlowast

1 and Xlowast2 The design point is shown as a and ab is the minimum distance

to g = 0 Except for very simple problems the search for the minimum ab isdone via numerical solution Low et al (1997) show the use of spread sheet

X2

g(X2 X2)=0

X1

a

b

Figure 115 First-order reliability method

Reliability analysis of slopes 421

to search for the critical circle A detailed treatment on the use of spreadsheet and an example are given by Low in Chapter 3

The distribution of Fs and Pf can be obtained by simulation Values of theinput parameters (eg cprime ϕprime etc) that represent the mean values along thepotential slip surface are random variables generated from the distributionfunctions and are used to calculate the distribution of Fs From this the fail-ure probability P(Fs le 1) can be determined Examples include Chong et al(2000) and El-Ramly et al (2003) Several commercial software packagesalso have an option to calculate β and Pf by simulation Most do simulationson the critical slip surface determined from limit equilibrium analysis

1123 Displacement analysis

The Newmark (1965) method for computing displacement of a slope duringan earthquake considers a block sliding on a plane slip surface (Figure 116a)Figure 116b shows the acceleration calculated for a given ground accelera-tion record Slip starts when ai exceeds the yield acceleration ay at Fs le 1The displacement during each slip is

yi = (ai minus ay)t2i 2 (1110a)

where ai = acceleration during time interval ti For n cycles with Fs le 1 thedisplacement is

Y =nsumi

yi (1110b)

a

ai

ti

ay

t

(a)

(b)

Figure 116 Displacement by Newmarkrsquos method

422 Tien H Wu

The displacement analysis assumes that the soil strength is constantLiquefaction is not considered Displacement analysis has been used to eval-uate the stability of dams and landfills Statistics of ground motion recordswere used to compute distribution of displacements by Lin and Whitman(1986) and Kim and Sitar (2003) Kramer (1996) gives a comprehensivereview of slopes subjected to seismic hazards

1124 Finite element method

The finite element method (FEM) is widely used to calculate stress strainand displacement in geotechnical structures In probabilistic FEM materialproperties are represented as random variables or random fields

A simplified case is where only the uncertainties about the average materialproperties need be considered Auvinet et al (1996) show a procedure whichtreats the properties xi as random variables Consider the finite elementequations for an elastic material

P = [K]Y (1111a)

where P = applied nodal forces [K] = stiffness matrix Y = nodaldisplacements Differentiating and transposing terms yields

[K]partYpartxi

= partPpartxi

minus partKpartxi

Y (1111b)

where xi = material properties This is analogous to Equation (1111a) ifwe let

partYpartxi

= Ylowast (1111c)

Plowast = partPpartxi

minus partKpartxi

Y (1111d)

Then the same FEM program can be used to solve for Ylowast which is equal tothe sensitivity of displacement to the properties The means and variancesof material properties are used to calculate the first two moments of thedisplacement via FOSM Equations (113) Moments of strain or stress canbe calculated in the same manner

The perturbation method considers the material as a random field Equa-tions (1111) are extended to second-order and used to compute the first twomoments of displacement stress and strain (eg Liu et al 1986) Sudretand Berveiller describe an efficient spectral-based stochastic finite elementmethod in Chapter 7

Another approach is simulation A FEM analysis is done with materialproperties generated from the probability distribution of the properties

Reliability analysis of slopes 423

Griffith and Fenton (2004) studied the influence of spatial variation insoil strength on failure probability for a saturated soil slope subjected toundrained loading The strength is represented as a random field and thegenerated element properties account for spatial variation and correlation

For dynamic loads deterministic analyses with FEM and various materialmodels have been made (eg Seed et al 1975 Lacy and Prevost 1987 Zerfaand Loret 2003) to yield pore pressure generation and embankment defor-mation for given earthquake motion records See Popescu et al (Chapter 6)for stochastic FEM analysis of liquefaction

1125 Pore pressure seepage and infiltration

For deep-seated failures where the pore pressure is controlled primarily bygroundwater it can be estimated from measurements or from seepage calcu-lations It is often assumed that the pore pressure in the zone above the watertable the vadose zone is zero This may be satisfactory where the vadosezone is thin Estimation of pore pressure or suction (ψ) in the vadose zoneis complex Infiltration from the surface due to rainfall and evaporation isneeded to calculate the suction Where the soil layer is thin one-dimensionalsolution may be used to calculate infiltration A commonly used solution isRichards (1931) equation which in one-dimension is

partθ

partψ

partψ

partt= part

partz

[Kpartψ

partz

]+ Q (1112)

where θ = volumetric moisture content ψ = soil suction K = permeabilityQ = source or sink term partθpartψ = soil moisture versus suction relationshipNumerical solutions are usually used This equation has been used formoisture flow in landfill covers (Khire et al 1999) and slopes (Andersonet al 1988) Two-dimensional flow may be solved via FEM Fredlund andRahardjo (1993) provide the details on the numerical solutions

The soil moisture versus suction relation in Equation (1112) also calledthe soil water characteristic curve is a material property A widely usedrelationship is the van Genuchten (1980) equation

θ = θr + θs minus θr

[1 + (aψ)n]m (1113)

where θr θs = residual and saturated volumetric water contents a m andn = shape parameters The Fredlund and Xing (1994) Equation which hasa rational basis is

θ = θs

[1

ln[e + (ψa)n

]m

(1114)

where a and m = shape parameters

424 Tien H Wu

The calculated pore pressures are used in a limit equilibrium or finiteelement analysis Examples of application to landslides on unsaturated slopessubjected to rainfall infiltration include Fourie et al (1999) and Wilkinsonet al (2000)

Perturbation methods have been applied to continuity equations such asEquation (1112) to study flow through soils with spatial variations in per-meability (eg Gelhar 1993) Simulation was used by Griffith and Fenton(1997) for saturated flow and by Chong et al (2000) and Zhang et al(2005) to study the effect of uncertainties about the hydraulic properties ofunsaturated soils on soil moisture and slope stability A more complicatedissue is the presence of macro-pores and fractures (eg Beven and Germann1982) This is difficult to incorporate into seepage analysis largely becausetheir geometry is not well known Field measurements (eg Pierson 1980Johnson and Sitar 1990) have shown seepage patterns that differ substan-tially from those predicted by simple models Although the causes were notdetermined flow through macropores is one likely cause

113 Uncertainties in stability analysis

Uncertainties encountered in predictions of stability are considered underthree general categories material properties boundary conditions andanalytical methods The uncertainties involved in estimating the relevantsoil properties are familiar to geotechnical engineers Boundary conditionsinclude geometry loads and groundwater levels infiltration and evapora-tion at the boundaries and ground motions during earthquakes Errors andassociated uncertainties may be random or systematic Uncertainties due todata scatter are random while model errors are systematic Random errorsdecrease when averaged over a number of samples but systematic errorsapply to the average value

1131 Soil properties

The uncertainties about a soil property s can be expressed as

Nssm = s + ζ (1115)

where sm = value measured in a test Ns = bias or inaccuracy of the testmethod ζ = testing error which is the precision of the test procedure Therandom variable sm has mean = sm variance = V(sm) and COV =∆(sm) =σ (sm) sm It includes the natural variability of the material with COV ∆(s)and variance V(s) and testing error with zero mean and variance V(ζ ) Themodel error Ns is the combination of Nirsquos (i = 12 ) that represent biasfrom the ith source with mean Ni and COV ∆i The true bias is of courseunknown since the perfect test is not available

Reliability analysis of slopes 425

For n independent samples tested the uncertainty about sm due toinsufficient samples has a COV

∆0 = ∆(sm)radic

n (1116)

For high-quality laboratory and in-situ tests the COV of ζ is around 01(Phoon and Kulhawy 1999) Hence unless n is very small the testing erroris not likely to an important source of uncertainty

Input into analytical models requires the average s over a given distance(eg limiting equilibrium analysis) or a given area or volume (eg FEM)Consider the one-dimensional case The average of sm over a distance Lsm(L) has a mean and variance sm and V [sm(L)] To evaluate V [sm(L)]the common procedure is to fit a trend to the data to obtain the mean asa function of distance The departures from the trend are considered to berandom with variance V(sm) To evaluate the uncertainty about sm(L) dueto the random component it is necessary to consider its spatial nature Thedepartures from the mean at points i and j separated by distance r arelikely to be correlated To account for this correlation VanMarcke (1977)introduced the variance reduction factor

Γ 2 = V[ sm(L)]V[sm] (1117a)

A simple expression is

Γ 2 = 1 if L le δ (1117b)

Γ 2 = δL if L gt δ (1117c)

where δ = correlation distance which measures the decay in the correlationcoefficient

ρ = eminus2rδ (1118)

Lδ can also be considered as the equivalent number of independent samplesne contained within L Then the variance and COV are

V[ sm(L)] = Γ 2V[sm] = V[sm]ne (1119a)

∆e = Γ∆(sm) = ∆(sm)radic

ne (1119b)

Extensions to two and three dimensions are given in VanMarcke (1977)It should be noted that ∆0 applies to the average strength and is system-atic while ∆e changes with L and ne A more detailed treatment of thissubject is given by Baecher and Christian in Chapter 2 Data on V(sm)∆(sm) and δ for various soils have been summarized by Lumb (1974)

426 Tien H Wu

DeGroot and Baecher (1993) Phoon and Kulhawy (1999) and Baecher andChristian (2003) Data on some unsaturated soils have been reviewed byChong et al (2000) and Zhang et al (2005)

In limit equilibrium analysis the average in-situ shear strength to be usedin Equations (112) contains all of the uncertainties described above It canbe expressed as (Tang et al 1976)

s = NsS (1120a)

where s = estimated shear strength and is usually taken as the average of themeasured values sm The combined uncertainty is represented by N

N = N0NeNs = N0Nei

Ni (1120b)

where N0 = uncertainty about the population mean Ne = uncertainty aboutthe mean value along the slip surface and Ni = uncertainty due to ith sourceof model error Equations (113) are used to obtain the mean and COV

s = i

Nism = Nssm (1121a)

∆(s) = [∆20 +∆2

e +∆2i ]12 (1121b)

Note that N0 = Ne = 1 It is assumed that the model errors are independentTwo examples of uncertainties about soil properties due to data scatter

∆(sm) are shown in Table 112 All of the data scatter is attributed to spatialvariation The values of ∆0 and ∆e representing the systematic and ran-dom components are also given For the Chicago clay the value of δ wasobtained from borehole samples and represents δy For the James Bay siteboth δx and δy were obtained Because a large segment of the slip surfaceis nearly horizontal δ is large Consequently Γ is small This illustrates thesignificance of δ

Table 112 Uncertainties due to data scatter

Site Soil sm (kPa) ∆(sm) ∆0 δ(m) ∆e Ref

Chicago upper clay 510 051 0083 02 0096 Tang et almiddle clay 300 026 0035 02 0035 (1976)lower clay 370 032 0056 02 0029

James Bay lacustrineclay

312 027 0045 40+ deGroot andBaecher (1993)

slipsurfacelowast 014 24 0063 Christian et al

(1994)

+δx lowastfor circular arc slip surface and H = 12 m

Reliability analysis of slopes 427

Examples of estimated bias in the strength model made between 1970and 1993 are shown in Table 113 Since the true model error is unknownthe estimates are based on data from laboratory or field studies and aresubjective in nature because judgment is involved in the interpretation ofpublished data Note that different individuals considered different possiblesources of error For the James Bay site the model error of the field vane sheartests consists of the data scatter in Bjerrumrsquos (1972) correction factor Thecorrection factors Ni for the various sources of errors are mostly between09 and 11 with an average close to 10 The ∆irsquos for the uncertainties fromthe different sources range between 003 and 014 and the total uncertainties(∆2

i )12 are about the same order of magnitude as those from data scatter(Table 112)

When two or more parameters describing a soil property are derived byfitting a curve to test data the correlation between the parameters may beimportant Data on correlations are limited Examples are the correlationbetween cprime and φprime (Lumb 1970) and between parameters a n and ks in theFredlund and Xing (1994) equation (Zhang et al 2003)

To illustrate the influence of correlation between properties consider theslope in a cprime ϕprime material (Figure 117) analyzed by Fredlund and Krahn(1977) by limit equilibrium and by Low et al (1997) by FORM The randomvariables X1 X2 in Equation (119) are cprime ϕprime The correlation coefficientρcϕ is ndash05 The critical circles from the limit equilibrium analysis (LEA)and that from FORM are also shown Note that FORM searches for themost unfavorable combination of cprime and ϕprime or point b in Figure 115 whichare cprime = 013 kPa and ϕprime = 235 The shallow circle is the result of thelow cprime and high ϕprime (Taylor 1948) The difference reflects the uncertaintyabout the material model and may be expected to decrease with decreasingvalues of ρcϕ and the COVs of cprime and ϕprime Lumbrsquos (1970) study on threeresidual soils show that ρcϕ le minus02 ∆(c) asymp 015 and ∆(ϕprime) asymp 005 whichare considerably less than the values used by Low et al It should be addedthat software packages that use simulation on the critical slip surface fromlimit equilibrium analysis cannot locate point b in Figure 115 and will notproperly account for correlation

1132 Loads and geometry

The geometry and live and dead loads for most geotechnical structures areusually well defined except for special cases When there is more than oneload on the slope a simple procedure is to use the sum of the loads Howeverthis may be overly conservative if the live loads have limited durations Thenfailure can be modeled as a zero-crossing process (Wen 1990) with the loadsrepresented as Poisson processes An application to landslide hazard is givenby Wu (2003)

Table 113 Uncertainties due to errors in strength model

Material Test Ni ∆i Ns (∆2i )12 Ref

Sampledisturbance

Stress state Anisotropy Samplesize

Strain rate Progressfailure

Total

Detroit clay unconfcomp

115008 100003 086009 X X X 099 012 Wu and Kraft(1970)

ChicagoUpper clay unconf

comp(a)1380024(b)105002

105003 10 003 075 009 080014 093003 Tang et al(1976)

Middle clay (a)1380024(b)105002

105003 10 003 093 005 080014 097003

Lower clay (a)1380024(b)105002

105003 10 003 093 005 080014 097003 105 017

Labrador clay fieldvane

X X X X X X 10 015 Christian et al(1994)

Reliability analysis of slopes 429

100

10

20

20 30 40 50 (m)

LEAcprime = 285 kPaφprime = 20degβ = 357FORMcprime = 130 kPaφprime = 23degβ = 290

Figure 117 FORM analysis of a slope (adapted from Low et al (1997))

1133 Soil boundaries

Subsoil profiles are usually constructed from borehole logs It is common tofind the thickness of individual soil layers to vary spatially over a site Wherethere is no sharp distinction between different soil types judgment is requiredto choose the boundaries Uncertainties about boundary and thickness resultin uncertainty about the mean soil property Both systematic and randomerrors may be present Their representation depends on the problem at handAn example is given in Christian et al (1994) Errors in the subsoil modelwhich result from failure to detect important geologic details could haveimportant consequences (eg Terzaghi 1929) but are not included herebecause available methods cannot adequately account for errors in judgment

1134 Flow at boundaries

Calculation of seepage requires water levels or flow rates at the bound-aries For levees and dams flood levels and durations at the upstream slopeare derived from hydrological studies (eg Maidman 1992) Impermeableboundaries often used to represent bedrock or clay layers may be perviousbecause of fractures or artesian pressures Modeling these uncertainties isdifficult

For calculation of infiltration rainfall depth and duration for returnperiod Tr are available (Hershfield 1961) However rainfall and dura-tion are correlated and simulation using rainfall statistics is necessary(Richardson 1981) Under some conditions the precipitation per storm con-trols (Wu and Abdel-Latif 2000) Then this can be used in a seepage modelto derive the mean and variance of pore pressure In unsaturated soils failurefrequently occurs due to reduction in suction from infiltration (eg Krahnet al 1989 Fourie et al 1999 Aubeny and Lytton 2004 and others)Usually both rainfall and duration are needed to estimate the suction or crit-ical depth (eg Fourie et al 1999) Then a probabilistic analysis requiressimulation

430 Tien H Wu

Evapotranspiration is a term widely used for water evaporation from avegetated surface It may be unimportant during rainfall events in humidand temperate regions but is often significant in arid areas (Blight 2003)The Penman equation with subsequent modifications (Penman 1948Monteith 1973) is commonly used to calculate the potential evapotran-spiration The primary input data are atmospheric conditions saturationvapor pressure vapor pressure of the air wind speed and net solar radiationMethods to estimate actual evapotranspiration from potential evapotranspi-ration are described by Shuttleworth (1993) and Dingman (2002) Genera-tion of evapotranspiration from climate data (Ritchie 1972) has been usedto estimate moisture in landfill covers (Schroeder et al 1994) Model errorand uncertainty about input parameters are not well known

1135 Earthquake motion

For pseudostatic analysis empirical rules for seismic coefficients are availablebut the accuracy is poor Seismic hazard curves that give the probabilitythat an earthquake of a given magnitude or given maximum accelerationwill be exceeded during given time exposures (T) are available for the US(Frankel et al 2002) Frankel and Safak (1998) reviewed the uncertaintiesin the hazard estimates The generation of ground motion parameters andrecords for design is a complicated process and subject of active currentresearch It is beyond the scope of this chapter A comprehensive reviewof this topic is given in Sommerville (1998) The errors associated with thevarious procedures are largely unknown

It should be noted that in some problems the magnitude and accelerationof future earthquakes may be the predominant uncertainty and estimates ofthis uncertainty with current methods are often sufficient for making designdecisions An example is the investigation of the seismic stability of theMormon Island Auxiliary Dam in California (Sykora et al 1991) Limitequilibrium analysis indicated that failure is likely if liquefaction occurs in agravel layer Dynamic FEM analysis indicated that an acceleration k = 037from a magnitude 525 earthquake or k = 025 from a magnitude 65 earth-quake would cause liquefaction However using the method of Algermissanet al (1976) the annual probability of occurrence of an earthquake with amagnitude of 525 or larger is less than 00009 which corresponds to a returnperiod of 1100 years Therefore the decision was made not to strengthenthe dam

1136 Analytical models

In limit equilibrium analysis different methods usually give different safetyfactors For plane strain the safety factors obtained by different methodswith the exception of the Fellenius (1936) method are generally within plusmn7

Reliability analysis of slopes 431

Table 114 Errors in analytical model

Analysis Ni∆i Na∆a Ref

3D Slip surf Numerical Total

Cut ϕ = 0 105003 095006 X 10 0067 Wu and Kraft(1970)

Cut ϕ = 0 X X X 098 0087 Tang et al(1976)

Embankment ϕ = 0 11005 10005 10002 10007 Christianet al (1994)

Landfill cprime ϕprime 11016 X X 11016 Gilbert et al(1998)

(Whitman and Bailey 1967 Fredlund and Krahn 1977) A more importanterror is the use of the plane-strain analysis for failure surfaces which areactually three-dimensional Azzouz et al (1983) Leshchinsky et al (1985)and Michalowski (1989) show that the error introduced by using a two-dimensional analysis has a bias factor of Na = 11 minus 13 for ϕ = 0 minus 25Table 114 shows estimates of various errors in the limit equilibrium modelThe largest model error ∆a = 016 is for three-dimensional (3D) effect in alandfill (Gilbert et al 1998)

Ideally a 3D FEM analysis using the correct material model should givethe correct result However even fairly advanced material models are notperfect The model error is unknown and has been estimated by comparisonwith observed performance The review by Duncan (1996) shows variousdegrees of success of FEM predictions of deformations under static loadingIn general Type A predictions (Lambe 1973) for static loading cannot beexpected to come within plusmn10 of observed performances This is wherethe profession has plenty of experience The model error and the associateduncertainty may be expected to be much larger in problems where expe-rience is limited One example is the prediction of seepage and suction inunsaturated soils

1137 Bayesian updating

Bayesian updating allows one to combine different sources of informationto obtain an ldquoupdatedrdquo estimate Examples include combining direct andindirect measurements of soil strength (Tang 1971) and properties of land-fill materials measured in different tests (Gilbert et al 1998) It can alsobe used to calibrate predictions with observation In geotechnical engineer-ing back-analysis or solving the inverse problem is widely used to verifydesign assumptions when measured performance is available An example is

432 Tien H Wu

a slope failure This observation (Fs = 1) may be used to provide an updatedestimate of the soil properties or other input parameters

In Bayes theorem the posterior probability is

P = P[Ai|B] = P[B|Ai]P[Ai]P[B] (1122a)

where

P[B] =nsum

i=1

P[B|Ai]P[Ai] (1122b)

P[Ai|B] = probability of Ai (shear strength is si) given B (failure) has beenobserved P[B|Ai] = probability that event B occurs (slope fails) given eventAi occurs (the correct strength is used) P[Ai] = prior probability that Aioccurs In a simple case P[Ai] represents the uncertainty about strength dueto data scatter etc P[B|Ai] represents the uncertainty about the safety factordue to uncertainties about the strength model stability analysis boundaryconditions etc Example 5 illustrates a simple application Gilbert et al(1998) describe an application of this method to the Kettleman Hills landfillwhere there are several sources of uncertainty

Regional records of slope performance when related to some significantparameter such as rainfall intensity are valuable as an initial estimate offailure probability Cheung and Tang (2005) collected data on failure prob-ability of slopes in Hong Kong as a function of age This was used as priorprobability in Bayesian updating to estimate the failure probability for aspecific slope The failure probability based on age is P[Ai] and P[B|Ai]is the pdf of β as determined from investigations for failed slopes and β

is the reliability index for the specific slope under investigation To applyEquation (1122a)

P[B] = P[B|Ai]P[Ai]+ P[B|Aprimei]P[Arsquo

i] (1122c)

P[Arsquoi] = 1 minus P[Ai]

where P[B|Aprimei] = pdf of β for stable slopes (1122d)

Example 3

The analysis of the slope failure in Chicago by Tang et al (1976) is reviewedhere to illustrate the combination of uncertainties using Equations (1120)and (1121) The undrained shear strength s was measured by unconfinedcompression tests on 508 cm Shelby tube samples The subsoil consistsmainly of ldquogritty blue clayrdquo whose average strength varies with depthFigure 113 The clay was divided into three layers with values of sm and

Reliability analysis of slopes 433

∆(sm) as given in Table 112 The value of δ is the same for the three claylayers The values of ∆0 calculated according to Equation (1116) are givenin Table 112 The correlation coefficients ρij between the strength of theupper and middle middle and lower and upper and lower layers are 066059 and 040 respectively

The sources of uncertainties about the shear strength model are listed inTable 113 The errors were evaluated by review of published data mostlylaboratory studies on the influence of the various factors on strength Thesewere used to establish a range of upper and lower limits for Ni To calculateNi and ∆i a distribution of Ni is necessary The rectangular and triangulardistributions between the upper and lower limits were used and the meanand variance of Ni were calculated with the relations given in Ang and Tang(1975) Clearly judgment was exercised in choosing the sources of errorand the ranges and distributions of Ni The estimated values of Ni and ∆iare shown in Table 113 Two sets of values are given for sample distur-bance (a) denotes the strength change due to mechanical disturbance and(b) denotes that due to stress changes from sampling

The resisting moment in Equations (112) can be written as

MR = ri

sili (1123a)

where i denotes the soil layer According to Equation (113a) the mean is

MR = Nri

sili (1123b)

According to Equation (1121a)

N = 138 times 105 times 105 times 10 times 075 times 080 times 093 = 085 (1123c)

for the upper clay and the values of Ni are as given in Table 113The calculated MR for the different segments are given in Table 115 andthe sum is MR = 2717MN minus mm

To evaluate the variance or COV of Mr the first step is to evaluate ∆(s) forthe segments of the slip surface passing through the different soil layers Forthe upper clay layer substituting ∆0 in Table 112 and the ∆irsquos in Table 113into Equation (1121b) gives

∆(s1) = [(0083)2 + (0024)2 + (002)2 + (003)2 + (003)2 + (009)2

+ (014)2 + (003)2]12 = 020 (1124a)

for failure surface 1 Since ne is large the contribution of ∆e is ignoredThe values of ∆(s) = ∆(MR) and σ (MR) for the clay layers are given in

434 Tien H Wu

Table 115 Mean and standard deviation of resisting moment Chicago slope

Layer MRMN minus mm N ∆(MR) σ (MR)MN minus mm

Upper clay 401 085 020 080Middle clay 570 110 016 091Lower clay 1690 10 017 288Sand 056 2717 015 412

018lowast

lowastWith model error ∆a

Table 115 Substitution into Equation (113b) gives

V(MR) = (080)2 + (091)2 + (288)2 + 2[(080 times 091 times 066)

+ (080 times 288 times 059) + (091 times 288 times 04)]= 1706(MN minus mm)2 (1124b)

∆(MR) = [1706]122717 = 0152 (1124c)

To include the error Na in the stability analysis model Na and ∆a givenin Table 113 are applied to MR The combined uncertainty is

∆(MR) = [∆2a +∆2

s ]12 = [(0087)2 + (0152)2]12 = 018 (1124d)

The calculation of β and Pf are described in Example 1

Example 4

The failure of a slope in Cleveland (Wu et al 1975) Figure 118 is used toillustrate the choice of soil properties and its influence on predicted failureprobability The subsoil consists of layers of silty clay and varved clay Failureoccurred shortly after construction and the shape of the failure surface (1)shown in Figure 118 suggested the presence of a weak layer near elev184 m An extensive investigation following failure revealed the presenceof one or more thin layers of gray clay Most of these were encounterednear elev 184 m Many samples of this material contained slickensides andhad very low strengths It was assumed that the horizontal portion of theslip surface passed through this weak material Because no samples of theweak material were recovered during the initial site exploration its presencewas not recognized A simplified analysis of the problem is used for thisexample The layers of silty clay and varved clay are considered as one unitand the weak gray clay is treated as thin layer at elev 184 m (Figure 118)

Reliability analysis of slopes 435

210

(2)

200

Failure surface (2)

Failure surface (1)

Silty Clay

0 200 kPasu

Varved Clay Till

190

Ele

v (

m)

180

Figure 118 Slope failure in Cleveland (adapted from Wu et al 1975) su = undrained shearstrength

Table 116 Strength properties Cleveland slope

Analysis One material Two materials

Combineddata

Varved clayand silty clay

Weak gray claylayer

sm kPa 671 831 395σm (sm) kPa 383 384 168∆(sm) 057 046 042n 19 12 7δy m 33 33 ndashδx m ndash ndash 33∆0 013 013 016ne 56 41 24∆e 0076 0072 027[(∆0)2 + (∆e)2]12 015 015 031

The undrained shear strengths sm of the two materials as measured by theunconfined compression test are summarized in Table 116 The correlationdistance of the silty clay and varved clay δy is determined by VanMarckersquos(1977) method The number of samples of the weak gray clay is too smallto allow calculation of δx Hence it is arbitrarily taken as 10δy

436 Tien H Wu

Consider first the case where the silty clay and varved clay and the weakmaterial are treated as one material All the measured strengths are treatedas samples from one population (ldquocombined datardquo in Table 116) For sim-plification the model errors Ni and ∆i are ignored Then only ∆0 and ∆econtribute to ∆(s) in Equation (1121b) The values of ∆0 and ∆e are eval-uated as described in Example 2 and given in Table 116 Limit equilibriumanalysis gives Fs = 136 for the circular arc shown as (2) in Figure 118The values of β and Pf calculated by FOSM are given in Table 117Values of β and Pf calculated by simulation with SLOPEW (GEO-SLOPEInternational) where s is modeled as a normal variate are also given inTable 117

Next consider the subsoil to be composed of two materials with sm and∆(sm) as given in Table 116 The values of ∆0 and ∆e are evaluated as beforeand given in Table 116 The larger values of∆e and [(∆0)2+(∆e)

2]12 are theresult of the large δx of the weak material Values of Fs β and Pf for the com-posite failure surface (1) calculated by FOSM and by simulation are givenin Table 117 The Pf is much larger than that for the circular arc (2) Thisillustrates the importance of choosing the correct subsoil model Figure 119shows the distribution of Fs from simulation Note that SLOPEW uses thenormal distribution to represent the simulated Fs Hence the mean of thenormal distribution may differ from the Fs obtained by limit equilibriumanalysis for the critical slip surface

In the preceding analysis of the two-material model it is assumed that thestrengths of the two materials are uncorrelated The available data are insuf-ficient to evaluate the correlation coefficient and geologic deductions couldbe made in favor of either correlation or no correlation To illustrate theinfluence of weak correlation it is assumed that ρij = 02 The β calculatedby FOSM is reduced from 164 to 149

Table 117 Calculated reliabilities Cleveland slope

Analysis One material Two materials

Limit equil Fs 136 126FOSM ∆(Fs) 015 014no model β 197 164error Pf 0025 008Simulation Fs 136 131no model β 248 164error Pf 0006 005FOSM [∆2

j ]12 014 014with model ∆a 0035 0035error ∆(Fs) 021 020

β 137 105Pf 009 015

Reliability analysis of slopes 437

025

020

015

Pf

010

005

006 08 10 12 14 16 18 20

Fs

Figure 119 Distribution of Fs Cleveland slope

Finally consider model errors The total uncertainty about strength modelbased on the unconfined compression test is estimated to be Ns = 10 and(∆2

i )12 = 014 Assume that the analytical model error has Na = 10∆a = 0035 Calculation with FOSM gives β = 137 and 105 for the one-and two-material models respectively (Table 117)

Example 5

If the slope in Example 4 is to be redesigned it is helpful to revise the shearstrength with the observation that failure has occurred Bayesrsquo TheoremEquations ( 1122) is used to update the shear strength of the weak layerin Example 4 Let P[B|Ai] = probability of failure given the shear strengthsi P[Ai] = prior probability that the shear strength is si In this simplifiedexample the distribution of si is discretized as shown in Figure 1110a whereP[Ai]= probabilities that the shear strength falls within the respective rangesThen P[B|Ai] is found from the distribution of Pf calculated with SLOPEWBecause of errors in the stability analysis it is assumed that failure will occurwhen the calculated Fs falls between 100plusmn005 Figure 119 shows that forsi = 395 kPa P[B|Ai] = P[095lt Fs lt 105] asymp 005 Other values of P[B|Ai]are found in the same way and plotted in Figure 1110b Substitution ofthe values in Figures 1110a and 1110b into Equations (1122) gives theposterior distribution Pprimeprime which is shown in Figure 1110c The mean of theupdated s is 303 kPa with a standard deviation of 83 kPa and COV = 027

438 Tien H Wu

04

P[Ai]

02

02

04

02

0 20 40Su kPa

60 80

P[B|Ai]

PPrime

01

0

0

Figure 1110 Updating of shear strength Cleveland slope

Note that ∆(s) after updating is still large This reflects the large COV ofP[B|Ai] in Figure 1110b due to the large ∆(sm) of the gray silty clay andvarved clay and expresses the dependence of the back-calculated strengthon the accuracy of the stability analysis and other input parameters (Leroueiland Tavanas 1981 Leonards 1982) Inclusion of model errors wouldfurther increase the uncertainty Gilbert et al (1998) provide a detailed

Reliability analysis of slopes 439

example of the evaluation of P[B|Ai] for the strength of a geomembranendashclayinterface

114 Case histories

Results from earlier studies are used to illustrate the relative importance ofuncertainties from different sources and their influence on the failure proba-bility Consider the uncertainties in a limit equilibrium analysis for undrainedloading of slopes in intact clays This is a common problem for which theprofession has plenty of experience Where the uncertainties about live anddead loads are negligible then there remain only the uncertainties aboutsoil properties due to data scatter and the strength model and the analyti-cal model Tables 112ndash114 show estimated uncertainties for several sitesAll three sources data scatter model errors for shear strength and stabilityanalysis contribute to Pf

Comparison of estimated failure probability with actual performance pro-vides a calibration of the reliability method Table 118 shows some relativelysimple cases with estimated uncertainties about input parameters The fail-ure probabilities calculated for mean safety factors larger than 13 withvalues of N asymp 1 and ∆(Fs) asymp 01 are well below 10minus2 The FEM analysisof Griffith and Fenton (2004) in which the undrained shear strength is arandom field with ∆(s) = 025 and ne = 15 shows that for Fs = 147 thefailure probability is less than 10minus2 This serves to confirm the results of thesimple analyses cited above In addition their analysis searches for the fail-ure surface in a spatially variable field which is not necessarily a circle Thedeparture from the critical circle in a limit equilibrium analysis depends onthe correlation distance (0 lt δ lt L) An important implication is that whenδx δy and δx is a very large the problem approaches that of a weak zone(Leonards 1982) as illustrated in Example 4

Table 118 Estimated failure probability and slope performance

Site Material Analysis Failuresurface

Fs ∆(Fs) Pf Stability Ref

Cleveland Clay Totalstress

Composite 118 026 03 Failure Wu et al1975

Chicago Clay Totalstress

Circular 118 018 02lowast Failure Tang et al1976

James Bay Clay Totalstress

Circular 153 013 0003 Stable Christianet al 1994

Alberta Clayshale

Effectivestress

Composite 131 012 00016 Stable El-Ramlyet al 2003

lowastObserved failure surface

440 Tien H Wu

The success of current practice using a safety factor of 15 or larger isenough to confirm that for simple cuts and embankments on intact clay theuncertainties (Tables 112ndash114) are not a serious problem Hence reliabilityanalysis and evaluation of uncertainties should be reserved for analysis ofmore complex site conditions or special design problems An example ofthe latter is the design of the dams at James Bay (Christian et al 1994)A reliability analysis was made to provide the basis for a rational decisionon the safety factors for the low dams (6 or 12 m) built in one stage andthe high dam (23 m) built by staged construction

The analysis using FOSM showed that for Fs asymp 15 β for the high damis much larger than that for the low dams However the uncertainty aboutFs for the low dams comes largely from spatial variation which is randomwhile that for the high dam comes largely from systematic errors Failures dueto spatial variation (eg low strength) are likely to be limited to a small zonebut failures due to systematic error would involve the entire structure andmay extend over a long segment Clearly the consequences of failure of alarge section in a high dam are much more serious than those of failureof small sections in a low dam Consideration of this difference led to thechoice of target βrsquos of 001 0001 and 00001 for the 6 m 12 m and 23 mdams respectively The corresponding Fsrsquos are 163 153 and 143 whenmodel error is not included These numbers are close enough to 150 thatFs = 150 was recommended for all three heights in the feasibility study andpreliminary cost estimates

Where experience is limited the estimation of uncertainties becomesdifficult because model errors are not well known The significance ofuncertainties about properties of unsaturated soils has only recently beeninvestigated Chong et al (2000) studied the influence of uncertainties aboutparameters in the van Genuchten equation Equation (1113) The parame-ters α and n have the largest influence on Fs and ∆(Fs) α alone can lead to a∆(Fs) asymp 01 and a reduction of Fs from 14 to 12 Zhang et al (2005) useddata on decomposed granite to evaluate the uncertainties about parametersa and n in the FredlundndashXing equation Equation (1114) Ks = saturatedpermeability θs = saturated moisture content and four parameters of thecritical-state model The correlations between a n Ks and θs were deter-mined from test data The values of ∆(ln a) ∆(ln n) and ∆(ln Ks) are largerthan 10 Under constant precipitation the calculated ∆(Fs) is 022 forFs = 142 These are model errors and are systematic It can be appreciatedthat ∆(Fs) and the corresponding failure probability are significantly largerthan those for the slopes in saturated soils given in Table 118 Uncertaintiesabout precipitation and evapotranspiration and spatial variation would fur-ther increase the overall uncertainty about pore pressure and stability Thesetwo examples should serve notice that for slopes on unsaturated soils thepractice of Fs = 15 should be viewed with caution and reliability analysisdeserves serious attention

Reliability analysis of slopes 441

115 Summary and conclusions

The limitations of reliability analysis are

1 Published values on ∆(sm) and δ of soils (eg Barker et al 1991 Phoonand Kulhawy 1999) are based on data from a limited number of sites(eg Appendix A Barker et al 1991) and show a wide range even forsoils with similar classification Therefore the choice of design parame-ters involves unknown errors Even estimation of these values from siteexploration data involves considerable errors unless careful samplingschemes are used (DeGroot and Baecher 1993 Baecher and Christian2003) The calculated failure probabilities may be expected to dependstrongly on these properties

2 Errors in strength and analysis models which result from simplificationsare often unknown Estimates are usually made from case historieswhich are limited in number particularly where there is little experi-ence Errors in climate and earthquake models are difficult to evaluateHence estimation of uncertainties about input variables contain a largesubjective element

3 The reliability analyses reviewed in this chapter consider only a lim-ited number of failure modes For a complex system where there aremany sources of uncertainties the system reliability can be evaluatedby expanding the scope via event trees This is beyond the scope of thischapter See Baecher and Christian (2003) for a detailed treatment

4 Available methods of reliability analysis cannot account for omissionsor errors of judgment A particularly serious problem in geotechnicalengineering is misjudgment of the site conditions

The limitations cited above should not discourage the use of reliabilityanalysis Difficulties due to inadequate information about site conditionsand model errors are familiar to geotechnical engineers and apply to bothdeterministic and probabilistic design Some examples of use of reliabilityanalysis in design are given below

1 Reliability can be used to compare different design options and helpdecision-making (eg Christian et al 1994)

2 Even when a comprehensive reliability analysis cannot be made sensitiv-ity analysis can be used to identify the random variables that contributemost to failure probability The design process can then concentrate onthese issues (eg Sykora et al 1991)

3 Bayesrsquos theorem provides a logical basis for combining different sourcesof information (eg Cheung and Tang 2005)

4 The methodology of reliability is well established and it should bevery useful in evaluating the safety of new and emerging geotechnical

442 Tien H Wu

problems as has been illustrated in the case of unsaturated soils (Chonget al 2000 Zhang et al 2005)

Acknowledgments

The writer thanks K K Phoon for valuable help and advice during thewriting of this chapter and W H Tang for sharing his experience and insightthrough many discussions and comments

References

Algermissen S T Perkins D M Thenhaus P C Hanson S L and Bender B L(1982) Probabilistic estimates of maximum acceleration and velocity in rock inthe contiguous United States Openfile Report 82-10033 Geological Survey USDepartment of the Interior Denver

Anderson M G Kemp M J and Lloyd D M (1988) Application of soil waterdifference models to slope stability problems In 5th International Symposium onLandslides A A Balkema Rotterdam Lausanne 1 pp 525ndash30

Ang A H-S and Tang W H (1975) Probability Concepts in Engineering Planningand Design Vol 1 John Wiley and Sons New York

Aubeny C P and Lytton R L (2004) Shallow slides in compacted high plastic-ity clay slopes Journal of Geotechnical and Geoenvironmental Engineering 130717ndash27

Auvinet G Bouyad A Orlandi S and Lopez A (1996) Stochastic finite ele-ment method in geomechanics In Uncertainty in the Geologic EnvironmentEds C D Shackelford P P Nelson and M J S Roth Geotechnical SpecialPublications 58 ASCE Reston VA 2 pp 1239ndash53

Azzouz A S Baligh MM and Ladd C C (1983) Corrected field vane strengthfor embankment design Journal of Geotechnical Engineering 120 730ndash4

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley and Sons Chichester UK

Barker R M Duncan J M Rojiani K B Ooi P S K Tan C K and KimS G (1991) Manuals for the design of bridge foundations NCHRP Report 343Transportation Research Board Washington DC

Beven K and Germann P (1982) Macropores and water flow through soils WaterResources Research 18 1311ndash25

Bjerrum L (1972) Embankments on soft ground state of the art report In Pro-ceedings Specialty Conference on Performance of Earth and Earth-supportedStructures ASCE New York 2 pp 1ndash54

Blight G B (2003) The vadose zone soil-water balance and transpiration rates ofvegetation Geotechnique 53 55ndash64

Cheung R W M and Tang W H (2005) Realistic assessment of slope stabilityfor effective landslide hazard management Geotechnique 55 85ndash94

Chong P C Phoon K K and Tan T S (2000) Probabilistic analysis of unsaturatedresidual soil slopes In Applications of Statistics and Probability Eds R E Melchersand M G Stewart Balkema Rotterdam pp 375ndash82

Reliability analysis of slopes 443

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental Engineering130 985ndash1003

Christian J T Ladd C C and Baecher G B (1994) Reliability applied to slopestability analysis Journal of Geotechnical Engineering 120 2180ndash207

DeGroot D J and Baecher G B (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering 119 147ndash67

Dingman S L (2002) Physical Hydrology Prentice Hall Upper Saddle River NJDuncan J M (1996) State of the art limit equilibrium and finite element analysis of

slopes Journal Geotechnical and Geoenvironmental Engineering 122 577ndash97El-Ramly H Morgenstern N R and Cruden D M (2003) Probabilistic stabil-

ity analysis of a tailings dyke on pre-sheared clay-shale Canadian GeotechnicalJournal 40 192ndash208

Fellenius W (1936) Calculation of the stability of earth dams Proceedings SecondCongress on Large Dams 4 445ndash63

Fourie A B Rowe D and Blight G E (1999) The effect of infiltration on thestability of the slopes of a dry ash dump Geotechnique 49 1ndash13

Frankel A D and Safak E (1998) Recent trends and future prospects in seismichazard analysis In Geotechnical Earthquake Engineering and Soil Dynamics IIIGeotechnical Special Pub 75 Eds P Dakoulas M Yegian and R D Holtz ASCEReston VA

Frankel A D Petersen M D Mueller C S Haller K M WheelerR L Leyendecker E V Wesson R L Harmsen S C Cramer C H PerkinsD M and Rukstales K S (2002) Documentation for the 2002 update of thenational seismic hazard maps U S Geological Survey Open-file report 02-420

Fredlund D G and Krahn J (1977) Comparison of slope stability methods ofanalysis Canadian Geotechnical Journal 14 429ndash39

Fredlund D G and Rahardjo H (1993) Soil Mechanics for Unsaturated Soils JohnWiley and Sons New York

Fredlund D G and Xing A (1994) Equations for the soil-water suction curveCanadian Geotechnical Journal 31 521ndash32

Gelhar L W (1993) Stochastic Subsurface Hydrology Prentice Hall EnglewoodCliffs NJ

Gilbert R B Wright S G and Liedke E (1998) Uncertainty in back analysis ofslopes Kettleman Hills case history Journal of Geotechnical and Geoenvironmen-tal Engineering 124 1167ndash76

Griffith V D and Fenton G A (1997) Three-dimensional seepage through spatiallyrandom soil Journal of Geotechnical and Geoenvironmental Engineering 123153ndash60

Griffith V D and Fenton G A (2004) Probabilistic slope stability analysis byfinite elements Journal of Geotechnical and Geoenvironmental Engineering 130507ndash18

Hansen J Brinch (1965) The philosophy of foundation design design criteria safetyfactors and settlement limits In Bearing Capacity and Settlement of FoundationsEd A S Vesic Duke University Durham pp 9ndash14

Hershfield D M (1961) Rainfall frequency atlas of the United States TechPaper 40 US Department of Commerce Washington DC

444 Tien H Wu

Ireland H O (1954) Stability analysis of the Congress Street open cut in ChicagoGeotechnique 40 161ndash80

Johnson K A and Sitar N (1990) Hydrologic conditions leading to debris-flowinitiation Canadian Geotechnical Journal 27 789ndash801

Khire M V Benson C H and Bosscher P J (1999) Field data from a capil-lary barrier and model predictions with UNSAT-H Journal of Geotechnical andGeoenvironmental Engineering 125 518ndash27

Kim J and Sitar N (2003) Importance of spatial and temporal variability in theanalysis of seismically induced slope deformation In Applications of Statisticsand Probability in Civil Engineering Eds A Der Kiureghian S Madanat andJ M Pestana Balkema Rotterdam pp 1315ndash22

Krahn J Fredlund D G and Klassen M J (1989) Effect of soil suction on slopestability at Notch Hill Canadian Geotechnical Journal 26 2690ndash78

Kramer S L (1996) Geotechnical Earthquake Engineering Prentice Hall UpperSaddle NJ

Lacy S J and Prevost J H (1987) Nonlinear seismic analysis of earth dams SoilDynamics and Earthquake Engineering 6 48ndash63

Lambe T W (1973) Predictions in soil engineering Geotechnique 23 149ndash202Leonards G A (1982) Investigation of failures Journal of Geotechnical

Engineering 108 185ndash246Leroueil S and Tavanas F (1981) Pitfalls of back-analysis In 10th Interna-

tional Conference on Soil Mechanics and Foundation Engineering A A BalkemaRotterdam 1 pp 185ndash90

Leshchinsky D Baker R and Silver ML (1985) Three dimensional analysisof slope stability International Journal Numerical and Analytical Methods inGeomechanics 9 199ndash223

Lin J-S and Whitman R V (1986) Earthquake induced displacements of slidingblocks Journal of Geotechnical Engineering 112 44ndash59

Liu W K Belytschko T and Mani A (1986) Random field finite elementsInternational Journal of Numerical Methods in Engineering 23 1831ndash45

Low B F Gilbert R B and Wright S G (1998) Slope reliability analysis usinggeneralized method of slices Journal of Geotechnical and GeoenvironmentalEngineering 124 350ndash62

Lumb P (1970) Safety factors and the probability distribution of strength CanadianGeotechnical Journal 7 225ndash42

Lumb P (1974) Application of statistics in soil mechanics In Soil Mechanics ndash NewHorizons Ed IK Lee Elsevier New York pp 44ndash111

Maidman D R (1992) Handbook of Hydrology McGraw-Hill New YorkMichalowski R L (1989) Three-dimensional analysis of locally loaded slopes

Geotechnique 39(1) 27ndash38Monteith J L (1973) Principles of Environmental Physics Elsevier New YorkNewmark N M (1965) Effects of earthquakes on dams and embankments

Geotechnique 15 139ndash60Penman H L (1948) Natural evapotranspiration from open water bare soil and

grass Proceedings of the Royal Society A 193 120ndash46Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variability

Canadian Geotechnical Journal 36 612ndash24

Reliability analysis of slopes 445

Pierson T C (1980) Piezometric response to rainstorms in forested hillslopedrainage depression Journal of Hydrology (New Zealand) 19 1ndash10

Richards L A (1931) Capillary conduction of liquids through porous mediumJournal of Physics 72 318ndash33

Richardson C W (1981) Stochastic simulation of daily precipitation temperatureand solar radiation Water Resources Research 17 182ndash90

Ritchie J T (1972) A model for predicting evaporation from a row crop withincomplete plant cover Water Resources Research 8 1204ndash13

Rojiani K B Ooi P S K and Tan C K (1991) Calibration of load factor designcode for highway bridge foundations In Geotechnical Engineering CongressGeotechnical Special Publication No 217 ASCE Reston VA 2 pp 1353ndash64

Schroeder P R Morgan J M Walski T M and Gibson A C (1994) HydraulicEvaluation of Landfill Performance ndash HELP EPA6009-94xxx US EPA RiskReduction Engineering Laboratory Cincinnati OH

Seed H B Lee K L Idriss I M and Makdisi F I (1975) The slides in theSan Fernando dams during the earthquake of Feb 9 1971 Journal GeotechnicalEngineering Division ASCE 10 651ndash88

Shuttleworth W J (1993) Evaporation In Handbook of HydrologyEd D R Maidman McGraw-Hill New York

Sommerville P (1998) Emerging art earthquake ground motion In GeotechnicalEarthquake Engineering and Soil Dynamics III Eds P Dakoulas M Yegian andR D Holtz Geotechnical Special Pub 75 ASCE Reston VA pp 1ndash38

Soong T-Y and Koerner R M (1996) Seepage induced slope instability Journalof Geotextiles and Geomembranes 14 425ndash45

Sykora D W Koester J P and Hynes M E (1991) Seismic hazard assess-ment of liquefaction potential at Mormon Island Auxiliary Dam California USAIn Proceedings 23rd US-Japan Joint panel Meeting on Wind and Seismic EffectsNIST SP 820 Gaithersburg National Institute of Standards and Technologypp 247ndash67

Tang W H (1971) A Bayesian evaluation of information for foundation engineeringdesign In Statistics and Probability in Civil Engineering Ed P Lumb Hong KongUniversity Press Hong Kong pp 173ndash85

Tang W H Yucemen M S and Ang A H-S (1976) Probability-based short termdesign of soil slopes Canadian Geotechnical Journal 13 201ndash15

Taylor D W (1948) Fundamentals of Soil Mechanics John Wiley and SonsNew York

Terzaghi K (1929) Effect of minor geologic details on the safety of dams TechnicalPub 215 American Institute of Mining and Metallurgical Engineering New YorkNY pp 31ndash44

van Genuchten M Th (1980) A closed-form equation for predicting hydraulicconductivity of unsaturated soils Soil Science Society of America Journal 44892ndash8

VanMarcke E H (1977) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103 1227ndash46

Wilkinson P L Brooks S M and Anderson M G (2000) Design and applicationof an automated non-circular slip surface search within a combined hydrology andstability model (CHASM) Hydrological Processes 14 2003ndash17

446 Tien H Wu

Wen Y K (1990) Structural Load Modeling and Combinations for Performanceand Safety Evaluation Elsevier Amsterdam

Whitman R V and Bailey W A (1967) Use of computers for slope stabilityanalysis Journal of Soil Mechanics and Foundations Division ASCE 93 475ndash98

Wu T H (2003) Assessment of landslide hazard under combined loading CanadianGeotechnical Journal 40 821ndash9

Wu T H and Abdel-Latif M A (2000) Prediction and mapping of landslidehazard Canadian Geotechnical Journal 37 579ndash90

Wu T H and Kraft L M (1970) Safety analysis of slopes Journal of Soil Mechanicsand Foundations Division ASCE 96 609ndash30

Wu T H Thayer W B and Lin S S (1975) Stability of embankment on clayJournal of Geotechnical Engineering Division ASCE 101 913ndash32

Yucemen M S and Tang W H (1975) Long-term stability of soil slopes ndasha reliability approach In Applications of Statistics and Probability in Soil andStructural Engineering Deutsche Gesellschaft fur Erd-und Grundbau eV Essen2 pp 215ndash30

Zerfa F Z and Loret B (2003) Coupled dynamic elasticndashplastic analysis of earthstructures Soil Dynamics and Earthquake Engineering 23 4358ndash454

Zhang L L Zhang L M and Tang W H (2005) Rainfall-induced slope failureconsidering variability of soil properties Geotechnique 55 183ndash8

Notations

c = cohesionF = forceFs = safety factorh = depth of soil layerk = seismic coefficientK = permeability coefficientl = lengthM = momentN = model biasP = normal forcePf = failure probabilityQ = source or sink termr = distances = soil propertysm = measured soil propertyu = pore pressureT = return periodV() = varianceW = weightXY = random variableα = angle of inclination of failure surfaceβ = reliability index

Reliability analysis of slopes 447

γ = unit weightδ = correlation distanceζ = testing errorθ = volumetric water contentρ = corelation coefficientσ () = standard deviationϕ = angle of internal frictionψ = suction∆() = coefficient of variationamp = variance reduction function

Chapter 12

Reliability of levee systems

Thomas F Wolff

121 About levees

1211 Dams levees dikes and floodwalls

Dams and levees are two types of flood protection structures Both consistprimarily of earth or concrete masses intended to retain water Howeverthere are a number of important differences between the two and these dif-ferences drive considerations about their reliability As shown in Figure 121dams are constructed perpendicular to a river and are seldom more than 1 or2 km long They reduce flooding of downstream floodplains by retainingrunoff from storms or snowmelt behind the dam and releasing it in a con-trolled manner over a period of time The peak stage of the flood is reducedbut downstream stages will be higher than natural for some time afterwardBecause dam sites are fairly localized they can be chosen very deliberatelyand a high level of geotechnical exploration testing and analysis is usuallydone during design Many dams are multiple-use structures with one ormore of the following beneficial uses flood control water supply powersupply and recreation These often provide substantial economic benefitspermitting substantial costs to be expended in design and constructionFinally they are generally used on a daily basis and have some operatingstaff that can provide some level of regular observation

In contrast levees are constructed parallel to rivers and systems of leveesare typically tens to thousands of kilometers long (see Figure 121) Theyprevent flooding of the adjacent landward floodplain by keeping the riverconfined to one side of the levee or where levees are present on both sides of ariver between the levees As levees restrict the overbank flow area they mayactually increase river stages making a flood peak even higher than it wouldnaturally be However they can be designed high enough to account for thiseffect and to protect landside areas against some design flood event eventhough that event will occur at a higher stage with the levee in place Becauselevees are long and parallel to riverbanks designers often have a much

Reliability of levee systems 449

Dam

Levee

Figure 121 Dams and levees

more limited choice of locations than for dams Furthermore riverbank soildeposits may be complex and highly variable alternating between weak andstrong and pervious and impervious Levees tend to be single-use (floodcontrol) projects that may go unneeded and unused for long periods of timeCompared to dam design an engineer designing a levee may face greater lev-els of uncertainties yet proportionately smaller budgets for exploration andtesting Given all of these uncertainties it would seem that levees are inher-ently more fraught with uncertainty than dams If there are any ldquobalancingfactorsrdquo to this dilemma it may be that most dams tend to be much higherthan most levees and that levees designed for very rare events have a verylow probability of loading

There are some structures that lie in a ldquogray areardquo between dams andlevees Like levees they tend to be very long and parallel to watercoursesor water bodies But like dams they may permanently retain water to somepart of their height and provide further protection against temporary highwaters Notable examples are the levees around New Orleans Louisianathe dikes protecting much of the Netherlands and the Herbert Hoover Dikearound Lake Okeechobee Florida If more precise terminology is desiredthe author suggests using dike to refer to long structures that run parallelto a water body provide permanent retention of water to some level andperiodic protection against higher water levels

Finally another related structure is a floodwall (see Figure 122)A floodwall performs the same function as a levee but is constructed ofconcrete steel sheetpile or a combination As these materials provide much

450 Thomas F Wolff

RiverLandside

Figure 122 Inverted-T floodwall with sheetpile

greater structural strength floodwalls are much more slender and requiremuch less width than levees They are used primarily in urban areas wherethe cost of the floodwall would be less than the cost of real estate requiredfor a levee

1212 Levee terminology

Figure 123 illustrates the terminology commonly used to describe thevarious parts of the levee Directions perpendicular to the levee are statedas landside and riverside The crown refers to the flat area at the top ofthe levee or sometimes to the edges of that area The toe of a levee refersto the point where the slope meets the natural ground surface The fore-shore is the area between the levee and the river Construction material forthe levee may be dredged from the river but often is taken from borrowpits which are broad shallow excavations These are commonly located

River

Berm

Crown

Toe

Levee

Foreshore

Borrow pit

Riverside Landside

Figure 123 Levee terminology

Reliability of levee systems 451

riverside of the levee within the foreshore but occasionally may be foundon the landside

As one proceeds along the length of a levee the embankment geometry ofthe levee may vary as may the subsurface materials and their configurationA levee reach is a length of levee (often several hundred meters) for whichthe geometry and subsurface conditions are sufficiently similar that theycan be represented for analysis and design by a single two-dimensionalcross-section and foundation profile Using the cross-section representing thereach analyses are performed to develop a design template for use through-out that reach A design template specifies slope inclination crown and bermwidths and crown and berm elevations

The line of protection refers to the continuous locus of levees or otherfeatures (floodwalls gates or temporary works such as sandbags) thatseparate the flooded area from the protected area The line of protectionmust either tie to high ground or connect back to itself The protected areabehind a single continuous line of protection is referred to as a polderespecially in the Netherlands

An unintended opening in the line of protection is referred to as abreach When destructive flow passes through a breach creating significanterosive damage to the levee and significant landward flooding the breach issometimes called a crevasse

The following appurtenances may be used to provide increased protectionagainst failure

bull Stability berms are extensions of the primary embankment sectionMoving downslope from the crown the slope is interrupted by a nearlyflat area of some width (the berm) and then the slope continuesThe function of a stability berm is to provide additional weight andshear resistance to prevent sliding failures

bull Seepage berms may be similar in shape to stability berms but theirfunction is to provide weight over the landside toe area to counteractupward seepage forces due to underseepage

bull Cutoffs are impervious vertical features provided beneath a leveeThey may consist of driven sheet piles slurry walls or compacted claytrenches They reduce underseepage problems by physically reducingseepage under the levee and lengthening the seepage path

bull Relief wells are vertical cylindrical drains placed into the groundnear the landside toe If piezometric levels in the landside foundationmaterials exceed the ground surface water will flow from the relief wellskeeping uplift pressure buildup to a safe level as flood levels continueto rise

bull Riverside slope protection such as stone riprap or paving may beprovided to prevent wave erosion of the levee where there is potentialfor wave attack

452 Thomas F Wolff

122 Failure Modes

Levees are subject to failure by a variety of modes The following paragraphssummarize the five failure modes most commonly considered and which willbe addressed further

1221 Overtopping

The simplest and most obvious failure mode is overtopping The levee is builtto some height and the flood water level exceeds that height flooding theprotected area While the probability of flooding for water above the top ofa levee will obviously be near unity it may be somewhat lower as the leveemight be raised with sandbags during a floodfight operation

1222 Slope instability

Embankments or berms may slide due to the soil strength being insuffi-cient to resist the driving forces from the embankment weight and waterloading Of most concern is an earth slide during flood loading that wouldlower the levee crown below the water elevation resulting in breaching andsubsequent failure Usually such a slide would occur on the landside of thelevee Figure 124 shows the slide at the 17th St Canal levee in New Orleansfollowing the storm surge from Hurricane Katrina in 2005 At that sitea portion of the levee slid landward about 15ndash20 m landward as a rela-tively intact block ldquoDrawdownrdquo slope failures may occur on the riversideslope of a levee when flood waters recede more quickly than pore pressuresin the foundation soils dissipate Slope failures may occur on either slope

Figure 124 Slide at 17th St Canal levee New Orleans 2005 (photo by T Wolff)

Reliability of levee systems 453

during non-flood conditions one scenario involves levees built of highlyplastic clays cracking during dry periods and the cracks becoming filledwith water during rainy periods leading to slides Slope slides during non-flood periods can leave a deficient levee height if not repaired before the nextflood event

1223 Underseepage

During floods the higher water on the riverside creates a gradient and subse-quent seepage water under the levee as shown in Figure 125 Seepage mayemerge near-vertically from the ground on the landside of the levee Wherefoundation conditions are uniform the greatest pressure upward gradientand flow will be concentrated at the landside levee toe In some instancesthe water may seep out gently leaving the landside ground surface soft andspongy but not endangering the levee Where critical combinations of waterlevels soil types and foundation stratigraphy (layering) are present waterpressures below the surface or the velocity of the water at the surface maybe sufficient to move the soil near the levee toe Sand boils (Figure 126)may appear these are small volcano-like cones of sand built from founda-tion sands carried to the surface by the upward-flowing water Continuederosion of the subsoil may lead to settlement of the levee permitting overtop-ping or to catastrophic uncontrolled subsurface erosion sometimes termeda blowout

1224 Throughseepage

Throughseepage refers to detrimental seepage coming through the leveeabove the ground surface When seepage velocity is sufficient to move materi-als the resulting internal erosion is called piping Piping can occur if there arecracks or voids in the levee due to hydraulic fracturing tensile stresses decayof vegetation animal activity low density adjacent to conduits or any simi-lar location where there may be a preferential path of seepage For piping tooccur the tractive shear stress exerted by the flowing water must exceed thecritical tractive shear stress of the soil In addition to internal discontinuities

Figure 125 Underseepage

454 Thomas F Wolff

Figure 126 Sand boil (photo by T Wolff)

leading to piping high exit gradients on the downstream face of the leveemay cause piping and progressive backward erosion

1225 Surface erosion

Erosion of the levee section during flood events can lead to failure byreduction of the levee section especially near the crown As flood stagesincrease the potential increases for surface erosion from two sources(1) excessive current velocities parallel to the levee slope and (2) erosiondue to wave attack directly against the levee slope Protection is typicallyprovided by a thick grass cover occasionally with stone or concrete revet-ment at locations expected to be susceptible to wave attack During floodsadditional protection may be provided where necessary using dumped rocksnow fence or plastic sheeting

123 Research on geotechnical reliability of levees

The research literature is replete with publications discussing the hydrologicand economic aspects of levees in a probabilistic framework These focusprimarily on setting levee heights in a manner that optimizes the level ofprotection against parameters such as costs benefits damage and possible

Reliability of levee systems 455

loss of life In many of these studies the geotechnical reliability is eitherneglected suggesting that the relative probability of geotechnical failure isnegligible or left for others to provide suitable probabilities of failure forgeotechnical failure modes

Duncan and Houston (1983) estimated failure probabilities for Californialevees constructed from a heterogeneous mixture of sand silt and peat andfounded on peat of uncertain strength Stability failure due to riverside waterload was analyzed using a horizontal sliding block model The factor ofsafety was expressed as a function of the shear strength which is a randomvariable due to its uncertainty and the water level for which there is adefined annual exceedance probability Values for the annual probabilityof failure for 18 islands in the levee system were calculated by integratingnumerically over the joint events of high-water levels and insufficient shearstrength The obtained probability of failure values were adjusted based onseveral practical considerations first they were normalized with respect tolength of levee reach modeled (longer reaches should be more likely to failthan shorter one) and second they were adjusted from relative probabilityvalues to more absolute values by adjusting them with respect to the observednumber of failures

Peter (1982) in Canal and River Levees provides the most completereference-book treatise on levee design based on work in Slovakia Notableamong Peterrsquos work is a more up-to-date and extended treatment ofmathematical and numerical modeling than in most other references Under-seepage safety is cast as a function of particle size and size distribution andnot just gradient alone While Peterrsquos work does not address probabilisticmethods it is of note because it provides a very comprehensive collection ofanalytical models for a large number of potential failure modes which couldbe incorporated into probabilistic analyses

Vrouwenvelder (1987) provides a very thorough treatise on a proposedprobabilistic approach to the design of dikes and levees in the NetherlandsNotable aspects of this work include the following

bull It is recognized that exceedance frequency of the crest elevation is nottaken as the frequency of failure there is some probability of failurefor lower elevations and there is some probability of no failure orinundation above this level as an effort might be made to raise theprotection

bull Considered failure modes are overflowing and overtopping macro-instability (deep sliding) micro-instability (shallow sliding or erosionof the landside slope due to seepage) and piping (as used equivalent tounderseepage as used herein) In an example 11 parameters are taken asrandom variables which are used in conjunction with relatively simplemathematical physical models Aside from overtopping piping (under-seepage) is found to be the governing mode for the section studied slope

456 Thomas F Wolff

stability is of little significance to probability of failure Surface erosiondue to wave attack or parallel currents is not considered

bull The probabilistic procedure is aimed at optimizing the height and slopeangle of new dikes with respect to total costs including constructionand expected losses including property and life In the examplemacro-instability (slope failure) of the inner slope was found to havea low risk much less than 8 times 10minus8 per year Piping was found to besensitive to seepage path length probabilities of failure for piping var-ied but were several orders of magnitude higher (10minus2 to 10minus3per year)Micro-instability (landside sloughing due to seepage) was found to havevery low probabilities of failure Based on these results it was consid-ered that only overtopping and piping need be considered in a combinedreliability evaluation

bull The ldquolength problemrdquo (longer dikes are less reliable than equivalentshort ones) is discussed

The US Army Corps of Engineers first introduced probabilistic concepts tolevee evaluation in the United States in their Policy Guidance Letter No 26(USACE 1991) Prior to that time planning studies for federally funded leveeimprovements to existing non-federal levees were based on the assumptionthat the existing levee was essentially absent and provided no protectionFollowing 1991 it was assumed that the levee was present with some prob-ability which was a function of water elevation The function was definedas a straight line between two points The probable failure point (PFP) wastaken as the water elevation for which the probability of levee failure wasestimated to be 085 and the probable non-failure point (PNP) was definedas the water elevation for which the probability of failure was estimated at015 The only guidance for setting the coordinates of these points was aldquotemplate methodrdquo based only on the geometry of the levee section with-out regard for geotechnical conditions or the engineerrsquos judgment based onother studies

To better define the shape of the function relating probability of failureto water level research by Wolff (1994) led to a report for the US ArmyCorps of Engineers entitled Evaluating the Reliability of Existing LeveesIt reviewed past and current practice for deterministic analysis and designof levees and illustrated how they could be incorporated into a probabilisticmodel Similar to Vrouwenvelder (1987) the report considers slope stabilityunderseepage and through seepage it also considers surface erosion and aprovision for incorporating other information through judgmental proba-bilities The overall probability of failure considering multiple failure modesis treated by assuming the failure modes form a series system The reportwas later incorporated as Appendix B in the Corpsrsquo Engineer TechnicalLetter ETL 1110-2-556 ldquoRisk-Based Analysis in Geotechnical Engineeringfor Support of Planning Studiesrdquo (USACE 1999) It should be noted that the

Reliability of levee systems 457

guidance was intended to apply only to the problem of characterizing exist-ing levees in probabilistic-based economic and planning studies to evaluatethe feasibility of levee improvements as of this writing the Corps has notissued any guidance recommending that probabilistic procedures be usedfor the geotechnical aspects of levee design Some of the report was alsobriefly summarized by Wolff et al (1996) This chapter draws heavily onWolff (1994) and USACE (1999) with some additional consideration ofother failure mode models and subsequent work by others

In 1999 the Corpsrsquo Policy Guidance Letter No 26 (USACE 1999) wasupdated and that version is posted on the Internet at the time of this writingThe current version recommends consideration of multiple failure modes andpermits the PFP to be assigned probability values between 085 and 099and the PNP values between 001 and 085

In 2000 the National Research Council published Risk and Uncertaintyin Flood Damage Reduction Studies (National Research Council 2000)a critical review of the Corps of Engineersrsquo approach to the application ofprobabilistic methods in flood control planning The recommendations ofthe report included the following

the Corpsrsquo risk analysis method (should) evaluate the performance ofa levee as a spatially distributed system Geotechnical evaluation of alevee which may be many miles long should account for the potentialof failure at any point along the levee during a flood Such an analy-sis should consider multiple modes of levee failure (eg overtoppingembankment instability) correlation of embankment and foundationproperties hazards associated with flood stage (eg debris waves floodduration) and the potential for multiple levee section failures during aflood The current procedure treats a levee within each damage reach asindependent and distinct from one reach to the next Further within areach the analysis focuses on the portion of each levee that is most likelyto fail This does not provide a sufficient analysis of the performance ofthe entire levee

Further it notes

The Corpsrsquo new geotechnical reliability model would benefit greatlyfrom field validation hellip The committee recommends that the Corpsundertake statistical ex post studies to compare predictions of geotech-nical levee failure probabilities made by the reliability model againstfrequencies of actual levee failures during floods

The report noted that the simplified concepts of the Corpsrsquo Policy GuidanceLetter No 26 had been updated via the procedures in the 1999 guidanceIt noted that ldquothe numerical difference in risk analysis results compared

458 Thomas F Wolff

to the initial model (PFP and PNP) may not be large The updated modelhowever supports a more complete geotechnical analysis and should replacethe initial modelrdquo It further notes that the Corpsrsquo geotechnical model doesnot separate natural variability and model uncertainty (the former could beuncorrelated from reach to reach with the latter perfectly correlated) doesnot include duration of flooding and remains to be calibrated against thefrequency of actual levee failures Nevertheless no further changes have beenmade to the Corpsrsquo methodology as of this writing in 2006

Apel et al (2004) describe a comprehensive probabilistic approach to floodrisk assessment across the entire spectrum from rainfall way to economicdamage They incorporate the probability of failure vs water elevation func-tions introduced by Wolff (1994) in USACE (1999) but extend them from2D curves to 3D surfaces to include duration of flooding

Buijs et al (2003) describe the application of the reliability-based designtools (PC-Ring) developed in the Netherlands to levees in the UK andprovides some comparison of the issues around flood defenses in the twocountries In this study overtopping was found to be the dominatingfailure mode

A doctoral thesis by Voortman (2003) summarizes the history of dikedesign in the Netherlands Voortman notes that a complete probabilisticanalysis considering all variables was explored for dike design in the 1980sbut that ldquothe legislative safety requirements are still prescribed as probabil-ities of exceedance of the design water level and thus the full probabilisticapproach has not been officially adopted to date Probabilistic methods aresometimes applied but a required failure probability for flood defenses is notdefined in Dutch lawrdquo The required level of safety is still couched in termsof a dike or levee being ldquosaferdquo against a flood elevation of some statisticalfrequency

In summary the literature to date largely consists of probabilistic modelsfor evaluation of the most tractable levee failure modes such as slope stabilityand underseepage Rudimentary methods of systems probability have beenused to consider multiple failure modes and long systems of levee reachesWork remains to better model throughseepage spatial variability systemsreliability and duration of flooding and to calibrate predicted probabilitiesof failure to the frequency of actual failures

124 A framework for levee reliability analysis

1241 Accuracy of probabilistic measures

Before proceeding a note of caution is in order The application of proba-bilistic analysis in civil engineering is still an emerging technology more soin geotechnical engineering and even more so in application to levees Muchexperience remains to be gained and the appropriate form and shape of

Reliability of levee systems 459

probability distributions for most of the relevant parameters are not knownwith certainty The methods described herein should not be expected toprovide ldquotruerdquo or ldquoabsoluterdquo probability-of-failure values but can provideconsistent measures of relative reliability when reasonable assumptions areemployed Such comparative measures can be used to indicate for examplewhich reach (or length) of levee which typical section or which alternativedesign may be more reliable than another They also can be used to judgewhich of several performance modes (seepage slope stability etc) governsthe reliability of a particular levee

1242 Calibration of models

Any reliability-based evaluation must be calibrated ie tested against a suf-ficient number of well-understood engineering problems to ensure that itproduces reasonable results Performance modes known to be problematical(such as seepage) should be found to have a lower reliability than those forwhich problems are seldom observed larger and more stable sections shouldbe found to be more reliable than smaller less stable sections etc As rec-ommended by the National Research Council (2000) as additional analysesare performed by researchers and practitioners on a wide range of real leveecross sections using real data refinements in the procedures may be neededto produce reasonable results

1243 The conditional probability-of-failure function

For the purposes at hand failure will be defined as the unintended floodingof the protected area and thus includes both overtopping and breaching ofthe levee at water elevations below the crown For an existing levee subjectedto a flood the probability of failure pf can be expressed as a function of theflood water elevation and other factors including soil strength permeabilityembankment geometry foundation stratigraphy etc The analysis of leveereliability will start with the development of a conditional probability offailure function given the flood water elevation which will be constructedusing engineering estimates of the probability functions or moments of theother relevant variables

The conditional probability of failure can be written as

pf = Pr(failure |FWE) = f (FWEX1X2 Xn) (121)

In the above equation the first expression (denoting probability of failure)will be used as a shorthand version of the second term The symbol ldquo|rdquois read given and the variable FWE is the flood water elevation In thesecond expression the random variables X1 through Xn denote relevantparameters such as soil strength conductivity top stratum thickness etc

460 Thomas F Wolff

Equation (121) can be restated as follows ldquoThe probability of failure giventhe flood water elevation is a function of the flood water elevation and otherrandom variablesrdquo

Two extreme values of the function can be readily estimated by engineeringjudgment

bull For flood water at the same level as the landside toe (base elevation) ofthe levee the levee is not loaded hence pf = 0

bull For flood water at or near the levee crown (top elevation) pf rarr 100

In principle the probability of failure value may be something less than 10with water at the crown elevation as additional protection can be providedby emergency measures

The question of primary interest however is the shape of the functionbetween these extremes Quantifying this shape is the focus of the proceduresto follow

Reliability (R) is defined as

R = 1 minus pf (122)

hence for any flood water elevation the probability of failure and reliabilitymust sum to unity

For the case of flood water partway up a levee pf could be very near zeroor very near unity depending on engineering factors such as levee geometrysoil strength hydraulic conductivity foundation stratigraphy etc In turnthese differences in the conditional probability of failure function could resultin very different scenarios Four possible shapes of the pf and R functionsare illustrated in Figure 127 For a well-designed and constructed ldquogoodrdquo

000 100Probability of Failure

000100 Reliability

ldquopoorrdquo levee

ldquogoodrdquo levee

Flood waterElevationLevee

Figure 127 Possibble probability of failure vs flood water elevation functions

Reliability of levee systems 461

levee the probability of failure may remain low and the reliability remainhigh until the flood water elevation is rather high In contrast a ldquopoorrdquolevee may experience greatly reduced reliability when subjected to even asmall flood head It is hypothesized that some real levees may follow theintermediate curve which is similar in shape to the ldquogoodrdquo case for smallfloods but reverses to approach the ldquopoorrdquo case for floods of significantheight

1244 Steps in levee system evaluation

To evaluate the annual probability of failure for an entire levee system foursteps are required

Step 1

A set of conditional probability of failure versus flood water elevation func-tions is developed For each levee reach this requires a separate function foreach of the following failure modes slope stability underseepage through-seepage and surface erosion Overtopping and other failure modes (eghuman error failure to close a gate) may be added as desired This leadsto N functions where N = rm r is the number of reaches and m is thenumber of failure modes considered

Step 2

For each reach the functions for the various failure modes developed in step 1are systematically combined into one composite conditional probability offailure function for each reach This leads to r composite functions

Step 3

Given the composite reach function and a probability of exceedance functionfor the flood water elevation the annual probability of levee failure for thereach can be determined by integrating the product of flood probability andconditional probability of failure over the range of water elevations from thelevee toe to the crown

Step 4

Using the results of step 3 an overall annual probability of levee systemfailure can be developed by combining the annual probabilities for the entireset of reaches If only a few reaches dominate (by having comparatively highprobabilities of failure) this step might be simplified by considering onlythose selected reaches In any case length effects must first be consideredbefore the number of reaches is finalized

462 Thomas F Wolff

125 Division into reaches

The first step in any levee analysis or design deterministic or probabilistic isdivision of the levee into reaches for geotechnical analysis Note that thoseanalyzing the economic benefits of a levee may divide the levee into ldquodamagereachesrdquo based on interior topography and value of property at risk andthose studying interior drainage may use another system of dividing a leveeinto reaches for their purposes In a comprehensive risk analysis reach iden-tification may require careful coordination among an analysis team to ensureproper combination of probabilities of failure and associated consequences

Analysis of slope stability and seepage conditions is usually performedusing a two-dimensional perpendicular cross-section of the levee and foun-dation topography and stratigraphy These are in fact an idealization ofthe actual conditions which have some uncertainty at any cross-sectionconsidered and furthermore are variable in the direction of the leveeA geotechnical reach is a length of levee for which the topography andsoil conditions are sufficiently similar that the engineer considers they canbe represented by a single cross-section for analysis This is illustrated inFigure 128 where the solid lines represent the best understanding (alsouncertain) of the foundation soil profile under the levee (often along thelandside toe) and the dashed lines represent the idealization into reachesfor analysis For each reach a cross-section is developed and assumed to berepresentative of the conditions along the reach (See Figure 129) Calculatedperformance measures (factor of safety exit gradients) for the section areassumed to be valid for the length of the reach In design practice reachesare typically several hundred meters long but the lengths and endpointsshould be carefully selected based on topographic and geotechnical infor-mation not on a specified length Within a reach the design cross-sectionis often selected at a point considered to be the most critical such as thegreatest height

In a probabilistic analysis it may be required to further subdivide leveereaches into statistically independent shorter reaches for analysis of overallsystem reliability This will be further discussed later under length effects

Figure 128 Division into reaches

Reliability of levee systems 463

440

420

400

380

360

0minus100 100

10 crown at el 420

1V on 2H side slopes

Clay top blanket

Pervious sand substratum extends to el 3120

Clay levee el 395

Figure 129 Two-dimensional cross-section representing a reach (from Wolff 1994)

126 Step 1 failure mode models

1261 Slope instability

It is desired to define the conditional probability of slope failure as a functionof the flood water elevation FWE To do so the relevant parameters arecharacterized as random variables a performance function is defined thatexpresses the slope safety as a function of those variables and a probabilisticmodel is selected to determine the probability of failure

12611 Random variables

Probabilistic slope stability analysis typically involves modeling most orall of the following parameters as random variables unit weight drainedstrength of sand and other pervious materials and drained and undrainedstrength of cohesive materials such as clays In addition some researchershave included geometric uncertainty of the soil boundaries and model uncer-tainty Table 121 summarizes some typical coefficients of variation for theseparameters The coefficients of variation shown are for the point strengthcharacterizing the strength at a random point However the uncertaintyin the average strength over some spatial distance may govern the stabilitymore than the point strength To consider the effects of spatial correlationin a slope stability analysis it may be necessary to reduce these variances orprovide some other modification in the analysis model

In slope stability problems uncertainty in unit weight usually pro-vides little contribution to the overall uncertainty which is dominatedby soil strength For stability problems it can usually be taken as a

464 Thomas F Wolff

Table 121 Typical coefficients of variation for soil properties used in slope stabilityanalysis

Parameter Coefficient ofvariation ()

Reference

Unit weight γ 3 Hammitt (1966) cited by Harr (1987)4ndash8 Assumed by Shannon and Wilson (1994)

Drained strength ofsand φprime

37ndash93 Direct shear tests Mississippi River Lock andDam No 2 Shannon and Wilson (1994)

12 Schultze (1972) cited by Harr (1987)Drained strength of

clay φprime75ndash101 S tests on compacted clay at Cannon Dam

(Wolff 1985)Undrained strength

of clays su40 Fredlund and Dahlman (1972) cited by

Harr (1987)30ndash40 Assumed by Shannon and Wilson (1994)11ndash45 UU tests on compacted clay at Cannon Dam

Wolff (1985)Strength-to-effective

stress ratio suσ primevo

31 Clay at Mississippi River Lock and Dam No 2Shannon and Wilson (1994)

deterministic variable to reduce the number of random variables and simplifycalculations

Reported coefficients of variation for the drained friction angle (φ) ofsands are in the range of 3ndash12 Lower values can be used where thereis some confidence that the materials considered are of consistent qualityand relative density and the higher values should be used where there isconsiderable uncertainty regarding material type or density For the directshear tests on sands from Lock and Dam No 2 cited in Table 121 (Shannonand Wilson and Wolff 1994) the lower coefficients of variation correspondto higher confining stresses and vice versa

Ladd et al (1977) and others have shown that the undrained strength su(or c) of clays of common geologic origin can be normalized with respect toeffective overburden stress σ prime

vo and overconsolidation ratio OCR and definedin terms of the ratio suσ prime

vo Analysis of test data on clay at Mississippi RiverLock and Dam No 2 (Shannon and Wilson and Wolff 1994) showed thatit was reasonable to characterize uncertainty in clay strength in terms of theprobabilistic moments of the suσ prime

v parameter The ratio of suσ primevo for 24 tested

samples was found to have a mean value of 035 a standard deviation of011 and a coefficient of variation of 31

12612 Deterministic model and performance function

For slope stability the choice of a deterministic analysis model is straight-forward A conventional limit-equilibrium slope stability model is used thatyields a factor of safety defined in terms of the shear strength of the soil

Reliability of levee systems 465

Many well documented programs are available to run the most popularslope stability analysis methods (eg Bishoprsquos method Spencerrsquos methodand the MorgensternndashPrice method) For a specific failure surface manystudies have shown that resulting factors of safety differ little for any of themethods that satisfy moment equilibrium or complete equilibrium How-ever finding the geometry of the critical failure surface is another matter Aslevees are typically located over stratified alluvial foundations the selecteddeterministic model and computer program should be capable of analyzingfailure surfaces with planar bases (eg ldquowedgerdquo surfaces) and other generalshapes not only circular arcs Furthermore there remains a philosophicalquestion as to what exactly is the ldquocritical surfacerdquo In many probabilisticslope stability studies the critical deterministic surface (lowest FS) has beenfound first and the probability of failure for that surface has been calcu-lated However it can be argued that one should locate the surface with thehighest probability of failure which can be greatly different when soils withdifferent levels of uncertainty are present Hassan and Wolff (1999) haveinvestigated this problem and presented an approximate empirical methodto locate the critical probabilistic surface when the available programs cansearch only for the surface of minimum FS

Depending on the probabilistic model being used it may be sufficient tobe able to simply determine the factor of safety for various realizations of therandom variables and use these to determine the probability that the factorof safety is less than one However some methods such as the advancedfirst-order second-moment method (AFOSM) may require a performancefunction that assumes a value of zero at the limit state For the latter theperformance function for slope stability can be taken as

FS minus 1 = 0 (123)

or

In FS = 0 (124)

The second expression is preferred as it works consistently with the assump-tion that the factor of safety is lognormally distributed which is appealingas FS cannot assume negative values

12613 Probabilistic modeling

Having defined a set of random variables a deterministic model and per-formance function a probabilistic model is then needed to determine theprobability of failure (or probability that the performance function assumesa negative value) Given the probability distributions or at least the proba-bilistic moments of the random variables the probabilistic model determines

466 Thomas F Wolff

the distribution or at least the probabilistic moments of the factor of safety orperformance function Details are described elsewhere in this book but com-mon models include the first-order reliability method (FORM) the advancedfirst-order second-moment method (AFOSM) both based on Taylorrsquos seriesexpansion and simulation (or Monte Carlo) methods

In the examples in this chapter a variation called the Taylorrsquos series ndashfinite difference method used by the Corps of Engineers (USACE 1995) isused to calculate the mean and standard deviation of the factor of safetyknowing the mean and standard deviations of the random variables TheTaylorrsquos series is expanded around the mean value of the FS function not theldquodesign pointrdquo found by iteration in the AFOSM Hence the nonlinearity ofthe performance function is not directly considered This deficiency is partlymitigated by determining the required partial derivatives numerically over arange of plus to minus one standard deviation rather than a tangent at themean Using this large increment captures some of the information aboutthe functional shape

12614 Example problem

The example to follow is taken from Wolff (1994) The assumed levee cross-section is shown in Figure 1210 and consists of a sand levee on a thin clay topstratum overlaying thick foundation sands Units are in English system feetconsistent with the original reference (1 ft = 03048 m) Despite their highconductivity sand levees have been used in a number of locations where claymaterials are scarce a notable example is along the middle Mississippi Riverin western Illinois Three random variables were defined and their assumedprobabilistic moments were assigned as shown in Table 122

440

420

400

380

360

0minus100 100

10 crown at el 420

1V on 25 side slopes8 ft clay top blanket

80 ft thick pervious sand substratumExtends to el 3120

Sand levee with clay face

Figure 1210 Cross-section for slope stability and underseepage example (elevations anddistances in feet)

Reliability of levee systems 467

Table 122 Random variables for slope stability example problem

Parameter Expectedvalue

Standarddeviation

Coefficient ofvariation ()

Friction angle of sand levee embankment φemb 32 deg 2 deg 67Undrained strength of clay foundation c or su 800 lbft2 320 lbft2 40Friction angle of sand foundation φfound 34 deg 2 deg 59

For slope stability analysis the piezometric surface in the embankmentsand was approximated as a straight line from the point where the floodwater intersects the riverside slope to the landside levee toe The piezometricsurface in the foundation sands was taken as that obtained for the expectedvalue condition in the underseepage analysis reported later in this chapterIf desired the piezometric surface could be modeled as an additional randomvariable using the results of a probabilistic seepage analysis

Using the Taylorrsquos seriesndashfinite difference method seven runs of a slopestability program are required for each flood water level considered one forthe expected value case and two runs to determine the variance componentof each random variable

The seven resulting solutions and partial variance terms are summarizedfor FWE = 400 ft (base of the levee) in Table 123 The seven failure surfacesare nearly coincident as seen in Figure 1211 The expected value of thefactor of safety is the factor of safety calculated using the expected values ofall variables

E[FS] = 1568 (125)

The variance of the factor of safety is calculated per USACE (1995) usinga finite-difference increment of 2σ

VarFS=(partFSpartφe

)2

σ 2φe

+(partFSpartc

)2

σ 2c +(partFSpartφf

)2

σ 2φf

=(

FS+minusFSminus2σφe

)2

σ 2φe

+(

FS+minusFSminus2σc

)2

σ 2c +(

FS+minusFSminus2σφf

)2

σ 2φf

=(

FS+minusFSminus2

)2

+(

FS+minusFSminus2

)2

+(

FS+minusFSminus2

)2

=(

1448minus16932

)2

+(

1365minus15682

)2

+(

1568minus15672

)2

=0025309 (126)

468 Thomas F Wolff

Water Elev = 400

385

390

395

400

405

410

415

420

425

minus10 0 10 20 30 40 50 60

Horizontal Distance (feet)

Ele

vati

on

(fe

et)

Figure 1211 Failure surfaces for slope stability example

Table 123 Example slope stability problem undrained conditions water at elevation 400(H = 0 ft)

Run (levee) c (clay) (found) FS Variance Percent of total variance

1 32 800 34 15682 30 800 34 14483 34 800 34 1693 0015006 59294 32 480 34 13655 32 1120 34 1568 00010302 40716 32 800 32 15687 32 800 36 1567 25 times 10minus7 00

Total 025309 1000

In the above expression φe is the friction angle of the embankment sand cis the undrained strength (or cohesion) of the top blanket clay and φf is thefriction angle of the foundation sand

The factor of safety is assumed to be a lognormally distributed randomvariable with E[FS] = 1568 and σFS = 002530912 = 0159 From the prop-erties of the lognormal distribution the coefficient of variation of the factorof safety is

VFS = σFS

E[FS] = 01591568

= 01015 (127)

Reliability of levee systems 469

The standard deviation of ln FS is

σlnFS =radic

ln(1 + V2FS) =

radicln(1 + 010152) = 01012 (128)

The expected value of ln FS is

E[lnFS] = lnE[FS]minus σ 2lnFS

2= ln1568 minus 00102

2= 0447 (129)

The reliability index is then

β = E[lnFS]σlnFS

= 044701012

= 4394 (1210)

As FS is assumed lognormally distributed ln FS is normally distributed Fromthe cumulative distribution function of the standard normal distributionevaluated at minusβ the conditional probability of failure for water at elevation400 is

Prf = 6 times 10minus6

This is illustrated in Figure 1212 The results for all water elevations aresummarized in Table 124

12615 Interpretation

Note that the calculated probability of failure infers that the existinglevee is taken to have approximately a six in one million probability of

E[ln FS] = 0445

ln FS

βσln FS = (4394)(01012)

Pr(f) = 0000006

limit stateln 1 = 00

Figure 1212 Probability of failure for slope stability example water at elevation 400

470 Thomas F Wolff

Table 124 Example slope stability problem undrained conditions conditional probabilityof failure function

Water elevation E[FS] σFS Prf

4000 1568 0159 4394 6 times10minus6

4050 1568 0161 4351 7 times10minus6

4100 1568 0170 4114 19 times10minus5

4150 1502 0107 5699 6 times10minus9

4200 1044 0084 0499 03087

instability with flood water to its base elevation of 400 even though itmay in fact be existing and observed stable under such conditions Thereliability index model was developed for the analysis of yet-unconstructedstructures When applied to existing structures it will provide probabili-ties of failure greater than zero This can be interpreted as follows givena large number of different levee reaches each with the same geometryand with the variability in the strength of their soils distributed accord-ing to the same density functions about six in one million of those leveesmight be expected to have slope stability problems Expressing the reliabilityof existing structures in this manner provides a consistent probabilis-tic framework for use in economic evaluation of improvements to thosestructures

12616 Conditional probability of failure function

The probabilities of failure for all water elevations are summarized inTable 124 and plotted in Figure 1213 Contrary to what might beexpected the reliability index reaches a maximum and the probabilityof failure reaches a minimum at a flood water elevation of 4150 orthree-quarters the levee height For water above 410 the critical failuresurface moves from the clay foundation materials up into the embank-ment sands As this occurs the factor of safety becomes more dependenton the shear strength of the embankment sands and less dependent onthe shear strength of the foundation clays Although the factor of safetydrops as water rises from 410 to 415 the reliability index increases Asthere is more certainty regarding the strength of the sand (the coeffi-cient of variations are about 6versus 40 for the clay) this indicatesthat a sand embankment with a low factor of safety can be more reli-able than a clay embankment with a higher factor of safety Finally asthe water surface reaches the top of the levee at 420 the increasing seep-age forces and reduction in effective stress leads to the lowest values forFS and β

Reliability of levee systems 471

0000000001

000000001

00000001

0000001

000001

00001

0001

001

01

10 5 10 15 20 25

H ft

Pr(

failu

re)

Figure 1213 Conditional probability of failure function for slope stability

1262 Underseepage

12621 Deterministic model

Underseepage is a common failure mode for levees and floodwalls that hasbeen widely researched and for which analysis methods have been widelypublished (Peter 1982 USACE 2000) The example presented here uses anequation-based deterministic method developed by the Corps of Engineers(USACE 2000) for a specific but common set of idealized foundation con-ditions a relatively thin top semi-pervious top stratum of uniform thicknessoverlying a thick pervious substratum of uniform thickness A typical cross-section is shown in Figure 1214 For this case the exit gradient io at thelandside toe can be calculated as

io = ho

z(1212)

where

ho is the residual head at the levee toe andz is the effective thickness of the landside top blanket

The residual head can be calculated as

ho = H x3

x1 + x2 + x3(1213)

472 Thomas F Wolff

Landside ho

z

d

Riverside

Top blanket

Pervious substratum

piezometricsurface

X1 X2 X3

Figure 1214 Terms used in Crops of Engineersrsquo underseepage model

where

H is the net height of water on leveex1 is the effective seepage entrance distancex2 is the base width of levee andx3 is the effective seepage exit distance

For uniform blanket conditions of infinite landside length x3 can becalculated as

x3 =radic

kf

kbzd (1214)

where kf is the horizontal permeability of the pervious substratum kb isthe vertical permeability of the semi-pervious top blanket z is the thicknessof the top blanket and d is the thickness of the substratum The coefficientof permeability k is a combined property of the soil and the permeant andis now more commonly referred to as hydraulic conductivity The earliernomenclature is retained for consistency with the original references

Procedures for determining these values for a variety of conditions can befound in USACE (2000)

The exit gradient io is compared to the critical gradient ic which can becalculated as follows

ic = γ primeγw

(1215)

Reliability of levee systems 473

where

γ prime is the effective of submerged unit weight of the top stratum andγw is unit weight of water

An exit gradient in excess of the critical gradient implies failure Exit gra-dients can also be calculated for much more complex foundation conditionsusing methods such as finite element analysis

The formulation above considers the initiation of sand boils or piping tobe related only to gradient conditions Other researchers (eg Peter 1982)have proposed analysis models that include other parameters notably grainsize and grain size distribution

12622 Random variables

Table 125 provides some typical coefficients of variation for the parametersused in underseepage analysis

12623 Example problem

The levee cross section for this example is the same as that previouslyshown in Figure 1210 Four random variables are considered kf kb zand d The assigned probabilistic moments for these variables are given inTable 126

As borings are not available at every possible cross-section there is someuncertainty regarding the thicknesses of the soil strata at the critical locationHence z and d are modeled as random variables Their standard deviationsare set by engineering judgment regarding the probable range of actual valuesat the site For the blanket thickness z assigning the standard deviation at20 ft models a high probability that the actual blanket thickness will bebetween 40 and 120 ft ( plusmn2 standard deviations) and a very high probabilitythat the blanket thickness will be between 20 and 140 ft ( plusmn3 standard

Table 125 Typical coefficients of variation for soil parameters used in underseepageanalysis

Parameter Coefficient ofvariation ()

Reference

Coefficient ofpermeability k

90 For saturated soils Nielson et al (1973) citedby Harr (1987)

Permeability of topblanket clay kb

20 Derived from assumed distribution Shannonand Wilson (1994)

Permeability offoundation sands kf

20ndash275 For average permeability over thickness ofaquifer Shannon and Wilson (1994)

Permeability ratiokf kb

40 Derived using 30 for kf and kb seeAppendix A

474 Thomas F Wolff

Table 126 Random variables for underseepage example problem

Parameter Expected value Standard deviation Coefficient ofvariation()

Substratum permeability kf 1000 times 10minus4 cms 300 times 10minus4cms 30Top blanket permeability kb 1 times 10minus4 cms 03 times 10minus4cm s 30Blanket thickness z 80 ft 20 ft 25Substratum thickness d 80 ft 5 ft 625

deviations) For the substratum thickness d the two standard deviation rangeis 70ndash90 ft and the three standard deviation range is 65ndash95 ft For analysis ofreal levee systems it is suggested that the engineer review the geologic historyand stratigraphy of the area and assign a range of likely strata thicknessesthat are considered the thickest and thinnest probable values These can thenbe taken to correspond to plus and minus 25 or 30 standard deviations fromthe expected value

As the exit gradient is a function of the permeability ratio kf kb and notthe absolute magnitude of the values the number of analyses can be reducedby treating the permeability ratio as a single random variable To do so it isnecessary to determine the coefficient of variation of the permeability ratiogiven the coefficient of variation of the two permeability values Using severalmethods to determine the probabilistic moments of the ratio of two randomvariables it appears reasonable to take the expected value of the permeabilityratio as 1000 and its coefficient of variation as 40 This corresponds to astandard deviation of 400 for kf kb

The performance function is taken as the exit gradient landside of thelevee and the value of the critical gradient assumed to be 085 is taken asthe limit state

For each water elevation the exit gradient is calculated using the Taylorrsquosseriesndashfinite difference method This requires considering seven combinationsof the input parameters Results for a 20 ft head on the levee are summarizedin Table 127

For Run 1 the three random variables are all taken at their expectedvalues First the effective exit distance x3 is calculated as

x3 =radic

kf

kbmiddot z middot d = radic

1000 middot 8 middot 80 = 800ft (1216)

As the problem is symmetrical the distance from the riverside toe to theeffective source of seepage entrance x1 is also 800 ft

From the geometry of the given problem the base width of the levee x2is 110 ft

Reliability of levee systems 475

Table 127 Underseepage example Taylorrsquos series method water at elevation 420 (H =20 ft)

Run kf kb z d ho i Variance Percent of total variance

1 1000 80 800 9357 11702 600 80 800 9185 11483 1400 80 800 9451 1181 0000276 0304 1000 60 800 9265 15445 1000 100 800 9421 0942 0090606 99696 1000 80 750 9337 11677 1000 80 850 9375 1172 0000006 001

Total 0090888 1000

The net residual head at the levee toe is

ho = Hx3

x1 + x2 + x3= 20 middot 800

800 + 110 + 800= 9357ft (1217)

and the landside toe exit gradient is

i = ho

z= 9357

80= 1170 (1218)

For the second and third analyses the permeability ratio is adjusted to theexpected value plus and minus one standard deviation while the other twovariables are held at their expected values These are used to determine thecomponent of the total variance related to the permeability ratio

(parti

part(kfkb)

)2

σ 2kfkb

asymp(

i+ minus iminus2σkfkb

)2

σ 2kfkb

=(

i+ minus iminus2

)2

=(

1181 minus 11482

)2

= 0000276

(1219)

A similar calculation is performed to determine the variance componentscontributed by the other random variables

When the variance components are summed the total variance of the exitgradient is obtained as 0090888 Taking the square root of the variancegives the standard deviation of 0301

The exit gradient is assumed to be a lognormally distributed random vari-able with probabilistic moments E[i] = 1170 and σi = 0301 Using theproperties of the lognormal distribution the equivalent normally distributedrandom variable has moments E[ln i] = 0124 and σln i = 0254

476 Thomas F Wolff

The critical exit gradient is assumed to be 085 The probability of failureis then

Prf = Pr (ln i gt ln 085) (1220)

This probability can be found by first calculating the standard normalizedvariate z

z = ln icrit minus E[ln i]σln i

= minus016252 minus 0124490253629

= minus1132 (1221)

For this value the cumulative distribution function F(z) is 0129 and repre-sents the probability that the gradient is below critical The probability thatthe gradient is above critical is

Prf = 1 minus F(z) = 1 minus 0129 = 0871 (1222)

Note that the z value is analogous to the reliability index β and it couldbe stated that β = minus113 The probability calculation is illustrated inFigure 1215

Repeating this procedure for a range of flood water elevations the condi-tional probability of failure function can be plotted as shown in Figure 1216The probability of failure is very low until the head on the levee exceeds about8 ft after which it curves up sharply It reverses curvature when the headreaches about 15 ft and the probability of failure is near 50 When theflood water elevation is near the top of the levee the conditional probabilityof failure approaches 87

The results of one intermediate calculation should be noted As indicatedby the relative size of the variance components shown in Table 127 virtu-ally all of the uncertainty is in the top blanket thickness A similar effect wasfound in other underseepage analyses by the writer reported in the UpperMississippi River report (Shannon and Wilson and Wolff 1994) where

E[ln i] = 0124

ln i crit = minus0163

ln i

minus1132 sigma ln i

Figure 1215 Probability of failure for underseepage example water at el 420

Reliability of levee systems 477

0

01

02

03

04

05

06

07

08

09

1

0 5 10 15 20 25H ft

Pr(

failu

re)

Figure 1216 Conditional probability of failure function for underseepage example

the top blanket thickness was treated as a random variable its uncertaintydominated the problem This has two implications

bull Probability of failure functions for preliminary economic analysis mightbe developed using a single random variable the top blanket thickness z

bull In expending resources to design levees against underseepage failureobtaining more data to determine the blanket thickness profile may bebetter justified than obtaining more data on material properties

1263 Throughseepage

Three types of internal erosion or piping can occur as a result of seepagethrough a levee

bull Cracks in a levee due to hydraulic fracturing tensile stresses decay ofvegetation animal activity along the contours of hydraulic structuresetc can all provide a preferential seepage path along which piping mayoccur For piping (movement of soil material) to occur the tractive shearstress exerted by the flowing water must exceed the critical tractive shearstress of the soil

bull High exit gradients on the downstream face of the levee may cause pipingand possible progressive backward erosion

bull Internal erosion or removal of fine grains by excessive seepage forces mayoccur This type of piping occurs when the seepage gradient exceeds acritical value

478 Thomas F Wolff

Well-constructed clay levees are generally considered resistant againstinternal erosion but such erosion can occur where there are defects suchas above For sand levees throughseepage is a common occurrence duringflood Water flowing through the embankment will cut small channels onthe landside slope If the slope is sufficiently flat the levee will hold and theslope can be re-dressed after the flood Construction of sand levees is rela-tively common on the middle Mississippi River for levees constructed by theRock Island District of the US Army Corps of Engineers

12631 Deterministic models

There is no single widely accepted analytical technique or performance func-tion in common use for predicting internal erosion Review of various modelsindicates that erosion susceptibility may be taken to be a function of somecombination of the following parameters

bull permeability or hydraulic conductivity kbull hydraulic gradient ibull porosity nbull critical tractive stress τc (the shear stress required for flowing water to

dislodge a soil particle)bull particle size expressed as some representative size such as D50 or

D85 andbull friction angle φ or angle of repose

Essentially the models use the gradient critical tractive stress and particlesize to determine whether the shear stresses induced by seepage head lossare sufficient to dislodge soil particles and use the gradient permeabilityand porosity to determine whether the seepage flow rate is sufficient to carryaway or transport the particles once they have been dislodged

Very fine sands and silt-sized materials are among the most erosion-susceptible soils This arises from their having a critical balance of relativelyhigh permeability low particle weight and low critical tractive stress Parti-cles larger than fine sand sizes are generally too heavy to be moved easilyas particle weight increases with the cube of the diameter Particles smallerthan silts (ie clay sizes) although of light weight may have relatively largeelectro-chemical forces acting on them which can substantially increase thecritical tractive stress τc and also have sufficiently small permeability as toinhibit particle transport in significant quantity

12632 Schwartzrsquos method

As analytical models for throughseepage are complex and not well provenan illustrative example based on a design procedure for the landside slope of

Reliability of levee systems 479

sand levees developed by Schwartz (1976) will be used herein Where thereis experience with other erosion models they could be substituted for theillustrated method using the same approach of defining the probability offailure as the probability that the performance function crosses the limit stateSchwartzrsquos method includes some elements of erosion analysis However theresult of the method is a parameter to determine the need for providing toeberms according to semi-empirical criteria rather than to directly determinethe threshold of erosion conditions or predict whether erosion will occurPresumably some conservatism is present in the berm criteria and thus thecriteria do not represent a true limit state Accordingly the actual probabilityof failure should be somewhat lower than calculated

The procedure involves the calculation of two parameters the maximumerosion susceptibility M and the relative erosion susceptibility R The cal-culated values are compared to critical combinations for which toe bermsare considered necessary The parameters are functions of the embankmentgeometry and soil properties First the vertical distance of the seepage exitpoint on the downstream slope yc is determined using the well-known solu-tion for the ldquobasic parabolardquo by L Casagrande Two parameters λ1 and λ2are then calculated as

λ1 =cosβ minus γw

γbsinβ tan(β minus δ) minus γsat

λb

sinβ

tanφ(1223)

λ2 =γw sin07 β( n

149

)06 [k tan(β minus δ)

]06 (1224)

where β is the downstream slope angle

δ is taken as zero for a horizontal exit gradientn is Manningrsquos coefficient for sand typically 002γsat is the saturated density of the sand in lbft3

γb is the submerged effective density of the sand in lbft3

k is the sand permeability in ftsφ is the friction angle

It is important to note that the parameter λ2 is not dimensionless and theunits stated above must be used

The erosion susceptibility parameters are then calculated as

M =λ2y06e

λ1τco(1225)

R =ye minus(λ1τco

λ2

)167

H(1226)

480 Thomas F Wolff

In the above equations τco is the critical tractive stress which can be takenas about 003 lbft2 (1436 dynescm2) for medium sand and H is thefull embankment height measured in feet According to Schwartzrsquos crite-ria toe berms are recommended when M and R values fall above the shadedregion shown in Figure 1217 To simplify probabilistic analysis Shannonand Wilson and Wolff (1994) suggested replacing this region with a linearapproximation (also shown in Figure 1217) and taken to be the limit stateThe approximation is

M + 144R minus 130 = 0 (1227)

Positive values of the expression to the left of the equals sign indicate theneed for toe berms

12633 Example problem

The same embankment section previously analyzed will be used as an exam-ple of a throughseepage analysis Random variables were characterized asshown in Table 128 The method which assesses erosion at the landsideseepage face was numerically unstable (λ1 becomes negative) for the slopespreviously assumed To make the problem stable for purposes of illustra-tion the slopes had to be flattened to 1V on 3H riverside and 1V on 5Hlandside

The results for a 20 ft water height are summarized in Table 129 The mostsignificant random variables based on descending order of their variancecomponents are the unit weight the friction angle and the permeability

01 02 03 04 05 06 07 08 09 10

Relative Erosion Susceptibility R

0

5

10

15

20

0

Maximum Erosion Susceptibility M

No bermNo bermNo berm

Figure 1217 Schwartzrsquos method berm criteria and assumed linear limit state

Reliability of levee systems 481

Table 128 Random variables for throughseepage example

Variable Expected value Coefficient of variation()

Manningrsquos coefficient n 002 10Unit weight γsat 125 lbft3 8Friction angle φ 30 deg 67Coefficient of permeability k 2000 times 10minus4cms 30Critical tractive stress τ 18 dynescm2 10

Table 129 Results of internal erosion analysis example problem (modified to flatterslopes) H = 20 ft

n γsat φ k τ Performancefunction

Variancecomponent

Percent of totalvariance

002 125 30 2000 18 175240022 125 30 2000 18 184910018 125 30 2000 18 16515 09761 21002 135 30 2000 18 14798002 115 30 2000 18 23667 196648 429002 125 32 2000 18 14817002 125 28 2000 18 22179 135498 295002 125 30 2600 18 20321002 125 30 1400 18 14339 8961 195002 125 30 2000 198 16046002 125 30 2000 162 19369 27606 60

Total 458974 1000

The effects of Manningrsquos coefficient and the critical tractive stress at leastfor the coefficients of variation assumed are relatively insignificant

The reliability index was calculated as the expected value of the per-formance function divided by the standard deviation of the performancefunction The probability of failure was calculated using the reliability indexand assuming the performance function was normally distributed (Note thatthe assumption of a lognormally distributed performance function was notused for this failure mode as negative values of the performance function arepermitted)

When the probabilities of failure for various flood water elevations areplotted the conditional probability of failure function shown in Figure 1218is obtained Again it takes the expected reverse-curve shape Below heads10 ft or about half the levee height the probability of failure against throughseepage failure is virtually nil The probability of failure becomes greater than05 for a head of about 165 feet and approaches unity at the full head of20 ft

482 Thomas F Wolff

00

01

02

03

04

05

06

07

08

09

10

0 5 10 15 20 25

H ft

Pr(

failu

re)

Figure 1218 Conditional probability of failure function for throughseepage example

12634 Surface erosion

As flood stages increase the potential increases for surface erosion from twosources

bull erosion due to excessive current velocities parallel to the levee slope andbull erosion due to wave attack directly against the levee slope

Preventing surface erosion requires providing adequate slope protection typ-ically a thick grass cover and stone revetment at locations expected to besusceptible to wave attack During flood emergencies additional protectionmay be provided where necessary using dumped rock snow fence or plasticsheeting

12635 Analytical model

Although there are established criteria for determining the need for slopeprotection and for designing slope protection they are not in the form of alimit state or performance function (ie one does not typically calculate afactor of safety against scour) To perform a reliability analysis one needs todefine the problem as a comparison between the probable velocity or waveheight and the velocity or wave height that will result in damaging scourAs a first approximation for the purpose of illustration this section will usea simple adaptation of Manningrsquos formula for average flow velocity andassume that the critical velocity for a grassed slope can be expressed by anexpected value and coefficient of variation

Reliability of levee systems 483

For channels that are very wide relative to their depth (width gt 10timesdepth) the flow velocity can be expressed as

V = 1486y23S12

n(1228)

where y is the depth of flowS is the slope of the energy line andn is Manningrsquos roughness coefficient

It will be assumed that the velocity of flow parallel to a levee slope forwater heights from 0 to 20 ft can be approximated using the above formulawith y taken from 0 to 20 ft For real levees in the field it is likely thatbetter estimates of flow velocities at the location of the riverside slope canbe obtained by more detailed hydraulic models

The following probabilistic moments are assumed More detailed and site-specific studies would be necessary to determine appropriate values

E[S] =00001 VS = 10

E[n] =003 Vn = 10

It is assumed that the critical velocity that will result in damaging scour canbe expressed as

E[Vcrit] = 50fts Vvcrit = 20

Further research is necessary to develop guidance on appropriate values forprototype structures

The Manning equation is of the form

G(x1x2x3 ) = axg11 xg2

2 xg33 (1229)

For equations of this form Harr (1987) shows that the probabilisticmoments can be determined using a special form of Taylorrsquos series approx-imation he refers to as the vector equation In such cases the expectedvalue of the function is evaluated as the function of the expected valuesThe coefficient of variation of the function can be calculated as

V2G = g2

1V2(x1) + g22V2(x2) + g2

3V2(x3) +middotmiddot middot (1230)

For the case considered the coefficient of variation of the flow velocityis then

VV =radic

V2n + ( 1

4 )V2S (1231)

Note that although the velocity increases with flood water height y thecoefficient of variation of the velocity is constant for all heights

484 Thomas F Wolff

Knowing the expected value and standard deviation of the velocity andthe critical velocity a performance function can be defined as the ratio ofcritical velocity to the actual velocity (ie the factor of safety) and the limitstate can be taken as this ratio equaling the value 10 If the ratio is assumedto be lognormally distributed the reliability index is

β =ln(

E [C]E [D]

)radic

V2C + V2

D

=ln

(E[Vcrit]

E [V]

)radic

V2Vcrit + V2

V

(1232)

and the probability of failure can be determined from the cumulativedistribution function for the normal distribution

The assumed model and probabilistic moments were used to constructthe conditional probability of failure function in Figure 1219 It is againobserved that a typical levee may be highly reliable for water levels up toabout one-half the height and then the probability of failure may increaserapidly

127 Step 2 Reliability of a reach ndash combiningthe failure modes

Having developed a conditional probability of failure function for each con-sidered failure mode the next step is to combine them to obtain a totalor composite conditional probability of failure function for the reach that

0

001

002

003

004

005

006

007

008

009

0 5 10 15 20

H (feet)

Pr(

failu

re)

Figure 1219 Conditional probability of failure function for surface erosion example

Reliability of levee systems 485

combines all modes As a first approximation it may be assumed that thefailure modes are independent and hence uncorrelated This is not necessar-ily true as some of the conditions increasing the probability of failure forone mode may likely increase the probability of failure by another Notablyincreasing pressures due to underseepage will adversely affect slope stabilityHowever there is insufficient research to better quantify such possible cor-relation between modes Assuming independence considerably simplifies themathematics involved and may be as good a model as can be expected atpresent

1271 Judgmental evaluation of other modes

Before combining failure mode probabilities consider the probability of fail-ure due to other circumstances not readily treated by analytical modelsDuring a field inspection one might observe other items and features thatmight compromise the levee during a flood event These might include animalburrows cracks roots and poor maintenance that might impede detectionof defects or execution of flood-fighting activities To factor in such infor-mation a judgment-based conditional probability function could be addedby answering the following question

Discounting the likelihood of failure accounted for in the quantitativeanalyses but considering observed conditions what would an experi-enced levee engineer consider the probability of failure of this levee fora range of water elevations

For the example problem considered herein the function in Table 1210was assumed While this may appear to be guessing leaving out such infor-mation has the greater danger of not considering the obvious Formalizedtechniques for quantifying expert opinion exist and merit further researchfor application to levees

Table 1210 Assumed conditional probabilityof failure function for judgmental evaluationof observed conditions

Flood water elevation Probability of failure

4000 04050 0014100 0024150 0204175 0404200 080

486 Thomas F Wolff

1272 Composite conditional probability of failurefunction for a reach

For N independent failure modes the reliability or probability of no failureinvolving any mode is the probability of no failure due to mode 1 and nofailure due to mode 2 and no failure due to mode 3 etc As and impliesmultiplication the overall reliability at a given flood water elevation is theproduct of the modal reliability values for that flood elevation or

R = RSSRUSRTSRSERJ (1233)

where the subscripts refer to the identified failure modes Hence theprobability of failure at any flood water elevation is

Pr(f) =1 minus R

=1 minus (1 minus pSS)(1 minus pUS)(1 minus pTS)(1 minus pSE)(1 minus pJ)(1234)

The total conditional probability of failure function is shown in Figure 1220It is observed that probabilities of failure are generally quite low for waterelevations less than one-half the levee height then rise sharply as water levelsapproach the levee crest While there is insufficient data to judge whetherthis shape is a general trend for all levees it has some basis in experienceand intuition

00

02

04

06

08

10

12

395 400 405 410 415 420 425

Flood Water Elevation

Pro

bab

ility

of

Fai

lure

Underseepage

Slope Stability

Through Seepage

Surface Erosion

Judgment

Combined

Figure 1220 Combined conditional probability of failure function for a reach

Reliability of levee systems 487

128 Step 3 Annualizing the probability of failure

Returning to Equation (121) the probability of levee failure is conditionedon the flood water elevation FWE which is a random variable that varieswith time Flood water elevation is usually characterized by an annualexceedance probability function which gives the probability that the highestwater level in a given year will exceed a given elevation

It is desired to find the annual probability of the joint event of floodingto any and all levels and a levee failure given that level An example ofthe methodology is provided in Table 1211 The first column shows thereturn period and the second shows the annual probability of exceedancefor the water level shown in the third column The return period is simplythe inverse of the annual exceedance probability These values are obtainedfrom hydrologic studies In the fourth column titled lumped increment allwater elevations between those in the row above and the row below areassumed to be lumped as a single elevation at the increment midpoint Theannual probability of the maximum water level in a given year being withinthat increment can be taken as the difference of the annual exceedance prob-abilities for the top and bottom of the increment For example the annualprobability of the maximum flood water elevation being between 408 and410 is

Pr(408 lt FWE lt 410) =Pr(FWE gt 408) minus Pr(FWE gt 410)

=0500 minus 0200

=0300

(1235)

Table 1211 Annual probability of flooding for a reach

Returnperiod (yr)

AnnualPr(Exceed)

Water elev Lumpedincrement

AnnualPr(FWE)

Pr(F |FWE) Pr (F)

lt= 408 0505 002 0010102 0500 408

409 +minus 1 0300 003 0009005 0200 410

411 +minus 1 0100 008 00080010 0100 412

413 +minus 1 0050 045 00225020 0050 414

415 +minus 1 0030 072 00216050 0020 416

417 +minus 1 0010 086 000860100 0010 418

419 +minus 1 0005 097 000485200 0005 420

Sum 1000 008465

488 Thomas F Wolff

The flood water elevations for this increment are lumped at elevation 4090and hence the annual probability of the maximum flood water being eleva-tion 4090 is 0300 The annual probability of each flood water elevationis multiplied by the conditional probability of failure given that flood waterelevation (sixth column) to obtain the annual probability of levee failure forthat FWE (seventh column) For this example the values in the sixth columnare taken from Figure 1216 The annual probability of the joint event ofmaximum flood being between 408 and 410 and the levee failing under thisload is

Pr(408 lt FWE lt 410) times Pr(failure|FWE) = 0300 times 003 = 00090(1236)

Finally the values in column seven are summed to obtain the annual prob-ability of the joint event of flooding and failure for all possible waterelevations This can be expressed as

AnnualPr(f) =sum

all FWE

AnnualPr(FWE) times Pr(failure|FWE)FWE

(1237)

As the water level increment becomes small the equation above approaches

AnnualPr(f) =int

all FWE

AnnualPr(FWE) times Pf (failure|FWE)dFWE

(1238)

From Table 1211 the annual probability of failure for this reach is 008465which considers all flood levels (for which annual probabilities decrease withheight) and the probability of failure associated with each flood level (whichincrease with height) Note that this is a rather high probability the exampleproblem was deliberately selected to be of marginal safety to provide relativelarge numbers to easily view the mathematics

Baecher and Christian (2003 488ndash500) use an event-tree approach toillustrate an approach similar to that of Equations (1237) and (1238) Theprocess is started with probabilities of different river discharges Q For eachdischarge river stage may be uncertain For each river stage the levee mayor may not fail by various modes Following all branches of the event treeleads to one of two events failure or non-failure Summing up all the branchprobabilities leading to failure is equivalent to the equations above

Reliability of levee systems 489

129 Step 4 The Levee as a system ndash combiningthe reaches

Having an annual probability of failure for each reach the final step is todetermine the overall annual probability of failure for the entire levee systemwhich is a set of reaches

If a levee were ldquoperfectly uniformrdquo in cross-section and soil profile alongits length intuition would dictate that the probability of a failure for a verylong levee should be greater than that for a short identical levee as morelength of levee is at risk

First it will be assumed that each reach is statistically independent of allothers and then that assumption will be examined further For each reach anannual probability of failure has been determined Subtracting these valuesfrom unity the annual reach reliability Ri is obtained If the levee systemis considered to be a series system of discrete independent reaches such aslinks in a chain the system reliability is the product of the reliabilities foreach link and the same methods can be used for combining probabilities forreaches as was used for combining modes hence

R = R1R2R3 RN (1239)

where the subscripts refer to the separate reaches Hence for a given waterlevel the probability of failure for the system is

Pr(f) =1 minus R

=1 minus (1 minus p1)(1 minus p2)(1 minus p3) (1 minus pN)(1240)

Where the p values are ldquovery smallrdquo the above equation approaches the sumof the pi values

The problem remaining is to determine what is the distance along thelevee beyond which soil properties are statistically independent At shortdistances soil properties are highly correlated and if failure conditions arerealized they might occur over the entire length At long distances in a verylong statistically homogeneous reach soil properties may be very differentfrom each other due to inherent randomness There could be a failure due toweak strength values in one area but values could be very strong in anotherThe long reach may need to be subdivided into an equivalent number ofstatistically independent reaches to model the appropriate total number ofstatistically independent reaches in Equations (1239) and (1240)

Much research has been done in the areas of spatial correlation autocor-relation functions variance reduction functions etc which have a directbearing on this problem However there are seldom sufficient data toaccurately quantify such functions Spatial variability can be most simplyexpressed by using a ldquocorrelation distancerdquo δ discussed by a number of

490 Thomas F Wolff

researchers notably VanMarcke (1977a) For distances less than δ soilproperties can be considered highly correlated ie values are uncertainbut nearly the same from point to point For points separated by distancesconsiderably greater than δ soil property values are independent and uncor-related The degree of correlation between soil properties at separated pointsis assumed to follow some function which is scaled by the δ distance For alevee the longer a ldquostatistically homogeneousrdquo levee is as a multiple of thecorrelation distance the greater the probability of failure

For a levee with non-homogeneous foundation conditions already dividedinto a set discrete reaches based on the local geology the concept ofcorrelation distance can be included as follows

bull Where the reach length is less than the correlation distance δ theprobability of failure for the two-dimensional section could simply betaken as the probability of failure for the reach Alternatively if thereach length is quite small relative to the correlation distance (not com-mon) reaches could be combined and represented by the conditionalprobability function for the most critical reach

bull Where the reach length is greater than δ the probability of failure ofthat reach must be increased to account for length effects This can beapproximated by subdividing the reach into an additional integral num-ber of equivalent reaches with each reach of length δ and one additionalreach for the fractional remainder

The required correlation distance δ is a difficult parameter to estimateas it requires a detailed statistical analysis of a relatively large set ofequally spaced data which is generally not available The reported val-ues for soil strength in the horizontal direction are typically a few hundredfeet (eg VanMarcke 1977b uses 150 ft) On the other hand for leveesVrouwenvelder (1987) uses 500 m for Dutch levees While this is a con-siderable difference maximum reach lengths of 100ndash300 m would seemto be reasonable in levee systems analysis there should always be a newreach assumed when the levee geometry or foundation conditions changesignificantly

1210 Remaining shortcomings

One significant shortcoming remaining is the effect of flood duration As theduration of a flood increases the probability of failure inevitably increasesas extended flooding increases pore pressures and increases the likelihoodand intensity of damaging erosion The analyses herein essentially assumethat the flood has been of sufficient duration that steady-state seepageconditions have developed in pervious substratum materials and perviousembankment materials but no pore pressure adjustment has occurred in

Reliability of levee systems 491

impervious clayey foundation and embankment materials These are rea-sonable assumptions for economic analysis of most levees Further researchwill be required to provide a rational basis for modifying these functions forflood duration

Other shortcomings are enumerated by the National Research Council(2000) and have been discussed earlier

1211 Epilogue New Orleans 2005

During the preparation of this chapter Hurricane Katrina struck the GulfCoast of the United States creating storm surges that led to numerous leveebreaks flooding much of New Orleans Louisiana causing over 100 billiondollars of damage and the loss of over 1000 lives Much of New Orleansis situated below sea level protected by a system of levees (more properlycalled dikes) and pumping stations

A preliminary joint report on the performance of the levees and floodwallswas prepared by two field investigation teams one from the American Soci-ety of Civil Engineers and one sponsored by the National Science Foundationand led by civil engineering faculty at the University of California at Berkeley(Seed et al 2005) The report noted that three leveefloodwall failures (oneat the 17th St Canal and two along the London Avenue Canal) occurredat water levels lower than the top of the protection These were attributedto some combination of sliding instability and inadequate seepage controlThree additional major breaks along the Inner Harbor Navigation Canalwere attributed to overtopping and a large number of additional failuresmore remote from the urban part of the city occurred due to overtoppingandor wave erosion

Subsequently an extensive analysis of the New Orleans levee failures wasconducted by the Interagency Project Evaluation Team (IPET 2006) whichinvolved a number of government agencies academics and consultants ledby the US Army Corps of Engineers The IPET report was in turn reviewedby a second team from the American Society Society of Civil Engineers(ASCE) the External Review Panel (ERP) The IPET report found that oneof the breaches on the Inner Harbor Navigation Canal also likely failed priorto overtopping

This event the most catastrophic levee failure in recent history raisesquestions as to how the New Orleans levee system would have been viewedfrom a reliability perspective and how application of reliability-based prin-ciples might have possibly reduced the level of the damage The writerwas in the area shortly after the failures as a member of the first ASCElevee assessment team However the observations below should be con-strued only as the opinions of the writer and not to reflect any positionof the ASCE which has published its own recommendations at its web site(wwwasceorg)

492 Thomas F Wolff

12111 Level of protection and probability of systemfailure

The system of levees and floodwalls forming the New Orleans hurricaneprotection system were reported to be constructed to heights that wouldcorrespond to return periods of 200ndash300 years or an equivalent annualprobability of 0005ndash00033 While this is several times the expected lifetimeof a person the probability of such an event occurring in onersquos lifetime isnot negligible From the Poisson distribution the probability of at least oneevent in an interval of t years is given as

Pr(x gt 0) = 1 minus eminusλt (1241)

where x is the number of events in the interval t and λ is a parameter denotingthe expected number of events per unit time For an annual probability of 1in 200 λ = p = 0005

Hence the probability of getting at least one 200-year event in a 50-yeartime period (a reasonable estimate of the length of time one might residebehind the levee) is

Pr(x gt 0) = 1 minus e(minus0005)(50) = 0221

which is between 1 in 4 and 1 in 5 It is fair to say the that the publicperception of the following statement

There is between a 1 in 4 and 1 in 5 chance that your home will beflooded sometime in your life

is much different than

Your home is protected by a levee designed higher than the 200 yearflood

A 200-year level of protection for an urban area leaves a significant chanceof flood exceedance in any 50-year period In comparison the primary dikesprotecting the Netherlands are set to height corresponding to 10000 yearreturn period (Voortman 2003) and the interior levees protecting againstthe Rhine are set to a return period of about 1250 years (Vrouwenvelder1987)

In addition to the annual probability of the flood water exceeding thelevee there is some additional probability that the levee will fail at waterlevels below the top of the levee Hence flood return periods only provide anupper bound on reliability and perhaps a poor one Estimating conditionalfailure probabilities to determine an overall probability of levee failure (asopposed to probability of height exceedance) is the purpose of the methods

Reliability of levee systems 493

discussed in this chapter as well as by Wolff (1994) Vrouwenvelder (1987)the National Research Council (2000) and others The three or four failuresin New Orleans that occurred without overtopping illustrate the importanceof considering material variability model uncertainty and other sources ofuncertainty

Finally the levee system in New Orleans is extremely long Local pressreports indicated that system was nearly 170 miles long The probability ofat least one failure in the system can be calculated from Equation (1240)Using a large amount of conservatism assume that there are 340 statisticallyindependent reaches of one half mile each and that conditional probabilityof failure for each reach with water at the top of the levee for each reach is10minus3 The probability of system failure with water at the top of all leveesis then

Pr(f) =1 minus (1 minus 0001)340

=1 minus 07116

=02884

or about 29 But if the conditional probability of failure for each reachdrops to 10minus2 the probability of system failure rises to almost 97 It isevident from the above that very long levees protecting developed areas needto be designed to very high levels of reliability with conditional probabilitiesof failure on the order of 10minus3 or smaller to ensure a reasonably smallprobability of system failure

12112 Reliability considerations in project design

In hindsight applying several principles of reliability engineering may haveprevented some damage and enhanced response in the Katrina disaster

bull Parallel (redundant) systems are inherently much more reliable thanseries systems Long levee systems are primarily series systems a fail-ure at any one point is a failure of the system leading to widespreadflooding Had the interior of New Orleans been subdivided into a setof compartments by interior levees only a fraction of the area mayhave been flooded Levees forming containment compartments are com-mon around tanks in petroleum tank farms Such interior levees wouldundoubtedly run against public perception which would favor buildinga larger stronger levee that ldquocould not failrdquo over interior levees on dryland far from the water that would limit interior flooding

bull However small the probability of the design event one should considerthe consequences of even lower probability events The levee systems inNew Orleans included no provisions for passing water in a controlled

494 Thomas F Wolff

non-destructive manner for water heights exceeding the design event(Seed et al 2005) Had spillways or some other means of landside hard-ening been provided the landside area would have still been flooded butsome of the areas would not have breached This would have reducedthe number of buildings flattened by the walls of water coming throughthe breaches and facilitated the ability to begin pumping out the inte-rior areas after the storm surge had passed It can be perceived that the200ndash300-year level of protection may have been perceived as such a lowprobability event that it would never occur

bull Overall consequences can be reduced by designing critical facilities toa higher level of reliability With the exception of tall buildings essen-tially all facilities in those parts of New Orleans below sea level werelower than the tops of the levees Had critical facilities such as policeand fire stations military bases medical care facilities communicationsfacilities and pumping station operating floors been constructed on highfills or platforms the emergency response may have been significantlyimproved

References

Apel H Thieken A H Merz B and Bloschl G (2004) Flood risk assess-ment and associated uncertainty Natural Hazards and Earth System Sciences 4295ndash308

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering J Wiley and Sons Chichester UK

Buijs F A van Gelder H A J M and Hall J W (2003) Application ofreliability-based flood defence design in the UK ESREL 2003 ndash European Safetyand Reliability Conference 2003 Maastricht Netherlands Available online athttpherontudelftnl2004_1Art2pdf

Duncan J M and Houston W N (1983) Estimating failure probabilities forCalifornia levees Journal of Geotechnical Engineering ASCE 109 2

Fredlund D G and Dahlman A E (1972) Statistical geotechnical properties ofglacial lake edmonton sediments In Statistics and Probability in Civil EngineeringHong Kong University Press Hong Kong

Hammitt G M (1966) Statistical analysis of data from a comparative laboratorytest program sponsored by ACIL Miscellaneous Paper 4-785 US Army EngineerWaterways Experiment Station Corps of Engineers

Harr M E (1987) Reliability Based Design in Civil Engineering McGraw-HillNew York

Hassan A M and Wolff T F (1999) Search algorithm for minimum reliabil-ity index of slopes Journal of Geotechnical and Geoenvironmental EngineeringASCE 124(4) 301ndash8

Interagency Project Evaluation Team (IPET 2006) Performance Evaluation of theNew Orleans and Southeast Louisiana Hurricane Protection System Draft FinalReport of the Interagency Project Evaluation Team 1 June 2006 Available onlineat httpsipetwesarmymil

Reliability of levee systems 495

Ladd C C Foote R Ishihara K Schlosser F and Poulos H G (1977)Stress-deformation and strength characteristics State-of-the-Art report In Pro-ceedings of the Ninth International Conference on Soil Mechanics and Foun-dation Engineering Tokyo Soil Mechanics and Foundation Engineering 14(6)410ndash14

National Research Council (2000) Risk Analysis and Uncertainty in Flood DamageReduction Studies National Academy Press Washington DC httpwwwnapedubooks0309071364html

Peter P (1982) Canal and River Levees Elsevier Scientific Publishing CompanyAmsterdam

Schultze E (1972) ldquoFrequency Distributions and Correlations of Soil Propertiesrdquoin Statistics and Probability in Civil Engineering Hong Kong

Schwartz P (1976) Analysis and performance of hydraulic sand-hill levees PhDdissertation Iowa State University Anes IA

Seed R B Nicholson P G et al (2005) Preliminary Report on the Perfor-mance of the New Orleans Levee Systems in Hurricane Katrina on August29 2005 National Science Foundation and American Society of Civil Engi-neers University of California at Berkeley Report No UCBCITRIS ndash 050117 November 2005 Available online at httpwwwasceorgfilespdfkatrinateamdatareport1121pdf

Shannon and Wilson Inc and Wolff T F (1994) Probability models for geotech-nical aspects of navigation structures Report to the St Louis District US ArmyCorps of Engineers

US Army Corps of Engineers (USACE) (1991) Policy Guidance Letter No 26Benefit Determination Involving Existing Levees Department of the Army Officeof the Chief of Engineers Washington DC Available online at httpwwwusacearmymilinetfunctionscwcecwpbranchesguidance _devpglspgl26htm

US Army Corps of Engineers (USACE) (1995) Introduction to Probability andReliability Methods for Use in Geotechnical Engineering Engineering TechnicalLetter 1110-2-547 30 September 1995 Available online at httpwwwusacearmymilinetusace-docseng-tech-ltrsetl1110-2-547entirepdf

US Army Corps of Engineers (USACE) (1999) Risk-Based Analysis in GeotechnicalEngineering for Support of Planning Studies Engineering Technical Letter 1110-2-556 28 May 1999 Available online at httpwwwusacearmymilinetusace-docseng-tech-ltrsetl1110-2-556

US Army Corps of Engineers (USACE) (2000) Design and Construction of LeveesEngineering Manual 1110-2-1913 April 2000 Available online at httpwwwusacearmymilinetusace-docseng-manualsem1110-2-1913tochtm

VanMarcke E (1977a) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103 1227ndash46

VanMarcke E (1977b) Reliability of earth slopes Journal of the GeotechnicalEngineering Division ASCE 103 1247ndash65

Voortmann H G (2003) Risk-based design of flood defense systems Doctoral dis-sertation Delft Technical University Available online at httpwwwwaterbouwtudelftnlindexphpmenu_items_id=49

Vrouwenvelder A C W M (1987) Probabilistic Design of Flood Defenses ReportNo B-87-404 IBBC-TNO (Institute for Building Materials and Structures of theNetherlands Organization for Applied Scientific Research) The Netherlands

496 Thomas F Wolff

Wolff T F (1985) Analysis and design of embankment dam slopes a probabilisticapproach PhD thesis Purdue University Lafayette IN

Wolff T F (1994) Evaluating the Reliability of Existing Levees prepared forUS Army Engineer Waterways Experiment Station Geotechnical LaboratoryVicksburg MS September 1994 This is also Appendix B to US Army Corps ofEngineers ETL 1110-2-556 Risk-Based Analysis in Geotechnical Engineering forSupport of Planning Studies 28 May 1999

Wolff T F Demsky E C Schauer J and Perry E (1996) Reliability assessment ofdike and levee embankments In Uncertainty in the Geologic Environment FromTheory to Practice Proceedings of Uncertainty rsquo96 ASCE Geotechnical SpecialPublication No 58 eds C D Shackelford P P Nelson and M J S Roth ASCEReston VA pp 636ndash50

Chapter 13

Reliability analysis ofliquefaction potential of soilsusing standardpenetration test

Charng Hsein Juang Sunny Ye Fang and David Kun Li

131 Introduction

Earthquake-induced liquefaction of soils may cause ground failure such assurface settlement lateral spreading sand boils and flow failures whichin turn may cause damage to buildings bridges and lifelines Examplesof such structural damage due to soil liquefaction have been extensivelyreported in the last four decades As stated in Kramer (1996) ldquosome of themost spectacular examples of earthquake damage have occurred when soildeposits have lost their strength and appeared to flow as fluidsrdquo Duringliquefaction ldquothe strength of the soil is reduced often drastically to thepoint where it is unable to support structures or remain stablerdquo

Liquefaction is ldquothe act of process of transforming any substance intoa liquid In cohesionless soils the transformation is from a solid state toa liquefied state as a consequence of increased pore pressure and reducedeffective stressrdquo (Marcuson 1978) The basic mechanism of the initiation ofliquefaction may be elucidated from the observation of behavior of a sandsample undergoing cyclic loading in a cyclic triaxial test In such laboratorytests the pore water pressure builds up steadily as the cyclic deviatoric stressis applied and eventually approaches the initially applied confining pressureproducing an axial strain of about 5 in double amplitude (DA) Such astate has been referred to as ldquoinitial liquefactionrdquo or simply ldquoliquefactionrdquoThus the onset condition of liquefaction or cyclic softening is specified interms of the magnitude of cyclic stress ratio required to produce 5 DAaxial strain in 20 cycles of uniform load application (Seed and Lee 1966Ishihara 1993 Carraro et al 2003)

From an engineerrsquos perspective three aspects of liquefaction are ofparticular interest they include (1) the likelihood of liquefaction occurrenceor triggering of a soil deposit in a given earthquake referred to herein asliquefaction potential (2) the effect of liquefaction (ie the extent of ground

498 Charng Hsein Juang Sunny Ye Fang and David Kun Li

failure caused by liquefaction) and (3) the response of foundations in aliquefied soil In this chapter the focus is on the evaluation of liquefactionpotential

The primary factors controlling the liquefaction of a saturated cohe-sionless soil in level ground are the intensity and duration of earthquakeshaking and the density and effective confining pressure of the soil Sev-eral approaches are available for evaluating liquefaction potential includingthe cyclic stress-based approach the cyclic strain-based approach and theenergy-based approach In the cyclic strain-based approach (eg Dobry et al1982) both ldquoloadingrdquo and ldquoresistancerdquo are described in terms of cyclicshear strain Although the cyclic strain-based approach has an advantageover the cyclic stress-based approach in that pore water pressure gener-ation is more closely related to cyclic strains than cyclic stresses cyclicstrain amplitudes cannot be predicted as accurately as cyclic stress ampli-tudes and equipment for cyclic strain-controlled testing is less readilyavailable than equipment for cyclic stress-controlled testing (Kramer andElgamal 2001) Thus the cyclic strain-based approach is less commonlyused than the cyclic stress-based approach The energy-based approach isconceptually attractive as the dissipated energy reflects both cyclic stressand strain amplitudes Several investigators have established relationshipsbetween the pore pressure development and the dissipated energy dur-ing ground shaking (Davis and Berrill 1982 Berrill and Davis 1985Figueroa et al 1994 Ostadan et al 1996) The initiation of liquefac-tion can be formulated by comparing the calculated unit energy from thetime series record of a design earthquake with the resistance to liquefac-tion in terms of energy based on in-situ soil properties (Liang et al 1995Dief 2000) The energy-based methods however are also less commonlyused than the cyclic stress-based approach Thus the focus in this chapteris on the evaluation of liquefaction potential using the cyclic stress-basedmethods

Two general types of cyclic stress based-approach are available for assess-ing liquefaction potential One is by means of laboratory testing (eg cyclictriaxial test and cyclic simple shear test) of undisturbed samples and theother involves use of empirical relationships that relate observed field behav-ior with in situ tests such as standard penetration test (SPT) cone penetrationtest (CPT) shear wave velocity measurement (Vs) and the Becker penetrationtest (BPT) Because of the difficulties and costs associated with high-qualityundisturbed sampling and subsequent high-quality testing of granular soilsuse of in-situ tests along with the case histories-calibrated empirical relation-ships (ie liquefaction boundary curves) has been and is still the dominantapproach in engineering practise

The most widely used cyclic stress-based method for liquefactionpotential evaluation in North America and throughout much of theworld is the simplified procedure pioneered by Seed and Idriss (1971)

Analysis of liquefaction potential 499

The simplified procedure was developed based on field observations andfield and laboratory tests with a strong theoretical basis Case histo-ries of liquefactionno-liquefaction were collected from sites on level togently sloping ground underlain by Holocene alluvial or fluvial sedi-ments at shallow depths (lt 15 m) In a case history the occurrence ofliquefaction was primarily identified with surface manifestations such aslateral spread ground settlement and sand boils Because the simplifiedprocedure was eventually ldquocalibratedrdquo based on such case histories theldquooccurrence of liquefactionrdquo should be interpreted accordingly that isthe emphasis is on the surface manifestations This definition of liquefac-tion does not always correspond to the initiation of liquefaction definedbased on the 5 DA axial strain in 20 cycles of uniform load typi-cally adopted in the laboratory testing The stress-based approach thatfollows the simplified procedure by Seed and Idriss (1971) is consideredherein

The state of the art for evaluating liquefaction potential was reviewedin 1985 by a committee of the National Research Council The report ofthis committee became the standard reference for practicing engineers inNorth America (NRC 1985) About 10 years later another review wassponsored by the National Center for Earthquake Engineering Research(NCEER) at the State University of New York at Buffalo This workshopfocused on the stress-based simplified methods for liquefaction potentialevaluation The NCEER Committee issued a report in 1997 (Youd andIdriss 1997) but continued to re-assess the state of the art and in 2001published a summary paper (Youd et al 2001) which represents the cur-rent state of the art on the subject of liquefaction evaluation It focuseson the fundamental problem of evaluating the potential for liquefaction inlevel or nearly level ground using in-situ tests to characterize the resistanceto liquefaction and the Seed and Idriss (1971 1982) simplified method tocharacterize the duration and intensity of the earthquake shaking Amongthe methods recommended for determination of liquefaction resistanceonly the SPT-based method is examined herein as the primary purposeof this chapter is on the reliability analysis of soil liquefaction The SPT-based method is used only as an example to illustrate the probabilisticapproach

In summary the SPT-based method as described in Youd et al (2001) isadopted here as the deterministic model for liquefaction potential evaluationThis method is originated by Seed and Idriss (1971) but has gone throughseveral stages of modification (Seed and Idriss 1982 Seed et al 1985 Youdet al 2001) In this chapter a limit state of liquefaction triggering is definedbased on this SPT-based method and the issues of parameter and modeluncertainties are examined in detail followed by probabilistic analyses usingreliability theory Examples are presented to illustrate both the deterministicand the probabilistic approaches

500 Charng Hsein Juang Sunny Ye Fang and David Kun Li

132 Deterministic approach

1321 Formulation of the SPT-based method

In the current state of knowledge the seismic loading that could cause asoil to liquefy is generally expressed in terms of cyclic stress ratio (CSR)Because the simplified stress-based methods were all developed based oncalibration with field data with different earthquake magnitudes and over-burden stresses CSR is often ldquonormalizedrdquo to a reference state with momentmagnitude Mw = 75 and effective overburden stress σ prime

v = 100 kPa At thereference state the CSR is denoted as CSR75σ which may be expressed as(after Seed and Idriss 1971)

CSR75σ = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ (131)

where σv = the total overburden stress at the depth of interest (kPa) σ primev = the

effective stress at the depth of interest (kPa) g = the unit of the accelerationof gravity amax = the peak horizontal ground surface acceleration (amaxgis dimensionless) rd = the depth-dependent stress reduction factor (dimen-sionless) MSF = the magnitude scaling factor (dimensionless) and Kσ = theoverburden stress adjustment factor for the calculated CSR (dimensionless)For the peak horizontal ground surface acceleration the geometric meanis preferred for use in engineering practice although use of the larger ofthe two orthogonal peak accelerations is conservative and allowable (Youdet al 2001)

For routine practice and no critical projects the following equations maybe used to estimate the values of rd (Liao and Whitman 1986)

rd = 10 minus 000765d for d lt 915m (132a)

rd = 1174 minus 00267d for 915m lt d le 20m (132b)

where d = the depth of interest (m) The variable MSF may be calculatedwith the following equation (Youd et al 2001)

MSF = (Mw75)minus256 (133)

It should be noted that different formulas for rd and MSF have been pro-posed by many investigators (eg Youd et al 2001 Idriss and Boulanger2006 Cetin et al 2004) To be consistent with the SPT-based deter-ministic method presented herein use of Equations (132) and (133) isrecommended

As noted previously the variable Kσ is a stress adjustment factor usedto adjust CSR to the effective overburden stress of σ prime

v = 100 kPa This is

Analysis of liquefaction potential 501

different from the overburden stress correction factor (CN) that is applied tothe SPT blow count (N60) which is described later The adjustment factorKσ is defined as follows (Hynes and Olsen 1999)

Kσ = (σ primevPa)(f minus1) (134)

where f asymp 06 minus 08 and Pa is the atmosphere pressure (asymp100 kPa) Datafrom the existing database are insufficient for precise determination of thecoefficient f For routine practice and no critical projects f = 07 may beassumed and thus the exponent in Equation (134) would be minus03

For the convenience of presentation hereinafter the normalized cyclicstress ratio CSR75σ is simply labeled as CSR whenever no confusion wouldbe caused by such use For liquefaction potential evaluation CSR (as theseismic loading) is compared with liquefaction resistance expressed as cyclicresistance ratio (CRR) As noted previously the simplified stress-based meth-ods were all developed based on calibration with field observations Suchcalibration process is generally based on the concept that cyclic resistanceratio (CRR) is the limiting CSR beyond which the soil will liquefy Basedprimarily on this concept and with engineering judgment the followingequation is recommended by Youd et al (2001) for the determination ofCRR using SPT data

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

(135)

where N160cs (dimensionless) is the clean-sand equivalence of the overbur-den stress-corrected SPT blow count defined as follows

N160cs = α+βN160 (136)

where α and β are coefficients to account for the effect of fines contentdefined later and N160 is the SPT blow count normalized to the referencehammer energy efficiency of 60 and effective overburden stress of 100 kPadefined as

N160 = CNN60 (137)

where N60 = the SPT blow count at 60 hammer energy efficiency andcorrected for rod length sampler configuration and borehole diameter(Skempton 1986 Youd et al 2001)

CN = (Paσprimev)05 le 17 (138)

502 Charng Hsein Juang Sunny Ye Fang and David Kun Li

The coefficients α and β in Equation (136) are related to fines content (FC)as follows

α = 0 for FC le 5 (139a)

α = exp [176 minus (190FC2)] for 5 lt FC lt 35 (139b)

α = 50 for FC ge 35 (139c)

β = 10 for FC le 5 (1310a)

β = [099 + (FC151000)] for 5 lt FC lt 35 (1310b)

β = 12 for FC ge 35 (1310c)

Equations (131) through (1310) collectively represent the SPT-base deter-ministic model for liquefaction potential evaluation recommended by Youdet al (2001) This model is recognized as the current state of the art forliquefaction evaluation using SPT The reader is referred to Youd et al(2001) for additional details on this model and its parameters In a deter-ministic evaluation factor of safety (FS) defined as FS = CRRCSR is usedto ldquomeasurerdquo liquefaction potential In theory liquefaction is said to occurif FS le 1 and no liquefaction if FS gt 1 However caution must be exer-cised when interpreting the calculated FS In the back analysis of a casehistory or in a post-earthquake investigation analysis use of FS le 1 to judgewhether liquefaction had occurred could be misleading as the existing sim-plified methods tend to be conservative (in other words there could be modelbias toward the conservative side) Because of model and parameter uncer-tainties FS gt 1 does not always correspond to no-liquefaction and FS le 1does not always correspond to liquefaction

The selection of a minimum required FS value for a particular project ina design situation depends on factors such as the perceived level of modeland parameter uncertainties the consequence of liquefaction in terms ofground deformation and structures damage potential the importance of thestructures and the economic consideration Thus the process of selectingan appropriate FS is not a trivial exercise In a design situation a factor ofsafety of 12ndash15 is recommended by the Building Seismic Safety Council(1997) in conjunction with the use of the Seed et al (1985) method for liq-uefaction evaluation Since the Youd et al (2001) method is essentially anupdated version of and is perceived as conservative as the Seed et al (1985)method the recommended range of FS by the Building Seismic Safety Coun-cil (1997) should be applicable In recent years however there is growingtrend to assess liquefaction potential in terms of probability of liquefaction(Liao et al 1988 Juang et al 2000 2002 Cetin et al 2004) To facilitatethe use of probabilistic methods calibration of the calculated probabilityto the previous engineering experience is needed In a previous study by

Analysis of liquefaction potential 503

Juang et al (2002) a factor of safety of 12 in the Youd et al (2001) methodwas found to correspond approximately to a mean probability of 030 Inthis chapter further calibration of the calculated probability of liquefactionis presented later

1322 Example No 1 deterministic evaluation of anon-liquefied case

This example concerns a non-liquefied case Field observation of the sitewhich is designated as San Juan B-5 (Idriss et al as cited in Cetin 2000) indi-cated no occurrence of liquefaction during the 1977 Argentina earthquakeThe mean values of seismic and soil parameters at the critical depth (29 m)are given as follows N60 = 80 FC = 3 σ prime

v = 381 kPa σv = 456kPa amax = 02 g and Mw = 74 (Cetin 2000) First CRR is calculatedas follows

Using Equation (138)

CN = (Paσprimev)05 = (100381)05 = 162 lt 17

Using Equation (137)

N160 = CNN60 = (162)(80) = 130

Since FC = 3 lt 5 thus α = 0 and β = 1 according to Equations (139)and (1310) Thus according to Equation (136)

N160cs = α+βN160 = 130

Finally using Equation (135) we have

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

= 0141

Next the intermediate parameters of CSR are calculated as follows

MSF = (Mw75)minus256 = (7475)minus256 = 1035

Kσ = (σ primevPa)(f minus1) = (381100)minus03 = 1335

rd = 10 minus 000765d = 10 minus 000765(29) = 0978

504 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Finally using Equation (131) we have

CSR75σ = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ

= 065(

456381

)(02)(0978)[(1035)(1335)]

= 0110

The factor of safety is calculated as follows

FS = CRRCSR = 01410110 = 128

As a back analysis of a case history this FS value would suggest no liq-uefaction which agrees with the filed observation However a probabilisticanalysis might be necessary or desirable to complement the judgment basedon the calculated FS value

1323 Example No 2 deterministic evaluation of aliquefied case

This example concerns a liquefied case Field observation of the site des-ignated as Ishinomaki-2 (Ishihara et al as cited in Cetin 2000) indicatedoccurrence of liquefaction during the 1978 Miyagiken-Oki earthquake Themean values of seismic and soil parameters at the critical depth (37 m) aregiven as follows N160 = 5 FC = 10 σ prime

v = 3628 kPa σv = 5883 kPaamax = 02 g and Mw = 74 (Cetin 2000)

Similar to the analysis performed in Example 1 the calculations of theCRR CSR and FS are carried out as follows

α = exp [176 minus (190FC2)] = exp[176 minus (190102)] = 0869

β = [099 + (FC151000)] = [099 + (10151000)] = 1022

N160cs = α+βN160 = 598

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

= 134 minus 598

+ 598135

+ 50[10 times 598 + 45]2 minus 1

200

= 0080

MSF = (Mw75)minus256 = (7475)minus256 = 1035

Kσ = (σ primevPa)(f minus1) = (3628100)minus03 = 1356

Analysis of liquefaction potential 505

rd = 10 minus 000765d = 10 minus 000765(37) = 0972

CSR = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ = 0146

FS = CRRCSR = 0545

The calculated FS value is much below 1 As a back analysis of a case his-tory the calculated FS value would confirm the field observation with agreat certainty However a probabilistic analysis might still be desirable tocomplement the judgment based on the calculated FS value

133 Probabilistic approach

Various models for estimating the probability of liquefaction have been pro-posed (Liao et al 1988 Juang et al 2000 2002 Cetin et al 2004) Thesemodels are all data-driven meaning that they are established based on statis-tical analyses of the databases of case histories To calculate the probabilityusing these empirical models only the best estimates (ie the mean values) ofthe input variables are required the uncertainty in the model termed modeluncertainty and the uncertainty in the input variables termed parameteruncertainty are excluded from the analysis Thus the calculated proba-bilities might be subject to error if the effect of model andor parameteruncertainty is significant A more fundamental approach to this problemwould be to adopt a reliability analysis that considers both model and param-eter uncertainties The formulation and procedure for conducting a rigorousreliability analysis is described in the sections that follow

1331 Limit state of liquefaction triggering

In the context of reliability analysis presented herein the limit state of lique-faction triggering is essentially the boundary curve that separates ldquoregionrdquoof liquefaction from the region of no-liquefaction An example of a limitstate is shown in Figure 131 where the SPT-based boundary curve recom-mended by Youd et al (2001) is shown with 148 case histories As reflectedin the scattered data shown in Figure 131 uncertainty exists as to wherethe boundary curve should be ldquopositionedrdquo This uncertainty is the modeluncertainty mentioned previously The issue of model uncertainty is dis-cussed later At this point the limit state may be expressed symbolically asfollows

h(x) = CRR minus CSR = 0 (1311)

where x is a vector of input variables that consist of soil and seismic param-eters that are required in the calculation of CRR and CSR and h(x) lt 0indicates liquefaction

506 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Figure 131 An example limit state of liquefaction triggering

As noted previously Equations (131) through (1310) collectively repre-sent the SPT-base deterministic model for liquefaction potential evaluationrecommended by Youd et al (2001) Two parameters N160 and FC arerequired in the calculation of CRR and both are assumed to be randomvariables Since MSF is a function of Mw and Kσ is a function of σ prime

v fiveparameters including amaxMw σ prime

vσv and rd are required for calculat-ing CSR The first four parameters amaxMw σ prime

v and σv are assumed tobe random variables The parameter rd is a function of depth (d) and is notconsidered as a random variable (since CSR is evaluated for soil at a given d)Based on the above discussions a total of six random variables are identi-fied in the deterministic model by Youd et al (2001) described previouslyThus the limit state of liquefaction based on this deterministic model maybe expressed as follows

h(x) = CRR minus CSR = h(N160FCMwamaxσprimev and σv) = 0 (1312)

It should be noted that while the parameter rd is not considered as a randomvariable the uncertainty does exist in the model for rd (Equation (132))just as the uncertainty exists in the model for MSF (Equation (133)) and inthe model for Kσ (Equation (134)) The uncertainty in these ldquocomponentrdquomodels contributes to the uncertainty of the calculated CSR which inturn contributes to the uncertainty of the CRR model since CRR is con-sidered as the limiting CSR beyond which the soil will liquefy Ratherthan dealing with the uncertainty of each component model where thereis a lack of data for calibration the uncertainty of the entire limit state

Analysis of liquefaction potential 507

model is characterized as a whole since the only data available for modelcalibration are field observations of liquefaction (indicated by h(x) le 0) orno liquefaction (indicated by h(x) gt 0) Thus the limit state model thatconsiders model uncertainty may be rewritten as

h(x) = c1CRR minus CSR = h(c1N160FCMwamaxσprimev and σv) = 0

(1313)

where random variable c1 represents the uncertainty of the limit state modelthat is yet to be characterized Use of a single random variable to characterizethe uncertainty of the entire limit state model is adequate since CRR isdefined as the limiting CSR beyond which the soil will liquefy as notedpreviously Thus only one random variable c1 applied to CRR is requiredIn fact Equation (1313) may be interpreted as h(x) = c1 CRR ndash CSR =c1 FS ndash1 = 0 Thus the uncertainty of the entire limit state model is seenhere as the uncertainty in the calculated FS where data (field observations)are available for calibration For convenience of presentation hereinafterthe random variable c1 is referred to as the model bias factor or simplymodel factor

1332 Parameter uncertainty

For a realistic estimate of liquefaction probability the reliability analy-sis must consider both parameter and model uncertainties The issue ofmodel uncertainty is discussed later Thus for reliability analysis of a futurecase the uncertainties of input random variables must first be assessedFor each input variable this process involves the estimation of the meanand standard deviation if the variable is assumed to follow normal orlognormal distribution The engineer usually can make a pretty good esti-mate of the mean of a variable even with limited data This probably hasto do with the well-established statistics theory that the ldquosample meanrdquois a best estimate of the ldquopopulation meanrdquo Thus the following discus-sion focuses on the estimation of standard deviation of each input randomvariable

Duncan (2000) suggested that the standard deviation of a random variablemay be obtained by one of the following three methods (1) direct calculationfrom data (2) estimate based on published coefficient of variation (COV)and (3) estimate based on the ldquothree-sigma rulerdquo (Dai and Wang 1992) Inthe last method the knowledge of the highest conceivable value (HCV) andthe lowest conceivable value (LCV) of the variable is used to calculate thestandard deviation σ as follows (Duncan 2000)

σ = HCV minus LCV6

(1314)

508 Charng Hsein Juang Sunny Ye Fang and David Kun Li

It should be noted that the engineer tends to under-estimate the range of agiven variable (and thus the standard deviation) particularly if the estimatewas based on very limited data and judgment was required Thus for asmall sample size a value of less than 6 should be used for the denominatorin Equation (1314) Whenever in doubt a sensitivity analysis should beconducted to investigate the effect of different levels of COV of a particularvariable on the results of reliability analysis

Typical ranges of COVs of the input variables according to the publisheddata are listed in Table 131 It should be noted that the COVs of the earth-quake parameters amax and Mw listed in Table 131 are based on valuesreported in the published databases of case histories where recorded strongground motions andor locally calibrated data were available The COVof amax based on general attenuation relationships could easily be as highas 050 (Haldar and Tang 1979) According to Youd et al (2001) for afuture case the variable amax may be estimated using one of the followingmethods

1 Using empirical correlations of amax with earthquake magnitude dis-tance from the seismic energy source and local site conditions

2 Performing local site response analysis (eg using SHAKE or othersoftware) to account for local site effects

3 Using the USGS National Seismic Hazard web pages and the NEHRPamplification factors

Table 131 Typical coefficients of variation of input random variables

Random variable Typical range of COVa References

N160 010ndash040 Harr (1987)Gutierrez et al (2003)Phoon and Kulhawy (1999)

FC 005ndash035 Gutierrez et al (2003)σ prime

v 005ndash020 Juang et al (1999)σv 005ndash020 Juang et al (1999)amax 010ndash020b Juang et al (1999)Mw 005ndash010 Juang et al (1999)

NoteaThe word ldquotypicalrdquo here implies the range approximately bounded by the 15th percentile andthe 85th percentile estimated from case histories in the existing databases such as Cetin (2000)Published COVs are also considered in the estimate given here The actual COV values could behigher or lower depending on the variability of the site and the quality and quantity of data that areavailablebThe range is based on values reported in the published databases of case histories where recordedstrong ground motions and locally calibrated data were available However the COV of amaxbased on general attenuation relationships or amplification factors could easily be as high as orover 050

Analysis of liquefaction potential 509

Use of the amplification factors approach is briefly summarized inthe following The USGS National Hazard Maps (Frankel et al 1997)provide rock peak ground acceleration (PGA) and spectral acceleration(SA) for a specified locality based on latitudelongitude or zip codeThe USGS web page (httpearthquakeusgsgovresearchhazmaps) pro-vides PGA value and SA values at selected spectral periods (For exam-ple T = 02 03 and 10 s) each with six levels of probability ofexceedance including 1 probability of exceedance in 50 years (annualrate of 00002) 2 probability of exceedance in 50 years (annualrate of 00004) 5 probability of exceedance in 50 years (annual rateof 0001) 10 probability of exceedance in 50 years (annual rate of0002) and 20 probability of exceedance in 50 years (annual rateof 0004) and 50 probability of exceedance in 75 years (annual rateof 0009) The six levels of probability of exceedance are often referredto as the six seismic hazard levels with corresponding earthquake returnperiods of 4975 2475 975 475 224 and 108 years respectively Fora given locality a PGA can be obtained for a specified probability ofexceedance in an exposure time from the USGS National Seismic HazardMaps

For liquefaction analysis the rock PGA needs to be converted to peakground surface acceleration at the site amax Ideally the conversion shouldbe carried out based on site response analysis Various simplified proce-dures are also available for an estimate of amax (eg Green 2001 Gutierrezet al 2003 Stewart et al 2003 Choi and Stewart 2005) As an examplea simplified procedure for estimating amax perhaps in the simplest form isexpressed as follows

amax = Fa (PGA) (1315)

where Fa is the amplification factor which in a simplest form may beexpressed as a function of rock PGA and the NEHRP site class (NEHRP1998) Figure 132 shows an example of a simplified chart for the ampli-fication factor The NEHRP site classes used in Figure 132 are basedon the mean shear wave velocity of soils in the top 30 m as listed inTable 132

Choi and Stewart (2005) developed a more sophisticated model for groundmotion amplification that is a function of the average shear wave velocityover the top 30 m of soils VS30 and ldquorockrdquo reference PGA The amplifica-tion factors are defined relative to ldquorockrdquo reference motions from severalattenuation relationships for active tectonic regions including those ofAbrahamson and Silva (1997) Sadigh et al (1997) and Campbell andBozorgnia (2003) The databases used in model development cover theparameter spaces VS30 = 130 sim 1300 ms and PGA = 002 sim 08 g andthe model is considered valid only in these ranges of parameters The Choi

510 Charng Hsein Juang Sunny Ye Fang and David Kun Li

0 01 02 03 04 05

Rock PGA (g)

0

1

2

3

4

Am

plifi

catio

n fa

ctor

Fa

Site B (Rock)

Site C (Very dense soil and soft rock)

Site D (Stiff soil)

Site E (Soft soil)

Figure 132 Amplification factor as a function of rock PGA and the NEHRP site class (re-produced from Gutierrez et al 2003)

Table 132 Site classes (categories) in NEHRP provisions

NEHRP category(soil profile type)

Description

A Hard rock with measured mean shear wave velocity in the top30 m vs gt 1500 ms

B Rock with 760 ms lt vs le 1500 msC Dense soil and soft rock with 360 ms lt vs le 760 msD Stiff soil with 180 ms lt vs le 360 msE Soil with vs le 180 ms or any profile with more than 3 m of soft

clay (plasticity index PI gt 20 water content w gt 40 andundrained shear strength su lt 25 kPa)

F Soils requiring a site-specific study eg liquefiable soils highlysensitive clays collapsible soils organic soils etc

and Stewart (2005) model for amplification factor (Fij) is expressed asfollows

ln(Fij) = c ln

(VS30ij

Vref

)+ b ln

(PGArij

01

)+ηi + εij (1316)

where PGAr is the rock PGA expressed in units of g b is a function ofregression parameters c and Vref are regression parameters ηi is a random

Analysis of liquefaction potential 511

effect term for earthquake event i and εij represents the intra-event modelresidual for motion j in event i

Choi and Stewart (2005) provided many sets of empirical constants foruse of Equation (1316) As an example for a site where the attenuationrelationship by Abrahamson and Silva (1997) is applicable and a spectralperiod T= 02 sec is specified Equation (1316) becomes

ln(Fa) = minus031 ln(

VS30

453

)+ b ln

(PGAr

01

)(1317)

In Equation (1317) Fa is the amplification factor VS30 is obtained fromsite characterization PGAr is obtained for reference rock conditions usingthe attenuation relationship by Abrahamson and Silva (1997) and b isdefined as follows (Choi and Stewart 2005)

b = minus052 for Site Category E (1318a)

b = minus019 minus 0000023 (VS30 minus 300)2 for 180 lt VS30 lt 300(ms)(1318b)

b = minus019 for 300 lt VS30 lt 520(ms) (1318c)

b = minus019 + 000079(VS30 minus 520) for 520 lt VS30 lt 760(ms)(1318d)

b = 0 for VS30 gt 760(ms) (1318e)

The total standard deviation for the amplification factor Fa obtainedfrom Equation (1317) came from two sources the inter-event standarddeviation of 027 and the intra-event standard deviation of 053 Thusfor the given scenario (the specified spectral period and the chosen atten-uation model) the total standard deviation is

radic(027)2 + (053)2 = 059

The peak ground surface acceleration amax can be obtained from Equation(1315)

For subsequent reliability analysis amax obtained from Equation (1315)may be considered as the mean value For a specified probability ofexceedance (and thus a given PGA) the variation of this mean amax isprimarily caused by the uncertainty in the amplification factor model Useof simplified amplification factors for estimating amax tends to result in alarge variation and thus for important projects concerted effort to reducethis uncertainty using more accurate methods andor better quality datashould be made whenever possible The reader is referred to Bazzurro andCornell (2004 ab) and Juang et al (2008) for detailed discussions of thissubject

The magnitude of Mw can also be derived from the USGS National SeismicHazard web pages through a de-aggregation procedure Detailed information

512 Charng Hsein Juang Sunny Ye Fang and David Kun Li

may be obtained from httpeqintcrusgsgovdeaggint2002indexphpA summary of the procedure is provided in the following

The task of seismic hazard de-aggregation involves the determination ofearthquake parameters principally magnitude and distance for use in aseismic-resistant design In particular calculations are made to determinethe statistical mean and modal sources for any given US site for the sixhazard levels (or the corresponding probabilities of exceedance)

The seismic hazard presented in the USGS Seismic Hazard web page is de-aggregated to examine the ldquocontribution to hazardrdquo (in terms of frequency)as a function of magnitude and distance These plots of ldquocontribution tohazardrdquo as a function of magnitude and distance are useful for specify-ing design earthquakes On the available de-aggregation plots from theUSGS website the height of each bar represents the percent contributionof that magnitude and distance pair (or bin) to the specified probabilities ofexceedance The distribution of the heights of these bars (ie frequencies)is essentially a joint probability mass function of magnitude and distanceWhen this joint mass function is ldquointegratedrdquo along the axis of distancethe ldquomarginalrdquo or conditional probability mass function of the magnitude isobtained This distribution of Mw is obtained for the same specified probabil-ity of exceedance as the one from which the PGA is derived The distribution(or the uncertainty) of Mw here is due primarily to the uncertainty in seismicsources

It should also be noted that for selection of a design earthquake in a deter-ministic approach the de-aggregation results are often described in termsof the mean magnitude However use of the modal magnitude is preferredby many engineers because the mode represents the most likely source inthe seismic-hazard model whereas the mean might represent an unlikelyor even unconsidered source especially in the case of a strongly bimodaldistribution

In summary a pair of PGA and Mw may be selected at a specified hazardlevel or probability of exceedance The selected PGA is converted to amaxand the pair of amax and Mw is then used in the liquefaction evaluationFor reliability analysis the values of amax and Mw determined as describedpreviously are taken as the mean values and the variations of these variablesare estimated and expressed in terms of the COVs The reader is referred toJuang et al (2008) for additional discussions on this subject

1333 Correlations among input random variables

The correlations among the input random variables should be considered in areliability analysis The correlation coefficients may be estimated empiricallyusing statistical methods Except for the pair of amax and Mw the correla-tion coefficient between each pair of input variables used in the limit statemodel is estimated based on an analysis of the actual data in the existing

Analysis of liquefaction potential 513

Table 133 Coefficients of correlation among the six input random variables

Variable Variable N160 FC primev v amax Mw

N160 1 0 03 03 0 0FC 0 1 0 0 0 0σ prime

v 03 0 1 09 0σv 03 0 09 1 0 0amax 0 0 0 0 1 09a

Mw 0 0 0 0 09a 1

NoteaThis is estimated based on local attenuation relationships calibrated to given historic earthquakesThis correlation may be used for back-analysis of a case history The correlation of the two parametersat a locality subject to uncertain sources as in the analysis of a future case could be much lower oreven negligible

databases of case histories The correlation coefficient between amax andMw is taken to be 09 which is based on statistical analysis of the datagenerated from the attenuation relationships (Juang et al 1999) The coef-ficients of correlation among the six input random variables are shown inTable 133 The correlation between the model uncertainty factor (c1 inEquation 1313) and each of the six input random variables is assumedto be 0

It should be noted that the correlation matrix as shown in Table 133 mustbe symmetric and ldquopositive definiterdquo (Phoon 2004) If this condition is notsatisfied a negative variance might be obtained which would contradict thedefinition of the variance In ExcelTM the condition can be easily checkedusing ldquoMAT_CHOLESKYrdquo It should be noted that MAT_CHOLESKYcan be executed with a free ExcelTM add-in ldquomatrixxlardquo which mustbe installed once by the user The file ldquomatrixxlardquo may be downloadedfrom httpdigilanderliberoitfoxesindexhtm For the correlation matrixshown in Table 133 the diagonal entries of the matrix of Choleskyfactors are all positive thus the condition of ldquopositive definitenessrdquo issatisfied

1334 Model uncertainty

The issue of model uncertainty is important but rarely emphasized in thegeotechnical engineering literature perhaps because it is difficult to addressInstead of addressing this issue directly Zhang et al (2004) suggested aprocedure for reducing the uncertainty of model prediction using Bayesianupdating techniques However since a large quantity of liquefaction casehistories (Cetin 2000) is available for calibration of the calculated relia-bility indexes an estimate of the model factor (c1 in Equation (1313)) is

514 Charng Hsein Juang Sunny Ye Fang and David Kun Li

possible as documented previously by Juang et al (2004) The procedurefor estimating model factor involves two steps (1) deriving a Bayesian map-ping function based on the database of case histories and (2) using thecalibrated Bayesian mapping function as a reference to back-figure the modelfactor c1 The detail of this procedure is not repeated herein only a briefsummary of the results obtained from the calibration of the limit state model(Equation (1313)) is provided and the reader is referred to Juang et al(2004 2006) for details of the procedure

The model factor c1 is assumed to follow lognormal distribution With theassumption of lognormal distribution the model factor c1 can be character-ized with a mean microc1 and a standard deviation (or coefficient of variationCOV) In a previous study (Juang et al 2004) the effect of the COV ofthe model factor (c1) on the final probability obtained through reliabilityanalysis was found to be insignificant relative to the effect of microc1 Thusfor the calibration (or estimation) of mean model factor microc1 an assump-tion of COV = 0 is made It should be noted however that because of theassumption of COV = 0 and the effect of other factors such as data scatterthere will be variation on the calibrated microc1 which would be reflected in itsstandard deviation σmicroc1

The first step in the model calibration process is to develop Bayesian map-ping functions based on the distributions of the values of reliability index β

for the group of liquefied cases and the group of non-liquefied cases (Juanget al 1999)

PL = P(L|β) = P(β|L)P(L)P(β|L)P(L) + P(β|NL)P(NL)

(1319)

where P(L|β) = probability of liquefaction for a given β P(β|L) = probabilityof β given that liquefaction did occur P(β|NL) = probability of β given thatliquefaction did not occur P(L) = prior probability of liquefaction P(NL) =prior probability of no-liquefaction

The second step in the model calibration process is to back-figure themodel factor microc1 using the developed Bayesian mapping functions Bymeans of a trial-and-error process with varying microc1 values the uncer-tainty of the limit state model (Equation (1313)) can be calibrated usingthe probabilities interpreted from Equation (1319) for a large number ofcase histories The essence of the calibration here is to find an ldquooptimumrdquomodel factor microc1 so that the calibrated nominal probabilities match thebest with the reference Bayesian mapping probabilities for all cases in thedatabase Using the database compiled by Cetin (2000) the mean modelfactor is calibrated to be microc1 = 096 and the variation of the calibratedmean model factor is reflected in the estimated standard deviation ofσmicroc1 = 004

Analysis of liquefaction potential 515

With the given limit state model (Equation (1313) and associatedequations) and the calibrated model factor reliability analysis of a futurecase can be performed once the mean and standard deviation of each inputrandom variable are obtained

1335 First-order reliability method

Because of the complexity of the limit state model (Equation (1313)) andthe fact that the basic variables of the model are non-normal and cor-related no closed-form solution for reliability index of a given case ispossible For reliability analysis of such problems numerical methods suchas the first-order reliability method (FORM) are often used The generalapproach undertaken by the FORM is to transform the original randomvariables into independent standard normal random variables and theoriginal limit state function into its counterpart in the transformed orldquostandardrdquo variable space The reliability index β is defined as the short-est distance between the limit state surface and the origin in the standardvariable space The point on the limit state surface that has the shortestdistance from the origin is referred to as the design point FORM requiresan optimization algorithm to locate the design point and to determine thereliability index Several algorithms are available and the reader is referredto the literature (eg Ang and Tang 1984 Melchers 1999 Baecher andChristian 2003) for details of these algorithms Once the reliability index β

is obtained using FORM the nominal probability of liquefaction PL canbe determined as

PL = 1 minus(β) (1320)

where is the standard normal cumulative distribution functionIn Microsoft ExcelTM numerical value of (β) can be obtained using thefunction NORMSDIST(β)

The FORM procedure can easily be programmed (eg Yang 2003)Efficient implementation of the FORM procedure in ExcelTM was firstintroduced by Low (1996) and many geotechnical applications are foundin the literature (eg Low and Tang 1997 Low 2005 Juang et al2006) The spreadsheet solution introduced by Low (1996) is a clever solu-tion of reliability index based on the formulation by Hasofer and Lind(1974) on the original variable space It utilized a feature of ExcelTMcalled ldquoSolverrdquo for performing the optimization process Phoon (2004)developed a similar spreadsheet solution using ldquoSolverrdquo however thesolution of reliability index was obtained in the standard variable spacewhich tends to produce more stable numerical results Both spreadsheetapproaches yield a solution (reliability index) that is practically identicalto each other and to the solution obtained by a dedicated computer program

516 Charng Hsein Juang Sunny Ye Fang and David Kun Li

(Yang 2003) that implement the well-accepted algorithms for reliabilityindex using FORM

The mean probability of liquefaction PL may be obtained by the FORManalysis considering the mean model factor microc1 To determine the variationof the estimated mean probability as a result of the variation in microc1 in termsof standard deviation σPL

a large number of microc1 values may be ldquosampledrdquowithin the range of microc1plusmn 3σmicroc1 The FORM analysis with each of thesemicroc1 values will yield a large number of corresponding PL values and thenthe standard deviation σPL

can be determined Alternatively a simplifiedmethod may be used to estimate σPL

Two additional FORM analyses maybe performed one with microc1+ 1σmicroc1 as the model factor and the other withmicroc1minus 1σmicroc1 as the model factor Assuming that the FORM analysis usingmicroc1+ 1σmicroc1 as the model factor yields a probability of P+

L and the FORManalysis using microc1minus 1σmicroc1 as the model factor yields a probability of Pminus

L then the standard deviation σPL

may be estimated approximately with thefollowing equation (after Gutierrez et al 2003)

σPL= (P+

L minus PminusL )2 (1321)

1336 Example No 3 probabilistic evaluation of anon-liquefied case

This example concerns a non-liquefied case that was analyzed previouslyusing the deterministic approach (See Example No 1 in Section 1322)As described previously field observation of the site indicated no occurrenceof liquefaction during the 1977 Argentina earthquake The mean values ofseismic and soil parameters at the critical depth (29 m) are given as followsN160 = 13 FC= 3 σvprime = 381 kPa σv = 456 kPa amax = 02 g andMw =74 and the corresponding coefficients of variation of these parametersare assumed to be 023 0333 0085 0107 0075 and 010 respectively(Cetin 2000)

In the reliability analysis based on the limit state model expressed inEquation (1313) the parameter uncertainty and the model uncertaintyare considered along with the correlation between each pair of input ran-dom variables However no correlation is assumed between the modelfactor c1 and each of the six input random variables of the limit statemodel This assumption is supported by the finding of a recent study byPhoon and Kulhawy (2005) that the model factor is weakly correlatedto the input variables To facilitate the use of this reliability analysis aspreadsheet that implements the procedure of the FORM is developedThis spreadsheet shown in Figure 133 is designed specifically for lique-faction evaluation using the SPT-based method by Youd et al (2001) andthus all the formulas of the limit state model (Equation (1313) along with

Analysis of liquefaction potential 517

Equations (131)ndash(1310)) are implemented For users of this spreadsheetthe input data consists of four categories

1 the depth of interest (d = 29 m in this example)2 the mean and standard deviation of each of the six random variables

N160 FC σ primev σ v amax and Mw

3 the matrix of the correlation coefficients (use of default values as listed inTable 133 is recommended however a user-specified matrix could beused if it is deemed more accurate and it satisfies the positive definitenessrequirement as described previously) and

4 the model factor statistics microc1 and COV (note COV is set to 0 here asexplained previously)

Figure 133 shows a spreadsheet solution that is adapted from thespreadsheet originally designed by Low and Tang (1997) The input datafor this example along with the intermediate calculations and the finaloutcome (reliability index and the corresponding nominal probability) areshown in this spreadsheet It should be noted that the solution shown in

Mean COV h l x mN sN

N160 1300

300

3814

4561

020

740

096

0231

0333

0085

0107

0075

0100

0000

0228

0325

0085

0107

0075

0100

0000

2539

1046

3638

3814

minus1612

1997

minus0041

1196

286

3790

4532

021

771

096

12647

2846

37999

45345

0199

7355

0960

2723

0927

3221

4853

0015

0769

0000

Depth 290 a 0

FC MSF 0932 b 1

svrsquo

sv

rd 0978 N160cs 1196

f 07 Kσ 1338

amax CSR75σ 0126 CRR 0131

Mw FS 1042

c1

Correlation Matrix r Results

1

0

03

03

0

0

0

0

1

0

0

0

0

0

03

0

1

09

0

0

0

03

0

09

1

0

0

0

0

0

0

0

1

09

0

0

0

0

0

09

1

0

0

0

0

0

0

0

1

minus02536

00119

minus00296

minus00059

04338

04595

00000

FSoriginal 1278 0533

h() -1E-09 PL 0297

Notes

For this case in solverrsquos option use automatic scaling

Quadratic for Estimates Central for Derivatives and Newton for Search

others as default options

Initially enter original mean values for x column followed by invoking Excel Solver to automaticallyminimize reliability index b by changing x column subject to h(x) = 0

Original inputEquivalent normalparameters Calculate CSR and CRREquivalent normal

parameters at design point

To enter ARRAY FORMULA hold down ldquoCtrlrdquo and ldquoShiftrdquo keys then press Enter

h () = c1sdot CRR ndash CSR

xi minus mi

T

xisinF

minus1

|b|

b = minsi

xi minus mi

si

r

(x minus mN)sN

Figure 133 A spreadsheet that implements the FORM analysis of liquefaction potential(after Low and Tang 1997)

518 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Figure 133 was obtained using the mean model factor microc1 = 096 Becausethe spreadsheet uses the ldquoSolverrdquo feature in ExcelTM to perform the optimiza-tion procedure as part of the FORM analysis the user needs to make sure thisfeature is selected in ldquoToolsrdquo Within the ldquoSolverrdquo screen various optionsmay be selected for numerical solutions and the user may want to experimentwith different choices In particular the option of ldquoUse Automatic Scalingrdquoin Solver should be activated if the optimization is carried out in the originalvariable space (Figure 133) After selecting the solution options the userreturns to the Solver screen and chooses ldquoSolverdquo to perform the optimiza-tion and when the convergence is achieved the reliability index and theprobability of liquefaction are obtained Because the results depend on theinitial guess of the ldquodesign pointrdquo which is generally assumed to be equalto the vector of the mean values of the input random variables the user maywant to repeat the solution process a few times using different initial trialvalues to make certain ldquostablerdquo results have indeed been obtained Usingmicroc1 = 096 the spreadsheet solution (Figure 133) yields a probability ofliquefaction of PL = 0297 asymp 030

As a comparison the spreadsheet solution that is adapted from the spread-sheet originally designed by Phoon (2004) is shown in Figure 134 Practicallyidentical solution (PL = 030) is obtained However experience with bothspreadsheet solutions one with optimization carried out in the original vari-able space and the other in the standard variable space indicates that thesolution with the latter approach is significantly more robust and is generallyrecommended

Following the procedure described previously the FORM analyses usingmicroc1 plusmn 1σmicroc1 as the model factors respectively are performed with thespreadsheet shown in Figure 133 (or Figure 134) The standard deviation ofthe computed mean probability is then estimated to be σPL

= 0039 accord-ing to Equation (1321) If the three sigma rule is applied the probability PLwill approximately be in the range of 018ndash042 with a mean of 030

As noted previously (Section 133) a preliminary estimate of the meanprobability may be obtained from empirical models Using the proceduredeveloped by Juang et al (2002) the following equation is developed forinterpreting FS determined by the adopted SPT-based method

PL = 1

1 +(

FS105

)38 (1322)

This equation is intended to be used only for a preliminary estimate of theprobability of liquefaction in the absence of the knowledge of parameteruncertainties For this non-liquefied case FS = 128 and thus PL = 032according to Equation (1322) This PL value falls in the range of 018ndash042determined by the probabilistic approach using FORM As noted previously

Analysis of liquefaction potential 519

Mean COV h l Y=H(x) x=LU trial values Ux1 N160 Depth 290 a 000x2 FC MSF 0932 b 100x3 svrsquo rd 0978 N160cs 1196

x4 sv f 07

0126

Kσ 1338

x5 amax CSR75σ CRR 0131

x6 Mw FS 1042x7 c1

Correlation Matrix r

Results FSorginal 1278 h() 5E-09 05333 PL 0297

H 0a 1 2 3 4 5 6 7

Y1

Y2

Y3

Y4

Y5

Y6

Y7

Cholesky L

Initially enter original mean values for x column followed by invoking Excel Solver to automatically minimizereliability index b by changing x column subject to h(x) = 0

Original inputEquivalent normalparameters

Equivalent normal parameters Calculate CSR and CRR

minus0254

0000

0049

0066

0434

0158

0000

minus0254

0000

minus0030

minus0006

0434

0460

0000

11956

2846

37903

45316

0206

7709

096

2539

1046

3638

3814

minus1612

1997

minus0041

0228

0325

0085

0107

0075

0100

0000

0231

0333

0085

0107

0075

0100

0000

1300

300

3814

4561

020

740

096

0

1

1

1

1

1

1

1

1

minus02536

0

minus00296

minus00059

04338

04595

-1E-07

2

minus09357

minus1

minus09991

minus1

minus08118

minus07889

minus1

3

07446

0

00889

00178

minus12198

minus12815

4E-07

4

26182

3

29947

29998

19063

17777

3000

5

minus36423

0

minus04445

minus0089

57061

59428

-2E-06

6

minus12167

minus15

minus1496

minus14998

minus7056

minus61579

minus15

7

minus2494

0

31104

06232

minus37298

minus38486

1E-05

13

3

38136

45606

02

74

096

29612

09738

32404

48841

0015

07382

1E-07

03373

0158

01377

02615

00006

00368

5E-15

00256

00171

00039

00093

1E-05

00012

2E-22

00015

00014

8E-05

00002

3E-07

3E-05

4E-30

7E-05

9E-05

1E-06

5E-06

4E-09

6E-07

8E-38

3E-06

5E-06

2E-08

1E-07

5E-11

1E-08

1E-45

8E-08

2E-07

2E-10

1E-09

5E-13

1E-10

2E-53

1

00

03

03

0

0

0

00

1

00

00

0

0

0

03

00

1

09

0

0

0

03

00

09

1

0

0

0

0

0

0

0

1

09

0

0

0

0

0

09

1

0

0

0

0

0

0

0

1

1

0

03

03

0

0

0

0

1

0

0

0

0

0

0

0

09539

08491

0

0

0

0

0

0

04348

0

0

0

0

0

0

0

1

09

0

0

0

0

0

0

04359

0

0

0

0

0

0

0

1

|b|

Figure 134 A spreadsheet that implements the FORM analysis of liquefaction potential(after Phoon 2004)

in Section 133 the models such as Equation (1322) require only the bestestimates (ie the mean values) of the input variables and thus the calculatedprobabilities might be subject to error if the effect of parameter uncertaintiesis significant

Table 134 summarizes the solutions obtained by using the deterministicand the probabilistic approaches for Example No 3 and for Example No 4which is presented later The results obtained for Example No 3 from bothapproaches confirm field observation of no liquefaction However the cal-culated probability of liquefaction could still be as high as 042 even witha factor of safety of FS = 128 which reflects significant uncertainties in theparameters as well as in the limit state model

1337 Example No 4 probabilistic evaluation of aliquefied case

This example concerns a liquefied case that was analyzed previouslyusing the deterministic approach (See Example No 2 in Section 1323)

520 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Table 134 Summary of the deterministic and probabilistic solutions

Case FS Mean Standard Range of PLprobability PL deviation of PL PL

San Juan B-5(Example Nos 1 and 3)

128 030 0039 018ndash042

Ishinomaki-2(Example Nos 2 and 4)

055 091 0015 086ndash095

Field observation of the site indicated occurrence of liquefaction duringthe 1978 Miyagiken-Oki earthquake The mean values of seismic and soilparameters at the critical depth (37 m) are given as follows N160 = 5FC = 10 σ prime

v = 3628 kPaσv = 5883 kPa amax = 02 g and Mw = 74and the corresponding coefficients of variation of these parameters are 01800200 0164 0217 02 and 01 respectively (Cetin 2000)

Using the spreadsheet and the same procedure as described in ExampleNo 3 the following results are obtained PL = 091 and σPL

= 0015Estimating with the three sigma rule PL falls approximately in the rangeof 086ndash095 with a mean of 091 Similarly a preliminary estimate of theprobability of liquefaction may be obtained by Equation (1322) using onlythe mean values of the input variables For this liquefied case FS = 055 andthus PL = 092 according to Equation (1322) This PL value falls within therange of 086ndash095 determined by the probabilistic approach using FORMAlthough the general trend of the results obtained from Equation (1322)appears to be correct based on the two examples presented use of simplifiedmodels such as Equation (1322) for estimating the probability of liquefac-tion should be limited to preliminary analysis of cases where there is a lackof the knowledge of parameter uncertainties

Table 134 summaries the solutions obtained by using the deterministicand the probabilistic approaches for this liquefied case (Example No 4)The results obtained from both approaches confirm field observation ofliquefaction

Finally one observation of the standard deviation of the estimated prob-ability obtained in Example Nos 3 and 4 is perhaps worth mentioningIn general the standard deviation of the estimated mean probability is muchsmaller in the cases with extremely high or low probability (PL gt 09 orPL lt 01) than those with medium probability (03 lt PL lt 07) In the caseswith extremely high or low probability the potential for liquefaction or no-liquefaction is almost certain it will either liquefy or not liquefy This lowerdegree of uncertainty is reflected in the smaller standard deviation In thecases with medium probability there is a higher degree of uncertainty as towhether liquefaction will occur and thus it tends to have a larger standarddeviation in the estimated mean probability

Analysis of liquefaction potential 521

134 Probability of liquefaction in a givenexposure time

For performance-based earthquake engineering (PBEE) design it is oftennecessary to determine the probability of liquefaction of a soil at a site in agiven exposure time The probability of liquefaction obtained by reliabilityanalysis as presented in Section 133 (in particular in Example Nos 3 and4) is a conditional probability for a given set of seismic parameters amaxand Mw at a specified hazard level For a future case with uncertain seismicsources the probability of liquefaction in a given exposure time (PLT) may beobtained by integrating the conditional probability over all possible groundmotions at all hazard levels

PLT =sum

All pairs of (amax Mw)

p[L|(amaxMw)] middot p(amaxMw)

(1323)

where the term p[L|(amaxMw)] is the conditional probability of lique-faction given a pair of seismic parameters amax and Mw and the termp(amaxMw) is the joint probability of amax and Mw It is noted that thejoint probability p(amaxMw) may be thought of as the likelihood of an event(amax Mw) and the conditional probability of liquefaction p[L|(amaxMw)]as the consequence of the event Thus the product of p[L|(amaxMw)] andp(amaxMw) can be thought of as the weighted consequence of a single eventSince all mutually exclusive and collectively exhaustive events [ie all pos-sible pairs of (amax Mw)] are considered in Equation 1323 the sum ofall weighted consequences yields the total probability of liquefaction at thegiven site

While Equation (1323) is conceptually straightforward the implemen-tation of this equation is a significant undertaking Care must be exercisedto evaluate the joint probability of the pair (amax and Mw) for a local sitefrom different earthquake sources using appropriate attenuation and ampli-fication models This matter however is beyond the scope of this chapterand the reader is referred to Kramer et al (2007) and Juang et al (2008) fordetailed treatment of this subject

135 Summary

Evaluation of liquefaction potential of soils is an important task in geotechni-cal earthquake engineering In this chapter the simplified procedure based onthe standard penetration test (SPT) as recommended by Youd et al (2001) isadopted as the deterministic model for liquefaction potential evaluation Thismodel was established originally by Seed and Idriss (1971) based on in-situand laboratory tests and field observations of liquefactionno-liquefactionin the seismic events Field evidence of liquefaction generally consistedof surficial observations of sand boils ground fissures or lateral spreads

522 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Case histories of liquefactionno-liquefaction were collected mostly fromsites on level to gently sloping ground underlain by Holocene alluvial orfluvial sediments at shallow depths (lt 15 m) Thus the models presented inthis chapter are applicable only to sites with similar conditions

The focus of this chapter is on the probabilistic evaluation of liquefactionpotential using the SPT-based boundary curve recommended by Youd et al(2001) as the limit state model The probability of liquefaction is obtainedthrough reliability analysis using the first-order reliability method (FORM)The FORM analysis is carried out considering both parameter and modeluncertainties as well as the correlations among the input variables Proce-dures for estimating the parameter uncertainties in terms of coefficient ofvariation are outlined In particular the estimation of the seismic parame-ters amax and Mw is discussed in detail The limit state model based on Youdet al (2001) is characterized with a mean model factor of microc1 = 096 and astandard deviation of the mean σmicroc1 = 004 Use of these mean model fac-tor statistics for the estimation of the mean probability PL and its standarddeviation σPL

is illustrated in two examples Spreadsheet solutions specifi-cally developed for this probabilistic liquefaction evaluation using FORMare presented The spreadsheets can facilitate the use of FORM for predictingthe probability of liquefaction considering the mean and standard deviationof the input variables It may also be used to investigate the effect of thedegree of uncertainty of individual parameters on the calculated probabilityto aid in design considerations in cases where there is insufficient knowledgeof parameter uncertainties

The probability of liquefaction obtained in this chapter is a conditionalprobability at a given set of seismic parameters amax and Mw correspond-ing to a specified hazard level For a future case with uncertain seismicsources the probability of liquefaction in a given exposure time (PLT) may beobtained by integrating the conditional probability over all possible groundmotions at all hazard levels

References

Abrahamson N A and Silva W J (1997) Empirical response spectral attenua-tion relations for shallow crustal earthquakes Seismological Research Letters 6894ndash127

Ang A H-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Vol II Design Risk and Reliability John Wiley and SonsNew York

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley and Sons New York

Bazzurro P and Cornell C A (2004a) Ground-motion amplification in nonlin-ear soil sites with uncertain properties Bulletin of the Seismological Society ofAmerica 94(6) 2090ndash109

Analysis of liquefaction potential 523

Bazzurro P and Cornell C A (2004b) Nonlinear soil-site effects in probabilisticseismic-hazard analysis Bulletin of the Seismological Society of America 94(6)2110ndash23

Berrill J B and Davis R O (1985) Energy dissipation and seismic liquefaction ofsands Soils and Foundations 25(2) 106ndash18

Building Seismic Safety Council (1997) NEHRP Recommended Provisions forSeismic Regulations for New Buildings and Other Structures Part 2 CommentaryFoundation Design Requirements Washington DC

Campbell K W and Bozorgnia Y (2003) Updated near-source ground-motion(attenuation) relations for the horizontal and vertical components of peak groundacceleration and acceleration response spectra Bulletin of the SeismologicalSocoety of America 93 314ndash31

Carraro J A H Bandini P and Salgado R (2003) Liquefaction resistanceof clean and nonplastic silty sands based on cone penetration resistanceJournal of Geotechnical and Geoenvironmental Engineering ASCE 129(11)965ndash76

Cetin K O (2000) Reliability-based assessment of seismic soil liquefaction initiationhazard PhD dissertation University of California Berkeley CA

Cetin K O Seed R B Kiureghian A D Tokimatsu K Harder L FJr Kayen R E and Moss R E S (2004) Standard penetration test-basedprobabilistic and deterministic assessment of seismic soil liquefaction potentialJournal of Geotechnical and Geoenvironmental Engineering ASCE 130(12)1314ndash40

Choi Y and Stewart J P (2005) Nonlinear site amplification as function of 30 mshear wave velocity Earthquake Spectra 21(1) 1ndash30

Dai S H and Wang M O (1992) Reliability Analysis in Engineering ApplicationsVan Nostrand Reinhold New York

Davis R O and Berrill J B (1982) Energy dissipation and seismic liquefaction insands Earthquake Engineering and Structural Dynamics 10 59ndash68

Dief H M (2000) Evaluating the liquefaction potential of soils by the energymethod in the centrifuge PhD dissertation Case Western Reserve UniversityCleveland OH

Dobry R Ladd R S Yokel F Y Chung R M and Powell D (1982) Predictionof Pore Water Pressure Buildup and Liquefaction of Sands during Earthquakes bythe Cyclic Strain Method National Bureau of Standards Publication No NBS-138 Gaithersburg MD

Duncan J M (2000) Factors of safety and reliability in geotechnical engineer-ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

Figueroa J L Saada A S Liang L and Dahisaria M N (1994) Evaluation ofsoil liquefaction by energy principles Journal of Geotechnical Engineering ASCE120(9) 1554ndash69

Frankel A Harmsen S Mueller C et al (1997) USGS national seismichazard maps uniform hazard spectra de-aggregation and uncertainty InProceedings of FHWANCEER Workshop on the National Representation ofSeismic Ground Motion for New and Existing Highway Facilities NCEERTechnical Report 97-0010 State University of New York at Buffalo New Yorkpp 39ndash73

524 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Green R (2001) The application of energy concepts to the evaluation of remedia-tion of liquefiable soils PhD dissertation Virginia Polytechnic Institute and StateUniversity Blacksburg VA

Gutierrez M Duncan J M Woods C and Eddy E (2003) Development of asimplified reliability-based method for liquefaction evaluation Final TechnicalReport USGS Grant No 02HQGR0058 Virginia Polytechnic Institute and StateUniversity Blacksburg VA

Haldar A and Tang W H (1979) Probabilistic evaluation of liquefaction potentialJournal of Geotechnical Engineering ASCE 104(2) 145ndash62

Hasofer A M and Lind N C (1974) Exact and invariant second momentcode format Journal of the Engineering Mechanics Division ASCE 100(EM1)111ndash21

Harr M E (1987) Reliability-based Design in Civil Engineering McGraw-HillNew York

Hynes M E and Olsen R S (1999) Influence of confining stress on liquefactionresistance In Proceedings International Workshop on Physics and Mechanics ofSoil Liquefaction Balkema Rotterdam pp 145ndash52

Idriss I M and Boulanger R W (2006) ldquoSemi-empirical procedures for evalu-ating liquefaction potential during earthquakesrdquo Soil Dynamics and EarthquakeEngineering Vol 26 115ndash130

Ishihara K (1993) Liquefaction and flow failure during earthquakes The 33rdRankine lecture Geacuteotechnique 43(3) 351ndash415

Juang C H Rosowsky D V and Tang W H (1999) Reliability-based methodfor assessing liquefaction potential of soils Journal of Geotechnical and Geoenvi-ronmental Engineering ASCE 125(8) 684ndash9

Juang C H Chen C J Rosowsky D V and Tang W H (2000) CPT-based liquefaction analysis Part 2 Reliability for design Geacuteotechnique 50(5)593ndash9

Juang C H Jiang T and Andrus R D (2002) Assessing probability-based meth-ods for liquefaction evaluation Journal of Geotechnical and GeoenvironmentalEngineering ASCE 128(7) 580ndash9

Juang C H Yang S H Yuan H and Khor E H (2004) Characterization ofthe uncertainty of the Robertson and Wride model for liquefaction potential SoilDynamics and Earthquake Engineering 24(9ndash10) 771ndash80

Juang C H Fang S Y and Khor E H (2006) First-order reliabilitymethod for probabilistic liquefaction triggering analysis using CPTJournal of Geotechnical and Geoenvironmental Engineering ASCE 132(3)337ndash50

Juang C H Li D K Fang S Y Liu Z and Khor E H (2008) A simplified proce-dure for developing joint distribution of amax and Mw for probabilistic liquefactionhazard analysis Journal of Geotechnical and Geoenvironmental EngineeringASCE in press

Kramer S L (1996) Geotechnical Earthquake Engineering Prentice-HallEnglewood Cliffs NJ

Kramer S L and Elgamal A (2001) Modeling Soil Liquefaction Hazards forPerformance-Based Earthquake Engineering Report No 200113 PacificEarthquake Engineering Research (PEER) Center University of CaliforniaBerkeley CA

Analysis of liquefaction potential 525

Kramer S L and Mayfield R T (2007) ldquoReturn period of soil liquefactionrdquoJournal of Geotechnical and Geoenvironmental Engineering ASCE 133(7)802ndash13

Liang L Figueroa J L and Saada A S (1995) Liquefaction under randomloading unit energy approach Journal of Geotechnical and GeoenvironmentalEngineering 121(11) 776ndash81

Liao S S C and Whitman R V (1986) Overburden correction factors for SPT insand Journal of Geotechnical Engineering ASCE 112(3) 373ndash7

Liao S S C Veneziano D and Whitman R V (1988) Regression model forevaluating liquefaction probability Journal of Geotechnical Engineering ASCE114(4) 389ndash410

Low B K (1996) Practical probabilistic approach using spreadsheet In Uncer-tainty in the Geologic Environment from Theory to Practice Geotechnical SpecialPublication No 58 Eds C D Shackelford P P Nelson and M J S Roth ASCEReston VA pp 1284ndash302

Low B K and Tang W H (1997) Efficient reliability evaluation using spreadsheetJournal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K (2005) Reliability-based design applied to retaining walls Geacuteotechnique55(1) 63ndash75

Marcuson W F III (1978) Definition of terms related to liquefaction JournalGeotechnical Engineering Division ASCE 104(9) 1197ndash200

Melchers R E (1999) Structural Reliability Analysis and Prediction 2nd edWiley Chichester UK

NEHRP (1998) NEHRP Recommended Provisions for Seismic Regulationsfor New Buildings and Other Structures Part 1 ndash Provisions FEMA 302Part 2 ndash Commentary FEMA 303 Federal Emergency Management AgencyWashington DC

National Research Council (1985) Liquefaction of Soil during Earthquake NationalResearch Council National Academy Press Washington DC

Ostadan F Deng N and Arango I (1996) Energy-based Method forLiquefaction Potential Evaluation Phase 1 Feasibility Study Report NoNIST GCR 96-701 National Institute of Standards and TechnologyGaithersburg MD

Phoon K K (2004) General Non-Gaussian Probability Models for First OrderReliability Method (FORM) A State-of-the Art Report ICG Report 2004-2-4 (NGI Report 20031091-4) International Center for Geohazards OsloNorway

Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanada Geotechnical Journal 36 612ndash24

Phoon K K and Kulhawy F H (2005) Characterization of modeluncertainties for laterally loaded rigid drilled shafts Geacuteotechnique 55(1)45ndash54

Sadigh K Chang C -Y Egan J A Makdisi F and Youngs R R (1997) Attenu-ation relations for shallow crustal earthquakes based on California strong motiondata Seismological Research Letters 68 180ndash9

Seed H B and Idriss I M (1971) Simplified procedure for evaluating soil lique-faction potential Journal of the Soil Mechanics and Foundation Division ASCE97(9) 1249ndash73

526 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Seed H B and Idriss I M (1982) lsquoGround Motions and Soil Liquefactionduring Earthquakes Earthquake Engineering Research Center Monograph EERIBerkeley CA

Seed H B and Lee K L (1966) Liquefaction of saturated sands during cyclicloading Journal of the Soil Mechanics and Foundations Division ASCE 92(SM6)Proceedings Paper 4972 Nov 105ndash34

Seed H B Tokimatsu K Harder L F and Chung R (1985) Influence of SPTprocedures in soil liquefaction resistance evaluations Journal of GeotechnicalEngineering ASCE 111(12) 1425ndash45

Skempton A K (1986) Standard penetration test procedures and the effects in sandsof overburden pressure relative density particle size aging and overconsolidationGeacuteotechnique 36(3) 425ndash47

Stewart J P Liu A H and Choi Y (2003) Amplification factors for spectral accel-eration in tectonically active regions Bulletin of Seismological Society of America93(1) 332ndash52

Yang S H (2003) Reliability analysis of soil liquefaction using in situ tests PhDdissertation Clemson University Clemson SC

Youd T L and Idriss I M Eds (1997) Proceedings of the NCEER Workshopon Evaluation of Liquefaction Resistance of Soils Technical report NCEER-97-0022 National Center for Earthquake Engineering Research State University ofNew York at Buffalo Buffalo NY

Youd T L Idriss I M Andrus R D Arango I Castro G Christian J TDobry R Liam Finn W D Harder L F Jr Hynes M E Ishihara KKoester J P Laio S S C Marcuson W F III Martin G R Mitchell J KMoriwaki Y Power M S Robertson P K Seed R B and Stokoe K H II(2001) Liquefaction resistance of soils summary report from the 1996 NCEERand 1998 NCEERNSF workshops on evaluation of liquefaction resistance of soilsJournal of Geotechnical and Geoenvironmental Engineering ASCE 127(10)817ndash33

Zhang L Tang W H Zhang L and Zheng J (2004) Reducing uncer-tainty of prediction from empirical correlations Journal of Geotechnical andGeoenvironmental Engineering ASCE 130(5) 526ndash34

Index

17th St Canal 452 491

acceptance criteria 403ndash8action 299 300 307ndash10 316 327AFOSM 465ndash6allowable displacement 345ndash6 369anchored sheet pile wall 142 145 151Arias Intensity 247ndash50augered cast-in-place pile 346

349 357autocorrelation 82ndash6axial compression 346ndash8

Bayesian estimation 108ndash9Bayesian theory 101 108Bayesian updating 431ndash9bearing capacity 136ndash8 140ndash1 225ndash6

254ndash5 389ndash92bearing resistance 317 322 323 326berm 228ndash9 451 479ndash80bivariate probability model

357ndash9borrow pit 450breach 451 452 459 491 494

calibration of partial factors 319ndash21Case Pile Wave Analysis Program

(CAPWAP) 392Cauchy distribution 174characteristic value 147 244 309 316closed-form solutions 27ndash32coefficient of correlation 512ndash13coefficient of variation 219ndash20

238 247 252 283 287 387ndash8309 399 401 464 473ndash4482ndash3

combination factor 309ndash10conditional probability 521ndash2

conditional probability of failurefunction 459ndash61 470 471 477482 484 486

cone tip resistance 227 229consequences classes 321constrained optimization 135 137

139 163ndash4construction control 392correlation distance 87 217 232 236correlation factor 315correlation matrix 137 151 162ndash3covariance matrix 18ndash21 45crevasse 451crown 450ndash2 460ndash1 463cutoffs 451cyclic resistance ratio 501cyclic stress ratio 500ndash1

decision criteria 195ndash201decision making 192 202 204 210

213 220ndash1decision tree 193ndash5deformation factor 376ndash8Design Approaches 316ndash19

322ndash3design life 202 204 206ndash7 209design point 141ndash2 146ndash7 164ndash5

180ndash1design value 298 313 323ndash4deterministic approach 137 500ndash5deterministic model 281ndash2 464ndash5

471ndash3 478dikes 448ndash50 455ndash6displacement analysis 421ndash2drained strength 463ndash4drilled shaft 349 356ndash7 371 373

377ndash81

528 Index

earthquake 497ndash500 508ndash9511ndash13 521

erosion 423 454 477ndash84 486Eurocode 7 9 145 298ndash341Excel Solver 156 164excess pore water pressure 241ndash9expansion optimal linear estimation

method (EOLE) 265ndash6exponential distribution 174

factor of safety 99ndash100 104ndash5 160ndash1202ndash3 365 386 398 405 407

failure mechanism 225 238 254failure modes 37ndash9 452ndash4 463ndash84failure probability 177 182 184 288

417ndash21 439first-order reliability method (FORM)

32ndash9 58ndash9 261 278ndash80 364ndash5515ndash16

first-order second moment method261 413 416

flood water elevation 459ndash63 485486 487ndash8

floodwalls 448ndash50FORM analyses 280 285 287 325ndash9

429 517 519foundation displacement 361fragility curves 244ndash5 250ndash3Frechet distribution 174

Generalized Pareto distribution 175Geotechnical Categories 302ndash7geotechnical risk 302ndash7geotechnical uncertainties 6ndash9global sensitivity analysis 275ndash6Gumbel distribution 174

Hasofer-Lind reliability index 140ndash2Hermite polynomials 15ndash18 274 293

353 358horizontal correlation distance

216ndash17 249

importance sampling 179ndash81 280interpolation 116ndash17

Johnson system 10ndash15

Karhunen-Loegraveve expansion 264ndash5kurtosis 10 16 290

landside 450ndash3levees 448ndash51

level crossing 123ndash5limit equilibrium analysis 415ndash20limit state 140ndash2 283ndash7 505ndash7limit state design concept 300ndash1line of protection 451liquefaction 240ndash4 245ndash9 505ndash7 521liquefaction potential 227 497ndash522Load and Resistance Factor Design

(LRFD) 376ndash9 56ndash8load factor 338ndash40load tests 389ndash92 402ndash8load-displacement curves 345ndash9 372

374 379ndash80low discrepancy sequences 170ndash2lower-bound capacity 205ndash8 213ndash15lumped factor of safety 137ndash9 143ndash4

marginal distribution 351ndash4Markov chain Monte Carlo (MCMC)

simulation 181ndash7materials factor approach (MFA) 318MATLAB codes 50ndash63maximum likelihood estimation 103

106ndash8measurement noise 78 90ndash3 97Metropolis-Hastings algorithm 47

189ndash91model calibration 210ndash20model errors 337ndash8model factor 329 365ndash7 507 513ndash18model uncertainty 96 319 513ndash15moment estimation 102ndash3Monte Carlo analysis 331ndash4Monte Carlo simulation 160ndash1

169ndash90 176ndash9 233ndash4 240 242277ndash8

most probable failure point 137140ndash1 158

New Orleans 125ndash9 452 491ndash4non-intrusive methods 261 271ndash4 292nonnormal distributions 149ndash51

offshore structures 192ndash4 199overtopping 452

parameter uncertainty 507ndash12partial action factor 308partial factor method 302 307ndash8

322ndash5partial factors 137ndash9 316ndash21partial material factor 299 308 318

Index 529

partial resistance factor 219ndash20317ndash18

peak ground acceleration (PGA)247ndash8 509ndash12

Pearson system 10ndash11permanent action 318permeability 472ndash5 480ndash1pile capacity 197 206ndash7 213ndash20 386

387ndash92pile foundations 402 407 408pile resistance 315ndash16polynomial chaos expansions 267ndash9pore pressure 423ndash4positive definite correlation matrix

162ndash3pressure-injected footing 349 354

356ndash7probabilistic approach 505ndash20probabilistic hyperbolic model 345

346ndash51 361ndash70 379probabilistic model 4 465ndash6probability 479 492ndash3 502ndash3 509

514ndash20 521probability density function 160ndash1

182ndash3 229 261ndash2probability of failure 26ndash32 66

69 78 125ndash6 141ndash2 160ndash1 321326 328ndash9 331 333 338ndash41470ndash1 486

product-moment correlation 349 359proof load tests 389ndash92 398 402ndash8pseudo random numbers 169ndash70 173

quality assurance 392quasi random numbers 170ndash2

random field 111ndash29 263ndash4random number generation 169ndash76random process 22ndash6random variables 6ndash18 52ndash3 66

463ndash4 467 473 480ndash1random vector 18ndash22 262ndash3 266

267ndash8rank model 359ndash61reach 462ndash91reliability 125ndash8 497 522reliability index 137ndash42 151ndash2reliability-based design 142ndash51 301ndash2

386ndash7 402ndash3 370relief wells 451representative value 309resistance factor approach (RFA) 318

response surface 163 274riprap 451riverside 450ndash3 471ndash2

sample moments 10 104 112sand boil 453ndash4 473scale of fluctuation 22ndash3 119ndash20Schwartzrsquos method 478ndash80second-order reliability method

(SORM) 69ndash70 66 261seepage berm 451serviceability limit state (SLS) 300

320ndash1 344ndash80SHANSEP 95ndash101skewness 10 16slope stability 152ndash61 463ndash71Sobolrsquo indices 275ndash6soil borings 215ndash20soil liquefaction 240ndash4soil properties 11 76ndash95 127 224ndash33

236 424ndash6soil property variability 6 377 379ndash80soil variability 225 232 244 251ndash3spatial variability 78ndash9 82 96ndash7 129

215ndash20spectral approach 22ndash3Spencer method of slices 134

152ndash9spread foundation 311 322ndash5

331ndash4spreadsheet 134ndash66 515ndash20stability berm 451standard penetration test 228

497ndash522stochastic data 6ndash26stochastic finite element methods

260ndash94strength 463ndash4structural reliability 32 261 276ndash80subset MCMC 61ndash3 169 181ndash7system reliability 36ndash9 208ndash10

target reliability index 321369ndash70 402

Taylorrsquos series - finite differencemethod 466ndash7 474

throughseepage 453ndash4 477ndash84toe 313ndash14 450ndash1 453 460ndash2 467

471 474ndash5 479ndash80translation model 12 13 20ndash2 49

357ndash9trend analysis 79ndash82

530 Index

ultimate limit state (ULS) 300308 316 321ndash3 332ndash3338ndash40 344 363 365370ndash1

underseepage 451 453 455 466471ndash7 485

undrained strength 93 126ndash8 463464 467

undrained uplift 377ndash8

US Army Corps of Engineers 456478 491

variable action 309variance reduction function

119ndash20variogram 109ndash11 113 117

Weibull distribution 175

  • Book Cover
  • Title
  • Copyright
  • Contents
  • List of contributors
  • Chapter 1 Numerical recipes for reliability analysis ndash a primer
  • Chapter 2 Spatial variability and geotechnical reliability
  • Chapter 3 Practical reliability approach using spreadsheet
  • Chapter 4 Monte Carlo simulation in reliability analysis
  • Chapter 5 Practical application of reliability-based design in decision-making
  • Chapter 6 Randomly heterogeneous soils under static and dynamic loads
  • Chapter 7 Stochastic finite element methods in geotechnical engineering
  • Chapter 8 Eurocode 7 and reliability-based design
  • Chapter 9 Serviceability limit state reliability-based design
  • Chapter 10 Reliability verification using pile load tests
  • Chapter 11 Reliability analysis of slopes
  • Chapter 12 Reliability of levee systems
  • Chapter 13 Reliability analysis of liquefaction potential of soils using standard penetration test
  • Index
Page 2: Reliability-Based Design in Geotechnical Engineering

Reliability-Based Design inGeotechnical Engineering

Also available from Taylor amp Francis

Geological HazardsFred Bell Hb ISBN 0419-16970-9

Pb ISBN 0415-31851-3

Rock Slope EngineeringDuncan Wyllie and Chris Mah Hb ISBN 0415-28000-1

Pb ISBN 0415-28001-X

Geotechnical ModellingDavid Muir Wood Hb ISBN 9780415343046

Pb ISBN 9780419237303

Soil LiquefactionMike Jefferies and Ken Been Hb ISBN 9780419161707

Advanced Unsaturated SoilMechanics and EngineeringCharles WW Ng and Bruce Menzies Hb ISBN 9780415436793

Advanced Soil Mechanics 3rd editionBraja Das Hb ISBN 9780415420266

Pile Design and Construction Practice5th editonMichael Tomlinson and John Woodward Hb ISBN 9780415385824

Reliability-Based Design inGeotechnical Engineering

Computations and Applications

Kok-Kwang Phoon

First published 2008by Taylor amp Francis2 Park Square Milton Park Abingdon Oxon OX14 4RN

Simultaneously published in the USA and Canadaby Taylor amp Francis270 Madison Avenue New York NY 10016 USA

Taylor amp Francis is an imprint of the Taylor amp Francis Groupan informa business

copy Taylor amp Francis

All rights reserved No part of this book may be reprinted or reproducedor utilised in any form or by any electronic mechanical or other meansnow known or hereafter invented including photocopying and recordingor in any information storage or retrieval system without permission inwriting from the publishers

The publisher makes no representation express or implied with regardto the accuracy of the information contained in this book and cannotaccept any legal responsibility or liability for any efforts or omissions thatmay be made

British Library Cataloguing in Publication DataA catalogue record for this book is availablefrom the British Library

Library of Congress Cataloging in Publication DataPhoon Kok-Kwang

Reliability-based design in geotechnical engineering computations andapplicationsKok-Kwang Phoon

p cmIncludes bibliographical references and indexISBN 978-0-415-39630-1 (hbk alk paper) ndash ISBN 978-0-203-93424-1

(e-book) 1 Rock mechanics 2 Soil mechanics 3 Reliability I Title

TA706P48 20086241prime51ndashdc22 2007034643

ISBN10 0-415-39630-1 (hbk)IBSN10 0-213-93424-5 (ebk)

ISBN13 978-0-415-39630-1 (hbk)ISBN13 978-0-203-93424-1 (ebk)

ISBN 0-203-93424-5 Master e-book ISBN

ldquoTo purchase your own copy of this or any of Taylor amp Francis or Routledgersquoscollection of thousands of eBooks please go to wwweBookstoretandfcoukrdquo

This edition published in the Taylor amp Francis e-Library 2008

Contents

List of contributors vii

1 Numerical recipes for reliability analysis ndash a primer 1KOK-KWANG PHOON

2 Spatial variability and geotechnical reliability 76GREGORY B BAECHER AND JOHN T CHRISTIAN

3 Practical reliability approach using spreadsheet 134BAK KONG LOW

4 Monte Carlo simulation in reliability analysis 169YUSUKE HONJO

5 Practical application of reliability-based design indecision-making 192ROBERT B GILBERT SHADI S NAJJAR YOUNG-JAE CHOI AND

SAMUEL J GAMBINO

6 Randomly heterogeneous soils under static anddynamic loads 224RADU POPESCU GEORGE DEODATIS AND JEAN-HERVEacute PREacuteVOST

7 Stochastic finite element methods in geotechnicalengineering 260BRUNO SUDRET AND MARC BERVEILLER

8 Eurocode 7 and reliability-based design 298TREVOR L L ORR AND DENYS BREYSSE

vi Contents

9 Serviceability limit state reliability-based design 344KOK-KWANG PHOON AND FRED H KULHAWY

10 Reliability verification using pile load tests 385LIMIN ZHANG

11 Reliability analysis of slopes 413TIEN H WU

12 Reliability of levee systems 448THOMAS F WOLFF

13 Reliability analysis of liquefaction potential of soilsusing standard penetration test 497CHARNG HSEIN JUANG SUNNY YE FANG AND DAVID KUN LI

Index 527

List of contributors

Gregory B Baecher is Glenn L Martin Institute Professor of Engineeringat the University of Maryland He holds a BSCE from UC Berkeley andPhD from MIT His principal area of work is engineering risk manage-ment He is co-author with JT Christian of Reliability and Statistics inGeotechnical Engineering (Wiley 2003) and with DND Hartford ofRisk and Uncertainty in Dam Safety (Thos Telford 2004) He is recipientof the ASCE Middlebrooks and State-of-the-Art Awards and a memberof the US National Academy of Engineering

Marc Berveiller received his masterrsquos degree in mechanical engineering fromthe French Institute in Advanced Mechanics (Clermont-Ferrand France2002) and his PhD from the Blaise Pascal University (Clermont-FerrandFrance) in 2005 where he worked on non-intrusive stochastic finiteelement methods in collaboration with the French major electrical com-pany EDF He is currently working as a research engineer at EDF onprobabilistic methods in mechanics

Denys Breysse is a Professor of Civil Engineering at Bordeaux 1 UniversityFrance He is working on randomness in soils and building materialsteaches geotechnics materials science and risk and safety He is the chair-man of the French Association of Civil Engineering University Members(AUGC) and of the RILEM Technical Committee TC-INR 207 on Non-Destructive Assessment of Reinforced Concrete Structures He has alsocreated (and chaired 2003ndash2007) a national research scientific networkon Risk Management in Civil Engineering (MR-GenCi)

Young-Jae Choi works for Geoscience Earth amp Marine Services Inc inHouston Texas He received his Bachelor of Engineering degree in 1995from Pusan National University his Master of Science degree from theUniversity of Colorado at Boulder in 2002 and his PhD from TheUniversity of Texas at Austin in 2007 He also has six years of practicalexperience working for Daelim Industry Co Ltd in South Korea

viii List of contributors

John T Christian is a consulting geotechnical engineer in WabanMassachusetts He holds BSc MSc and PhD degrees from MIT Hisprincipal areas of work are applied numerical methods in soil dynamicsand earthquake engineering and reliability methods A secondary inter-est has been the evolving procedures and standards for undergraduateeducation especially as reflected in the accreditation process He was the39th Karl Terzaghi Lecturer of the ASCE He is recipient of the ASCEMiddlebrooks and the BSCE Desmond Fitzgerald Medal and a memberof the US National Academy of Engineering

George Deodatis received his Diploma in Civil Engineering from theNational Technical University of Athens in Greece He holds MS andPhD degrees in Civil Engineering from Columbia University He startedhis academic career at Princeton University where he served as a Post-doctoral Fellow Assistant Professor and eventually Associate ProfessorHe subsequently moved to Columbia University where he is currently theSantiago and Robertina Calatrava Family Professor at the Department ofCivil Engineering and Engineering Mechanics

Sunny Ye Fang is currently a consulting geotechnical engineer withArdaman amp Associates Inc She received her BS degree from NingboUniversity Ningbo China MSCE degree from Zhejiang UniversityHangzhou Zhejiang China and PhD degree in Civil Engineering fromClemson University South Carolina Dr Fang has extensive consultingprojects experience in the field of geoenvironmental engineering

Samuel J Gambino has ten years of experience with URS Corporation inOakland California His specialties include foundation design tunnelinstallations and dams reservoirs and levees Samuel received his bach-elors degree in civil and environmental engineering from the Universityof Michigan at Ann Arbor in 1995 his masters degree in geotechnicalengineering from the University of Texas at Austin in 1998 and has helda professional engineers license in the State of California since 2002

Robert B Gilbert is the Hudson Matlock Professor in Civil Architecturaland Environmental Engineering at The University of Texas at AustinHe joined the faculty in 1993 Prior to that he earned BS (1987) MS(1988) and PhD (1993) degrees in civil engineering from the Universityof Illinois at Urbana-Champaign He also practiced with Golder Asso-ciates Inc as a geotechnical engineer from 1988 to 1993 His expertise isthe assessment evaluation and management of risk in civil engineeringApplications include building foundations slopes pipelines dams andlevees landfills and groundwater and soil remediation systems

Yusuke Honjo is currently a professor and the head of the Civil Engineer-ing Department at Gifu University in Japan He holds ME from Kyoto

List of contributors ix

University and PhD from MIT He was an associate professor and divi-sion chairman at Asian Institute of Technology (AIT) in Bangkok between1989 and 1993 and joined Gifu University in 1995 He is currentlythe chairperson of ISSMGE-TC 23 lsquoLimit state design in geotechnicalengineering practicersquo He has published more than 100 journal papersand international conference papers in the area of statistical analyses ofgeotechnical data inverse analysis and reliability analyses of geotechnicalstructures

Charng Hsein Juang is a Professor of Civil Engineering at Clemson Universityand an Honorary Chair Professor at National Central University TaiwanHe received his BS and MS degrees from National Cheng Kung Universityand PhD degree from Purdue University Dr Juang is a registered Profes-sional Engineering in the State of South Carolina and a Fellow of ASCEHe is recipient of the 1976 Outstanding Research Paper Award ChineseInstitute of Civil and Hydraulic Engineering the 2001 TK Hsieh Awardthe Institution of Civil Engineers United Kingdom and the 2006 BestPaper Award the Taiwan Geotechnical Society Dr Juang has authoredmore than 100 refereed papers in geotechnical related fields and is proudto be selected by his students at Clemson University as the Chi EpsilonOutstanding Teacher in 1984

Fred H Kulhawy is Professor of Civil Engineering at Cornell Univer-sity Ithaca New York He received his BSCE and MSCE from NewJersey Institute of Technology and his PhD from University of Californiaat Berkeley His teaching and research focuses on foundations soil-structure interaction soil and rock behavior and geotechnical computerand reliability applications and he has authored over 330 publicationsHe has lectured worldwide and has received numerous awards fromASCE ADSC IEEE and others including election to Distinguished Mem-ber of ASCE and the ASCE Karl Terzaghi Award and Norman Medal Heis a licensed engineer and has extensive experience in geotechnical practicefor major projects on six continents

David Kun Li is currently a consulting geotechnical engineer with GolderAssociates Inc He received his BSCE and MSCE degrees from ZhejiangUniversity Hangzhou Zhejiang China and PhD degree in Civil Engi-neering from Clemson University South Carolina Dr Li has extensiveconsulting projects experience on retaining structures slope stabilityanalysis liquefaction analysis and reliability assessments

Bak Kong Low obtained his BS and MS degrees from MIT and PhD degreefrom UC Berkeley He is a Fellow of the ASCE and a registered pro-fessional engineer of Malaysia He currently teaches at the NanyangTechnological University in Singapore He had done research while onsabbaticals at HKUST (1996) University of Texas at Austin (1997)

x List of contributors

and Norwegian Geotechnical Institute (2006) His research interest andpublications can be found at httpalummiteduwwwbklow

Shadi S Najjar joined the American University of Beirut (AUB) as an Assis-tant Professor in Civil Engineering in September 2007 Dr Najjar earnedhis BE and ME in Civil Engineering from AUB in 1999 and 2001 respec-tively In 2005 he graduated from the University of Texas at Austin witha PhD in civil engineering Dr Najjarrsquos research involves analytical stud-ies related to reliability-based design in geotechnical engineering Between2005 and 2007 Dr Najjar taught courses on a part-time basis in severalleading universities in Lebanon In addition he worked with Polytech-nical Inc and Dar Al-Handasah Consultants (Shair and Partners) as ageotechnical engineering consultant

Trevor Orr is a Senior Lecturer at Trinity College Dublin He obtainedhis PhD degree from Cambridge University He has been involved inEurocode 7 since the first drafting committee was established in 1981In 2003 he was appointed Chair of the ISSMGE European Technical Com-mittee 10 for the Evaluation of Eurocode 7 and in 2007 was appointeda member of the CEN Maintenance Group for Eurocode 7 He is theco-author of two books on Eurocode 7

Kok-Kwang Phoon is Director of the Centre for Soft Ground Engineering atthe National University of Singapore His main research interest is relatedto development of risk and reliability methods in geotechnical engineer-ing He has authored more than 120 scientific publications includingmore than 20 keynoteinvited papers and edited 15 proceedings He is thefounding editor-in-chief of Georisk and recipient of the prestigious ASCENormal Medal and ASTM Hogentogler Award Webpage httpwwwengnusedusgcivilpeoplecvepkkpkkhtml

Radu Popescu is a Consulting Engineer with URS Corporation and aResearch Professor at Memorial University of Newfoundland CanadaHe earned PhD degrees from the Technical University of Bucharest andfrom Princeton University He was a Visiting Research Fellow at PrincetonUniversity Columbia University and Saitama University (Japan) andLecturer at the Technical University of Bucharest (Romania) andPrinceton University Radu has over 25 years experience in computationaland experimental soil mechanics (dynamic soil-structure interaction soilliquefaction centrifuge modeling site characterization) and publishedover 100 articles in these areas In his research he uses the tools ofprobabilistic mechanics to address various uncertainties manifested in thegeologic environment

Jean H Prevost is presently Professor of Civil and Environmental Engineer-ing at Princeton University He is also an affiliated faculty at the Princeton

List of contributors xi

Materials Institute in the department of Mechanical and AerospaceEngineering and in the Program in Applied and Computational Math-ematics He received his MSc in 1972 and his PhD in 1974 from StanfordUniversity He was a post-doctoral Research Fellow at the NorwegianGeotechnical Institute in Oslo Norway (1974ndash1976) and a ResearchFellow and Lecturer in Civil Engineering at the California Institute ofTechnology (1976ndash1978) He held visiting appointments at the EcolePolytechnique in Paris France (1984ndash1985 2004ndash2005) at the EcolePolytechnique in Lausanne Switzerland (1984) at Stanford University(1994) and at the Institute for Mechanics and Materials at UCSD (1995)He was Chairman of the Department of Civil Engineering and Opera-tions Research at Princeton University (1989ndash1994) His principal areasof interest include dynamics nonlinear continuum mechanics mixturetheories finite element methods XFEM and constitutive theories He isthe author of over 185 technical papers in his areas of interest

Bruno Sudret has a masterrsquos degree from Ecole Polytechnique (France 1993)a masterrsquos degree in civil engineering from Ecole Nationale des Ponts etChausseacutees (1995) and a PhD in civil engineering from the same insti-tution (1999) After a post-doctoral stay at the University of Californiaat Berkeley in 2000 he joined in 2001 the RampD Division of the Frenchmajor electrical company EDF He currently manages a research group onprobabilistic engineering mechanics He is member of the Joint Commit-tee on Structural Safety since 2004 and member of the board of directorsof the International Civil Engineering Risk and Reliability Associationsince 2007 He received the Jean Mandel Prize in 2005 for his work onstructural reliability and stochastic finite element methods

Thomas F Wolff is an Associate Dean of Engineering at Michigan StateUniversity From 1970 to 1985 he was a geotechnical engineer with theUS Army Corps of Engineers and in 1986 he joined MSU His researchand consulting has focused on design and reliability analysis of damslevees and hydraulic structures He has authored a number of Corps ofEngineersrsquo guidance documents In 2005 he served on the ASCE LeveeAssessment Team in New Orleans and in 2006 he served on the InternalTechnical Review (ITR) team for the IPET report which analyzed theperformance of the New Orleans Hurricane Protection System

Tien H Wu is a Professor Emeritus at Ohio State University He receivedhis BS from St Johns University Shanghai China and MS and PhDfrom University of Illinois He has lectured and conducted researchat Norwegian Geotechnical Institute Royal Institute of Technology inStockholm Tonji University in Shanghai National Polytechnical Insti-tute in Quito Cairo University Institute of Forest Research in ChristChurch and others His teaching research and consulting activities

xii List of contributors

involve geotechnical reliability slope stability soil properties glaciologyand soil-bioengineering He is the author of two books Soil Mechanicsand Soil Dynamics He is an Honorary Member of ASCE and has receivedASCErsquos State-of-the-Art Award Peck Award and Earnest Award and theUS Antarctic Service Medal He was the 2008 Peck Lecturer of ASCE

Limin Zhang is an Associate Professor of Civil Engineering and AssociateDirector of Geotechnical Centrifuge Facility at the Hong Kong Universityof Science and Technology His research areas include pile foundationsdams and slopes centrifuge modelling and geotechnical risk and relia-bility He is currently secretary of Technical Committee TC23 on lsquoLimitState Design in Geotechnical Engineeringrsquo and member of TC18 on lsquoDeepFoundationsrsquo of the ISSMGE and Vice Chair of the International Press-InAssociation

Chapter 1

Numerical recipes forreliability analysis ndash a primer

Kok-Kwang Phoon

11 Introduction

Currently the geotechnical community is mainly preoccupied with the tran-sition from working or allowable stress design (WSDASD) to Load andResistance Factor Design (LRFD) The term LRFD is used in a loose wayto encompass methods that require all limit states to be checked using aspecific multiple-factor format involving load and resistance factors Thisterm is used most widely in the United States and is equivalent to LimitState Design (LSD) in Canada Both LRFD and LSD are philosophicallyakin to the partial factors approach commonly used in Europe although adifferent multiple-factor format involving factored soil parameters is usedOver the past few years Eurocode 7 has been revised to accommodate threedesign approaches (DAs) that allow partial factors to be introduced at thebeginning of the calculations (strength partial factors) or at the end of thecalculations (resistance partial factors) or some intermediate combinationsthereof The emphasis is primarily on the re-distribution of the original globalfactor safety in WSD into separate load and resistance factors (or partialfactors)

It is well accepted that uncertainties in geotechnical engineering design areunavoidable and numerous practical advantages are realizable if uncertain-ties and associated risks can be quantified This is recognized in a recentNational Research Council (2006) report on Geological and Geotechni-cal Engineering in the New Millennium Opportunities for Research andTechnological Innovation The report remarked that ldquoparadigms for dealingwith hellip uncertainty are poorly understood and even more poorly practicedrdquoand advocated a need for ldquoimproved methods for assessing the potentialimpacts of these uncertainties on engineering decisions helliprdquo Within the arenaof design code development increasing regulatory pressure is compellinggeotechnical LRFD to advance beyond empirical re-distribution of the orig-inal global factor of safety to a simplified reliability-based design (RBD)framework that is compatible with structural design RBD calls for a willing-ness to accept the fundamental philosophy that (a) absolute reliability is an

2 Kok-Kwang Phoon

unattainable goal in the presence of uncertainty and (b) probability theorycan provide a formal framework for developing design criteria that wouldensure that the probability of ldquofailurerdquo (used herein to refer to exceedingany prescribed limit state) is acceptably small Ideally geotechnical LRFDshould be derived as the logical end-product of a philosophical shift in mind-set to probabilistic design in the first instance and a simplification of rigorousRBD into a familiar ldquolook and feelrdquo design format in the second The needto draw a clear distinction between accepting reliability analysis as a nec-essary theoretical basis for geotechnical design and downstream calibrationof simplified multiple-factor design formats with emphasis on the formerwas highlighted by Phoon et al (2003b) The former provides a consis-tent method for propagation of uncertainties and a unifying frameworkfor risk assessment across disciplines (structural and geotechnical design)and national boundaries Other competing frameworks have been suggested(eg λ-method by Simpson et al 1981 worst attainable value method byBolton 1989 Taylor series method by Duncan 2000) but none has thetheoretical breadth and power to handle complex real-world problems thatmay require nonlinear 3D finite element or other numerical approaches forsolution

Simpson and Yazdchi (2003) proposed that ldquolimit state design requiresanalysis of un-reality not of reality Its purpose is to demonstrate thatlimit states are in effect unreal or alternatively that they are lsquosufficientlyunlikelyrsquo being separated by adequate margins from expected statesrdquo It isclear that limit states are ldquounlikelyrdquo states and the purpose of design is toensure that expected states are sufficiently ldquofarrdquo from these limit states Thepivotal point of contention is how to achieve this separation in numericalterms (Phoon et al 1993) It is accurate to say that there is no consensus onthe preferred method and this issue is still the subject of much heated debatein the geotechnical engineering community Simpson and Yazdchi (2003)opined that strength partial factors are physically meaningful because ldquoit isthe gradual mobilisation of strength that causes deformationrdquo This is con-sistent with our prevailing practice of applying a global factor of safety tothe capacity to limit deformations Another physical justification is that vari-ations in soil parameters can create disproportionate nonlinear variations inthe response (or resistance) (Simpson 2000) In this situation the author feltthat ldquoit is difficult to choose values of partial factors which are applicableover the whole range of the variablesrdquo The implicit assumption here is thatthe resistance factor is not desirable because it is not practical to prescribea large number of resistance factors in a design code However if main-taining uniform reliability is a desired goal a single partial factor for sayfriction angle would not produce the same reliability in different problemsbecause the relevant design equations are not equally sensitive to changes inthe friction angle For complex soilndashstructure interaction problems apply-ing a fixed partial factor can result in unrealistic failure mechanisms In fact

Numerical recipes for reliability analysis 3

given the complexity of the limit state surface it is quite unlikely for thesame partial factor to locate the most probable failure point in all designproblems These problems would not occur if resistance factors are cali-brated from reliability analysis In addition it is more convenient to accountfor the bias in different calculation models using resistance factors How-ever for complex problems it is possible that a large number of resistancefactors are needed and the design code becomes too unwieldy or confusing touse Another undesirable feature is that the engineer does not develop a feelfor the failure mechanism if heshe is only required to analyze the expectedbehavior followed by application of some resistance factors at the end ofthe calculation

Overall the conclusion is that there are no simple methods (factoredparameters or resistances) of replacing reliability analysis for sufficientlycomplex problems It may be worthwhile to discuss if one should insiston simplicity despite all the known associated problems The more recentperformance-based design philosophy may provide a solution for thisdilemma because engineers can apply their own calculations methods forensuring performance compliance without being restricted to following rigiddesign codes containing a few partial factors In the opinion of the authorthe need to derive simplified RBD equations perhaps is of practical impor-tance to maintain continuity with past practice but it is not necessary andit is increasingly fraught with difficulties when sufficiently complex prob-lems are posed The limitations faced by simplified RBD have no bearing onthe generality of reliability theory This is analogous to arguing that limita-tions in closed-form elastic solutions are related to elasto-plastic theory Theapplication of finite element softwares on relatively inexpensive and power-ful PCs (with gigahertz processors a gigabyte of memory and hundreds ofgigabytes ndash verging on terabyte ndash of disk) permit real-world problems to besimulated on an unprecedented realistic setting almost routinely It sufficesto note here that RBD can be applied to complex real-world problems usingpowerful but practical stochastic collocation techniques (Phoon and Huang2007)

One common criticism of RBD is that good geotechnical sense and judg-ment would be displaced by the emphasis on probabilistic analysis (Boden1981 Semple 1981 Bolton 1983 Fleming 1989) This is similar to the on-going criticism of numerical analysis although this criticism seems to havegrown more muted in recent years with the emergence of powerful and user-friendly finite element softwares The fact of the matter is that experiencesound judgment and soil mechanics still are needed for all aspects of geotech-nical RBD (Kulhawy and Phoon 1996) Human intuition is not suited forreasoning with uncertainties and only this aspect has been removed from thepurview of the engineer One example in which intuition can be misleadingis the common misconception that a larger partial factor should be assignedto a more uncertain soil parameter This is not necessarily correct because

4 Kok-Kwang Phoon

the parameter may have little influence on the overall response (eg capacitydeformation) Therefore the magnitude of the partial factor should dependon the uncertainty of the parameter and the sensitivity of the response tothat parameter Clearly judgment is not undermined instead it is focusedon those aspects for which it is most suited

Another criticism is that soil statistics are not readily available becauseof the site-specific nature of soil variability This concern only is true fortotal variability analyses but does not apply to the general approach whereinherent soil variability measurement error and correlation uncertainty arequantified separately Extensive statistics for each component uncertain-ties have been published (Phoon and Kulhawy 1999a Uzielli et al 2007)For each combination of soil type measurement technique and correlationmodel the uncertainty in the design soil property can be evaluated systemat-ically by combining the appropriate component uncertainties using a simplesecond-moment probabilistic approach (Phoon and Kulhawy 1999b)

In summary we are now at the point where RBD really can be used asa rational and practical design mode The main impediment is not theoret-ical (lack of power to deal with complex problems) or practical (speed ofcomputations availability of soil statistics etc) but the absence of simplecomputational approaches that can be easily implemented by practitionersMuch of the controversies reported in the literature are based on qualitativearguments If practitioners were able to implement RBD easily on their PCsand calculate actual numbers using actual examples they would gain a con-crete appreciation of the merits and limitations of RBD Misconceptions willbe dismissed definitively rather than propagated in the literature generatingfurther confusion The author believes that the introduction of powerful butsimple-to-implement approaches will bring about a greater acceptance ofRBD amongst practitioners in the broader geotechnical engineering commu-nity The Probabilistic Model Code was developed by the Joint Committeeon Structural Safety (JCSS) to achieve a similar objective (Vrouwenvelderand Faber 2007)

The main impetus for this book is to explain RBD to students andpractitioners with emphasis on ldquohow to calculaterdquo and ldquohow to applyrdquoPractical computational methods are presented in Chapters 1 3 4 and 7Geotechnical examples illustrating reliability analyses and design are pro-vided in Chapters 5 6 8ndash13 The spatial variability of geomaterials isone of the distinctive aspects of geotechnical RBD This important aspectis covered in Chapter 2 The rest of this chapter provides a primer onreliability calculations with references to appropriate chapters for follow-up reading Simple MATLAB codes are provided in Appendix A andat httpwwwengnusedusgcivilpeoplecvepkkprob_libhtml By focus-ing on demonstration of RBD through calculations and examples thisbook is expected to serve as a valuable teaching and learning resource forpractitioners educators and students

Numerical recipes for reliability analysis 5

12 General reliability problem

The general stochastic problem involves the propagation of input uncer-tainties and model uncertainties through a computation model to arriveat a random output vector (Figure 11) Ideally the full finite-dimensionaldistribution function of the random output vector is desired although par-tial solutions such as second-moment characterizations and probabilities offailure may be sufficient in some applications In principle Monte Carlosimulation can be used to solve this problem regardless of the complexi-ties underlying the computation model input uncertainties andor modeluncertainties One may assume with minimal loss of generality that a com-plex geotechnical problem (possibly 3D nonlinear time and constructiondependent etc) would only admit numerical solutions and the spatialdomain can be modeled by a scalarvector random field Monte Carlosimulation requires a procedure to generate realizations of the input andmodel uncertainties and a numerical scheme for calculating a vector ofoutputs from each realization The first step is not necessarily trivial andnot completely solved even in the theoretical sense Some ldquouser-friendlyrdquomethods for simulating random variablesvectorsprocesses are outlined inSection 13 with emphasis on key calculation steps and limitations Detailsare outside the scope of this chapter and given elsewhere (Phoon 2004a2006a) In this chapter ldquouser-friendlyrdquo methods refer to those that canbe implemented on a desktop PC by a non-specialist with limited pro-gramming skills in other words methods within reach of the generalpractitioner

Model errors

Computationmodel

Uncertain modelparameters

Uncertainoutput vector

Uncertaininput vector

Figure 11 General stochastic problem

6 Kok-Kwang Phoon

The second step is identical to the repeated application of a determin-istic solution process The only potential complication is that a particularset of input parameters may be too extreme say producing a near-collapsecondition and the numerical scheme may become unstable The statisticsof the random output vector are contained in the resulting ensemble ofnumerical output values produced by repeated deterministic runs Fentonand Griffiths have been applying Monte Carlo simulation to solve soil-interaction problems within the context of a random field since the early1990s (eg Griffiths and Fenton 1993 1997 2001 Fenton and Griffiths1997 2002 2003) Popescu and co-workers developed simulation-basedsolutions for a variety of soil-structure interaction problems particularlyproblems involving soil dynamics in parallel Their work is presented inChapter 6 of this book

For a sufficiently large and complex soil-structure interaction problemit is computationally intensive to complete even a single run The rule-of-thumb for Monte Carlo simulation is that 10pf runs are needed to estimatea probability of failure pf within a coefficient of variation of 30 Thetypical pf for a geotechnical design is smaller than one in a thousand andit is expensive to run a numerical code more than ten thousand times evenfor a modest size problem This significant practical disadvantage is wellknown At present it is accurate to say that a computationally efficient andldquouser-friendlyrdquo solution to the general stochastic problem remains elusiveNevertheless reasonably practical solutions do exist if the general stochasticproblem is restricted in some ways for example accept a first-order esti-mate of the probability of failure or accept an approximate but less costlyoutput Some of these probabilistic solution procedures are presented inSection 14

13 Modeling and simulation of stochastic data

131 Random variables

Geotechnical uncertainties

Two main sources of geotechnical uncertainties can be distinguished Thefirst arises from the evaluation of design soil properties such as undrainedshear strength and effective stress friction angle This source of geotechni-cal uncertainty is complex and depends on inherent soil variability degreeof equipment and procedural control maintained during site investigationand precision of the correlation model used to relate field measurementwith design soil property Realistic statistical estimates of the variability ofdesign soil properties have been established by Phoon and Kulhawy (1999a1999b) Based on extensive calibration studies (Phoon et al 1995) threeranges of soil property variability (low medium high) were found to be

Numerical recipes for reliability analysis 7

sufficient to achieve reasonably uniform reliability levels for simplified RBDchecks

Geotechnical parameter Property variability COV ()

Undrained shear strength Low 10ndash30Medium 30ndash50High 50ndash70

Effective stress friction angle Low 5ndash10Medium 10ndash15High 15ndash20

Horizontal stress coefficient Low 30ndash50Medium 50ndash70High 70ndash90

In contrast Reacutethaacuteti (1988) citing the 1965 specification of the AmericanConcrete Institute observed that the quality of concrete can be evaluated inthe following way

Quality COV ()

Excellent lt 10Good 10ndash15Satisfactory 15ndash20Bad gt 20

It is clear that the coefficients of variations or COVs of natural geomaterialscan be much larger and do not fall within a narrow range The ranges ofquality for concrete only apply to the effective stress friction angle

The second source arises from geotechnical calculation models Althoughmany geotechnical calculation models are ldquosimplerdquo reasonable predictionsof fairly complex soilndashstructure interaction behavior still can be achievedthrough empirical calibrations Model factors defined as the ratio of themeasured response to the calculated response usually are used to correctfor simplifications in the calculation models Figure 12 illustrates modelfactors for capacity of drilled shafts subjected to lateral-moment loadingNote that ldquoSDrdquo is the standard deviation ldquonrdquo is the sample size and ldquopADrdquois the p-value from the AndersonndashDarling goodness-of-fit test (gt 005 impliesacceptable lognormal fit) The COVs of model factors appear to fall between30 and 50

It is evident that a geotechnical parameter (soil property or model factor)exhibiting a range of values possibly occurring at unequal frequencies isbest modeled as a random variable The existing practice of selecting onecharacteristic value (eg mean ldquocautiousrdquo estimate 5 exclusion limit etc)is attractive to practitioners because design calculations can be carried outeasily using only one set of input values once they are selected However thissimplicity is deceptive The choice of the characteristic values clearly affects

04 12 20 28 36

HhHu(Reese)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 142 SD = 014 COV = 029

n = 74 pAD = 0186

Hyperbolic capacity (Hh)

04 12 20 28 36HhHu(Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 228 SD = 085 COV = 037

n = 74 pAD = 0149

04 12 20 28 36HhHu(Hansen)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 098 SD = 032 COV = 033

n = 77 pAD = 0229

04 12 20 28 36HhHu(Randolph amp Houlsby)HhHu(Randolph amp Houlsby)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 132 SD = 038 COV = 029

n = 74 pAD = 0270

04 12 20 28 36

HhHu(Simplified Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 130 SD = 049 COV = 038

n = 77 pAD = 0141

00 08 16 24 32

HhHu(Reese)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 092 SD = 027 COV = 029

n = 72 pAD = 0633

Lateral on moment limit (HL)Capacity model

Reese (1958)(undrained)

Broms (1964a)(undrained)

Randolph amp Houlsby(1984) (undrained)

Hansen (1961)(drained)

Broms (1964b)(drained)

00 08 16 24 32HhHu(Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 149 SD = 057 COV = 038

n = 72 pAD = 0122

00 08 16 24 32HhHu(Hansen)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 067 SD = 026 COV = 038

n = 75 pAD = 0468

00 08 16 24 32

HhHu(Simplified Broms)

0

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 088 SD = 036 COV = 041

n = 75 pAD = 0736

00 08 16 24 320

01

02

03

04

Rel

ativ

e F

requ

ency

Mean = 085 SD = 024 COV = 028

n = 72 pAD = 0555

Figure 12 Capacity model factors for drilled shafts subjected to lateral-moment loading(modified from Phoon 2005)

Numerical recipes for reliability analysis 9

the overall safety of the design but there are no simple means of ensur-ing that the selected values will achieve a consistent level of safety In factthese values are given by the design point of a first-order reliability anal-ysis and they are problem-dependent Simpson and Driscoll (1998) notedin their commentary to Eurocode 7 that the definition of the characteristicvalue ldquohas been the most controversial topic in the whole process of draftingEurocode 7rdquo If random variables can be included in the design process withminimal inconvenience the definition of the characteristic value is a mootissue

Simulation

The most intuitive and possibly the most straightforward method for per-forming reliability analysis is the Monte Carlo simulation method It onlyrequires repeated execution of an existing deterministic solution process Thekey calculation step is to simulate realizations of random variables This stepcan be carried in a general way using

Y = Fminus1(U) (11)

in which Y is a random variable following a prescribed cumulative distribu-tion F(middot) and U is a random variable uniformly distributed between 0 and 1(also called a standard uniform variable) Realizations of U can be obtainedfrom EXCELtrade under ldquoTools gt Data Analysis gt Random Number Gener-ation gt Uniform Between 0 and 1rdquo MATLABtrade implements U using theldquorandrdquo function For example U = rand(101) is a vector containing 10 real-izations of U Some inverse cumulative distribution functions are availablein EXCEL (eg norminv for normal loginv for lognormal betainv for betagammainv for gamma) and MATLAB (eg norminv for normal logninv forlognormal betainv for beta gaminv for gamma) More efficient methods areavailable for some probability distributions but they are lacking in generality(Hastings and Peacock 1975 Johnson et al 1994) A cursory examinationof standard probability texts will reveal that the variety of classical proba-bility distributions is large enough to cater to almost all practical needs Themain difficulty lies with the selection of an appropriate probability distribu-tion function to fit the limited data on hand A complete treatment of thisimportant statistical problem is outside the scope of this chapter Howeverit is worthwhile explaining the method of moments because of its simplicityand ease of implementation

The first four moments of a random variable (Y) can be calculated quitereliably from the typical sample sizes encountered in practice Theoreticallythey are given by

micro =int

yf (y)dy = E(Y) (12a)

10 Kok-Kwang Phoon

σ 2 = E[(Y minusmicro)2] (12b)

γ1 = E[(Y minusmicro)3]σ 3 (12c)

γ2 = E[(Y minusmicro)4]σ 4 minus 3 (12d)

in which micro = mean σ 2 = variance (or σ = standard deviation) γ1 =skewness γ2 = kurtosis f (middot) = probability density function = dF(y)dy andE[middot] = mathematical expectation The practical feature here is that momentscan be estimated directly from empirical data without knowledge of theunderlying probability distribution [ie f (y) is unknown]

y =

nsumi=1

yi

n(13a)

s2 = 1n minus 1

nsumi=1

(yi minus y)2 (13b)

g1 = n(n minus 1)(n minus 2)s3

nsumi=1

(yi minus y)3 (13c)

g2 = n(n + 1)(n minus 1)(n minus 2)(n minus 3)s4

nsumi=1

(yi minus y)4 minus 3(n minus 1)2

(n minus 2)(n minus 3)(13d)

in which n = sample size (y1y2 yn) = data points y = sample means2 = sample variance g1 = sample skewness and g2 = sample kurtosis Notethat the MATLAB ldquokurtosisrdquo function is equal to g2 + 3 If the sample sizeis large enough the above sample moments will converge to their respectivetheoretical values as defined by Equation (12) under some fairly general con-ditions The majority of classical probability distributions can be determineduniquely by four or less moments In fact the Pearson system (Figure 13)reduces the selection of an appropriate probability distribution function tothe determination of β1 = g2

1 and β2 = g2 + 3 Calculation steps with illus-trations are given by Elderton and Johnson (1969) Johnson et al (1994)provided useful formulas for calculating the Pearson parameters based onthe first four moments Reacutethaacuteti (1988) provided some β1 and β2 values ofSzeged soils for distribution fitting using the Pearson system (Table 11)

Johnson system

A broader discussion of distribution systems (in which the Pearson sys-tem is one example) and distribution fitting is given by Elderton and

0 02 04 06 08 1

β1

β 2

12 14 16 18

VI

VIV

III

Limit of possible distributions

I

2

VII

N

II

1

2

3

4

5

6

7

8

Figure 13 Pearson system of probability distribution functions based on β1 = g21 and

β2 = g2 + 3 N = normal distribution

Table 11 β1 and β2 values for some soil properties from Szeged Hungary (modifiedfrom Reacutethaacuteti 1988)

Soillayer

Watercontent

Liquidlimit

Plasticlimit

Plasticityindex

Consistencyindex

Voidratio

Bulkdensity

Unconfinedcompressivestrength

β1S1 276 412 681 193 013 650 128 002S2 001 074 034 049 002 001 009 094S3 003 096 014 085 000 130 289 506S4 005 034 013 064 003 013 106 480S5 110 002 292 000 098 036 010 272

β2S1 739 810 1230 492 334 1119 398 186S2 345 343 327 269 317 267 387 393S3 762 513 432 446 331 552 1159 1072S4 717 319 347 357 461 403 814 1095S5 670 231 915 217 513 414 494 674

NotePlasticity index (Ip) = wL minus wP in which wL = liquid limit and wP = plastic limit consistency index(Ic)= (wL minusw)Ip = 1minus IL in which w = water content and IL = liquidity index unconfined compressivestrength (qu) = 2su in which su = undrained shear strength

12 Kok-Kwang Phoon

Johnson (1969) It is rarely emphasized that almost all distribution functionscannot be generalized to handle correlated random variables (Phoon 2006a)In other words univariate distributions such as those discussed above cannotbe generalized to multivariate distributions Elderton and Johnson (1969)discussed some interesting exceptions most of which are restricted to bivari-ate cases It suffices to note here that the only convenient solution availableto date is to rewrite Equation (11) as

Y = Fminus1[Φ(Z)] (14)

in which Z = standard normal random variable with mean = 0 andvariance = 1 and Φ(middot) = standard normal cumulative distribution function(normcdf in MATLAB or normsdist in EXCEL) Equation (14) is calledthe translation model It requires all random variables to be related to thestandard normal random variable The significance of Equation (14) iselaborated in Section 132

An important practical detail here is that standard normal random vari-ables can be simulated directly and efficiently using the BoxndashMuller method(Box and Muller 1958)

Z1 =radic

minus2ln(U1)cos(2πU2) (15)

Z2 =radic

minus2ln(U1)sin(2πU2)

in which Z1Z2 = independent standard normal random variables andU1U2 = independent standard uniform random variables Equation (15)is computationally more efficient than Equation (11) because the inversecumulative distribution function is not required Note that the cumulativedistribution function and its inverse are not available in closed-form for mostrandom variables While Equation (11) ldquolooksrdquo simple there is a hiddencost associated with the numerical evaluation of Fminus1(middot) Marsaglia and Bray(1964) proposed one further improvement

1 Pick V1 and V2 randomly within the unit square extending between minus1and 1 in both directions ie

V1 = 2U1 minus 1

V2 = 2U2 minus 1(16)

2 Calculate R2 = V21 + V2

2 If R2 ge 10 or R2 = 00 repeat step (1)

Numerical recipes for reliability analysis 13

3 Simulate two independent standard normal random variables using

Z1 = V1

radicminus2ln(R2)

R2

Z2 = V2

radicminus2ln(R2)

R2

(17)

Equation (17) is computationally more efficient than Equation (15) becausetrigonometric functions are not required

A translation model can be constructed systematically from (β1 β2) usingthe Johnson system

1 Assume that the random variable is lognormal ie

ln(Y minus A) = λ+ ξZ Y gt A (18)

in which ln(middot) is the natural logarithm ξ2 = ln[1 + σ 2(microminus A)2] λ =ln(microminus A) minus 05ξ2 micro = mean of Y and σ 2 = variance of Y The ldquolog-normalrdquo distribution in the geotechnical engineering literature typicallyrefers to the case of A = 0 When A = 0 it is called the ldquoshiftedlognormalrdquo or ldquo3-parameter lognormalrdquo distribution

2 Calculate ω = exp(ξ2)3 Calculate β1 = (ω minus 1)(ω + 2)2 and β2 = ω4 + 2ω3 + 3ω2 minus 3 For the

lognormal distribution β1 and β2 are related as shown by the solid linein Figure 14

4 Calculate β1 = g21 and β2 = g2 + 3 from data If the values fall close to

the lognormal (LN) line the lognormal distribution is acceptable5 If the values fall below the LN line Y follows the SB distribution

ln(

Y minus AB minus Y

)= λ+ ξZ B gt Y gt A (19)

in which λξAB = distribution fitting parameters6 If the values fall above the LN line Y follows the SU distribution

ln

⎡⎣(Y minus A

B minus A

)+radic

1 +(

Y minus AB minus A

)2⎤⎦= sinhminus1

(Y minus AB minus A

)= λ+ ξZ

(110)

Examples of LN SB and SU distributions are given in Figure 15 Simulationof Johnson random variables using MATLAB is given in Appendix A1Carsel and Parrish (1988) developed joint probability distributions for

0 02 04 06 08 1

β1

β 2

12 14 16 18

SU

Limit of possible distributions

Pearson III

Pearson V

SB

2

1

2

3

4

5

6

7

8

LN

N

Figure 14 Johnson system of probability distribution functions based on β1 = g21 and

β2 = g2 + 3 N = normal distribution LN = lognormal distribution

00

01

02

03

04

05

06

07LNSBSU

08

1 2 3 4 5

Figure 15 Examples of LN (λ = 100ξ = 020) SB (λ = 100ξ = 036 A = minus300B = 500) and SU (λ= 100ξ = 009 A = minus188 B = 208) distributions withapproximately the same mean and coefficient of variation asymp 30

Numerical recipes for reliability analysis 15

parameters of soilndashwater characteristic curves using the Johnson system Themain practical obstacle is that the SB Johnson parameters (λξAB) arerelated to the first four moments (ys2g1g2) in a very complicated way(Johnson 1949) The SU Johnson parameters are comparatively easier toestimate from the first four moments (see Johnson et al 1994 for closed-form formulas) In addition both Pearson and Johnson systems require theproper identification of the relevant region (eg SB or SU in Figure 14)which in turn determines the distribution function [Equation (19) or (110)]before one can attempt to calculate the distribution parameters

Hermite polynomials

One-dimensional Hermite polynomials are given by

H0(Z) = 1

H1(Z) = Z

H2(Z) = Z2 minus 1

H3(Z) = Z3 minus 3Z

Hk+1(Z) = ZHk(Z) minus kHkminus1(Z)

(111)

in which Z is a standard normal random variable (mean = 0 andvariance = 1) Hermite polynomials can be evaluated efficiently using therecurrence relation given in the last row of Equation (111) It can be provenrigorously (Phoon 2003) that any random variable Y (with finite variance)can be expanded as a series

Y =infinsum

k=0

akHk(Z) (112)

The numerical values of the coefficients ak depend on the distribution of YThe key practical advantage of Equation (112) is that the randomness ofY is completely accounted for by the randomness of Z which is a knownrandom variable It is useful to observe in passing that Equation (112) maynot be a monotonic function of Z when it is truncated to a finite number ofterms The extrema are located at points with zero first derivatives but non-zero second derivatives Fortunately derivatives of Hermite polynomials canbe evaluated efficiently as well

dHk(Z)dZ

= kHkminus1(Z) (113)

16 Kok-Kwang Phoon

The Hermite polynomial expansion can be modified to fit the first fourmoments as well

Y = y + s k[Z + h3(Z2 minus 1) + h4(Z3 minus 3Z)] (114)

in which y and s = sample mean and sample standard deviation of Y respec-tively and k = normalizing constant = 1(1 + 2h2

3 + 6h24)05 It is important

to note that the coefficients h3 and h4 can be calculated from the sampleskewness (g1) and sample kurtosis (g2) in a relatively straightforward way(Winterstein et al 1994)

h3 = g1

6

(1 minus 0015

∣∣g1

∣∣+ 03g21

1 + 02g2

)(115a)

h4 = (1 + 125g2)13 minus 110

(1 minus 143g2

1

g2

)1minus01(g2+3)08

(115b)

The theoretical skewness (γ1) and kurtosis (γ2) produced by Equation (114)are (Phoon 2004a)

γ1 = k3(6h3 + 36h3h4 + 8h33 + 108h3h2

4) (116a)

γ2 = k4(3 + 48h43 + 3348h4

4 + 24h4 + 1296h34 + 60h2

3 + 252h24

+ 2232h23h2

4 + 576h23h4) minus 3 (116b)

Equation (115) is determined empirically by minimizing the error [(γ1 minusg1)2 + (γ2 minus g2)2] subjected to the constraint that Equation (114) is amonotonic function of Z It is intended for cases with 0 lt g2 lt 12 and0 le g2

1 lt 2g23 (Winterstein et al 1994) It is possible to minimize the errorin skewness and kurtosis numerically using the SOLVER function in EXCELrather than applying Equation (115)

In general the entire cumulative distribution function of Y F(y) canbe fully described by the Hermite expansion using the following simplestochastic collocation method

1 Let (y1y2 yn) be n realizations of Y The standard normal data iscalculated from

zi = Φminus1F(yi) (117)

Numerical recipes for reliability analysis 17

2 Substitute yi and zi into Equation (112) For a third-order expansionwe obtain

yi = a0 + a1zi + a2(z2i minus 1) + a3(z3

i minus 3zi) = a0 + a1hi1

+ a2hi2 + a3hi3 (118)

in which (1 hi1hi2hi3) are Hermite polynomials evaluated at zi3 The four unknown coefficients (a0a1a2a3) can be determined using

four realizations of Y (y1y2y3y4) In matrix notation we write

Ha = y (119)

in which H is a 4 times 4 matrix with ith row given by (1 hi1hi2hi3) y isa 4 times 1 vector with ith component given by yi and a is a 4 times 1 vectorcontaining the unknown numbers (a0a1a2a3)prime This is known as thestochastic collocation method Equation (119) is a linear system andefficient solutions are widely available

4 It is preferable to solve for the unknown coefficients using regression byusing more than four realizations of Y

(HprimeH)a = Hprimey (120)

in which H is an ntimes4 matrix and n is the number of realizations Notethat Equation (120) is a linear system amenable to fast solution as well

Calculation of Hermite coefficients using MATLAB is given in Appendix A2Hermite coefficients can be calculated with ease using EXCEL as well(Chapter 9) Figure 16 demonstrates that Equation (112) is quite efficient ndashit is possible to match very small probabilities (say 10minus4) using a third-orderHermite expansion (four terms)

Phoon (2004a) pointed out that Equations (14) and (112) are theoreti-cally identical Equation (112) appears to present a rather circuitous routeof achieving the same result as Equation (14) The key computational differ-ence is that Equation (14) requires the costly evaluation of Fminus1(middot) thousandsof times in a low-probability simulation exercise (typical of civil engineeringproblems) while Equation (117) requires less than 100 costly evaluationsof minus1F(middot) followed by less-costly evaluation of Equation (112) thousandsof times Two pivotal factors govern the computational efficiency of Equa-tion (112) (a) cheap generation of standard normal random variables usingthe BoxndashMuller method [Equation (15) or (17)] and (b) relatively smallnumber of Hermite terms

The one-dimensional Hermite expansion can be easily extended to ran-dom vectors and processes The former is briefly discussed in the next

18 Kok-Kwang Phoon

100

Theoretical4-term Hermite

Theoretical

4-term Hermite

Cum

ulat

ive

dist

ribut

ion

func

tion

10minus2

10minus4

10minus6minus1 0 1 2

Value3 4 5

08

07

06

05

Pro

babi

lity

dens

ity fu

nctio

n

04

03

02

01

0minus1 0 1 2

Value3 4 5

Theoretical

4-term Hermite

100

Theoretical4-term Hermite

Cum

ulat

ive

dist

ribut

ion

func

tion

10minus2

10minus4

10minus60 1 2 3

Value4 5 6

08

07

06

05

Pro

babi

lity

dens

ity fu

nctio

n

04

03

02

01

00 1 2 3

Value4 5 6

Figure 16 Four-term Hermite expansions for Johnson SB distribution with λ = 100ξ = 036 A = minus300 B = 500 (top row) and Johnson SU distribution withλ = 100ξ = 009 A = minus188 B = 208 (bottom row)

section while the latter is given elsewhere (Puig et al 2002 Sakamoto andGhanem 2002 Puig and Akian 2004) Chapters 5 7 9 and elsewhere(Sudret and Der Kiureghian 2000 Sudret 2007) present applications ofHermite polynomials in more comprehensive detail

132 Random vectors

The multivariate normal probability density function is available analyticallyand can be defined uniquely by a mean vector and a covariance matrix

f (X) = |C|minus 12 (2π )minus

n2 exp

[minus05(X minus micro)primeCminus1(X minus micro)

](121)

in which X= (X1X2 Xn)prime is a normal random vector with n componentsmicro is the mean vector and C is the covariance matrix For the bivariate

Numerical recipes for reliability analysis 19

(simplest) case the mean vector and covariance matrix are given by

micro =micro1micro2

C =[

σ 21 ρσ1σ2

ρσ1σ2 σ 22

] (122)

in which microi and σi = mean and standard deviation of Xi respectively andρ = product-moment (Pearson) correlation between X1 and X2

The practical usefulness of Equation (121) is not well appreciated Firstthe full multivariate dependency structure of a normal random vector onlydepends on a covariance matrix (C) containing bivariate information (cor-relations) between all possible pairs of components The practical advantageof capturing multivariate dependencies in any dimension (ie any number ofrandom variables) using only bivariate dependency information is obviousNote that coupling two random variables is the most basic form of depen-dency and also the simplest to evaluate from empirical data In fact there areusually insufficient data to calculate reliable dependency information beyondcorrelations in actual engineering practice Second fast simulation of cor-related normal random variables is possible because of the elliptical form(X minus micro)primeCminus1(X minus micro) appearing in the exponent of Equation (121) Whenthe random dimension is small the following Cholesky method is the mostefficient and robust

X = LZ + micro (123)

in which Z = (Z1Z2 Zn)prime contains uncorrelated normal random com-ponents with zero means and unit variances These components can besimulated efficiently using the BoxndashMuller method [Equations (15) or (17)]The lower triangular matrix L is the Cholesky factor of C ie

C = LLprime (124)

Cholesky factorization can be roughly appreciated as taking the ldquosquarerootrdquo of a matrix The Cholesky factor can be calculated in EXCEL usingthe array formula MAT_CHOLESKY which is provided by a free add-inat httpdigilanderliberoitfoxesindexhtm MATLAB produces Lprime usingchol (C) [note Lprime (transpose of L) is an upper triangular matrix] Choleskyfactorization fails if C is not ldquopositive definiterdquo The important practical ram-ifications here are (a) the correlation coefficients in the covariance matrixC cannot be selected independently and (b) an erroneous C is automaticallyflagged when Cholesky factorization fails When the random dimension ishigh it is preferable to use the fast fourier transform (FFT) as described inSection 133

20 Kok-Kwang Phoon

As mentioned in Section 131 the multivariate normal distribution playsa central role in the modeling and simulation of correlated non-normalrandom variables The translation model [Equation (14)] for one randomvariable (Y) can be generalized in a straightforward to a random vector(Y1Y2 Yn)

Yi = Fminus1i [Φ(Xi)] (125)

in which (X1X2 Xn) follows a multivariate normal probability densityfunction [Equation (121)] with

micro =

⎧⎪⎪⎪⎨⎪⎪⎪⎩

00

0

⎫⎪⎪⎪⎬⎪⎪⎪⎭

C =

⎡⎢⎢⎢⎣

1 ρ12 middot middot middot ρ1nρ21 1 middot middot middot ρ2n

ρn1 ρn2 middot middot middot 1

⎤⎥⎥⎥⎦

Note that ρij = ρji ie C is a symmetric matrix containing only n(n minus 1)2distinct entries Each non-normal component Yi can follow any arbitrarycumulative distribution function Fi(middot) The cumulative distribution func-tion prescribed to each component can be different ie Fi(middot) = Fj(middot) Thesimulation of correlated non-normal random variables using MATLAB isgiven in Appendix A3 It is evident that ldquotranslationmrdquo can be modifiedto simulate any number of random components as long as a compatiblesize covariance matrix is specified For example if there are three randomcomponents (n = 3) C should be a 3 times 3 matrix such as [1 08 04 081 02 04 02 1] This computational simplicity explains the popularityof the translation model As mentioned previously not all 3 times 3 matri-ces are valid covariance matrices An example of an invalid covariancematrix is C = [108 minus 08081 minus 02minus08 minus 021] An attempt to executeldquotranslationmrdquo produces the following error messages

Error using ==gt cholMatrix must be positive definite

Hence simulation from an invalid covariance matrix will not take placeThe left panel of Figure 17 shows the scatter plot of two correlated nor-mal random variables with ρ = 08 An increasing linear trend is apparentA pair of uncorrelated normal random variables will not exhibit any trendin the scatter plot The right panel of Figure 17 shows the scatter plot of twocorrelated non-normal random variables There is an increasing nonlineartrend The nonlinearity is fully controlled by the non-normal distributionfunctions (SB and SU in this example) In practice there is no reason forthe scatter plot (produced by two columns of numbers) to be related to the

Numerical recipes for reliability analysis 21

4

2

0

minus2

minus4minus4 minus2 0

X1 = normal distribution

X2 =

nor

mal

dis

trib

utio

n

2 4 01

2

3

4

Y2 =

SU

dis

trib

utio

n

Y1 = SB distribution

5

6

1 2 3 4 5

Figure 17 Scatter plots for correlated normal random variables with zero means and unitvariances (left) and correlated non-normal random variables with 1st compo-nent = Johnson SB distribution (λ = 100ξ = 036A = minus300B = 500) and2nd component = Johnson SU distribution (λ = 100ξ = 009 A = minus188B = 208) (right)

distribution functions (produced by treating each column of numbers sepa-rately) Hence it is possible for the scatter plot produced by the translationmodel to be unrealistic

The simulation procedure described in ldquotranslationmrdquo cannot be applieddirectly to practice because it requires the covariance matrix of X as aninput The empirical data can only produce an estimate of the covariancematrix of Y It can be proven theoretically that these covariance matricesare not equal although they can be approximately equal in some cases Thesimplest solution available so far is to express Equation (125) as Hermitepolynomials

Y1 = a10H0(X1) + a11H1(X1) + a12H2(X1) + a13H3(X1) +middotmiddot middotY2 = a20H0(X2) + a21H1(X2) + a22H2(X2) + a23H3(X2) +middotmiddot middot (126)

The relationship between the observed correlation coefficient (ρY1Y2) and the

underlying normal correlation coefficient (ρ) is

ρY1Y2=

infinsumk=1

ka1ka2kρk

radicradicradicradic( infinsumk=1

ka21k

)(infinsum

k=1ka2

2k

) (127)

This complication is discussed in Chapter 9 and elsewhere (Phoon 2004a2006a)

22 Kok-Kwang Phoon

From a practical viewpoint [only bivariate information needed cor-relations are easy to estimate voluminous n-dimensional data compiledinto mere n(n minus 1)2 coefficients etc] and a computational viewpoint(fast robust simple to implement) it is accurate to say that the multivariatenormal model and multivariate non-normal models generated using thetranslation model are already very good and sufficiently general for manypractical scenarios The translation model is not perfect ndash there are impor-tant and fundamental limitations (Phoon 2004a 2006a) For stochasticdata that cannot be modeled using the translation model the hunt forprobability models with comparable practicality theoretical power andsimulation speed is still on-going Copula theory (Schweizer 1991) pro-duces a more general class of multivariate non-normal models but it isdebatable at this point if these models can be estimated empirically andsimulated numerically with equal ease for high random dimensions Theonly exception is a closely related but non-translation approach based onthe multivariate normal copula (Phoon 2004b) This approach is outlinedin Chapter 9

133 Random processes

A natural probabilistic model for correlated spatial data is the randomfield A one-dimensional random field is typically called a random processA random process can be loosely defined as a random vector with an infi-nite number of components that are indexed by a real number (eg depthcoordinate z) We restrict our discussion to a normal random process X(z)Non-normal random processes can be simulated from a normal randomprocess using the same translation approach described in Section 132

The only computational aspect that requires some elaboration is thatsimulation of a process is usually more efficient in the frequency domainRealizations belonging to a zero-mean stationary normal process X(z) canbe generated using the following spectral approach

X(z) =infinsum

k=1

σk(Z1k sin2π fkz+Z2k cos2π fkz) (128)

in which σk =radic2S(fk)f f is the interval over which the spectral densityfunction S(f ) is discretized fk = (2k minus 1)f 2 and Z1k and Z2k are uncor-related normal random variables with zero means and unit variances Thesingle exponential autocorrelation function is commonly used in geostatis-tics R(τ ) = exp(minus2|τ |δ) in which τ is the distance between data points andδ the scale of fluctuation R(τ ) can be estimated from a series of numbers saycone tip resistances sampled at a vertical spacing of z using the method of

Numerical recipes for reliability analysis 23

moments

R(τ = jz) asymp 1(n minus j minus 1)s2

nminusjsumi=1

(xi minus x)(xi+j minus x) (129)

in which xi = x(zi)zi = i(z) x = sample mean [Equation (13a] s2 =sample variance [Equation (13b)] and n = number of data points The scaleof fluctuation is estimated by fitting an exponential function to Equation(129) The spectral density function corresponding to the single exponentialcorrelation function is

S(f ) = 4δ(2π f δ)2 + 4

(130)

Other common autocorrelation functions and their corresponding spectraldensity functions are given in Table 12 It is of practical interest to note thatS(f ) can be calculated numerically from a given target autocorrelation func-tion or estimated directly from x(zi) using the FFT Analytical solutions suchas those shown in Table 12 are convenient but unnecessary The simulationof a standard normal random process with zero mean and unit variance usingMATLAB is given in Appendix A4 Note that the main input to ldquoranpro-cessmrdquo is R(τ ) which can be estimated empirically from Equation (129)No knowledge of S(f ) is needed Some realizations based on the five com-mon autocorrelation functions shown in Table 12 with δ = 1 are given inFigure 18

Table 12 Common autocorrelation and two-sided power spectral density functions

Model Autocorrelation R(τ ) Two-sided power spectraldensity S(f )

Scale offluctuation δ

Singleexponential

exp(minusa |τ |) 2a

ω2 + a22a

Binary noise1 minus a |τ | |τ | le 1a0 otherwise

sin2 (ω2a)

a (ω2a)21a

Cosineexponential

exp(minusa |τ |)cos(aτ ) a(

1

a2 + (ω+ a)2+ 1

a2 + (ωminus a)2

)1a

Second-orderMarkov

(1 + a |τ |)exp(minusa |τ |) 4a3

(ω2 + a2)24a

Squaredexponential

exp[minus(aτ )2

] radicπ

aexp

(minus ω2

4a2

) radicπ

a

ω = 2π f

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(a)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(b)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(c)

Figure 18 Simulated realizations of normal process with mean = 0 variance = 1 scaleof fluctuation = 1 based on autocorrelation function = (a) single exponential(b) binary noise (c) cosine exponential (d) second-order Markov and (e) squareexponential

Numerical recipes for reliability analysis 25

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(d)

4

2

0

0 5 10 15 20

Depth z

25 30 35 40

X(z

)

minus2

minus4

(e)

Figure 18 Contrsquod

In geotechnical engineering spatial variability is frequently modeled usingrandom processesfields This practical application was first studied in geol-ogy and mining under the broad area of geostatistics The physical premisethat makes estimation of spatial patterns possible is that points close in spacetend to assume similar values The autocorrelation function (or variogram)is a fundamental tool describing similarities in a statistical sense as a func-tion of distance [Equation (129)] The works of G Matheron DG Krigeand FP Agterberg are notable Parallel developments also took place inmeteorology (LS Gandin) and forestry (B Mateacutern) Geostatistics is math-ematically founded on the theory of random processesfields developed byAY Khinchin AN Kolmogorov P Leacutevy N Wiener and AM Yaglomamong others The interested reader can refer to books by Cressie (1993)and Chilegraves and Delfiner (1999) for details VanMarcke (1983) remarked thatall measurements involve some degree of local averaging and that randomfield models do not need to consider variations below a finite scale becausethey are smeared by averaging VanMarckersquos work is incomplete in one

26 Kok-Kwang Phoon

crucial aspect This crucial aspect is that actual failure surfaces in 2D or 3Dproblems will automatically seek to connect ldquoweakestrdquo points in the randomdomain It is the spatial average of this failure surface that counts in practicenot simple spatial averages along pre-defined surfaces (or pre-specified mea-surement directions) This is an exceptionally difficult problem to solve ndash atpresent simulation is the only viable solution Chapter 6 presents extensiveresults on emergent behaviors resulting from spatially heterogeneous soilsthat illustrate this aspect quite thoroughly

Although the random processfield provides a concise mathematical modelfor spatial variability it poses considerable practical difficulties for statisti-cal inference in view of its complicated data structure All classical statisticaltests are invariably based on the important assumption that the data areindependent (Cressie 1993) When they are applied to correlated data largebias will appear in the evaluation of the test statistics (Phoon et al 2003a)The application of standard statistical tests to correlated soil data is thereforepotentially misleading Independence is a very convenient assumption thatmakes a large part of mathematical statistics tractable Statisticians can goto great lengths to remove this dependency (Fisher 1935) or be contentwith less-powerful tests that are robust to departures from the indepen-dence assumption In recent years an alternate approach involving the directmodeling of dependency relationships into very complicated test statisticsthrough Monte Carlo simulation has become feasible because desktop com-puting machines have become very powerful (Phoon et al 2003a Phoonand Fenton 2004 Phoon 2006b Uzielli and Phoon 2006)

Chapter 2 and elsewhere (Baecher and Christian 2003 Uzielli et al2007) provide extensive reviews of geostatistical applications in geotechnicalengineering

14 Probabilistic solution procedures

The practical end point of characterizing uncertainties in the design inputparameters (geotechnical geo-hydrological geometrical and possibly ther-mal) is to evaluate their impact on the performance of a design Reliabilityanalysis focuses on the most important aspect of performance namely theprobability of failure (ldquofailurerdquo is a generic term for non-performance)This probability of failure clearly depends on both parametric and modeluncertainties The probability of failure is a more consistent and completemeasure of safety because it is invariant to all mechanically equivalent def-initions of safety and it incorporates additional uncertainty informationThere is a prevalent misconception that reliability-based design is ldquonewrdquo Allexperienced engineers would conduct parametric studies when confidencein the choice of deterministic input values is lacking Reliability analysismerely allows the engineer to carry out a much broader range of para-metric studies without actually performing thousands of design checks with

Numerical recipes for reliability analysis 27

different inputs one at a time This sounds suspiciously like a ldquofree lunchrdquobut exceedingly clever probabilistic techniques do exist to calculate theprobability of failure efficiently The chief drawback is that these tech-niques are difficult to understand for the non-specialist but they are notnecessarily difficult to implement computationally There is no consensuswithin the geotechnical community whether a more consistent and com-plete measure of safety is worth the additional efforts or if the significantlysimpler but inconsistent global factor of safety should be dropped Reg-ulatory pressure appears to be pushing towards RBD for non-technicalreasons The literature on reliability analysis and RBD is voluminous Someof the main developments in geotechnical engineering are presented inthis book

141 Closed-form solutions

There is a general agreement in principle that limit states (undesirable statesin which the system fails to perform satisfactorily) should be evaluatedexplicitly and separately but there is no consensus on how to verify thatexceedance of a limit state is ldquosufficiently unlikelyrdquo in numerical terms Dif-ferent opinions and design recommendations have been made but there is alack of discussion on basic issues relating to this central idea of ldquoexceeding alimit staterdquo A simple framework for discussing such basic issues is to imag-ine the limit state as a boundary surface dividing sets of design parameters(soil load andor geometrical parameters) into those that result in satisfac-tory and unsatisfactory designs It is immediately clear that this surface canbe very complex for complex soil-structure interaction problems It is alsoclear without any knowledge of probability theory that likely failure sets ofdesign parameters (producing likely failure mechanisms) cannot be discussedwithout characterizing the uncertainties in the design parameters explicitlyor implicitly Assumptions that ldquovalues are physically boundedrdquo ldquoall valuesare likely in the absence of informationrdquo etc are probabilistic assump-tions regardless of whether or not this probabilistic nature is acknowledgedexplicitly If the engineer is 100 sure of the design parameters then onlyone design check using these fully deterministic parameters is necessary toensure that the relevant limit state is not exceeded Otherwise the situationis very complex and the only rigorous method available to date is reliabilityanalysis The only consistent method to control exceedance of a limit stateis to control the reliability index It would be very useful to discuss at thisstage if this framework is logical and if there are alternatives to it In fact ithas not been acknowledged explicitly if problems associated with strengthpartial factors or resistance factors are merely problems related to simpli-fication of reliability analysis If there is no common underlying purpose(eg to achieve uniform reliability) for applying partial factors and resis-tance factors then the current lack of consensus on which method is better

28 Kok-Kwang Phoon

cannot be resolved in any meaningful way Chapter 8 provides some usefulinsights on the reliability levels implied by the empirical soil partial factorsin Eurocode 7

Notwithstanding the above on-going debate it is valid to question if reli-ability analysis is too difficult for practitioners The simplest example is toconsider a foundation design problem involving a random capacity (Q) anda random load (F) The ultimate limit state is defined as that in which thecapacity is equal to the applied load Clearly the foundation will fail if thecapacity is less than this applied load Conversely the foundation should per-form satisfactorily if the applied load is less than the capacity These threesituations can be described concisely by a single performance function P asfollows

P = Q minus F (131)

Mathematically the above three situations simply correspond to the condi-tions of P = 0P lt 0 and P gt 0 respectively

The basic objective of RBD is to ensure that the probability of failure doesnot exceed an acceptable threshold level This objective can be stated usingthe performance function as follows

pf = Prob(P lt 0) le pT (132)

in which Prob(middot) = probability of an event pf =probability of failure andpT = acceptable target probability of failure A more convenient alternativeto the probability of failure is the reliability index (β) which is defined as

β = minusΦminus1(pf) (133)

in which Φminus1(middot) = inverse standard normal cumulative function The func-tion Φminus1(middot) can be obtained easily from EXCEL using normsinv (pf) orMATLAB using norminv (pf) For sufficiently large β simple approximateclosed-form solutions for Φ(middot) and Φminus1(middot) are available (Appendix B)

The basic reliability problem is to evaluate pf from some pertinent statisticsof F and Q which typically include the mean (microF or microQ) and the standarddeviation (σF or σQ) and possibly the probability density function A simpleclosed-form solution for pf is available if Q and F follow a bivariate normaldistribution For this condition the solution to Equation (132) is

pf = Φ

⎛⎜⎝minus microQ minusmicroFradic

σ 2Q +σ 2

F minus 2ρQFσQσF

⎞⎟⎠= Φ (minusβ) (134)

in which ρQF = product-moment correlation coefficient between Q and FNumerical values for Φ(middot) can be obtained easily using the EXCEL func-tion normsdist(minusβ) or the MATLAB function normcdf(minusβ) The reliability

Numerical recipes for reliability analysis 29

indices for most geotechnical components and systems lie between 1 and5 corresponding to probabilities of failure ranging from about 016 to3 times 10minus7 as shown in Figure 19

Equation (134) can be generalized to a linear performance function con-taining any number of normal components (X1X2 Xn) as long as theyfollow a multivariate normal distribution function [Equation (121)]

P = a0 +nsum

i=1

aiXi (135)

pf = Φ

⎛⎜⎜⎜⎜⎝minus

a0 +nsum

i=1aimicroiradic

nsumi=1

nsumj=1

aiajρijσiσj

⎞⎟⎟⎟⎟⎠ (136)

in which ai = deterministic constant microi = mean of Xiσi = standard devia-tion of Xiρij = correlation between Xi and Xj (note ρii = 1) Chapters 811 and 12 present some applications of these closed-form solutions

Equation (134) can be modified for the case of translation lognormals ieln(Q) and ln(F) follow a bivariate normal distribution with mean of ln(Q) =λQ mean of ln(F) =λF standard deviation of ln(Q) = ξQ standard deviation

Unsatisfactory

Hazardous1E+00

1Eminus01

1Eminus02

1Eminus03

Pro

bab

ility

of

failu

re

1Eminus04

1Eminus05

0 1 2 3

Reliability index4 5

1Eminus06

1Eminus07

PoorBelow average

Above average

Good

High

asymp10minus7

asymp10minus5

asymp10minus3

asymp2asymp7

asymp16

asymp5times10minus3

Figure 19 Relationship between reliability index and probability of failure (classificationsproposed by US Army Corps of Engineers 1997)

30 Kok-Kwang Phoon

of ln(F) = ξF and correlation between ln(Q) and ln(F) = ρprimeQF

pf = Φ

⎛⎜⎝minus λQ minusλFradic

ξ2Q + ξ2

F minus 2ρprimeQFξQξF

⎞⎟⎠ (137)

The relationships between the mean (micro) and standard deviation (σ ) of alognormal and the mean (λ) and standard deviation (ξ ) of the equivalentnormal are given in Equation (18) The correlation between Q and F (ρQF)is related to the correlation between ln(Q) and ln(F) (ρprime

QF) as follows

ρQF =exp(ξQξFρ

primeQF) minus 1radic

[exp(ξ2Q) minus 1][exp(ξ2

F ) minus 1](138)

If ρprimeQF = ρQF = 0 (ie Q and F are independent lognormals) Equation (137)

reduces to the following well-known expression

β =ln(

microQmicroF

radic1+COV2

F1+COV2

Q

)radic

ln[(

1 + COV2Q

)(1 + COV2

F

)] (139)

in which COVQ = σQmicroQ and COVF = σFmicroF If there are physical groundsto disallow negative values the translation lognormal model is more sensibleEquation (139) has been used as the basis for RBD (eg Rosenblueth andEsteva 1972 Ravindra and Galambos 1978 Becker 1996 Paikowsky2002) The calculation steps outlined below are typically carried out

1 Consider a typical Load and Resistance Factor Design (LRFD) equation

φQn = γDDn + γLLn (140)

in which φ = resistance factor γD and γL = dead and live load fac-tors and Qn Dn and Ln = nominal values of capacity dead loadand live load The AASHTO LRFD bridge design specifications rec-ommended γD = 125 and γL = 175 (Paikowsky 2002) The resistancefactor typically lies between 02 and 08

2 The nominal values are related to the mean values as

microQ = bQQn

microD = bDDn

microL = bLLn

(141)

Numerical recipes for reliability analysis 31

in which bQ bD and bL are bias factors and microQ microD and microL are meanvalues The bias factors for dead and live load are typically 105 and 115respectively Figure 12 shows that bQ (mean value of the histogram) canbe smaller or larger than one

3 Assume typical values for the mean capacity (microQ) and DnLn A reason-able range for DnLn is 1 to 4 Calculate the mean live load and meandead load as follows

microL = (φbQmicroQ)bL(γD

DnLn

+ γL

) (142)

microD =(

Dn

Ln

)microLbD

bL(143)

4 Assume typical coefficients of variation for the capacity dead load andlive load say COVQ = 03 COVD = 01 and COVL = 02

5 Calculate the reliability index using Equation (139) with microF = microD +microL and

COVF =radic

(microDCOVD)2 + (microLCOVL)2

microD +microL(144)

Details of the above procedure are given in Appendix A4 For φ = 05 γD =125 γL = 175 bQ = 1bD = 105bL = 115 COVQ = 03 COVD = 01COVL = 02 and DnLn = 2 the reliability index from Equation (139) is299 Monte Carlo simulation in ldquoLRFDmrdquo validates the approximationgiven in Equation (144) It is easy to show that an alternate approximationCOV2

F = COV2D+ COV2

L is erroneous Equation (139) is popular becausethe resistance factor in LRFD can be back-calculated from a target reliabilityindex (βT) easily

φ =bQ(γDDn + γLLn)

radic1+COV2

F1+COV2

Q

(bDDn + bLLn)expβT

radicln[(

1 + COV2Q

)(1 + COV2

F

)] (145)

In practice the statistics of Q are determined by comparing load test resultswith calculated values Phoon and Kulhawy (2005) compared the statisticsof Q calculated from laboratory load tests with those calculated from full-scale load tests They concluded that these statistics are primarily influencedby model errors rather than uncertainties in the soil parameters Hence it islikely that the above lumped capacity approach can only accommodate the

32 Kok-Kwang Phoon

ldquolowrdquo COV range of parametric uncertainties mentioned in Section 131To accommodate larger uncertainties in the soil parameters (ldquomediumrdquo andldquohighrdquo COV ranges) it is necessary to expand Q as a function of the gov-erning soil parameters By doing so the above closed-form solutions are nolonger applicable

142 First-order reliability method (FORM)

Structural reliability theory has a significant impact on the development ofmodern design codes Much of its success could be attributed to the advent ofthe first-order reliability method (FORM) which provides a practical schemeof computing small probabilities of failure at high dimensional space spannedby the random variables in the problem The basic theoretical result wasgiven by Hasofer and Lind (1974) With reference to time-invariant reliabil-ity calculation Rackwitz (2001) observed that ldquoFor 90 of all applicationsthis simple first-order theory fulfills all practical needs Its numerical accuracyis usually more than sufficientrdquo Ang and Tang (1984) presented numer-ous practical applications of FORM in their well known book ProbabilityConcepts in Engineering Planning and Design

The general reliability problem consists of a performance func-tion P(y1y2 yn) and a multivariate probability density functionf (y1y2 yn) The former is defined to be zero at the limit state less thanzero when the limit state is exceeded (ldquofailrdquo) and larger than zero otherwise(ldquosaferdquo) The performance function is nonlinear for most practical prob-lems The latter specifies the likelihood of realizing any one particular setof input parameters (y1y2 yn) which could include material load andgeometrical parameters The objective of reliability analysis is to calculatethe probability of failure which can be expressed formally as follows

pf =int

Plt0f(y1y2 yn

)dy1dy2 dyn (146)

The domain of integration is illustrated by a shaded region in the left panelof Figure 110a Exact solutions are not available even if the multivariateprobability density function is normal unless the performance function islinear or quadratic Solutions for the former case are given in Section 141Other exact solutions are provided in Appendix C Exact solutions are veryuseful for validation of new reliability codes or calculation methods Theonly general solution to Equation (146) is Monte Carlo simulation A simpleexample is provided in Appendix A5 The calculation steps outlined beloware typically carried out

1 Determine (y1y2 yn) using Monte Carlo simulation Section 132has presented fairly general and ldquouser friendlyrdquo methods of simulatingcorrelated non-normal random vectors

Numerical recipes for reliability analysis 33

βminuspoint

β

zlowast = (z1lowastz2

lowast)

PLlt 0

P lt 0FAIL

P gt 0SAFE PLgt 0

Actual P = 0

Approx PL=0

fz1z2(z1z2)

fy1y2(y1y2)

z2

z1

y2

y1

G(y1y2) = 0

Figure 110 (a) General reliability problem and (b) solution using FORM

2 Substitute (y1y2 yn) into the performance function and count thenumber of cases where P lt 0 (ldquofailurerdquo)

3 Estimate the probability of failure using

pf = nf

n(147)

in which nf = number of failure cases and n =number of simulations4 Estimate the coefficient of variation of pf using

COVpf=radic

1 minus pf

pfn(148)

For civil engineering problems pf asymp 10minus3 and hence (1 ndash pf) asymp 1 The samplesize (n) required to ensure COVpf

is reasonably small say 03 is

n = 1 minus pf

pfCOV2pf

asymp 1pf(03)2

asymp 10pf

(149)

It is clear from Equation (149) that Monte Carlo simulation is not prac-tical for small probabilities of failure It is more often used to validateapproximate but more efficient solution methods such as FORM

The approximate solution obtained from FORM is easier to visualize ina standard space spanned by uncorrelated Gaussian random variables withzero mean and unit standard deviation (Figure 110b) If one replaces theactual limit state function (P = 0) by an approximate linear limit state func-tion (PL = 0) that passes through the most likely failure point (also called

34 Kok-Kwang Phoon

design point or β-point) it follows immediately from rotational symmetryof the circular contours that

pf asymp Φ(minusβ) (150)

The practical result of interest here is that Equation (146) simply reduces toa constrained nonlinear optimization problem

β = minradic

zprimez forz G(z) le 0 (151)

in which z = (z1z2 zn)prime The solution of a constrained optimization prob-lem is significantly cheaper than the solution of a multi-dimensional integral[Equation (146)] It is often cited that the β-point is the ldquobestrdquo linearizationpoint because the probability density is highest at that point In actuality thechoice of the β-point requires asymptotic analysis (Breitung 1984) In shortFORM works well only for sufficiently large β ndash the usual rule-of-thumb isβ gt 1 (Rackwitz 2001)

Low and co-workers (eg Low and Tang 2004) demonstrated that theSOLVER function in EXCEL can be easily implemented to calculate thefirst-order reliability index for a range of practical problems Their studiesare summarized in Chapter 3 The key advantages to applying SOLVER forthe solution of Equation (151) are (a) EXCEL is available on almost allPCs (b) most practitioners are familiar with the EXCEL user interface and(c) no programming skills are needed if the performance function can becalculated using EXCEL built-in mathematical functions

FORM can be implemented easily within MATLAB as well Appendix A6demonstrates the solution process for an infinite slope problem (Figure 111)The performance function for this problem is

P =[γ(H minus h

)+ h(γsat minus γw

)]cosθ tanφ[

γ(H minus h

)+ hγsat]sinθ

minus 1 (152)

in which H = depth of soil above bedrock h = height of groundwater tableabove bedrock γ and γsat = moist unit weight and saturated unit weight ofthe surficial soil respectively γw = unit weight of water (981 kNm3) φ =effective stress friction angle and θ = slope inclination Note that the heightof the groundwater table (h) cannot exceed the depth of surficial soil (H)and cannot be negative Hence it is modeled by h = H times U in which U =standard uniform variable The moist and saturated soil unit weights are notindependent because they are related to the specific gravity of the soil solids(Gs) and the void ratio (e) The uncertainties in γ and γsat are characterizedby modeling Gs and e as two independent uniform random variables Thereare six independent random variables in this problem (HUφβe and Gs)

Numerical recipes for reliability analysis 35

ROCKh

SOIL

H

θγ

γsat

Figure 111 Infinite slope problem

Table 13 Summary of probability distributions for input random variables

Variable Description Distribution Statistics

H Depth of soil above bedrock Uniform [28] mh = H times U Height of water table U is uniform [0 1]φ Effective stress friction angle Lognormal mean = 35 cov = 8θ Slope inclination Lognormal mean = 20 cov = 5γ Moist unit weight of soil γsat Saturated unit weight of soil γw Unit weight of water Deterministic 981 kNm3

γ = γw (Gs + 02e)(1 + e) (assume degree of saturation = 20 for ldquomoistrdquo) γsat = γw (Gs + e)(1 + e) (degree of saturation = 100)Assume specific gravity of solids = Gs = uniformly distributed [25 27] and void ratio = e = uniformlydistributed [03 06]

and their probability distributions are summarized in Table 13 The first-order reliability index is 143 The reliability index calculated from MonteCarlo simulation is 157

Guidelines for modifying the code to solve other problems can besummarized as follows

1 Specify the number of random variables in the problem using theparameter ldquomrdquo in ldquoFORMmrdquo

2 The objective function ldquoobjfunmrdquo is independent of the problem3 The performance function ldquoPfunmrdquo can be modified in a straight-

forward way The only slight complication is that the physical vari-ables (eg HUφβe and Gs) must be expressed in terms of thestandard normal variables (eg z1z2 z6) Practical methods for

36 Kok-Kwang Phoon

converting uncorrelated standard normal random variables to correlatednon-normal random variables have been presented in Section 132

Low and Tang (2004) opined that practitioners will find it easier to appre-ciate and implement FORM without the above conversion procedureNevertheless it is well-known that optimization in the standard space is morestable than optimization in the original physical space The SOLVER optionldquoUse Automatic Scalingrdquo only scales the elliptical contours in Figure 110abut cannot make them circular as in Figure 110b Under some circum-stances SOLVER will produce different results from different initial trialvalues Unfortunately there are no automatic and dependable means of flag-ging this instability Hence it is extremely vital for the user to try differentinitial trial values and partially verify that the result remains stable Theassumption that it is sufficient to use the mean values as the initial trialvalues is untrue

In any case it is crucial to understand that a multivariate probabil-ity model is necessary for any probabilistic analysis involving more thanone random variable FORM or otherwise It is useful to recall thatmost multivariate non-normal probability models are related to the mul-tivariate normal probability model in a fundamental way as discussed inSection 132 Optimization in the original physical space does not elim-inate the need for the underlying multivariate normal probability modelif the non-normal physical random vector is produced by the translationmethod It is clear from Section 132 that non-translation methods exist andnon-normal probability models cannot be constructed uniquely from corre-lation information alone Chapters 9 and 13 report applications of FORMfor RBD

143 System reliability based on FORM

The first-order reliability method (FORM) is capable of handling any non-linear performance function and any combination of correlated non-normalrandom variables Its accuracy depends on two main factors (a) the cur-vature of the performance function at the design point and (b) the numberof design points If the curvature is significant the second-order reliabilitymethod (SORM) (Breitung 1984) or importance sampling (Rackwitz 2001)can be applied to improve the FORM solution Both methods are relativelyeasy to implement although they are more costly than FORM The calcu-lation steps for SORM are given in Appendix D Importance sampling isdiscussed in Chapter 4 If there are numerous design points FORM canunderestimate the probability of failure significantly At present no solutionmethod exists that is of comparable simplicity to FORM Note that problemscontaining multiple failure modes are likely to produce more than one designpoint

Numerical recipes for reliability analysis 37

Figure 112 illustrates a simple system reliability problem involving twolinear performance functions P1 and P2 A common geotechnical engineer-ing example is a shallow foundation subjected to inclined loading It isgoverned by bearing capacity (P1) and sliding (P2) modes of failure Thesystem reliability is formally defined by

pf = Prob[(P1 lt 0) cup (P2 lt 0)] (153)

There are no closed-form solutions even if P1 and P2 are linear and if theunderlying random variables are normal and uncorrelated A simple estimatebased on FORM and second-order probability bounds is available and is ofpractical interest The key calculation steps can be summarized as follows

1 Calculate the correlation between failure modes using

ρP2P1= α1 middotα2 = α11α21 +α12α22 = cosθ (154)

in which αi = (αi1αi2) = unit normal at design point for ith performancefunction Referring to Figure 110b it can be seen that this unit normalcan be readily obtained from FORM as αi1 = zlowast

i1βi and αi2 = zlowasti2βi

with (zlowasti1z

lowasti2) = design point for ith performance function

P2(P1 lt 0) cap (P2 lt 0)

fz1z2(z1z2)

z2

z1

θ

P1

β1

β2

α1

α2

Figure 112 Simple system reliability problem

38 Kok-Kwang Phoon

2 Estimate p21 = Prob[(P2 lt 0) cap (P1 lt 0)] using first-order probabilitybounds

pminus21 le p21 le p+

21

max[P(B1)P(B2)] le p21 le P(B1) + P(B2)(155)

in which P(B1) = Φ(minusβ1)Φ[(β1 cosθ minus β2)sinθ ] and P(B2) =Φ(minusβ2)Φ[(β2 cosθ minus β1)sinθ ] Equation (155) only applies forρP2P1

gt 0 Failure modes are typically positively correlated becausethey depend on a common set of loadings

3 Estimate pf using second-order probability bounds

p1 + max[(p2 minus p+21)0] le pf le min[p1 + (p2 minus pminus

21)1] (156)

in which pi = Φ(minusβi)

The advantages of the above approach are that it does not require infor-mation beyond what is already available in FORM and generalization to nfailure modes is quite straightforward

pf ge p1 + max[(p2 minus p+21)0]+ max[(p3 minus p+

31 minus p+320]+ middot middot middot

+ max[(pn minus p+n1 minus p+

n2 minusmiddotmiddot middotminus p+nnminus1)0] (157a)

pf le p1 + minp1 +[p2 minus pminus21]+ [p3 minus max(pminus

31pminus32)]+ middot middot middot

+ [pn minus max(pminusn1p

minusn2 middot middot middotpminus

nnminus1)]1 (157b)

The clear disadvantages are that no point probability estimate is availableand calculation becomes somewhat tedious when the number of failuremodes is large The former disadvantage can be mitigated using the followingpoint estimate of p21 (Mendell and Elston 1974)

a1 = 1

Φ(minusβ1)radic

2πexp

(minusβ2

1

2

)(158)

p21 asymp Φ

⎡⎢⎣ ρP1P2

a1 minusβ2radic1 minusρP1P2

2a1(a1 minusβ1)

⎤⎥⎦Φ(minusβ1) (159)

The accuracy of Equation (159) is illustrated in Table 14 Bold numbersshaded in gray are grossly inaccurate ndash they occur when the reliability indicesare significantly different and the correlation is high

Numerical recipes for reliability analysis 39

Table 14 Estimation of p21 using probability bounds point estimate and simulation

Performancefunctions

Correlation Probability bounds Point estimate Simulation

β1 = 1 01 (29 58) times10minus2 31 times10minus2 31 times10minus2

β2 = 1 05 (45 89) times10minus2 63 times10minus2 63 times10minus2

09 (06 13) times10minus1 12 times10minus1 12 times10minus1

β1 = 1 01 (48 93) times10minus3 50 times10minus3 50 times10minus3

β2 = 2 05 (11 18) times10minus2 13 times10minus2 13 times10minus2

09 (22 23) times10minus2 23 times10minus2 23 times10minus2

β1 = 1 01 (33 61) times10minus4 34 times10minus4 35 times10minus4

β2 = 3 05 (10 13) times10minus3 10 times10minus3 10 times10minus3

09 (13 13) times10minus3 05 times10minus3 13 times10minus3

β1 = 1 01 (86 157) times10minus6 89 times10minus6 85 times10minus6

β2 = 4 05 (28 32) times10minus5 23 times10minus5 28 times10minus5

09 (32 32) times10minus5 007 times10minus5 32 times10sminus5

β1 = 2 01 (80 160) times10minus4 87 times10minus4 88 times10minus4

β2 = 2 05 (28 56) times10minus3 41 times10minus3 41 times10minus3

09 (07 15) times10minus2 14 times10minus2 13 times10minus2

β1 = 2 01 (59 115) times10minus5 63 times10minus5 60 times10minus5

β2 = 3 05 (38 62) times10minus4 45 times10minus4 45 times10minus4

09 (13 13) times10minus3 12 times10minus3 13 times10minus3

β1 = 2 01 (17 32) times10minus6 18 times10minus6 25 times10minus6

β2 = 4 05 (16 22) times10minus5 16 times10minus5 15 times10minus5

09 (32 32) times10minus5 05 times10minus5 32 times10minus5

β1 = 3 01 (45 90) times10minus6 49 times10minus6 45 times10minus6

β2 = 3 05 (56 112) times10minus5 82 times10minus5 80 times10minus5

09 (33 66) times 10minus4 63 times 10minus4 60 times10minus4

Sample size = 5000000

144 Collocation-based stochastic response surfacemethod

The system reliability solution outlined in Section 143 is reasonably prac-tical for problems containing a few failure modes that can be individuallyanalyzed by FORM A more general approach that is gaining wider atten-tion is the spectral stochastic finite element method originally proposed byGhanem and Spanos (1991) The key element of this approach is the expan-sion of the unknown random output vector using multi-dimensional Hermitepolynomials as basis functions (also called a polynomial chaos expansion)The unknown deterministic coefficients in the expansion can be solved usingthe Galerkin or collocation method The former method requires significant

40 Kok-Kwang Phoon

modification of the existing deterministic numerical code and is impossibleto apply for most engineers with no access to the source code of their com-mercial softwares The collocation method can be implemented using a smallnumber of fairly simple computational steps and does not require the mod-ification of existing deterministic numerical code Chapter 7 and elsewhere(Sudret and Der Kiureghian 2000) discussed this important class of methodsin detail Verhoosel and Gutieacuterrez (2007) highlighted challenging difficultiesin applying these methods to nonlinear finite element problems involvingdiscontinuous fields It is interesting to observe in passing that the outputvector can be expanded using the more well-known Taylor series expansionas well The coefficients of the expansion (partial derivatives) can be calcu-lated using the perturbation method This method can be applied relativelyeasily to finite element outputs (Phoon et al 1990 Quek et al 1991 1992)but is not covered in this chapter

This section briefly explains the key computational steps for the morepractical collocation approach We recall in Section 132 that a vector of cor-related non-normal random variables Y = (Y1Y2 Yn)prime can be related to avector of correlated standard normal random variables X = (X1X2 Xn)primeusing one-dimensional Hermite polynomial expansions [Equation (126)]The correlation coefficients for the normal random vector are evaluatedfrom the correlation coefficients of the non-normal random vector usingEquation (127) This method can be used to construct any non-normal ran-dom vector as long as the correlation coefficients are available This is indeedthe case if Y represents the input random vector However if Y representsthe output random vector from a numerical code the correlation coefficientsare unknown and Equation (126) is not applicable Fortunately this practi-cal problem can be solved by using multi-dimensional Hermite polynomialswhich are supported by a known normal random vector with zero meanunit variance and uncorrelated or independent components

The multi-dimensional Hermite polynomials are significantly more com-plex than the one-dimensional version For example the second-order andthird-order forms can be expressed respectively as follows (Isukapalli1999)

Y asympao+nsum

i=1

aiZi +nsum

i=1

aii

(Z2

i minus1)+

nminus1sumi=1

nsumjgti

aijZiZj (160a)

Y asympao+nsum

i=1

aiZi +nsum

i=1

aii

(Z2

i minus1)+

nsumi=1

aiii

(Z3

i minus3Zi

)+

nminus1sumi=1

nsumjgti

aijZiZj

+nsum

i=1

nsumj=1j =i

aijj

(ZiZ

2j minusZi

)+

nminus2sumi=1

nminus1sumjgti

nsumkgtj

aijkZiZjZk (160b)

Numerical recipes for reliability analysis 41

For n = 3 Equation (160) produces the following expansions (indices forcoefficients are re-labeled consecutively for clarity)

Y asymp ao + a1Z1 + a2Z2 + a3Z3 + a4(Z21 minus 1) + a5(Z2

2 minus 1) + a6(Z23 minus 1)

+ a7Z1Z2 + a8Z1Z3 + a9Z2Z3 (161a)

Y asymp ao + a1Z1 + a2Z2 + a3Z3 + a4(Z21 minus 1) + a5(Z2

2 minus 1) + a6(Z23 minus 1)

+ a7Z1Z2 + a8Z1Z3 + a9Z2Z3 + a10(Z31 minus 3Z1) + a11(Z3

2 minus 3Z2)

+ a12(Z33 minus 3Z3) + a13(Z1Z2

2 minus Z1) + a14(Z1Z23 minus Z1)

+ a15(Z2Z21 minus Z2) + a16(Z2Z2

3 minus Z2) + a17(Z3Z21 minus Z3)

+ a18(Z3Z22 minus Z3) + a19Z1Z2Z3 (161b)

In general N2 and N3 terms are respectively required for the second-orderand third-order expansions (Isukapalli 1999)

N2 = 1 + 2n + n(n minus 1)2

(162a)

N3 = 1 + 3n + 3n(n minus 1)2

+ n(n minus 1)(n minus 2)6

(162b)

For a fairly modest random dimension of n = 5 N2 and N3 terms arerespectively equal to 21 and 56 Hence fairly tedious algebraic expressionsare incurred even at a third-order truncation One-dimensional Hermitepolynomials can be generated easily and efficiently using a three-term recur-rence relation [Equation (111)] No such simple relation is available formulti-dimensional Hermite polynomials They are usually generated usingsymbolic algebra which is possibly out of reach of the general practi-tioner This major practical obstacle is currently being addressed by Lianget al (2007) They have developed a user-friendly EXCEL add-in to gener-ate tedious multi-dimensional Hermite expansions automatically Once themulti-dimensional Hermite expansions are established their coefficients canbe calculated following the steps described in Equations (118)ndash(120) Twopractical aspects are noteworthy

1 The random dimension of the problem should be minimized to reducethe number of terms in the polynomial chaos expansion The spectraldecomposition method can be used

X = PD12Z + micro (163)

in which D = diagonal matrix containing eigenvalues in the leading diag-onal and P = matrix whose columns are the corresponding eigenvectors

42 Kok-Kwang Phoon

If C is the covariance matrix of X the matrices D and P can be calcu-lated easily using [PD] = eig(C) in MATLAB The key advantage ofreplacing Equation (123) by Equation (163) is that an n-dimensionalcorrelated normal vector X can be simulated using an uncorrelated stan-dard normal vector Z with a dimension less than n This is achieved bydiscarding eigenvectors in P that correspond to small eigenvalues A sim-ple example is provided in Appendix A7 The objective is to simulate a3D correlated normal vector following a prescribed covariance matrix

C =⎡⎣ 1 09 02

09 1 0502 05 1

⎤⎦

Spectral decomposition of the covariance matrix produces

D =⎡⎣0045 0 0

0 0832 00 0 2123

⎤⎦ P =

⎡⎣ 0636 0467 0614

minus0730 0108 06750249 minus0878 0410

⎤⎦

Realizations of X can be obtained using Equation (163) Results areshown as open circles in Figure 113 The random dimension can bereduced from three to two by ignoring the first eigenvector correspond-ing to a small eigenvalue of 0045 Realizations of X are now simulatedusing

⎧⎨⎩

X1X2X3

⎫⎬⎭=⎡⎣ 0467 0614

0108 0675minus0878 0410

⎤⎦[radic0832 0

0radic

2123

]Z1Z2

Results are shown as crosses in Figure 113 Note that three correlatedrandom variables can be represented reasonably well using only twouncorrelated random variables by neglecting the smallest eigenvalue andthe corresponding eigenvector The exact error can be calculated theo-retically by observing that the covariance of X produced by Equation(163) is

CX = (PD12)(D12Pprime) = PDPprime (164)

If the matrices P and D are exact it can be proven that PDPprime = C iethe target covariance is reproduced For the truncated P and D matrices

Numerical recipes for reliability analysis 43

Random dimension

ThreeTwo

3

2

1

0X3

X2

minus1

minus2

minus3minus3 minus2 minus1 0 2 2 3

Random dimension

ThreeTwo

3

2

1

0X3

X2

minus1

minus2

minus3minus3 minus2 minus1 0 2 2 3

Figure 113 Scatter plot between X1 and X2 (top) and X2 and X3 (bottom)

discussed above the covariance of X is

CX =⎡⎣ 0467 0614

0108 0675minus0878 0410

⎤⎦[0832 0

0 2123

]

times[0467 0108 minus08780614 0675 0410

]=⎡⎣0982 0921 0193

0921 0976 05080193 0508 0997

⎤⎦

44 Kok-Kwang Phoon

The exact error in each element of CX can be clearly seen by comparingwith the corresponding element in C In particular the variances ofX (elements in the leading diagonal) are slightly reduced For randomprocessesfields reduction in the random dimension can be achievedusing the KarhunenndashLoeve expansion (Phoon et al 2002 2004) It canbe shown that the spectral representation given by Equation (128) isa special case of the KarhunenndashLoeve expansion (Huang et al 2001)The random dimension can be further reduced by performing sensitiv-ity analysis and discarding input parameters that are ldquounimportantrdquoFor example the square of the components of the unit normal vec-tor α shown in Figure 112 are known as FORM importance factors(Ditlevsen and Madsen 1996) If the input parameters are independentthese factors indicate the degree of influence exerted by the corre-sponding input parameters on the failure event Sudret (2007) discussedthe application of Sobolrsquo indices for sensitivity analysis of the spectralstochastic finite element method

2 The number of output values (y1y2 ) in Equation (120) should beminimized because they are usually produced by costly finite elementcalculations Consider a problem containing two random dimensionsand approximating the output as a third-order expansion

yi = ao + a1zi1 + a2zi2 + a3(z2i1 minus 1) + a4(z2

i2 minus 1) + a5zi1zi2

+ a6(z3i1 minus 3zi1) + a7(z3

i2 minus 3zi2) + a8(zi1z2i2 minus zi1)

+ a9(zi2z2i1 minus zi2) (165)

Phoon and Huang (2007) demonstrated that the collocation points(zi1zi2) are best sited at the roots of the Hermite polynomial that is oneorder higher than that of the Hermite expansion In this example theroots of the fourth-order Hermite polynomial are plusmnradic

(3plusmnradic6) Although

zero is not one of the roots it should be included because the standardnormal probability density function is highest at the origin Twenty-five collocation points (zi1zi2) can be generated by combining the rootsand zero in two dimensions as illustrated in Figure 114 The roots ofHermite polynomials (up to order 15) can be calculated numerically asshown in Appendix A8

145 Subset simulation method

The collocation-based stochastic response surface method is very efficientfor problems containing a small number of random dimensions and perfor-mance functions that can be approximated quite accurately using low-orderHermite expansions However the method suffers from a rapid proliferation

Numerical recipes for reliability analysis 45

Z1

Z2

Figure 114 Collocation points for a third-order expansion with two random dimensions

of Hermite expansion terms when the random dimension andor order ofexpansion increase A critical review of reliability estimation procedures forhigh dimensions is given by Schueumlller et al (2004) One potentially practicalmethod that is worthy of further study is the subset simulation method (Auand Beck 2001) It appears to be more robust than the importance samplingmethod Chapter 4 and elsewhere (Au 2001) present a more extensive studyof this method

This section briefly explains the key computational steps involved inthe implementation Consider the failure domain Fm defined by the con-dition P(y1y2 yn) lt 0 in which P is the performance function and(y1y2 yn) are realizations of the uncertain input parameters The ldquofail-urerdquo domain Fi defined by the condition P(y1y2 yn) lt ci in which ci isa positive number is larger by definition of the performance function Weassume that it is possible to construct a nested sequence of failure domainsof increasing size by using an increasing sequence of positive numbers iethere exists c1 gt c2 gt gt cm = 0 such that F1 sup F2 sup Fm As shown inFigure 115 for the one-dimensional case it is clear that this is always possibleas long as one value of y produces only one value of P(y) The performancefunction will satisfy this requirement If one value of y produces two valuesof P(y) say a positive value and a negative value then the physical systemis simultaneously ldquosaferdquo and ldquounsaferdquo which is absurd

The probability of failure (pf ) can be calculated based on the above nestedsequence of failure domains (or subsets) as follows

pf = Prob(Fm) = Prob(Fm|Fmminus1)Prob(Fmminus1|Fmminus2)

times Prob(F2|F1)Prob(F1) (166)

46 Kok-Kwang Phoon

P(y)

y

C1

C2

F2 F2

F1

Figure 115 Nested failure domains

At first glance Equation (166) appears to be an indirect and more tediousmethod of calculating pf In actuality it can be more efficient because theprobability of each subset conditional on the previous (larger) subset canbe selected to be sufficiently large say 01 such that a significantly smallernumber of realizations is needed to arrive at an acceptably accurate resultWe recall from Equation (149) that the rule of thumb is 10pf ie only100 realizations are needed to estimate a probability of 01 If the actualprobability of failure is 0001 and the probability of each subset is 01 it isapparent from Equation (166) that only three subsets are needed implying atotal sample size = 3times100 = 300 In contrast direct simulation will require100001 = 10000 realizations

The typical calculation steps are illustrated below using a problemcontaining two random dimensions

1 Select a subset sample size (n) and prescribe p = Prob(Fi|Fiminus1) Weassume p = 01 and n = 500 from hereon

2 Simulate n = 500 realizations of the uncorrelated standard normal vector(Z1Z2)prime The physical random vector (Y1Y2)prime can be determined fromthese realizations using the methods described in Section 132

3 Calculate the value of the performance function gi = P(yi1yi2) associ-ated with each realization of the physical random vector (yi1yi2)prime i =12 500

4 Rank the values of (g1g2 g500) in ascending order The value locatedat the (np + 1) = 51st position is c1

Numerical recipes for reliability analysis 47

5 Define the criterion for the first subset (F1) as P lt c1 By construction atstep (4) P(F1) = npn = p The realizations contained in F1 are denotedby zj = (zj1zj2)prime j = 12 50

6 Simulate 1p = 10 new realizations from zj using the followingMetropolis-Hastings algorithm

a Simulate 1 realization using a uniform proposal distribution withmean located at micro = zj and range = 1 ie bounded by micro plusmn 05 Letthis realization be denoted by u = (u1u2)prime

b Calculate the acceptance probability

α = min(

10I(u)φ(u)φ(micro)

)(167)

in which I(u) = 1 if P(u1u2) lt c1 I(u) = 0 if P(u1u2) ge c1φ(u) = exp(minus05uprimeu) and φ(micro) = exp(minus05microprimemicro)

c Simulate 1 realization from a standard uniform distributionbounded between 0 and 1 denoted by v

d The first new realization is given by

w = u if v lt α

w = micro if v ge α(168)

Update the mean of the uniform proposal distribution in step (a) asmicro = w (ie centred about the new realization) and repeat the algo-rithm to obtain a ldquochainrdquo containing 10 new realizations Fiftychains are obtained in the same way with initial seeds at zj j = 1 50 It can be proven that these new realizations would followProb(middot|F1) (Au 2001)

7 Convert these realizations to their physical values and repeat Step (3)until ci becomes negative (note the smallest subset should correspondto cm = 0)

It is quite clear that the above procedure can be extended to any randomdimension in a trivial way In general the accuracy of this subset simula-tion method depends on ldquotuningrdquo factors such as the choice of np and theproposal distribution in step 6(a) (assumed to be uniform with range = 1)A study of some of these factors is given in Chapter 4 The optimal choice ofthese factors to achieve minimum runtime appears to be problem-dependentAppendix A9 provides the MATLAB code for subset simulation The perfor-mance function is specified in ldquoPfunmrdquo and the number of random variablesis specified in parameter ldquomrdquo Figure 116 illustrates the behavior of thesubset simulation method for the following performance function

P = 2 + 3radic

2 minus Y1 minus Y2 = 2 + 3radic

2 + ln[Φ(minusZ1)]+ ln[Φ(minusZ2)] (169)

minus1 0 1 2 3 4minus1

0

1

2

3

4

Z1

Z2

P lt 0

P lt c1

minus1 0 1 2 3 4minus1

0

1

2

3

4

Z1

Z2

P lt 0

P lt c1

Figure 116 Subset simulation method for P = 2 + 3radic

2 minus Y1 minus Y2 in which Y1 and Y2 areexponential random variables with mean = 1 first subset (top) and secondsubset (bottom)

Numerical recipes for reliability analysis 49

in which Y1Y2 = exponential random variables with mean = 1 and Z1Z2 =uncorrelated standard normal random variables The exact solution for theprobability of failure is 00141 (Appendix C) The solution based on subsetsimulation method solution is achieved as follows

1 Figure 116a Simulate 500 realizations (small open circles) and identifythe first 50 realizations in order of increasing P value (big open circles)The 51st smallest P value is c1 = 2192

2 These realizations satisfy the criterion P lt c1 (solid line) by construc-tion The domain above the solid line is F1

3 Figure 116b Simulate 10 new realizations from each big open circle inFig 116a The total number is 10 times 50 = 500 realizations (small opencircles)

4 There are 66 realizations (big open squares) satisfying P lt 0 (dashedline) The domain above the dashed line is F2 F2 is the actual failuredomain Hence Prob(F2|F1) = 66500 = 0132

5 The estimated probability of failure is pf = Prob(F2|F1)P(F1) = 0132times01 = 00132

Note that only 7 realizations in Figure 116a lie in F2 In contrast 66 real-izations in Figure 116b lie in F2 providing a more accurate pf estimate

15 Conclusions

This chapter presents general and user-friendly computational methods forreliability analysis ldquoUser-friendlyrdquo methods refer to those that can be imple-mented on a desktop PC by a non-specialist with limited programmingskills in other words methods within reach of the general practitionerThis chapter is organized under two main headings describing (a) how tosimulate uncertain inputs numerically and (b) how to propagate uncertaininputs through a physical model to arrive at the uncertain outputs Althoughsome of the contents are elaborated in greater detail in subsequent chaptersthe emphasis on numerical implementations in this chapter should shortenthe learning curve for the novice The overview will also provide a usefulroadmap to the rest of this book

For the simulation of uncertain inputs following arbitrary non-normalprobability distribution functions and correlation structure the translationmodel involving memoryless transform of the multivariate normal probabil-ity distribution function can cater for most practical scenarios Implemen-tation of the translation model using one-dimensional Hermite polynomialsis relatively simple and efficient For stochastic data that cannot be modeledusing the translation model the hunt for probability models with comparablepracticality theoretical power and simulation speed is still on-going Copulatheory produces a more general class of multivariate non-normal models but

50 Kok-Kwang Phoon

it is debatable at this point if these models can be estimated empirically andsimulated numerically with equal ease for high random dimensions Theonly exception is a closely related but non-translation approach based onthe multivariate normal copula

The literature is replete with methodologies on the determination of uncer-tain outputs from a given physical model and uncertain inputs The mostgeneral method is Monte Carlo simulation but it is notoriously tediousOne may assume with minimal loss of generality that a complex geotechni-cal problem (possibly 3D nonlinear time-and construction-dependent etc)would only admit numerical solutions and the spatial domain can be modeledby a scalarvector random field For a sufficiently large and complex problemit is computationally intensive to complete even a single run At present it isaccurate to say that a computationally efficient and ldquouser-friendlyrdquo solutionto the general stochastic problem remains elusive Nevertheless reasonablypractical solutions do exist if the general stochastic problem is restrictedin some ways for example accept a first-order estimate of the probabilityof failure or accept an approximate but less costly output The FORM isby far the most efficient general method for estimating the probability offailure of problems involving one design point (one dominant mode of fail-ure) There are no clear winners for problems beyond the reach of FORMThe collocation-based stochastic response surface method is very efficientfor problems containing a small number of random dimensions and perfor-mance functions that can be approximated quite accurately using low-orderHermite expansions However the method suffers from a rapid prolifera-tion of Hermite expansion terms when the random dimension andor order ofexpansion increase The subset simulation method shares many of the advan-tages of the Monte Carlo simulation method such as generality and tractabil-ity at high dimensions without requiring too many runs At low randomdimensions it does require more runs than the collocation-based stochas-tic response surface method but the number of runs is probably acceptableup to medium-scale problems The subset simulation method has numerousldquotuningrdquo factors that are not fully studied for geotechnical problems

Simple MATLAB codes are provided in the Appendix A to encouragepractitioners to experiment with reliability analysis numerically so as to gaina concrete appreciation of the merits and limitations Theory is discussedwhere necessary to furnish sufficient explanations so that users can modifycodes correctly to suit their purpose and to highlight important practicallimitations

Appendix A ndash MATLAB codes

MATLAB codes are stored in text files with extension ldquomrdquo ndash they are calledM-files M-files can be executed easily by typing the filename (eg hermite)within the command window in MATLAB The location of the M-file should

Numerical recipes for reliability analysis 51

be specified using ldquoFile gt Set Path helliprdquo M-files can be opened and editedusing ldquoFile gt Open helliprdquo Programming details are given under ldquoHelp gt

Contents gt MATLAB gt Programmingrdquo The M-files provided below areavailable at httpwwwengnusedusgcivilpeoplecvepkkprob_libhtml

A1 Simulation of Johnson distributions

Simulation of Johnson distributions Filename Johnsonm Simulation sample size nn = 100000 Lognormal with lambda xilambda = 1xi = 02Z = normrnd(0 1 n 1)X = lambda + ZxiLNY = exp(X) SB with lambda xi A Blambda = 1xi = 036A = -3B = 5X = lambda + ZxiSBY = (exp(X)B+A)(exp(X)+1) SU with lambda xi A Blambda = 1xi = 009A = -188B = 208X = lambda + ZxiSUY = sinh(X)(B-A)+A Plot probability density functions[f x] = ksdensity(LNY)plot(xf)hold[f x] = ksdensity(SBY)plot(xfrsquoredrsquo)[f x] = ksdensity(SUY)plot(xfrsquogreenrsquo)

52 Kok-Kwang Phoon

A2 Calculation of Hermite coefficients using stochasticcollocation method

Calculation of Hermite coefficients using stochasticcollocation method Filename herm_coeffsm Number of realizations nn = 20 Example Lognormal with lambda xilambda = 0xi = 1Z = normrnd(0 1 n 1)X = lambda + ZxiLNY = exp(X) Order of Hermite expansion mm = 6 Construction of Hermite matrix HH = zeros(nm+1)H(1) = ones(n1)H(2) = Zfor k = 3m+1H(k) = ZH(k-1) - (k-2)H(k-2)end Hermite coefficients stored in vector aK = HrsquoHf = HrsquoLNYa = inv(K)f

A3 Simulation of correlated non-normal randomvariables using translation method

Simulation of correlated non-normal random variablesusing translation method Filename translationm Number of random dimension nn = 2 Normal covariance matrix

Numerical recipes for reliability analysis 53

C = [1 08 08 1] Number of realizations mm = 100000 Simulation of 2 uncorrelated normal random variableswith mean = 0 and variance = 1Z = normrnd(0 1 m n) Cholesky factorizationCF = chol(C) Simulation of 2 correlated normal random variableswith with mean = 0 and covariance = CX = ZCF Example simulation of correlated non-normal with Component 1 = Johnson SBlambda = 1xi = 036A = -3B = 5W(1) = lambda + X(1)xiY(1) = (exp(W(1))B+A)(exp(W(1))+1) Component 2 = Johnson SUlambda = 1xi = 009A = -188B = 208W(2) = lambda + X(2)xiY(2) = sinh(W(2))(B-A)+A

A4 Simulation of normal random process using thetwo-sided power spectral density function S(f)

Simulation of normal random process using thetwo-sided power spectral density function S(f) Filename ranprocessm Number of data points based on power of 2 NN = 512

54 Kok-Kwang Phoon

Depth sampling interval delzdelz = 02 Frequency interval delfdelf = 1Ndelz Discretization of autocorrelation function R(tau) Example single exponential functiontau = zeros(1N)tau = -(N2-1)delzdelz(N2)delzd = 2 scale of fluctuationa = 2dR = zeros(1N)R = exp(-aabs(tau)) Numerical calculation of S(f) using FFTH = zeros(1N)H = fft(R) Notes 1 f=0 delf 2delf (N2-1)delf correspondsto H(1) H(2) H(N2) 2 Maximum frequency is Nyquist frequency= N2delf= 12delz 3 Multiply H (discrete transform) by delz to getcontinuous transform 4 Shift R back by tau0 ie multiply H byexp(2piiftau0)f = zeros(1N)S = zeros(1N) Shuffle f to correspond with frequencies orderingimplied in Hf(1) = 0for k = 2N2+1f(k)= f(k-1)+delfendf(N2+2) = -f(N2+1)+delffor k = N2+3Nf(k) = f(k-1)+delfendtau0 = (N2-1)delz

Numerical recipes for reliability analysis 55

for k = 1NS(k) = delzH(k)exp(2piif(k)tau0) i isimaginary numberendS = real(S) remove possible imaginary parts due toroundoff errors Shuffle S to correspond with f in increasing orderf = -(N2-1)delfdelf(N2)delftemp = zeros(1N)for k = 1N2-1temp(k) = S(k+N2+1)endfor k = N2Ntemp(k)=S(k-N2+1)endS = tempclear temp Simulation of normal process using spectralrepresentation mean of process = 0 and variance of process = 1 Maximum possible non-periodic process lengthLmax = 12fmin = 1delf Minimum frequency fmin = delf2Lmax = 1delf Simulation length L = bLmax with b lt 1L = 05Lmax Number of simulated data points nz Depth sampling interval dz Depth coordinates z(1) z(2) z(nz)nz = round(Ldelz)dz = Lnzz = dzdzL Number of realisations mm = 10000 Number of positive frequencies in the spectralexpansion nf = N2

56 Kok-Kwang Phoon

nf = N2 Simulate uncorrelated standard normal randomvariablesrandn(rsquostatersquo 1)Z = randn(m2nf)sigma = zeros(1nf)wa = zeros(1nf) Calculate energy at each frequency using trapezoidalrule 2S(f) for k = 1nfsigma(k) = 205(S(k+nf-1)+S(k+nf))delfwa(k) = 052pi(f(k+nf-1)+f(k+nf))endsigma = sigmaˆ05 Calculate realizations with mean = 0 andvariance = 1X = zeros(mnz)X = Z(1nf)diag(sigma)cos(warsquoz)+Z(nf+12nf)diag(sigma)sin(warsquoz)

A5 Reliability analysis of Load and Resistance FactorDesign (LRFD)

Reliability analysis of Load and Resistance FactorDesign (LRFD) Filename LRFDm Resistance factor phiphi = 05 Dead load factor gD Live load factor gLgD = 125gL = 175 Bias factors for Q D LbR = 1bD = 105bL = 115 Ratio of nominal dead to live load loadratioloadratio = 2 Assume mR = bRRn mD = bDDn mL = bLLn Design equation phi(Rn) = gD(Dn) + gL(Ln)

Numerical recipes for reliability analysis 57

mR = 1000mL = phibRmRbL(gDloadratio+gL)mD = loadratiomLbDbL Coefficients of variation for R D LcR = 03cD = 01cL = 02 Lognormal X with mean = mX and coefficent ofvariation = cXxR = sqrt(log(1+cRˆ2))lamR = log(mR)-05xRˆ2xD = sqrt(log(1+cDˆ2))lamD = log(mD)-05xDˆ2xL = sqrt(log(1+cLˆ2))lamL = log(mL)-05xLˆ2 Simulation sample size nn = 500000Z = normrnd(0 1 n 3)LR = lamR + Z(1)xRR = exp(LR)LD = lamD + Z(2)xDD = exp(LD)LL = lamL + Z(3)xLL = exp(LL) Total load = Dead load + Live LoadF = D + L Mean of FmF = mD + mL Coefficient of variation of F based on second-momentapproximationcF = sqrt((cDmD)ˆ2+(cLmL)ˆ2)mF Failure occurs when R lt Ffailure = 0for i = 1nif (R(i) lt F(i)) failure = failure+1 endend

58 Kok-Kwang Phoon

Probability of failure = no of failuresnpfs = failuren Reliability indexbetas = -norminv(pfs) Closed-form lognormal solutiona1 = sqrt(log((1+cRˆ2)(1+cFˆ2)))a2 = sqrt((1+cFˆ2)(1+cRˆ2))beta = log(mRmFa2)a1pf = normcdf(-beta)

A6 First-order reliability method

First-order reliability method Filename FORMm Number of random variables mm = 6 Starting guess is z0 = 0z0 = zeros(1 m) Minimize objective function exitflag = 1 for normal terminationoptions = optimset(rsquoLargeScalersquorsquooffrsquo)[zfvalexitflagoutput] =fmincon(objfunz0[][][][][][]Pfunoptions) First-order reliability indexbeta1 = fvalpf1 = normcdf(-beta1)

Objective function for FORM Filename objfunmfunction f = objfun(z) Objective function = distance from the origin z = vector of uncorrelated standard normal randomvariables f = norm(z)

Numerical recipes for reliability analysis 59

Definition of performance function P Filename Pfunmfunction [c ceq] = Pfun(z) Convert standard normal random variables to physicalvariables Depth of rock Hy(1) = 2+6normcdf(z(1)) Height of water tabley(2) = y(1)normcdf(z(2)) Effective stress friction angle (radians)xphi = sqrt(log(1+008ˆ2))lamphi = log(35)-05xphiˆ2y(3) = exp(lamphi+xphiz(3))pi180 Slope inclination (radians)xbeta = sqrt(log(1+005ˆ2))lambeta = log(20)-05xbetaˆ2y(4) = exp(lambeta+xbetaz(4))pi180 Specific gravity of solidsGs = 25+02normcdf(z(5)) Void ratioe = 03+03normcdf(z(6)) Moist unit weighty(5) = 981(Gs+02e)(1+e) Saturated unit weighty(6) = 981(Gs+e)(1+e) Nonlinear inequality constraints c lt 0c = (y(5)(y(1)-y(2))+y(2)(y(6)-981))cos(y(4))tan(y(3))((y(5)(y(1)-y(2))+y(2)y(6))sin(y(4)))-1 Nonlinear equality constraintsceq = []

60 Kok-Kwang Phoon

A7 Random dimension reduction using spectraldecomposition

Random dimension reduction using spectraldecomposition Filename eigendecompm Example Target covarianceC = [1 09 02 09 1 05 02 05 1] Spectral decomposition[PD] = eig(C) Simulation sample size nn = 10000 Simulate standard normal random variablesZ = normrnd(01n3) Simulate correlated normal variables following CX = Psqrt(D)ZrsquoX = Xrsquo Simulate correlated normal variables without 1steigen-componentXT = P(1323)sqrt(D(2323))Z(23)rsquoXT=XTrsquo Covariance produced by ignoring 1st eigen-componentCX = P(1323)D(2323)P(1323)rsquo

A8 Calculation of Hermite roots using polynomial fit

Calculation of Hermite roots using polynomial fit Filename herm_rootsm Order of Hermite polynomial m lt 15m = 5 Specification of z values for fittingz = (-412m4)rsquo Number of fitted points nn = length(z)

Numerical recipes for reliability analysis 61

Construction of Hermite matrix HH = zeros(nm+1)H(1) = ones(n1)H(2) = zfor k = 3m+1H(k) =zH(k-1)-(k-2)H(k-2)end Calculation of Hermite expansiony = H(m+1) Polynomial fitp = polyfit(zym) Roots of hermite polynomialr = roots(p) Validation of rootsnn = length(r)H = zeros(nnm+1)H(1) = ones(nn1)H(2) = rfor k = 3m+1H(k) =rH(k-1)-(k-2)H(k-2)endnorm(H(m+1))

A9 Subset Markov Chain Monte Carlo method

Subset Markov Chain Monte Carlo method Filename subsetm copy (2007) Kok-Kwang Phoon Number of random variables mm = 2 Sample size nn = 500 Probability of each subsetpsub = 01 Number of new samples from each seed samplens = 1psub

62 Kok-Kwang Phoon

Simulate standard normal variable zz = normrnd(01nm)stopflag = 0pfss = 1while (stopflag == 0) Values of performance functionfor i=1n g(i) = Pfun(z(i)) end Sort vector g to locate npsub smallest values[gsortindex]=sort(g) Subset threshold gt = npsub+1 smallest value of g if gt lt 0 exit programgt = gsort(npsub+1)if (gt lt 0)

i=1while gsort(i)lt0

i=i+1endpfss=pfss(i-1)nstopflag = 1break

elsepfss = pfsspsub

end npsub seeds satisfying Pfun lt gt stored inz(index(1npsub)) Simulate 1psub samples from each seed to get nsamples for next subsetw = zeros(nm)for i=1npsub

seed = z(index(i))for j=1ns

Proposal density standard uniform with mean =seedu = rand(1m)u = (seed-05)+ u Calculate acceptance probabilitypdf2 = exp(-05sum(seedˆ2))pdf1 = exp(-05sum(uˆ2))

Numerical recipes for reliability analysis 63

I = 0if Pfun(u)ltgt I=1 endalpha = min(1Ipdf1pdf2) Accept u with probability = alphaV = randif V lt alpha

w(ns(i-1)+j)= uelse

w(ns(i-1)+j)= seedend

seed = w(ns(i-1)+j)end

endz=wend Reliability indexbetass = -norminv(pfss)

Appendix B ndash Approximate closed-form solutionsfor Φ(middot) and Φminus1(middot)The standard normal cumulative distribution functionΦ(middot) can be calculatedusing normcdf in MATLAB For sufficiently large β it is also possible to esti-mate Φ(minusβ) quite accurately using the following asymptotic approximation(Breitung 1984)

Φ(minusβ) sim (2π )minus12exp(minusβ22)βminus1 (B1)

An extensive review of estimation methods for the standard normal cumu-lative distribution function is given by Johnson et al (1994) Comparisonbetween Equation (B1) and normcdf(middot) is given in Figure B1 It appearsthat β gt 2 is sufficiently ldquolargerdquo Press et al (1992) reported an extremelyaccurate empirical fit with an error less than 12 times 10minus7 everywhere

t = 1

1 + 025radic

2β05t times exp(minus05β2minus126551223 + t

times (100002368 + t times (037409196 +Φ(minusβ) asymp t times (009678418 + t

times (minus018628806 + t times (027886807 + t times (minus113520398 + t

times (148851587 + t times (minus082215223 + t times 017087277))))))))) (B2)

64 Kok-Kwang Phoon

0 1 2 3 4 510minus7

10minus6

10minus5

10minus4

10minus3

10minus2

10minus1

100

Reliability index β

Pro

babi

lity

of fa

ilure

pf

normcdfEq (B1)

Figure B1 Comparison between normcdf(middot) and asymptotic approximation of the standardnormal cumulative distribution function

However it seems difficult to obtain β = minusΦminus1(pf) which is of signifi-cant practical interest as well Equation (B1) is potentially more useful inthis regard To obtain the inverse standard normal cumulative distributionfunction we rewrite Equation (B1) as follows

β2 + 2ln(radic

2πpf) + 2ln(β) = 0 (B3)

To solve Equation (B3) it is natural to linearize ln(β) using Taylor seriesexpansion ln(β) = minusln[1 + (1β minus 1)] asymp 1 minus 1β with β gt 05 Using thislinearization Equation (B3) simplifies to a cubic equation

β3 + 2[In(radic

2πpf) + 1]β minus 2 = 0 (B4)

The solution is given by

Q = 2[ln(radic

2πpf) + 1]3

R = 1

β =(

R +radic

Q3 + R2)13

+(

R minusradic

Q3 + R2)13

pf lt exp(minus1)radic

(B5)

Numerical recipes for reliability analysis 65

0 1 2 3 4 510minus7

10minus6

10minus5

10minus4

10minus3

10minus2

10minus1

100

Reliability index β

Pro

babi

lity

of fa

ilure

pf

normcdfEq (B5)Eq (B7)

Figure B2 Approximate closed-form solutions for inverse standard normal cumulativedistribution function

Equation (B5) is reasonably accurate as shown in Figure B2 An extremelyaccurate and simpler linearization can be obtained by fitting ln(β) = b0 +b1β

over the range of interest using linear regression Using this linearizationEquation (B3) simplifies to a quadratic equation

β2 + 2b1β + 2[ln(radic

2πpf) + b0] = 0 (B6)

The solution is given by

β = minusb1 +radic

b21 minus 2[ln(

radic2πpf) + b0] pf lt exp(b2

12 minus b0)radic

2π (B7)

The parameters b0 = 0274 and b1 = 0266 determined from linear regressionover the range 2 le β le 6 were found to produce extremely good results asshown in Figure B2 To the authorrsquos knowledge Equations (B5) and (B7)appear to be original

Appendix C ndash Exact reliability solutions

It is well known that exact reliability solutions are available for problemsinvolving the sum of normal random variables or the product of lognormalrandom variables This appendix provides other exact solutions that areuseful for validation of new reliability codescalculation methods

66 Kok-Kwang Phoon

C1 Sum of n exponential random variables

Let Y be exponentially distributed with the following cumulative distributionfunction

F(y) = 1 minus exp(minusyb) (C1)

The mean of Y is b The sum of n independent identically distributed expo-nential random variables follows an Erlang distribution with mean = nband variance = nb2 (Hastings and Peacock 1975) Consider the followingperformance function

P = nb +αbradic

n minusnsum

i=1

Yi = c minusnsum

i=1

Yi (C2)

in which c and α are positive numbers The equivalent performance functionin standard normal space is

P = c +nsum

i=1

bln[Φ(minusZi)

](C3)

The probability of failure can be calculated exactly based on the Erlangdistribution (Hastings and Peacock 1975)

pf = Prob(P lt 0)

= Prob

(nsum

i=1

Y gt c

)(C4)

= exp(minus c

b

)nminus1sumi=0

(cb)i

i

Equation (C2) with b = 1 is widely used in the structural reliabilitycommunity to validate FORMSORM (Breitung 1984 Rackwitz 2001)

C2 Probability content of an ellipse in n-dimensionalstandard normal space

Consider the ldquosaferdquo elliptical domain in 2D standard normal space shownin Figure C1 The performance function is

P = α2 minus(

Z1

b1

)2

minus(

Z2

b2

)2

(C5)

Numerical recipes for reliability analysis 67

Z2

Z1

R + r = αb2

(Z1b1)2 + (Z2b2)2 = α2

R - r = αb1

R + r

R - r

Figure C1 Elliptical safe domain in 2D standard normal space

in which b2 gtb1 gt0 Let R =α(b1+b2)2 and r =α(b2minusb1)2 Johnson andKotz (1970) citing Ruben (1960) provided the following the exact solutionfor the safe domain

Prob(P gt 0) = Prob(χ prime22 (r2) le R2) minus Prob(χ prime2

2 (R2) le r2) (C6)

in which χ prime22 (r2) = non-central chi-square random variable with two degrees

of freedom and non-centrality parameter r2 The cumulative distributionfunction Prob(χ prime2

2 (r2) le R2) can be calculated using ncx2cdf(R22r2) inMATLAB

There is no simple generalization of Equation (C6) to higher dimen-sions Consider the following performance function involving n uncorrelatedstandard normal random variables (Z1Z2 Zn)

P = α2 minus(

Z1

b1

)2

minus(

Z2

b2

)2

minusmiddotmiddot middot(

Zn

bn

)2

(C7)

in which bn gt bnminus1 gt middot middot middot gt b1 gt 0 Johnson and Kotz (1970) citing Ruben(1963) provided a series solution based on the sum of central chi-squarecumulative distribution functions

Prob(P gt 0) =infinsum

r=0

er Prob[χ2

n+2r le (bnα)2] (C8)

in which χ2ν (middot) = central chi-square random variable with ν degrees of free-

dom The cumulative distribution function Prob(χ2ν le y) can be calculated

68 Kok-Kwang Phoon

using chi2cdf(yν) in MATLAB The coefficients er are calculated as

e0 =nprod

j=1

(bj

bn

)

er = (2r)minus1rminus1sumj=0

ejhrminusj r ge 1

(C9)

hr =nsum

j=1

(1 minus

b2j

b2n

)r

(C10)

It is possible to calculate the probabilities associated with n-dimensional non-central ellipsoidal domains as well The performance function is given by

P = α2 minus(

Z1 minus a1

b1

)2

minus(

Z2 minus a2

b2

)2

minusmiddotmiddot middot(

Zn minus an

bn

)2

(C11)

in which bn gt bnminus1 gt gt b1 gt 0 Johnson and Kotz (1970) citing Ruben(1962) provided a series solution based on the sum of non-central chi-squarecumulative distribution functions

Prob(P gt 0) =infinsum

r=0

erProb

⎡⎣χ prime2

n+2r

⎛⎝ nsum

j=1

a2j

⎞⎠le (bnα

)2⎤⎦ (C12)

The coefficients er are calculated as

e0 =nprod

j=1

(bj

bn

)

er = (2r)minus1rminus1sumj=0

ejhrminusj r ge 1

(C13)

h1 =nsum

j=1(1 minus a2

j )

(1 minus

b2j

b2n

)

hr =nsum

j=1

⎡⎣(1 minus

b2j

b2n

)r

+ rb2

na2

j b2j

(1 minus

b2j

b2n

)rminus1⎤⎦ r ge 2

(C14)

Remark It is possible to create a very complex performance function byscattering a large number of ellipsoids in nD space The parameters in

Numerical recipes for reliability analysis 69

Equation (C11) can be visualized geometrically even in nD space Hencethey can be chosen in a relatively simple way to ensure that the ellipsoidsare disjoint One simple approach is based on nesting (a) encase the firstellipsoid in a hyper-box (b) select a second ellipsoid that is outside the box(c) encase the first and second ellipsoids in a larger box and (d) select athird ellipsoid that is outside the larger box Repeated application will yielddisjoint ellipsoids The exact solution for such a performance function ismerely the sum of Equation (C12) but numerical solution can be very chal-lenging An example constructed by this ldquocookie-cutterrdquo approach may becontrived but it has the advantage of being as complex as needed to testthe limits of numerical methods while retaining a relatively simple exactsolution

Appendix D ndash Second-order reliability method(SORM)

The second-order reliability method was developed by Breitung andothers in a series of papers dealing with asymptotic analysis (Breitung1984 Breitung and Hohenbichler 1989 Breitung 1994) The calcula-tion steps are illustrated below using a problem containing three randomdimensions

1 Let the performance function be defined in the standard normal spaceie P(z1z2z3) and let the first-order reliability index calculated fromEquation (150) be β = (zlowastprimezlowast)12 in which zlowast = (zlowast

1zlowast2z

lowast3)prime is the design

point2 The gradient vector at zlowast is calculated as

nablaP(zlowast) =⎧⎨⎩

partP(zlowast)partz1partP(zlowast)partz2partP(zlowast)partz3

⎫⎬⎭ (D1)

The magnitude of the gradient vector is

||nablaP(zlowast)|| = [nablaP(zlowast)primenablaP(zlowast)]12 (D2)

3 The probability of failure is estimated as

pf = Φ(minusβ)|J|minus12 (D3)

in which J is the following 2 times 2 matrix

J =[

1 00 1

]+ β

nablaP(zlowast)[

part2P(zlowast)partz21 part2P(zlowast)partz1partz2

part2P(zlowast)partz2partz1 part2P(zlowast)partz22

](D4)

70 Kok-Kwang Phoon

Z2

Z1

4

201

5 6

3

∆ ∆

Figure D1 Central finite difference scheme

Equations (D1) and (D4) can be estimated using the central finite differencescheme shown in Figure D1 Let Pi be the value of the performance functionevaluated at node i Then the first derivative is estimated as

partPpartz1

asymp P2 minus P1

2(D5)

The second derivative is estimated as

part2P

partz21

asymp P2 minus 2P0 + P1

∆2 (D6)

The mixed derivative is estimated as

part2Ppartz1partz2

asymp P4 minus P3 minus P6 + P5

4∆2 (D7)

References

Ang A H-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Vol 2 (Decision Risk and Reliability) John Wiley and SonsNew York

Au S K (2001) On the solution of first excursion problems by simulation with appli-cations to probabilistic seismic performance assessment PhD thesis CaliforniaInstitute of Technology Pasadena

Au S K and Beck J (2001) Estimation of small failure probabilities in highdimensions by subset simulation Probabilistic Engineering Mechanics 16(4)263ndash77

Numerical recipes for reliability analysis 71

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley amp Sons New York

Becker D E (1996) Limit states design for foundations Part II ndash Developmentfor National Building Code of Canada Canadian Geotechnical Journal 33(6)984ndash1007

Boden B (1981) Limit state principles in geotechnics Ground Engineering14(6) 2ndash7

Bolton M D (1983) Eurocodes and the geotechnical engineer Ground Engineering16(3) 17ndash31

Bolton M D (1989) Development of codes of practice for design In Proceedings ofthe 12th International Conference on Soil Mechanics and Foundation EngineeringRio de Janeiro Vol 3 Balkema A A Rotterdam pp 2073ndash6

Box G E P and Muller M E (1958) A note on the generation of random normaldeviates Annals of Mathematical Statistics 29 610ndash1

Breitung K (1984) Asymptotic approximations for multinormal integrals Journalof Engineering Mechanics ASCE 110(3) 357ndash66

Breitung K (1994) Asymptotic Approximations for Probability Integrals LectureNotes in Mathematics 1592 Springer Berlin

Breitung K and Hohenbichler M (1989) Asymptotic approximations to mul-tivariate integrals with an application to multinormal probabilities Journal ofMultivariate Analysis 30(1) 80ndash97

Broms B B (1964a) Lateral resistance of piles in cohesive soils Journal of SoilMechanics and Foundations Division ASCE 90(SM2) 27ndash63

Broms B B (1964b) Lateral resistance of piles in cohesionless soils Journal of SoilMechanics and Foundations Division ASCE 90(SM3) 123ndash56

Carsel R F and Parrish R S (1988) Developing joint probability distributions ofsoil water retention characteristics Water Resources Research 24(5) 755ndash69

Chilegraves J-P and Delfiner P (1999) Geostatistics ndash Modeling Spatial UncertaintyJohn Wiley amp Sons New York

Cressie N A C (1993) Statistics for Spatial Data John Wiley amp Sons New YorkDitlevsen O and Madsen H (1996) Structural Reliability Methods John Wiley amp

Sons ChichesterDuncan J M (2000) Factors of safety and reliability in geotechnical engineer-

ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

Elderton W P and Johnson N L (1969) Systems of Frequency Curves CambridgeUniversity Press London

Fenton G A and Griffiths D V (1997) Extreme hydraulic gradient statistics in astochastic earth dam Journal of Geotechnical and Geoenvironmental EngineeringASCE 123(11) 995ndash1000

Fenton G A and Griffiths D V (2002) Probabilistic foundation settlement on spa-tially random soil Journal of Geotechnical and Geoenvironmental EngineeringASCE 128(5) 381ndash90

Fenton G A and Griffiths D V (2003) Bearing capacity prediction of spatiallyrandom c- soils Canadian Geotechnical Journal 40(1) 54ndash65

Fisher R A (1935) The Design of Experiments Oliver and Boyd EdinburghFleming W G K (1989) Limit state in soil mechanics and use of partial factors

Ground Engineering 22(7) 34ndash5

72 Kok-Kwang Phoon

Ghanem R and Spanos P D (1991) Stochastic Finite Element A SpectralApproach Springer-Verlag New York

Griffiths D V and Fenton G A (1993) Seepage beneath water retaining structuresfounded on spatially random soil Geotechnique 43(6) 577ndash87

Griffiths D V and Fenton G A (1997) Three dimensional seepage through spa-tially random soil Journal of Geotechnical and Geoenvironmental EngineeringASCE 123(2) 153ndash60

Griffiths D V and Fenton G A (2001) Bearing capacity of spatially random soilthe undrained clay Prandtl problem revisited Geotechnique 54(4) 351ndash9

Hansen J B (1961) Ultimate resistance of rigid piles against transversal forcesBulletin 12 Danish Geotechnical Institute Copenhagen 1961 5ndash9

Hasofer A M and Lind N C (1974) An exact and invariant first order reliabilityformat Journal of Engineering Mechanics Division ASCE 100(EM1) 111ndash21

Hastings N A J and Peacock J B (1975) Statistical Distributions A Handbookfor Students and Practitioners Butterworths London

Huang S P Quek S T and Phoon K K (2001) Convergence study of the truncatedKarhunenndashLoeve expansion for simulation of stochastic processes InternationalJournal of Numerical Methods in Engineering 52(9) 1029ndash43

Isukapalli S S (1999) Uncertainty analysis of transport ndash transformation modelsPhD thesis The State University of New Jersey New Brunswick

Johnson N L (1949) Systems of frequency curves generated by methods oftranslation Biometrika 73 387ndash96

Johnson N L Kotz S and Balakrishnan N (1970) Continuous UnivariateDistributions Vol 2 Houghton Mifflin Company Boston

Johnson N L Kotz S and Balakrishnan N (1994) Continuous UnivariateDistributions Vols 1 and 2 John Wiley and Sons New York

Kulhawy F H and Phoon K K (1996) Engineering judgment in the evolution fromdeterministic to reliability-based foundation design In Uncertainty in the Geo-logic Environment ndash From Theory to Practice (GSP 58) Eds C D ShackelfordP P Nelson and M J S Roth ASCE New York pp 29ndash48

Liang B Huang S P and Phoon K K (2007) An EXCEL add-in implementationfor collocation-based stochastic response surface method In Proceedings of the1st International Symposium on Geotechnical Safety and Risk Tongji UniversityShanghai China pp 387ndash98

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Marsaglia G and Bray T A (1964) A convenient method for generating normalvariables SIAM Review 6 260ndash4

Mendell N R and Elston R C (1974) Multifactorial qualitative traits geneticanalysis and prediction of recurrence risks Biometrics 30 41ndash57

National Research Council (2006) Geological and Geotechnical Engineering inthe New Millennium Opportunities for Research and Technological InnovationNational Academies Press Washington D C

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep founda-tions In Proceedings International Workshop on Foundation Design Codes andSoil Investigation in view of International Harmonization and Performance BasedDesign Hayama Balkema A A Lisse pp 59ndash94

Numerical recipes for reliability analysis 73

Phoon K K (2003) Representation of random variables using orthogonal poly-nomials In Proceedings of the 9th International Conference on Applicationsof Statistics and Probability in Civil Engineering Vol 1 Millpress RotterdamNetherlands pp 97ndash104

Phoon K K (2004a) General non-Gaussian probability models for first-order reli-ability method (FORM) a state-of-the-art report ICG Report 2004-2-4 (NGIReport 20031091-4) International Centre for Geohazards Oslo

Phoon K K (2004b) Application of fractile correlations and copulas to non-Gaussian random vectors In Proceedings of the 2nd International ASRANet(Network for Integrating Structural Safety Risk and Reliability) ColloquiumBarcelona (CDROM)

Phoon K K (2005) Reliability-based design incorporating model uncertainties InProceedings of the 3rd International Conference on Geotechnical EngineeringDiponegoro University Semarang Indonesia pp 191ndash203

Phoon K K (2006a) Modeling and simulation of stochastic data In Geo-Congress 2006 Geotechnical Engineering in the Information Technology AgeEds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K (2006b) Bootstrap estimation of sample autocorrelation functionsIn GeoCongress 2006 Geotechnical Engineering in the Information TechnologyAge Eds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K and Fenton G A (2004) Estimating sample autocorrelation func-tions using bootstrap In Proceedings of the 9th ASCE Specialty Conference onProbabilistic Mechanics and Structural Reliability Albuquerque (CDROM)

Phoon K K and Huang S P (2007) Uncertainty quantification using multi-dimensional Hermite polynomials In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Phoon K K and Kulhawy F H (1999a) Characterization of geotechnicalvariability Canadian Geotechnical Journal 36(4) 612ndash24

Phoon K K and Kulhawy F H (1999b) Evaluation of geotechnical propertyvariability Canadian Geotechnical Journal 36(4) 625ndash39

Phoon K K and Kulhawy F H (2005) Characterization of model uncertainties forlaterally loaded rigid drilled shafts Geotechnique 55(1) 45ndash54

Phoon K K Becker D E Kulhawy F H Honjo Y Ovesen N K and Lo S R(2003b) Why consider reliability analysis in geotechnical limit state design InProceedings of the International Workshop on Limit State design in GeotechnicalEngineering Practice (LSD2003) Cambridge (CDROM)

Phoon K K Kulhawy F H and Grigoriu M D (1993) Observations onreliability-based design of foundations for electrical transmission line struc-tures In Proceedings International Symposium on Limit State Design inGeotechnical Engineering Vol 2 Danish Geotechnical Institute Copenhagenpp 351ndash62

Phoon K K Kulhawy F H and Grigoriu M D (1995) Reliability-based designof foundations for transmission line structures Report TR-105000 Electric PowerResearch Institute Palo Alto

74 Kok-Kwang Phoon

Phoon K K Huang S P and Quek S T (2002) Implementation of KarhunenndashLoeve expansion for simulation using a wavelet-Galerkin scheme ProbabilisticEngineering Mechanics 17(3) 293ndash303

Phoon K K Huang H W and Quek S T (2004) Comparison between KarhunenndashLoeve and wavelet expansions for simulation of Gaussian processes Computersand Structures 82(13ndash14) 985ndash91

Phoon K K Quek S T and An P (2003a) Identification of statistically homo-geneous soil layers using modified Bartlett statistics Journal of Geotechnical andGeoenvironmental Engineering ASCE 129(7) 649ndash59

Phoon K K Quek S T Chow Y K and Lee S L (1990) Reliability anal-ysis of pile settlement Journal of Geotechnical Engineering ASCE 116(11)1717ndash35

Press W H Teukolsky S A Vetterling W T and Flannery B P (1992) Numer-ical Recipes in C The Art of Scientific Computing Cambridge University PressNew York

Puig B and Akian J-L (2004) Non-Gaussian simulation using Hermite polynomialsexpansion and maximum entropy principle Probabilistic Engineering Mechanics19(4) 293ndash305

Puig B Poirion F and Soize C (2002) Non-Gaussian simulation using Hermitepolynomial expansion convergences and algorithms Probabilistic EngineeringMechanics 17(3) 253ndash64

Quek S T Chow Y K and Phoon K K (1992) Further contributions toreliability-based pile settlement analysis Journal of Geotechnical EngineeringASCE 118(5) 726ndash42

Quek S T Phoon K K and Chow Y K (1991) Pile group settlement a proba-bilistic approach International Journal of Numerical and Analytical Methods inGeomechanics 15(11) 817ndash32

Rackwitz R (2001) Reliability analysis ndash a review and some perspectives StructuralSafety 23(4) 365ndash95

Randolph M F and Houlsby G T (1984) Limiting pressure on a circular pileloaded laterally in cohesive soil Geotechnique 34(4) 613ndash23

Ravindra M K and Galambos T V (1978) Load and resistance factor design forsteel Journal of Structural Division ASCE 104(ST9) 1337ndash53

Reese L C (1958) Discussion of ldquoSoil modulus for laterally loaded pilesrdquoTransactions ASCE 123 1071ndash74

Reacutethaacuteti L (1988) Probabilistic Solutions in Geotechnics Elsevier New YorkRosenblueth E and Esteva L (1972) Reliability Basis for some Mexican Codes

Publication SP-31 American Concrete Institute DetroitRuben H (1960) Probability constant of regions under spherical normal distribu-

tion I Annals of Mathematical Statistics 31 598ndash619Ruben H (1962) Probability constant of regions under spherical normal distribu-

tion IV Annals of Mathematical Statistics 33 542ndash70Ruben H (1963) A new result on the distribution of quadratic forms Annals of

Mathematical Statistics 34 1582ndash4Sakamoto S and Ghanem R (2002) Polynomial chaos decomposition for the simu-

lation of non-Gaussian non-stationary stochastic processes Journal of EngineeringMechanics ASCE 128(2) 190ndash200

Numerical recipes for reliability analysis 75

Schueumlller G I Pradlwarter H J and Koutsourelakis P S (2004) A criticalappraisal of reliability estimation procedures for high dimensions ProbabilisticEngineering Mechanics 19(4) 463ndash74

Schweizer B (1991) Thirty years of copulas In Advances in Probability Distribu-tions with Given Marginals Kluwer Dordrecht pp 13ndash50

Semple R M (1981) Partial coefficients design in geotechnics Ground Engineering14(6) 47ndash8

Simpson B (2000) Partial factors where to apply them In Proceedings of theInternational Workshop on Limit State Design in Geotechnical Engineering(LSD2000) Melbourne (CDROM)

Simpson B and Driscoll R (1998) Eurocode 7 A Commentary ConstructionResearch Communications Ltd Watford Herts

Simpson B and Yazdchi M (2003) Use of finite element methods in geotech-nical limit state design In Proceedings of the International Workshop onLimit State design in Geotechnical Engineering Practice (LSD2003) Cambridge(CDROM)

Simpson B Pappin J W and Croft D D (1981) An approach to limit statecalculations in geotechnics Ground Engineering 14(6) 21ndash8

Sudret B (2007) Uncertainty propagation and sensitivity analysis in mechanicalmodels Contributions to structural reliability and stochastic spectral methods1rsquoHabilitation a Diriger des Recherches Universite Blaise Pascal

Sudret B and Der Kiureghian A 2000 Stochastic finite elements and reliabilitya state-of-the-art report Report UCBSEMM-200008 University of CaliforniaBerkeley

US Army Corps of Engineers (1997) Engineering and design introduction to proba-bility and reliability methods for use in geotechnical engineering Technical LetterNo 1110-2-547 Department of the Army Washington D C

Uzielli M and Phoon K K (2006) Some observations on assessment of Gaussianityfor correlated profiles In GeoCongress 2006 Geotechnical Engineering in theInformation Technology Age Eds D J DeGroot J T DeJong J D Frost andL G Braise ASCE Reston (CDROM)

Uzielli M Lacasse S Nadim F and Phoon K K (2007) Soil variability analysisfor geotechnical practice In Proceedings of the 2nd International Workshop onCharacterisation and Engineering Properties of Natural Soils Vol 3 Taylor andFrancis Singapore pp 1653ndash752

VanMarcke E H (1983) Random Field Analysis and Synthesis MIT PressCambridge MA

Verhoosel C V and Gutieacuterrez M A (2007) Application of the spectral stochasticfinite element method to continuum damage modelling with softening behaviourIn Proceedings of the 10th International Conference on Applications of Statisticsand Probability in Civil Engineering Tokyo (CDROM)

Vrouwenvelder T and Faber M H (2007) Practical methods of structural reli-ability In Proceedings of the 10th International Conference on Applications ofStatistics and Probability in Civil Engineering Tokyo (CDROM)

Winterstein S R Ude T C and Kleiven G (1994) Springing and slow driftresponses predicted extremes and fatigue vs simulation Proceedings Behaviourof Offshore Structures Vol 3 Elsevier Cambridge pp 1ndash15

Chapter 2

Spatial variability andgeotechnical reliability

Gregory B Baecher and John T Christian

Quantitative measurement of soil properties differentiated the new disciplineof soil mechanics in the early 1900s from the engineering of earth workspracticed since antiquity These measurements however uncovered a greatdeal of variability in soil properties not only from site to site and stratumto stratum but even within what seemed to be homogeneous deposits Wecontinue to grapple with this variability in current practice although newtools of both measurement and analysis are available for doing so Thischapter summarizes some of the things we know about the variability ofnatural soils and how that variability can be described and incorporated inreliability analysis

21 Variability of soil properties

Table 21 illustrates the extent to which soil property data vary accordingto Phoon and Kulhawy (1996) who have compiled coefficients of variationfor a variety of soil properties The coefficient of variation is the standarddeviation divided by the mean Similar data have been reported by Lumb(1966 1974) Lee et al (1983) and Lacasse and Nadim (1996) amongothers The ranges of these reported values are wide and are only suggestiveof conditions at a specific site

It is convenient to think about the impact of variability on safety byformulating the reliability index

β = E[MS]SD[MS]or

E[FS]minus 1SD[FS] (21)

in which β = reliability index MS = margin of safety (resistance minusload) FS = factor of safety (resistance divided by load) E[middot] = expectationand SD[middot] = standard deviation It should be noted that the two definitionsof β are not identical unless MS = 0 or FS = 1 Equation 21 expressesthe number of standard deviations separating expected performance from afailure state

Spatial variability and geotechnical reliability 77

Table 21 Coefficient of variation for some common field measurements (Phoon andKulhawy 1996)

Test type Property Soil type Mean Units Cov()

qT Clay 05ndash25 MNm2 lt 20CPT qc Clay 05ndash2 MNm2 20ndash40

qc Sand 05ndash30 MNm2 20ndash60VST su Clay 5ndash400 kNm2 10ndash40SPT N Clay and sand 10ndash70 blowsft 25ndash50

A reading Clay 100ndash450 kNm2 10ndash35A reading Sand 60ndash1300 kNm2 20ndash50B reading Clay 500ndash880 kNm2 10ndash35

DMT B Reading Sand 350ndash2400 kNm2 20ndash50ID Sand 1ndash8 20ndash60KD Sand 2ndash30 20ndash60ED Sand 10ndash50 MNm2 15ndash65pL Clay 400ndash2800 kNm2 10ndash35

PMT pL Sand 1600ndash3500 kNm2 20ndash50EPMT Sand 5ndash15 MNm2 15ndash65wn Clay and silt 13ndash100 8ndash30WL Clay and silt 30ndash90 6ndash30WP Clay and silt 15ndash15 6ndash30

Lab Index PI Clay and silt 10ndash40 _a

LI Clay and silt 10 _a

γ γd Clay and silt 13ndash20 KNm3 lt 10Dr Sand 30ndash70 10ndash40

50ndash70b

NotesaCOV = (3ndash12)meanbThe first range of variables gives the total variability for the direct method of determination and thesecond range of values gives the total variability for the indirect determination using SPT values

The important thing to note in Table 21 is how large are the reportedcoefficients of variations of soil property measurements Most are tens ofpercent implying reliability indices between one and two even for conser-vative designs Probabilities of failure corresponding to reliability indiceswithin this range ndash shown in Figure 21 for a variety of common distribu-tional assumptions ndash are not reflected in observed rates of failure of earthstructures and foundations We seldom observe failure rates this high

The inconsistency between the high variability of soil property data andthe relatively low rate of failure of prototype structures is usually attributedto two things spatial averaging and measurement noise Spatial averagingmeans that if one is concerned about average properties within some volumeof soil (eg average shear strength or total compression) then high spotsbalance low spots so that the variance of the average goes down as thatvolume of mobilized soil becomes larger Averaging reduces uncertainty1

78 Gregory B Baecher and John T Christian

1

01

Probability of Failure for Different Distributions

001

0001

00001

Pro

babi

lity

000001

00000010 1 2 3

Beta

Normal

Lognormal COV = 01

Triangular

Lognormal COV = 005

Lognormal COV = 015

4 5

Figure 21 Probability of failure as a function of reliability index for a variety of commonprobability distribution forms

Measurement noise means that the variability in soil property data reflectstwo things real variability and random errors introduced by the processof measurement Random errors reduce the precision with which estimatesof average soil properties can be made but they do not affect the in-fieldvariation of actual properties so the variability apparent in measurementsis larger ndash possibly substantially so ndash than actual in situ variability2

211 Spatial variation

Spatial variation in a soil deposit can be characterized in detail but only witha great number of observations which normally are not available Thus itis common to model spatial variation by a smooth deterministic trend com-bined with residuals about that trend which are described probabilisticallyThis model is

z(x) = t(x) + u(x) (22)

in which z(x) is the actual soil property at location x (in one or more dimen-sions) t(x) is a smooth trend at x and u(x) is residual deviation from thetrend The residuals are characterized as a random variable of zero-meanand some variance

Var(u) = E[z(x) minus t(x)2] (23)

Spatial variability and geotechnical reliability 79

in which Var(x) is the variance The residuals are characterized as randombecause there are too few data to do otherwise This does not presume thatsoil properties actually are random The variance of the residuals reflectsuncertainty about the difference between the fitted trend and the actual valueof soil properties at particular locations Spatial variation is modeled stochas-tically not because soil properties are random but because information islimited

212 Trend analysis

Trends are estimated by fitting lines curves or surfaces to spatially refer-enced data The easiest way to do this is by regression analysis For exampleFigure 22 shows maximum past pressure measurements as a function ofdepth in a deposit of Gulf of Mexico clay The deposit appears homogeneous

Maximum Past Pressure σvm (KSF)

Ele

vatio

n (f

t)

0 2 4 6 8

minus60

minus50

minus40

minus30

minus20

minus10

0

σvo

mean σvm

standard deviations

Figure 22 Maximum past pressure measurements as a function of depth in Gulf of Mexicoclays Mobile Alabama

80 Gregory B Baecher and John T Christian

and mostly normally consolidated The increase of maximum past pressurewith depth might be expected to be linear Data from an over-consolidateddesiccated crust are not shown The trend for the maximum past pressuredata σ prime

vm with depth x is

σ primevm = t(x) + u(x) = α0 +α1x = u (24)

in which t(x) is the trend of maximum past pressure with depth x α0and α1 are regression coefficients and u is residual variation about thetrend taken to be constant with depth (ie it is not a function of x)Applying standard least squares analysis the regression coefficients mini-mizing Var[u] are α0 = 3 ksf (014 KPa) and α1 = 006 ksfft (14 times 10minus3

KPam) yielding Var(u) = 10 ksf (005 KPa) for which the correspondingtrend line is shown The trend t(x) = 3 + 006x is the best estimate or meanof the maximum past pressure as a function of depth (NB ksf = kip persquare foot)

The analysis can be made of data in higher dimensions which in matrixnotation becomes

z = Xα + u (25)

in which z is the vector of the n observations z=z1 hellip zn X=x1 x2 isthe 2 x n matrix of location coordinates corresponding to the observationsα = αα1 hellip αn is the vector of trend parameters and u is the vectorof residuals corresponding to the observations Minimizing the variance ofthe residuals u(x) over α gives the best-fitting trend surface in a frequentistsense which is the common regression surface

The trend surface can be made more flexible for example in the quadraticcase the linear expression is replaced by

z = α0 +α1x +α2x2 + u (26)

and the calculation for α performed the same way Because the quadraticsurface is more flexible than the planar surface it fits the observed datamore closely and the residual variations about it are smaller On the otherhand the more flexible the trend surface the more regression parameters thatneed to be estimated from a fixed number of data so the fewer the degrees offreedom and the greater the statistical error in the surface Examples of theuse of trend surfaces in the geotechnical literature are given by Wu (1974)Ang and Tang (1975) and many others

Historically it has been common for trend analysis in geotechnicalengineering to be performed using frequentist methods Although this istheoretically improper because frequentist methods yield confidence inter-vals rather than probability distributions on parameters the numericalerror is negligible The Bayesian approach begins with the same model

Spatial variability and geotechnical reliability 81

However rather than defining an estimator such as the least squarescoefficients the Bayesian approach specifies an a priori probability distri-bution on the coefficients of the model and uses Bayesrsquos Theorem to updatethat distribution in light of the observed data

The following summarizes Bayesian results from Zellner (1971) for theone-dimensional linear case of Equation (24) Let Var(u)= σ 2 so thatVar(u) = Iσ 2 in which I is the identity matrix The prior probability densityfunction (pdf) of the parameters ασ is represented as f (ασ ) Given a set ofobservations z = z1 hellip zn the updated or posterior pdf of ασ is foundfrom Bayesrsquos Theorem as f (ασ |z) prop f (ασ )L(ασ |z) in which L(ασ |z) isthe Likelihood of the data (ie the conditional probability of the observeddata for various values of the parameters) If variations about the trend lineor surface are jointly Normal the likelihood function is

L(ασ |z) = MN(z|ασ )prop expminus(z minus Xα)primeminus1(z minus Xα) (27)

in which MN() is the Multivariate-Normal distribution having mean Xα

and covariance matrix = Iσ Using a non-informative prior f (α σ ) prop σminus1 and measurements y made

at depths x the posterior pdf of the regression parameters is

f (α0α1σ |xy) prop 1σ n+1 exp

[minus 1

2σ 2

sumn

i=1(y1 minus (α0 +α1x1))2

](28)

The marginal distributions are

f (α0α1|xy) prop [νs2 + n(α0 minusα0) + 2(α0 minusα0)(α1 minusα1)xi

+ (α1 minusα1)2x21]minusn2

f (α0|xy) prop [ν + (xi minus x)2

s2x2i n

(α0 minusα0)2]minus(νminus1)2 (29)

f (α1|xy) prop [ν + (xi minus x)2

s2 (α1 minusα1)2]minus(νminus1)2

f (σ |xy) prop 1σνminus1 exp

(minus νs2

2σ 2

)

in which

ν = n minus 2

α0 = y minusα1x α1 =[sum(

xi minus x)(

yi minus y)][sum(

xi minus x)]

s2 = νminus1sum(

yi minusα0 minusα1xi

)2

82 Gregory B Baecher and John T Christian

y = nminus1sum

yi

x = nminus1sum

xi

The joint and marginal pdfrsquos of the regression coefficients are Student-tdistributed

213 Autocorrelation

In fitting trends to data as noted above the decision is made to dividethe total variability of the data into two parts one part explained by thetrend and the other as variation about the trend Residual variations notaccounted for by the trend are characterized by a residual variance Forexample the overall variance of the blow count data of Figure 23 is 45 bpf2

(475 bpm2) Removing a linear trend reduces this total to a residual varianceof about 11 bpf2(116 bpm2) The trend explains 33 bpf2 (349 bpm2)or about 75 of the spatial variation and 25 is unexplained by thetrend

The spatial structure remaining after a trend is removed usually displayscorrelations among the residuals That is the residuals off the trend are notstatistically independent of one another Positive residuals tend to clumptogether as do negative residuals Thus the probability of encountering a

570

575

580

585

590

595

600

605

610

0 10 20 30 40

STP Blowcount

50

Figure 23 Spatial variation of SPT blow count data in a silty sand (data from Hilldale 1971)

Spatial variability and geotechnical reliability 83

minus25

minus2

minus15

minus1

minus05

0

05

1

15

2

Distance along transect

Figure 24 Residual variations of SPT blow counts

continuous zone of weakness or high compressibility is greater than wouldbe predicted if the residuals were independent

Figure 24 shows residual variations of SPT blow counts measured at thesame elevation every 20 m beneath a horizontal transect at a site The dataare normalized to zero mean and unit standard deviation The dark line is asmooth curve drawn through the observed data The light line is a smoothcurve drawn through artificially simulated data having the same meanand same standard deviation but probabilistically independent Inspectionshows the natural data to be smoothly varying whereas the artificial dataare much more erratic

The remaining spatial structure of variation not accounted for by the trendcan be described by its spatial correlation called autocorrelation Formallyautocorrelation is the property that residuals off the mean trend are notprobabilistically independent but display a degree of association amongthemselves that is a function of their separation in space This degree ofassociation can be measured by a correlation coefficient taken as a functionof separation distance

Correlation is the property that on average two variables are linearlyassociated with one another Knowing the value of one provides informa-tion on the probable value of the other The strength of this association ismeasured by a correlation coefficient ρ that ranges between ndash1 and +1 Fortwo scalar variables z1 and z2 the correlation coefficient is defined as

ρ = Cov(z1z2)radicVar(z1)Var(z2)

= 1σz1

σz2

E[(z1 minusmicroz1)(z2 minusmicroz2

)] (210)

84 Gregory B Baecher and John T Christian

in which Cov(z1z2) is the covariance Var(zi) is the variance σ is thestandard deviation and micro is the mean

The two variables might be of different but related types for examplez1 might be water content and z2 might be undrained strength or the twovariables might be the same property at different locations for examplez1 might be the water content at one place on the site and z2 the watercontent at another place A correlation coefficient ρ = +1 means that tworesiduals vary together exactly When one is a standard deviation above itstrend the other is a standard deviation above its trend too A correlationcoefficient ρ = minus1 means that two residuals vary inversely When one is astandard deviation above its trend the other is a standard deviation belowits trend A correlation coefficient ρ = 0 means that the two residuals areunrelated In the case where the covariance and correlation are calculated asfunctions of the separation distance the results are called the autocovarianceand autocorrelation respectively

The locations at which the blow count data of Figure 23 were measuredare shown in Figure 25 In Figure 26 these data are used to estimate autoco-variance functions for blow count The data pairs at close separation exhibita high degree of correlation for example those separated by 20 m have acorrelation coefficient of 067 As separation distance increases correlationdrops although at large separations where the numbers of data pairs are

A

B

C

D E F G H I

Cprime

Bprime

Aprime

Dprime Eprime Fprime Gprime Hprime

3225

33

21

26

27

2220

37

8

3

210

15

14

169

1

19ST 40033 31 34

302924

23

181265

4 711 13

17

36

ST 40044

28

35

Iprime

Figure 25 Boring locations of blow count data used to describe the site (TW Lambe andAssociates 1982 Earthquake Risk at Patio 4 and Site 400 Longboat Key FLreproduced by permission of TW Lambe)

Spatial variability and geotechnical reliability 85

40

30

20

10

0

minus10

minus20

minus30

minus40

Aut

ocov

aria

nce

0 100 200 300 400 500 600

Separation (m)

8ndash10m

10ndash12m

6ndash8m

Figure 26 Autocorrelation functions for SPT data at at Site 400 by depth interval(TW Lambe and Associates 1982 Earthquake Risk at Patio 4 and Site 400Longboat Key FL reproduced by permission of TW Lambe)

smaller there is much statistical fluctuation For zero separation distancethe correlation coefficient must equal 10 For large separation distancesthe correlation coefficient approaches zero In between the autocorrelationusually falls monotonically from 10 to zero

An important point to note is that the division of spatial variation intoa trend and residuals about the trend is an assumption of the analysis it isnot a property of reality By changing the trend model ndash for example byreplacing a linear trend with a polynomial trend ndash both the variance of theresiduals and their autocorrelation function are changed As the flexibility ofthe trend increases the variance of the residuals goes down and in generalthe extent of correlation is reduced From a practical point of view theselection of a trend line or curve is in effect a decision on how much of thedata scatter to model as a deterministic function of space and how much totreat probabilistically

As a rule of thumb trend surfaces should be kept as simple as possiblewithout doing injustice to a set of data or ignoring the geologic settingThe problem with using trend surfaces that are very flexible (eg high-order polynomials) is that the number of data from which the parameters ofthose equations are estimated is limited The sampling variance of the trendcoefficients is inversely proportional to the degrees of freedom involvedν = (n minus k minus 1) in which n is the number of observations and k is the num-ber of parameters in the trend The more parameter estimates that a trend

86 Gregory B Baecher and John T Christian

surface requires the more uncertainty there is in the numerical values of thoseestimates Uncertainty in regression coefficient estimates increases rapidly asthe flexibility of the trend equation increases

If z(xi) = t(xi)+u(xi) is a continuous variable and the soil deposit is zonallyhomogeneous then at locations i and j which are close together the residualsui and uj should be expected to be similar That is the variations reflectedin u(xi) and u(xj) are associated with one another When the locations areclose together the association is usually strong As the locations becomemore widely separated the association usually decreases As the separationbetween two locations i and j approaches zero u(xi) and u(xj) become thesame the association becomes perfect Conversely as the separation becomeslarge u(xi) and u(xj) become independent the association becomes zeroThis is the behavior observed in Figure 26 for the Standard Peretrationtest(SPT) data

This spatial association of residuals off the trend t(xi) is summarized bya mathematical function describing the correlation of u(xi) and u(xj) asseparation distance increases This description is called the autocorrelationfunction Mathematically the autocorrelation function is

Rz(δ) = 1Varu(x)E[u(xi)u(xi+δ)] (211)

in which Rz(δ) is the autocorrelation function Var[u(x)] is the variance ofthe residuals across the site and E[u(xi)u(xi+δ)]=Cov[u(xi)u(xi+δ)] is thecovariance of the residuals spaced at separation distance δ By definitionthe autocorrelation at zero separation is Rz(0) = 10 and empirically formost geotechnical data autocorrelation decreases to zero as δ increases

If Rz(δ) is multiplied by the variance of the residuals Var[u(x)] theautocovariance function Cz(δ) is obtained

Cz(δ) = E[u(xi)u(xi+δ)] (212)

The relationship between the autocorrelation function and the autocovari-ance function is the same as that between the correlation coefficient and thecovariance except that autocorrelation and autocovariance are functions ofseparation distance δ

214 Example TONEN refinery Kawasaki Japan

The SPT data shown earlier come from a site overlying hydraulic bay fillin Kawasaki (Japan) The SPT data were taken in a silty fine sand betweenelevations +3 and minus7 m and show little if any trend horizontally so aconstant horizontal trend at the mean of the data was assumed Figure 27shows the means and variability of the SPT data with depth Figure 26 shows

Spatial variability and geotechnical reliability 87

Upper Fill

Blow Counts N - BlowsFt

0 5 10 15 20 25 350

2

4

6

8

10

12

14

Dep

th m

Sandy Fill

SandyAlluvium

+σminusσ

mea

n

Figure 27 Soil model and the scatter of blow count data (TW Lambe and Associates1982 Earthquake Risk at Patio 4 and Site 400 Longboat Key FL reproducedby permission of TW Lambe)

autocovariance functions in the horizontal direction estimated for threeintervals of elevation At short separation distances the data show distinctassociation ie correlation At large separation distances the data exhibitessentially no correlation

In natural deposits correlations in the vertical direction tend to have muchshorter distances than in the horizontal direction A ratio of about 1 to 10 forthese correlation distances is common Horizontally autocorrelation may beisotropic (ie Rz(δ) in the northing direction is the same as Rz(δ) in the eastingdirection) or anisotropic depending on geologic history However in prac-tice isotropy is often assumed Also autocorrelation is typically assumed tobe the same everywhere within a deposit This assumption called station-arity to which we will return is equivalent to assuming that the deposit isstatistically homogeneous

It is important to emphasize again that the autocorrelation function is anartifact of the way soil variability is separated between trend and residualsSince there is nothing innate about the chosen trend and since changingthe trend changes Rz(δ) the autocorrelation function reflects a modelingdecision The influence of changing trends on Rz(δ) is illustrated in data

88 Gregory B Baecher and John T Christian

Figure 28 Study area for San Francisco Bay Mud consolidation measurements (Javete1983) (reproduced with the authorrsquos permission)

analyzed by Javete (1983) (Figure 28) Figure 29 shows autocorrelationsof water content in San Francisco Bay Mud within an interval of 3 ft (1 m)Figure 210 shows the autocorrelation function when the entire site is con-sidered The difference comes from the fact that in the first figure the meantrend is taken locally within the 3 ft (1 m) interval and in the latter the meantrend is taken globally across the site

Autocorrelation can be found in almost all spatial data that are analyzedusing a model of the form of Equation (25) For example Figure 211shows the autocorrelation of rock fracture density in a copper porphyrydeposit Figure 212 shows autocorrelation of cone penetration resistancein North Sea Clay and Figure 213 shows autocorrelation of water con-tent in the compacted clay core of a rock-fill dam An interesting aspectof the last data is that the autocorrelations they reflect are more a func-tion of the construction process through which the core of the damwas placed than simply of space per se The time stream of borrowmaterials weather and working conditions at the time the core was

Spatial variability and geotechnical reliability 89

10

05

0rk

minus05

minus100 5 10 15 20

lag k

Figure 29 Autocorrelations of water content in San Francisco Bay Mud within an intervalof 3 ft (1 m) ( Javete 1983) (reproduced with the authorrsquos permission)

0 5 10

Lak k

15 20

rk

10

05

00

minus05

minus10

Figure 210 Autocorrelations of water content in San Francisco Bay Mud within entire siteexpressed in lag intervals of 25 ft ( Javete 1983) (reproduced with the authorrsquospermission)

placed led to trends in the resulting physical properties of the compactedmaterial

For purposes of modeling and analysis it is usually convenient to approx-imate the autocorrelation structure of the residuals by a smooth functionFor example a commonly used function is the exponential

Rz(δ) = exp(minusδδ0) (213)

in which δ0 is a constant having units of length Other functions commonlyused to represent autocorrelation are shown in Table 22 The distance atwhich Rz(δ) decays to 1e (here δ0) is sometimes called the autocorrelation(or autocovariance) distance

90 Gregory B Baecher and John T Christian

minus200

minus100

0

100

200

300

400

500

50

SEPARATION (M)

CO

VA

RIA

NC

E

10 15

Figure 211 Autocorrelation of rock fracture density in a copper porphyry deposit(Baecher 1980)

Distance of Separation (m)

ρ =exp [ minus rb2]

r = 30 m

At Depth 3 m

0 20 40 60 800 20 40 60 80

10

08

06

04

02

00Cor

rela

tion

Coe

ffici

ent10

08

06

04

02

00Cor

rela

tion

Coe

ffici

ent

Distance of Separation (m)

r = 30 m

At Depth 36 m

Figure 212 Autocorrelation of cone penetration resistance in North Sea Clay (Tang1979)

215 Measurement noise

Random measurement error is that part of data scatter attributable toinstrument- or operator-induced variations from one test to another Thisvariability may sometimes increase or decrease a measurement but itseffect on any one specific measurement is unknown As a first approxima-tion instrument and operator effects on measured properties of soils canbe represented by a frequency diagram In repeated testing ndash presumingthat repeated testing is possible on the same specimen ndash measured val-ues differ Sometimes the measurement is higher than the real value ofthe property sometimes it is lower and on average it may systematically

Spatial variability and geotechnical reliability 91

0 20 40 60 80 100

Lag Distance (test number)

050

025

000

minus025

minus050

Aut

ocov

aria

nce

Figure 213 Autocorrelation of water content in the compacted clay core of a rock-filldam (Beacher 1987)

Table 22 One-dimensional autocorrelation models

Model Equation Limits of validity(dimension of relevant space)

White noise Rx(δ) =

1 if δ = 00 otherwise Rn

Linear Rx(δ) =

1 minus|δ|δ0 if δ le δ00 otherwise R1

Exponential Rx(δ) = exp(minusδδ0) R1

Squared exponential(Gaussian)

Rx(δ) = exp2(minusδδ0) Rd

Power Cz(δ) = σ 21(|δ|2δ20)minusβ Rd β gt 0

differ from the real value This is usually represented by a simple model ofthe form

z = bx + e (214)

in which z is a measured value b is a bias term x is the actual propertyand e is a zero-mean independent and identically distributed (IID) errorThe systematic difference between the real value and the average of themeasurements is said to be measurement bias while the variability of themeasurements about their mean is said to be random measurement errorThus the error terms are b and e The bias is often assumed to be uncertainwith mean microb and standard deviation σb The IID random perturbation is

92 Gregory B Baecher and John T Christian

usually assumed to be Normally distributed with zero mean and standarddeviation σe

Random errors enter measurements of soil properties through a varietyof sources related to the personnel and instruments used in soil investiga-tions or laboratory testing Operator or personnel errors arise in many typesof measurements where it is necessary to read scales personal judgment isneeded or operators affect the mechanical operation of a piece of testingequipment (eg SPT hammers) In each of these cases operator differenceshave systematic and random components One person for example mayconsistently read a gage too high another too low If required to make aseries of replicate measurements a single individual may report numbersthat vary one from the other over the series

Instrumental error arises from variations in the way tests are set uploads are delivered or soil response is sensed The separation of mea-surement errors between operator and instrumental causes is not onlyindistinct but also unimportant for most purposes In triaxial tests soilsamples may be positioned differently with respect to loading platens insucceeding tests Handling and trimming may cause differing amounts ofdisturbance from one specimen to the next Piston friction may vary slightlyfrom one movement to another or temperature changes may affect fluidsand solids The aggregate result of all these variables is a number of dif-ferences between measurements that are unrelated to the soil properties ofinterest

Assignable causes of minor variation are always present because a verylarge number of variables affect any measurement One attempts to controlthose that have important effects but this leaves uncontrolled a large numberthat individually have only small effects on a measurement If not identifiedthese assignable causes of variation may influence the precision and possiblythe accuracy of measurements by biasing the results For example hammerefficiency in the SPT test strongly affects measured blow counts Efficiencywith the same hammer can vary by 50 or more from one blow to the nextHammer efficiency can be controlled but only at some cost If uncontrolledit becomes a source of random measurement error and increases the scatterin SPT data

Bias error in measurement arises from a number of reasonably well-understood mechanisms Sample disturbance is among the more importantof these mechanisms usually causing a systematic degradation of average soilproperties along with a broadening of dispersion The second major contrib-utor to measurement bias is the phenomenological model used to interpretthe measurements made in testing and especially the simplifying assump-tions made in that model For example the physical response of the testedsoil element might be assumed linear when in fact this is only an approxima-tion the reversal of principal stress direction might be ignored intermediateprincipal stresses might be assumed other than they really are and so forth

Spatial variability and geotechnical reliability 93

The list of possible discrepancies between model assumptions and the realtest conditions is long

Model bias is usually estimated empirically by comparing predictionsmade from measured values of soil engineering parameters against observedperformance Obviously such calibrations encompass a good deal more thanjust the measurement technique they incorporate the models used to makepredictions of field performance inaccuracies in site characterization and ahost of other things

Bjerrumrsquos (1972 1973) calibration of field vein test results for theundrained strength su of clay is a good example of how measurementbias can be estimated in practice This calibration compares values of sumeasured with a field vane against back-calculated values of su from large-scale failures in the field In principle this calibration is a regression analysisof back-calculated su against field vane su which yields a mean trend plusresidual variance about the trend The mean trend provides an estimate ofmicrob while the residual variance provides an estimate of σb The residual vari-ance is usually taken to be the same regardless of the value of x a commonassumption in regression analysis

Random measurement error can be estimated in a variety of wayssome direct and some indirect As a general rule the direct techniques aredifficult to apply to the soil measurements of interest to geotechnical engi-neers because soil tests are destructive Indirect methods for estimating Veusually involve correlations of the property in question either with otherproperties such as index values or with itself through the autocorrelationfunction

The easiest and most powerful methods involve the autocorrelationfunction The autocovariance of z after the trend has been removed becomes

Cz(δ) = Cx(δ) + Ce(δ) (215)

in which Cx(δ) is from Equation (212) and Cx(δ) is the autocovariancefunction of e However since ei and ej are independent except when i = jthe autocovariance function of e is a spike at δ = 0 and zero elsewhereThus Cx(δ) is composed of two functions By extrapolating the observedautocovariance function to the origin an estimate is obtained of the fractionof data scatter that comes from random error In the ldquogeostatisticsrdquo literaturethis is called the nugget effect

216 Example Settlement of shallow footings on sandIndiana (USA)

The importance of random measurement errors is illustrated by a case involv-ing a large number of shallow footings placed on approximately 10 mof uniform sand (Hilldale 1971) The site was characterized by Standard

94 Gregory B Baecher and John T Christian

Penetration blow count measurements predictions were made of settlementand settlements were subsequently measured

Inspection of the SPT data and subsequent settlements reveals an interest-ing discrepancy Since footing settlements on sand tend to be proportional tothe inverse of average blow count beneath the footing it would be expectedthat the coefficient of variation of the settlements equaled approximatelythat of the vertically averaged blow counts Mathematically settlement ispredicted by a formula of the form ρ prop qNc in which ρ = settlementq = net applied stress at the base of the footing and Nc = average cor-rected blow count (Lambe and Whitman 1979) Being multiplicative thecoefficient of variation of ρ should be the same as that of Nc

In fact the coefficient of variation of the vertically averaged blow countsis about Nc

= 045 while the observed values of total settlements for268 footings have mean 035 inches and standard deviation 012 inchesso ρ = (012035) = 034 Why the difference The explanation maybe found in estimates of the measurement noise in the blow count dataFigure 214 shows the horizontal autocorrelation function for the blowcount data Extrapolating this function to the origin indicates that the noise(or small scale) content of the variability is about 50 of the data scattervariance Thus the actual variability of the vertically averaged blow counts is

aboutradic

12

2N =radic

12 (045)2 = 032 which is close to the observed variability

minus04

minus02

00

02

04

06

08

10

0 200 400 600 800 1000 1200

Separation (ft)

Figure 214 Autocorrelation function for SPT blow count in sand (Adapted from Hilldale1971)

Spatial variability and geotechnical reliability 95

of the footing settlements Measurement noise of 50 or even more of theobserved scatter of in situ test data particularly the SPT has been noted onseveral projects

While random measurement error exhibits itself in the autocorrelation orautocovariance function as a spike at δ = 0 real variability of the soil at ascale smaller than the minimum boring spacing cannot be distinguished frommeasurement error when using the extrapolation technique For this reasonthe ldquonoiserdquo component estimated in the horizontal direction may not be thesame as that estimated in the vertical direction

For many but not all applications the distinction between measure-ment error and small-scale variability is unimportant For any engineeringapplication in which average properties within some volume of soil areimportant the small-scale variability averages quickly and therefore has lit-tle effect on predicted performance Thus for practical purposes it can betreated as if it were a measurement error On the other hand if perfor-mance depends on extreme properties ndash no matter their geometric scale ndashthe distinction between measurement error and small scale is importantSome engineers think that piping (internal erosion) in dams is such a phe-nomenon However few physical mechanisms of performance easily cometo mind that are strongly affected by small-scale spatial variability unlessthose anomalous features are continuous over a large extent in at least onedimension

22 Second-moment soil profiles

Natural variability is one source of uncertainty in soil properties the otherimportant source is limited knowledge Increasingly these are referred to asaleatory and epistemic uncertainty respectively (Hartford 1995)3 Limitedknowledge usually causes systematic errors For example limited numbersof tests lead to statistical errors in estimating a mean trend and if there isan error in average soil strength it does not average out In geotechnical reli-ability the most common sources of knowledge uncertainty are model andparameter selection (Figure 215) Aleatory and epistemic uncertainties canbe combined and represented in a second-moment soil profile The second-moment profile shows means and standard deviations of soil properties withdepth in a formation The standard deviation at depth has two componentsnatural variation and systematic error

221 Example SHANSEP analysis of soft claysAlabama (USA)

In the early 1980s Ideal Basic Industries Inc (IDEAL) constructed a cementmanufacturing facility 11 miles south of Mobile Alabama abutting a shipchannel running into Mobile Bay (Baecher et al 1997) A gantry crane at the

96 Gregory B Baecher and John T Christian

Temporal

Spatial

Natural Variability

Model

Parameters

Knowledge Uncertainty

Objectives

Values

Time Preferences

Decision ModelUncertainty

Risk Analysis

Figure 215 Aleatory epistemic and decision model uncertainty in geotechnical reliabilityanalysis

facility unloaded limestone ore from barges moored at a relieving platformand place the ore in a reserve storage area adjacent to the channel As the sitewas underlain by thick deposits of medium to soft plastic deltaic clay con-crete pile foundations were used to support all facilities of the plant exceptfor the reserve limestone storage area This 220 ft (68 m) wide by 750 ft(230 m) long area provides limestone capacity over periods of interrupteddelivery Although the clay underlying the site was too weak to supportthe planned 50 ft (15 m) high stockpile the cost of a pile supported matfoundation for the storage area was prohibitive

To solve the problem a foundation stabilization scheme was conceivedin which limestone ore would be placed in stages leading to consolidationand strengthening of the clay and this consolidation would be hastened byvertical drains However given large scatter in engineering property data forthe clay combined with low factors of safety against embankment stabilityfield monitoring was essential

The uncertainty in soil property estimates was divided between that causedby data scatter and that caused by systematic errors (Figure 216) These wereseparated into four components

bull spatial variability of the soil depositbull random measurement noisebull statistical estimation error andbull measurement or model bias

The contributions were mathematically combined by noting that the vari-ances of these nearly independent contributions are approximately additive

V[x] asymp Vspatial[x]+ Vnoise[x]+ Vstatistical[x]+ Vbias[x] (216)

Spatial variability and geotechnical reliability 97

Uncertainty

Data Scatter

SpatialVariability

MeasurementNoise

SystematicError

ModelBias

StatisticalError

Figure 216 Sources of uncertainty in geotechnical reliability analysis

E-6400

minus180 minus100

minus20

minus40

minus60

minus80

0

20

0

ELE

VAT

ION

(ft)

100 200 280

E-6500

DRAINED ZONE

DENSE SAND

CLCL

CL

CLCL

CL

SANDY MATERIAL

Edge of primary limestone storage areaand stacker

of Westaccess road

MEDIUM-SOFT TO MEDIUMCL-CH GRAY SILTY CLAY

of drainageditch

of 20ft berm Stockplie of 20ft berm

I-4P

SPS-2I-12I-3

N-5381 RLSANORTH

of East occess road

EAST COORDINATE (ft)

DISTANCE FROM THE STOCKPILE CENTERLINE (ft)

E-6600 E-6700 E-6800

Figure 217 West-east cross-section prior to loading

in which V[x] = variance of total uncertainty in the property x Vspatial[x] =variance of the spatial variation of x Vnoise[x]= variance of the measurementnoise in x Vstatistical[x] = variance of the statistical error in the expectedvalue of x and Vbias[x] = variance of the measurement or model bias in xIt is easiest to think of spatial variation as scatter around the mean trendof the soil property and systematic error as uncertainty in the mean trenditself The first reflects soil variability after random measurement error hasbeen removed the second reflects statistical error plus measurement biasassociated with the mean value

Initial vertical effective stresses σ vo were computed using total unitweights and initial pore pressures Figure 22 shows a simplified profile priorto loading (Figure 217) The expected value σ vm profile versus elevation

98 Gregory B Baecher and John T Christian

was obtained by linear regression The straight short-dashed lines showthe standard deviation of the σ vm profile reflecting data scatter about theexpected value The curved long-dashed lines show the standard deviation ofthe expected value trend itself The observed data scatter about the expectedvalue σ vm profile reflects inherent spatial variability of the clay plus ran-dom measurement error in the determination of σ vm from any one testThe standard deviation about the expected value σ vm profile is about 1 ksf(005 MPa) corresponding to a standard deviation in over-consolidationratio (OCR) from 08 to 02 The standard deviation of the expected valueranges from 02 to 05 ksf (001 to 0024 MPa)

Ten CKoUDSS tests were performed on undisturbed clay samples to deter-mine undrained stressndashstrainndashstrength parameters to be used in the stresshistory and normalized soil engineering properties (SHANSEP) procedure ofLadd and Foott (1974) Reconsolidation beyond the in situ σ vm was used tominimize the influence of sample disturbance Eight specimens were shearedin a normally consolidated state to assess variation in the parameter s withhorizontal and vertical locations The last two specimens were subjected to asecond shear to evaluate the effect of OCR The direct simple shear (DSS) testprogram also provided undrained stressndashstrain parameters for use in finiteelement undrained deformation analyses

Since there was no apparent trend with elevation expected valueand standard deviation values were computed by averaging all data toyield

su = σ vos(σ vm

σ vo

)m

(217)

in which s =(0213 plusmn 0028) and m =(085 plusmn 005) As a first approximationit was assumed that 50 of the variation in s was spatial and 50 wasnoise The uncertainty in m estimated from data on other clays is primarilydue to variability from one clay type to another and hence was assumedpurely systematic It was assumed that the uncertainty in m estimated fromonly two tests on the storage area clay resulted from random measurementerror

The SHANSEP su profile was computed using Equation (217) If σ vo σ vms and m are independent and σ vo is deterministic (ie there is no uncertaintyin σ vo) first-order second-moment error analysis leads to the expressions

E[su] = σ voE[s](

E[σ vo]σ vo

)E[m](218)

2[su] = 2[s]+ E2[m]2[σ vm]+ 1n2(

E[σ vm]σ vo

)V[m] (219)

Spatial variability and geotechnical reliability 99

in which E[X] = expected value of X V[X] = variance of X and [X] =radicV[X]E[X]= coefficient of variation of X The total coefficient of variation

of su is divided between spatial and systematic uncertainty such that

2[su] = 2sp[su]+2

sy[su] (220)

Figure 218 shows the expected value su profile and the standard deviationof su divided into spatial and systematic components

Stability during initial undrained loading was evaluated using two-dimensional (2D) circular arc analyses with SHANSEP DSS undrainedshear strength profiles Since these analyses were restricted to the east andwest slopes of the stockpile 2D analyses assuming plane strain conditionsappeared justified Azzouz et al (1983) have shown that this simplifiedapproach yields factors of safety that are conservative by 10ndash15 for similarloading geometries

Because of differences in shear strain at failure for different modes offailure along a failure arc ldquopeakrdquo shear strengths are not mobilized simul-taneously all along the entire failure surface Ladd (1975) has proposed aprocedure accounting for strain compatibility that determines an averageshear strength to be used in undrained stability analyses Fuleihan and Ladd(1976) showed that in the case of the normally consolidated AtchafalayaClay the CKoUDSS SHANSEP strength was in agreement with the averageshear strength computed using the above procedure All the 2D analyses usedthe Modified Bishop method

To assess the importance of variability in su to undrained stability it isessential to consider the volume of soil of importance to the performanceprediction At one extreme if the volume of soil involved in a failure wereinfinite spatial uncertainty would completely average out and the system-atic component uncertainty would become the total uncertainty At the otherextreme if the volume of soil involved were infinitesimal spatial and sys-tematic uncertainties would both contribute fully to total uncertainty Theuncertainty for intermediate volumes of soil depends on the character ofspatial variability in the deposit specifically on the rapidity with which soilproperties fluctuate from one point to another across the site A convenientindex expressing this scale of variation is the autocorrelation distance δ0which measures the distance to which fluctuations of soil properties abouttheir expected value are strongly associated

Too few data were available to estimate autocorrelation distance for thestorage area thus bounding calculations were made for two extreme casesin the 2D analyses Lδ0 rarr 0 (ie ldquosmallrdquo failure surface) and Lδ0 rarr infin(ie ldquolargerdquo failure surface) in which L is the length of the potential fail-ure surface Undrained shear strength values corresponding to significantaveraging were used to evaluate uncertainty in the factor of safety forlarge failure surfaces and values corresponding to little averaging for small

100 Gregory B Baecher and John T Christian

Symbol

20

08

10

12

14

20 25 30

20 25

=E [FS] minus 10

SD[FS]

FILL HEIGHT Hf (ft)30

Mean minusone standarddeviation (SD)

Mean FS = E [FS]

35

35

RE

LIA

BIL

ITY

IND

EX

bFA

CTO

R O

F S

AF

ET

Y F

S

15

10

05

0

Case Effect of uncertainty in

clay undrained shearstrength su

Lro

Lro

Lro

Lro

Lro

Lro

fill total unit weight gfclay undrained shear strengthsu and fill total unit weight gf

(I)

b

infin

infin

infin

0

0

0

Figure 218 Expected value su profile and the standard deviation of su divided into spatialand systematic components

failure surface The results of the 2D stability analysis were plotted as afunction of embankment height

Uncertainty in the FS was estimated by performing stability analyses usingthe procedure of Christian et al (1994) with expected value and expectedvalue minus standard deviation values of soil properties For a given expected

Spatial variability and geotechnical reliability 101

value of FS the larger the standard deviation of FS the higher the chancethat the realized FS is less than unity and thus the lower the actual safety ofthe facility The second-moment reliability index [Equation (21)] was usedto combine E[FS] and SD[FS] in a single measure of safety and related to aldquonominalrdquo probability of failure by assuming FS Normally distributed

23 Estimating autocovariance

Estimating autocovariance from sample data is the same as making anyother statistical estimate Sample data differ from one set of observa-tions to another and thus the estimates of autocovariance differ Theimportant questions are how much do these estimates differ and howmuch might one be in error in drawing inferences There are two broadapproaches Frequentist and Bayesian The Frequentist approach is morecommon in geotechnical practice For discussion of Bayesian approaches toestimating autocorrelation see Zellner (1971) Cressie (1991) or Berger et al(2001)

In either case a mathematical function of the sample observations is usedas an estimate of the true population parameters θ One wishes to determineθ = g(z1 zn) in which z 1 hellip zn is the set of sample observations and θ which can be a scalar vector or matrix For example the sample mean mightbe used as an estimator of the true population mean The realized value of θfor a particular sample z1 hellip zn is an estimate As the probabilistic proper-ties of the z1hellip zn are assumed the corresponding probabilistic propertiesof θ can be calculated as functions of the true population parameters This iscalled the sampling distribution of θ The standard deviation of the samplingdistribution is called the standard error

The quality of the estimate obtained in this way depends on how variablethe estimator θ is about the true value θ The sampling distribution andhence the goodness of an estimate has to do with how the estimate mighthave come out if another sample and therefore another set of observationshad been made Inferences made in this way do not admit of a probabilitydistribution directly on the true population parameter Put another way theFrequentist approach presumes the state of nature θ to be a constant andyields a probability that one would observe those data that actually wereobserved The probability distribution is on the data not on θ Of coursethe engineer or analyst wants the reverse the probability of θ given the dataFor further discussion see Hartford and Baecher (2004)

Bayesian estimation works in a different way Bayesian theory allowsprobabilities to be assigned directly to states of nature such as θ ThusBayesian methods start with an a priori probability distribution f (θ ) whichis updated by the likelihood of observing the sample using Bayesrsquos Theorem

f (θ |z1 zn)infin f (θ )L(θ |z1 zn) (221)

102 Gregory B Baecher and John T Christian

in which f (θ |z1hellip zn) is the a posteriori pdf of θ conditioned on the obser-vations and L(θ |z1 hellip zn) is the likelihood of θ which is the conditionalprobability of z1 hellip zn as a function of θ Note the Fisherian conceptof a maximum likelihood estimator is mathematically related to Bayesianestimation in that both adopt the likelihood principle that all informationin the sample relevant to making an estimate is contained in the Likelihoodfunction however the maximum likelihood approach still ends up with aprobability statement on the variability of the estimator and not on the stateof nature which is an important distinction

231 Moment estimation

The most common (Frequentist) method of estimating autocovariance func-tions for soil and rock properties is the method of moments This uses thestatistical moments of the observations (eg sample means variances andcovariances) as estimators of the corresponding moments of the populationbeing sampled

Given the measurements z1hellip zn made at equally spaced locationsx1hellip xn along a line as for example in a boring the sample autocovarianceof the measurements for separation is

Cz(δ) = 1(n minus δ)

nminusδsumi=1

[z(xi) minus t(xi)z(xi+δ) minus t(xi+δ)] (222)

in which Cz(δ) is the estimator of the autocovariance function at δ (nminus δ) isthe number of data pairs having separation distance δ and t(xi) is the trendremoved from the data at location xi

Often t(xi) is simply replaced by the spatial mean estimated by the meanof the sample The corresponding moment estimator of the autocorrelationR(δ) is obtained by dividing both sides by the sample variance

Rz(δ) = 1s2z (n minus δ)

nminusδsumi=1

[z(xi) minus t(xi)z(xi+δ) minus t(xi+δ)] (223)

in which sz is the sample standard deviation Computationally this simplyreduces to taking all data pairs of common separation distance d calcu-lating the correlation coefficient of that set then plotting the result againstseparation distance

In the general case measurements are seldom uniformly spaced at least inthe horizontal plane and seldom lie on a line For such situations the sampleautocovariance can still be used as an estimator but with some modificationThe most common way to accommodate non-uniformly placed measure-ments is by dividing separation distances into bands and then taking theaverages within those bands

Spatial variability and geotechnical reliability 103

The moment estimator of the autocovariance function requires no assump-tions about the shape of the autocovariance function except that secondmoments exist The moment estimator is consistent in that as the samplesize becomes large E[(θ minus θ )2]rarr 0 On the other hand the moment estima-tor is only asymptotically unbiased Unbiasedness means that the expectedvalue of the estimator over all ways the sample might have been taken equalsthe actual value of the function being estimated For finite sample sizes theexpected values of the sample autocovariance can differ significantly from theactual values yielding negative values beyond the autocovariance distance(Weinstock 1963)

It is well known that the sampling properties of the moment estima-tor of autocorrelation are complicated and that large sampling variances(and thus poor confidence) are associated with estimates at large sep-aration distances Phoon and Fenton (2004) and Phoon (2006a) haveexperimented with bootstrapping approaches to estimate autocorrelationfunctions with promising success These and similar approaches fromstatistical signal processing should be exploited more thoroughly in thefuture

232 Example James Bay

The results of Figure 219 were obtained from the James Bay data ofChristian et al (1994) using this moment estimator The data are froman investigation into the stability of dykes on a soft marine clay at theJames Bay Project Queacutebec (Ladd et al 1983) The marine clay at the site is

Separation Distance

Aut

ocov

aria

nce

30

0

10

20Maximum Likelihood estimate(curve)

Moment estimates

150m100500

Figure 219 Autocovariance of field vane clay strength data James Bay Project (Christianet al 1994 reproduced with the permission of the American Society of CivilEngineers)

104 Gregory B Baecher and John T Christian

approximately 8 m thick and overlies a lacustrine clay The depth-averagedresults of field vane tests conducted in 35 borings were used for the cor-relation analysis Nine of the borings were concentrated in one location(Figures 220 and 221)

First a constant mean was removed from the data Then the product ofeach pair of residuals was calculated and plotted against separation distanceA moving average of these products was used to obtain the estimated pointsNote the drop in covariance in the neighborhood of the origin and alsothe negative sample moments in the vicinity of 50ndash100 m separation Notealso the large scatter in the sample moments at large separation distanceFrom these estimates a simple exponential curve was fitted by inspectionintersecting the ordinate at about 60 of the sample variance This yieldsan autocovariance function of the form

Cz(δ) =

22 kPa2 for δ = 0

13expminusδ23 m for δgt0(224)

in which variance is in kPa2 and distance in m Figure 222 shows variancecomponents for the factor of safety for various size failures

INDEX PROPERTIES FIELD VANE STRESS HISTORY

DE

PT

H Z

(m

)

0

SOILPROFILE

0

0 1 2 3 0 20 6040 0 100 200 300

10 20 30

2

4

6

8

10

12

14

16

18

20

Mean from8 FV

Selectedcu Profile

Ip

IL

Ip () cu (FV) (kPa) s primevo and s primep (kPa)

IL

Selecteds primep Profile

BlockTube

Till

Lacustrineclay

Marineclay

Crust

s primep

s primevo

gb=90kNm3

gb=90kNm3

gb=105kNm3

Figure 220 Soil property data summary James Bay (Christian et al 1994 reproduced withthe permission of the American Society of Civil Engineers)

160

140

120

100

Y a

xis

(m)

80

200 180 160 140 120 100 80 60 40 20 0 minus20

X axis (m)

Fill properties = 20kNmsup3rsquo= 30

Note All slopes are 3 horizontal to1 vertical

Y 123 m

Stage 2Berm 2Berm 1

Foundation clay Critical wedge - stage 2

Critical circles - single stage

Till Limit of vertical drains

6030

Stage 1

Figure 221 Assumed failure geometries for embankments of three heights (Christianet al 1994 reproduced with the permission of the American Society of CivilEngineers)

Embankment Height

6 m 12 m 23 m0

002

004

006

008

Var

ianc

e of

Fac

tor

of S

afet

y

01Rest of Sp VarAve Spatial VarSystematic Error

F = 1500R = 07

F = 1453R = 02

F = 1427R = 007

Figure 222 Variance components of the factor of safety for three embankment heights(Christian et al 1994 reproduced with the permission of the American Societyof Civil Engineers)

106 Gregory B Baecher and John T Christian

233 Maximum likelihood estimation

Maximum likelihood estimation takes as the estimator that value of theparameter(s) θ leading to the greatest probability of observing the dataz1 hellip zn actually observed This is found by maximizing the likelihoodfunction L(θ |z1 hellip zn) Maximum likelihood estimation is parametricbecause the distributional form of the pdf f (z1 hellip zn|θ ) must be specifiedIn practice the estimate is usually found by maximizing the log-likelihoodwhich because it deals with a sum rather than a product and becausemany common probability distributions involve exponential terms is moreconvenient

The appeal of the maximum likelihood estimator is that it possessesmany desirable sampling properties Among others it has minimum vari-ance (although not necessarily unbiased) is consistent and asymptoticallyNormal The asymptotic variance of θML is

limnrarrinfin Var[θML]=Iz(θ ) = nE[minusδ2LLpartθ2] (225)

in which Iz(θ ) is Fisherrsquos Information (Barnett 1982) and LL is the log-likelihood

Figure 223 shows the results of simulated sampling experiments in whichspatial fields were generated from a multivariate Gaussian pdf with speci-fied mean trend and autocovariance function Samples of sizes n = 36 64and 100 were taken from these simulated fields and maximum likelihoodestimators used to obtain estimates of the parameters of the mean trend andautocovariance function The smooth curves show the respective asymp-totic sampling distributions which in this case conform well with the actualestimates (DeGroot and Baecher 1993)

An advantage of the maximum likelihood estimator over moment esti-mates in dealing with spatial data is that it allows simultaneous estimation ofthe spatial trend and autocovariance function of the residuals Mardia andMarshall (1984) provide an algorithmic procedure finding the maximumDeGroot and Baecher used the Mardia and Marshall approach in analyzingthe James Bay data First they removed a constant mean from the data andestimated the autocovariance function of the residuals as

Cz(δ) =

23forδ = 0133 exp minus δ214 for δ gt 0

(226)

in which variance is in kPa2 and distance is in m Then using estimating thetrend implicitly

β0 = 407 kPa

β1 = minus20 times 10minus3 kPam

Spatial variability and geotechnical reliability 107

20

15

Num

ber

of D

ata

Num

ber

of D

ata

10

5

0

15

10

5

0

20

Num

ber

of D

ata

15

10

5

0

20

04 06 08 10 12 14 16

04 06 08 10 12 14 16

04 06 08 10Variance

12 14 16

0

4

3

2

1

0

1

2 f(x)

f(x)

3

4(a) n=36

(b) n=64

Asymptotic

0

1

2 f(x)

3

4(c) n=100

Asymptotic

Asymptotic

Figure 223 Simulated sampling experiments in which spatial fields were generated froma multivariate Gaussian pdf with specified mean trend and autocovariancefunction (DeGroot and Baecher 1993 reproduced with the permission of theAmerican Society of Civil Engineers)

β2 = minus59 times 10minus3 kPam (227)

Cz(δ) =

23 kPa2 forδ = 0133 kPa2 expminus(δ214 m) forδ gt 0

The small values of β1 and β2 suggest that the assumption of constantmean is reasonable Substituting a squared-exponential model for the

108 Gregory B Baecher and John T Christian

autocovariance results in

β0 = 408 kPa

β1 = minus21 times 10minus3 kPam

β2 = minus61 times 10minus3 kPam (228)

Cz(δ) =

229 kPa2 forδ = 0127 kPa2 expminus(δ373 m)2 forδ gt 0

The exponential model is superimposed on the moment estimates ofFigure 219

The data presented in this case suggest that a sound approach to estimatingautocovariance should involve both the method of moments and maximumlikelihood The method of moments gives a plot of autocovariance versusseparation providing an important graphical summary of the data whichcan be used as a means of determining if the data suggest correlation and forselecting an autocovariance model This provides valuable information forthe maximum likelihood method which then can be used to obtain estimatesof both autocovariance parameters and trend coefficients

234 Bayesian estimation

Bayesian inference for autocorrelation has not been widely used in geotechni-cal and geostatistical applications and it is less well developed than momentestimates This is true despite the fact that Bayesian inference yields theprobability associated with the parameters given the data rather than theconfidence in the data given the probabilistic model An intriguing aspect ofBayesian inference of spatial trends and autocovariance functions is that formany of the non-informative prior distributions one might choose to reflectlittle or no prior information about process parameters (eg the Jeffreysprior the Laplace prior truncated parameter spaces) the posterior pdfrsquoscalculated through Bayesian theorem are themselves improper usually inthe sense that they do not converge toward zero at infinity and thus thetotal probability or area under the posterior pdf is infinite

Following Berger (1993) Boger et al (2001) and Kitanidis (1985 1997)the spatial model is typically written as a Multinomial random process

z(x) =ksum

i=1

fi(x)β + ε(x) (229)

in which fi(x) are unknown deterministic functions of the spatial loca-tions x and ε(x) is a zero-mean spatial random function The random

Spatial variability and geotechnical reliability 109

term is spatially correlated with an isotropic autocovariance function Theautocovariance function is assumed to be non-negative and to decreasemonotonically with distance to zero at infinite separation These assumptionsfit most common autocovariance functions in geotechnical applications TheLikelihood of a set of observations z = z1 hellip zn is then

L(βσ |z) = (2πσ 2)minusn2|Rθ |minus12 expminus 1

2σ 2 (z minus Xβ)tRminus1θ (z minus Xβ)

(230)

in which X is the (n times k) matrix defined by Xij=fj(Xi) Rθ is the matrix ofcorrelations among the observations dependent on the parameters and |Rθ |is the determinant of the correlation matrix of the observations

In the usual fashion a prior non-informative distribution on the param-eters (β σ θ ) might be represented as f (β σ θ )infin(σ 2)minusaf (θ ) for variouschoices of the parameter a and of the marginal pdf f (θ ) The obvious choicesmight be a = 1 f (θ ) = 1 a = 1 f (θ ) = 1θ or a = 1 f (θ ) = 1 but eachof these leads to an improper posterior pdf as does the well-known Jeffreysprior A proper informative prior does not share this difficulty but it iscorrespondingly hard to assess from usually subjective opinion Given thisproblem Berger et al (2001) suggest the reference non-informative prior

f (βσθ ) prop 1σ 2

(|W2

θ |minus |W2θ |

(n minus k)

)12

(231)

in which

W2θ = partRθ

partθRminus1

θ I minus X(XprimeRminus1θ X)minus1X

primeRminus1

θ (232)

This does lead to a proper posterior The posterior pdf is usually evaluatednumerically although depending on the choice of autocovariance functionmodel and the extent to which certain of the parameters of that model areknown closed-form solutions can be obtained Berger et al (2001) presenta numerically calculated example using terrain data from Davis (1986)

235 Variograms

In mining the importance of autocorrelation for estimating ore reserveshas been recognized for many years In mining geostatistics a functionrelated to the autocovariance called the variogram (Matheron 1971) iscommonly used to express the spatial structure of data The variogramrequires a less-restrictive statistical assumption on stationarity than doesthe autocovariance function and it is therefore sometimes preferred for

110 Gregory B Baecher and John T Christian

inference problems On the other hand the variogram is more difficult touse in spatial interpolation and engineering analysis and thus for geotech-nical purposes the autocovariance is used more commonly In practice thetwo ways of characterizing spatial structure are closely related

Whereas the autocovariance is the expected value of the product oftwo observations the variogram 2γ is the expected value of the squareddifference

2γ = E[z(xi) minus z(xj)2] = Var[z(xi) minus z(xj)] (233)

which is a function of only the increments of the spatial properties nottheir absolute values Cressie (1991) points out that in fact the commondefinition of the variogram as the mean squared difference ndash rather thanas the variance of the difference ndash limits applicability to a more restric-tive class of processes than necessary and thus the latter definition is tobe preferred None the less one finds the former definition more commonlyreferred to in the literature The term γ is referred to as the semivariogramalthough caution must be exercised because different authors interchangethe terms The concept of average mean-square difference has been usedin many applications including turbulence (Kolmogorov 1941) and timeseries analysis (Jowett 1952) and is alluded to in the work of Mateacutern(1960)

The principal advantage of the variogram over the autocovariance is thatit makes less restrictive assumptions on the stationarity of the spatial prop-erties being sampled specifically only that their increment and not theirmean is stationary Furthermore the use of geostatistical techniques hasexpanded broadly so that a great deal of experience has been accumulatedwith variogram analysis not only in mining applications but also in environ-mental monitoring hydrology and even geotechnical engineering (Chiassonet al 1995 Soulie and Favre 1983 Soulie et al 1990)

For spatial variables with stationary means and autocovariances (iesecond-order stationary processes) the variogram and autocovariance func-tion are directly related by

γ (δ) = Cz(0) minus Cz(δ) (234)

Common analytical forms for one-dimensional variograms are given inTable 23

For a stationary process as |δ rarr infinCz(δ) rarr 0 thus γ (δ) rarr Cz(0) =Var(z(x)) This value at which the variogram levels off 2Cz(δ) is called thesill value The distance at which the variogram approaches the sill is calledthe range The sampling properties of the variogram are summarized byCressie (1991)

Spatial variability and geotechnical reliability 111

Table 23 One-dimensional variogram models

Model Equation Limits of validity

Nugget g(δ) =

0 if δ = 01 otherwise Rn

Linear g(δ) =

0 if δ = 0c0 + b||δ|| otherwise R1

Spherical g(δ) =

(15)(δa) minus (12)(δa)3 if δ = 01 otherwise

Rn

Exponential g(δ) = 1 minus exp(minus3δa) R1

Gaussian g(δ) = 1 minus exp(minus3δ2a2) Rn

Power g(δ) = hω Rn 0lt δ lt2

24 Random fields

The application of random field theory to spatial variation is based on theassumption that the property of concern z(x) is the realization of a randomprocess When this process is defined over the space x isin S the variable z(x)is said to be a stochastic process In this chapter when S has dimensiongreater than one z(x) is said to be a random field This usage is more or lessconsistent across civil engineering although the geostatistics literature usesa vocabulary all of its own to which the geotechnical literature occasionallyrefers

A random field is defined as the joint probability distribution

Fx1middotmiddotmiddot xn

(z1 zn

) = Pz(x1)le z1 z

(xn

)le zn

(235)

This joint probability distribution describes the simultaneous variation of thevariables z within a space Sx Let E[z(x)] = micro(x) be the mean or trend of z(x)and let Var[z(x)] = σ 2(x) be the variance The covariances of z(x1) hellip z(xn)are defined as

Cov[z(xi)z(xj)] = E[(z(xi) minusmicro(xi)) middot (z(xj) minusmicro(xj))] (236)

A random field is said to be second-order stantionary (weak or wide-sensestationary) if E[z(x)]= micro for all x and Cov[z(xi)z(xj)] depends only onvector separation of xi and xj and not on location Cov[z(xi)z(xj)] =Cz(xi minus xj) in which Cz(xindashxj) is the autocovariance function The randomfield is said to be stationary (strong or strict stationarity) if the complete prob-ability distribution Fx1xn

(z1 zn) is independent of absolute location

112 Gregory B Baecher and John T Christian

depending only on vector separations among the xihellipxn Strong stationarityimplies second-order stationarity In the geotechnical literature stationar-ity is sometimes referred to as statistical homogeneity If the autocovariancefunction depends only on the absolute separation distance and not directionthe random field is said to be isotropic

Ergodicity is a concept originating in the study of time series in whichone observes individual time series of data and wishes to infer proper-ties of an ensemble of all possible time series The meaning and practicalimportance of ergodicity to the inference of unique realizations of spatialrandom fields is less well-defined and is debated in the literature Simplyergodicity means that the probabilistic properties of a random process(field) can be completely estimated from observing one realization of thatprocess For example the stochastic time series z(t) = ν + ε(t) in which ν

is discrete random variable and ε(t) is an autocorrelated random processof time is non-ergotic In one realization of the process there is but onevalue of ν and thus the probability distribution of ν cannot be estimatedOne would need to observe many realizations of zt in order to have suf-ficiently many observations of ν to estimate Fν(ν) Another non-ergoticprocess of more relevance to the spatial processes in geotechnical engi-neering is z(x) = ν + ε(x) in which the mean of z(x) varies linearly withlocation x In this case z(x) is non-stationary Var[z(x)] increases with-out limit as the window within which z(x) is observed increases andthe mean mz of the sample of z(x) is a function of the location of thewindow

The meaning of ergodicity for spatial fields of the sort encountered ingeotechnical engineering is less clear and has not been widely discussed inthe literature An assumption weaker than full erogodicity which none theless should apply for spatial fields is that the observed sample mean mz andsample autocovariance function Cz(δ) converge in mean-squared error to therespective random field mean and autocovariance function as the volumeof space within which they are sampled increases This means that as thevolume of space increases E[(mz minusmicro)2]rarr0 and E[(Cz(δ) minus Cz(δ)2] rarr 0Soong and Grigoriu (1993) provide conditions for checking ergodicity inmean and autocorrelation

When the joint probability distribution Fx1xn(z1 zn) is multivari-

ate Normal (Gaussian) the process z(x) is said to be a Gaussian randomfield A sufficient condition for ergodicity of a Gaussian random field isthat lim|δ|rarrinfinCz(δ) = 0 This can be checked empirically by inspecting thesample moments of the autocovariance function to ensure they convergeto 0 Cressie (1991) notes limitations of this procedure Essentially all theanalytical autocovariance functions common in the geotechnical literatureobey this condition and few practitioners appear concerned about verifyingergodicity Christakos (1992) suggests that in practical situations it isdifficult or impossible to verify ergodicity for spatial fields

Spatial variability and geotechnical reliability 113

A random field that does not meet the conditions of stationarity is saidto be non-stationary Loosely speaking a non-stationary field is statisticallyheterogeneous It can be heterogeneous in a number of ways In the sim-plest case the mean may be a function of location for example if there is aspatial trend that has not been removed In a more complex case the vari-ance or autocovariance function may vary in space Depending on the wayin which the random field is non-stationary sometimes a transformation ofvariables can convert a non-stationary field to a stationary or nearly station-ary field For example if the mean varies with location perhaps a trend canbe removed

In the field of geostatistics a weaker assumption is made on stationaritythan that described above Geostatisticians usually assume only that incre-ments of a spatial process are stationary (ie differences |z1 minus z2|) and thenoperate on the probabilistic properties of those increments This leads to theuse of the variogram rather than the autocovariance function Stationarityof the autocovariance function implies stationarity of the variogram but thereverse is not true

Like most things in the natural sciences stationarity is an assumption ofthe model and may only be approximately true in the world Also station-arity usually depends on scale Within a small region soil properties maybehave as if drawn from a stationary process whereas the same propertiesover a larger region may not be so well behaved

241 Permissible autocovariance functions

By definition the autocovariance function is symmetric meaning

Cz(δ) = Cz(minusδ) (237)

and bounded meaning

Cz(δ) le Cz(0) = σ 2z (238)

In the limit as distance becomes large

lim |δ|rarrinfinCz(δ)

|δ|minus(nminus1)2 = 0 (239)

In general in order for Cz(δ) to be a permissible autocovariance functionit is necessary and sufficient that a continuous mathematical expression ofthe form

msumi=1

msumj=1

kikjCz(δ) ge 0 (240)

114 Gregory B Baecher and John T Christian

be non-negative-definite for all integers m scalar coefficients k1hellipkmand δ This condition follows from the requirement that variances of linearcombinations of the z(xi) of the form

var

[msum

i=1

kiz(xi)

]=

msumi=1

msumj=1

kikjCz(δ) ge 0 (241)

be non-negative ie the matrix is positive definite (Cressie 1991) Christakos(1992) discusses the mathematical implications of this condition on selectingpermissible forms for the autocovariance Suffice it to say that analyticalmodels of autocovariance common in the geotechnical literature usuallysatisfy the condition

Autocovariance functions valid in a space of dimension d are validin spaces of lower dimension but the reverse is not necessarily trueThat is a valid autocovariance function in 1D is not necessarily validin 2D or 3D Christakos (1992) gives the example of the linearly decliningautcovariance

Cz(δ) =σ 2(1 minus δδ0) for 0 le δ le δ0

0 for δ gt δ0(242)

which is valid in 1D but not in higher dimensionsLinear sums of valid autocovariance functions are also valid This means

that if Cz1(δ) and Cz2(δ) are valid then the sum Cz1(δ)+Cz2(δ) is also a validautocovariance function Similarly if Cz(δ) is valid then the product with ascalar αCz(δ) is also valid

An autocovariance function in d-dimensional space is separable if

Cz(δ) =dprod

i=1

Czi(δi) (243)

in which δ is the d-dimensioned vector of orthogonal separation distancesδ1hellipδd and Ci(δi) is the one-dimensional autocovariance function indirection i For example the autocovariance function

Cz(δ) = σ 2 expminusa2|δ|2= σ 2 expminusa2(δ1

2 +middotmiddot middot+ δ2d) (244)

= σ 2dprod

i=1

expminusa2δ2i

is separable into its one-dimensional components

Spatial variability and geotechnical reliability 115

The function is partially separable if

Cz(δ) = Cz(δi)Cz(δj =i) (245)

in which Cz(δj =i) is a (d minus 1) dimension autocovariance function implyingthat the function can be expressed as a product of autocovariance func-tions of lower dimension fields The importance of partial separability togeotechnical applications as noted by VanMarcke (1983) is the 3D case ofseparating autocorrelation in the horizontal plane from that with depth

Cz(δ1δ2δ3) = Cz(δ1δ2)Cz(δ3) (246)

in which δ1 δ2 are horizontal distances and δ3 is depth

242 Gaussian random fields

The Gaussian random field is an important special case because it is widelyapplicable due to the Central Limit Theorem has mathematically conve-nient properties and is widely used in practice The probability densitydistribution of the Gaussian or Normal variable is

fz(z) = minus 1radic2πσ

exp

minus1

2

(x minusmicro

σ

)2

(247)

for minusinfin le z le infin The mean is E[z] = micro and variance Var[z] = σ 2 For themultivariate case of vector z of dimension n the correponding pdf is

fz(z) = (2π )minusn2||minus12 expminus1

2(z minusmicro)primeminus1(z minusmicro)

(248)

in which micro is the mean vector and the covariance matrix

ij =Cov[zi(x)zj(x)

](249)

Gaussian random fields have the following convenient properties (Adler1981) (1) they are completely characterized by the first- and second-ordermoments the mean and autocovarinace function for the univariate caseand mean vector and autocovariance matrix (function) for the multivari-ate case (2) any subset of variables of the vector is also jointly Gaussian(3) the conditional probability distributions of any two variables or vectorsare also Gaussian distributed (4) if two variables z1 and z2 are bivariateGaussian and if their covariance Cov[z1 z2] is zero then the variables areindependent

116 Gregory B Baecher and John T Christian

243 Interpolating random fields

A problem common in site characterization is interpolating among spa-tial observations to estimate soil or rock properties at specific locationswhere they have not been observed The sample observations themselvesmay have been taken under any number of sampling plans random sys-tematic cluster and so forth What differentiates this spatial estimationquestion from the sampling theory estimates in preceding sections of thischapter is that the observations display spatial correlation Thus the assump-tion of IID observations underlying the estimator results is violated in animportant way This question of spatial interpolation is also a problemcommon to the natural resources industries such as forestry (Mateacutern 1986)and mining (Matheron 1971) but also geohydrology (Kitanidis 1997) andenvironmental monitoring (Switzer 1995)

Consider the case for which the observations are sampled from aspatial population with constant mean micro and autocovariance functionCz(δ) = E[z(xi)z(xi+δ)] The set of observations z=zihellip zn thereforehas mean vector m in which all the terms are equal and covariancematrix

=⎡⎢⎣

Var(z1) middot middot middot Cov(z1zn)

Cov(znz1) middot middot middot Var(zn)

⎤⎥⎦ (250)

in which the terms z(xi) are replaced by zi for convenience These termsare found from the autocovariance function as Cov(z(xi)z(xj)) = Cz (δij) inwhich δij is the (vector) separation between locations xi and xj

In principle we would like to estimate the full distribution of z(x0) atan unobserved location x0 but in general this is computationally intensiveif a large grid of points is to be interpolated Instead the most commonapproach is to construct a simple linear unbiased estimator based on theobservations

z(x0) =nsum

i=1

wiz(xi) (251)

in which the weights w=w1hellip wn are scalar values chosen to makethe estimate in some way optimal Usually the criteria of optimality areunbiasedness and minimum variance and the result is sometimes called thebest linear unbiased estimator (BLUE)

The BLUE estimator weights are found by expressing the variance of theestimate z(x0) using a first-order second-moment formulation and minimiz-ing the variance over w using a Lagrange multiplier approach subject to the

Spatial variability and geotechnical reliability 117

condition that the sum of the weights equals one The solution in matrixform is

w = Gminus1h (252)

in which w is the vector of optimal weights and the matrices G and h relatethe covariance matrix of the observations and the vector of covariances of theobservations to the value of the spatial variable at the interpolated locationx0 respectively

G =

⎡⎢⎢⎢⎣

Var(z1) middot middot middot Cov(z1zn) 1

1

Cov(znz1) middot middot middot Var(zn) 11 1 1 0

⎤⎥⎥⎥⎦

h =

⎡⎢⎢⎢⎣

Cov(z1z0)

Cov(znz0)1

⎤⎥⎥⎥⎦

(253)

The resulting estimator variance is

Var(z0) = E[(z0 minus z0)2]

= Var(z0) minusnsum

i=1

wiCov(z0zi) minusλ (254)

in which λ is the Lagrange multiplier resulting from the optimization Thisis a surprisingly simple and convenient result and forms the basis of theincreasingly vast literature on the subject of so-called kriging in the field ofgeostatistics For regular grids of observations such as a grid of borings analgorithm can be established for the points within an individual grid celland then replicated for all cells to form an interpolated map of the largersite or region (Journel and Huijbregts 1978) In the mining industry andincreasingly in other applications it has become common to replace the auto-covariance function as a measure of spatial association with the variogram

244 Functions of random fields

Thus far we have considered the properties of random fields themselvesIn this section we consider the extension to properties of functions of ran-dom fields Spatial averaging of random fields is among the most importantconsiderations for geotechnical engineering Limiting equilibrium stabil-ity of slopes depends on the average strength across the failure surface

118 Gregory B Baecher and John T Christian

Settlements beneath foundations depend on the average compressibility ofthe subsurface soils Indeed many modes of geotechnical performance ofinterest to the engineer involve spatial averages ndash or differences amongspatial averages ndash of soil and rock properties Spatial averages also playa significant role in mining geostatistics where average ore grades withinblocks of rock have important implications for planning As a result thereis a rich literature on the subject of averages of random fields only a smallpart of which can be reviewed here

Consider the one-dimensional case of a continuous scalar stochastic pro-cess (1D random field) z(x) in which x is location and z(x) is a stochasticvariable with mean microz assumed to be constant and autocovariance functionCz(r) in which r is separation distance r = (x1 minusx2) The spatial average ormean of the process within the interval [0X] is

MXz(x) = 1X

int X

0z(x)dx (255)

The integral is defined in the common way as a limiting sum of z(x) valueswithin infinitesimal intervals of x as the number of intervals increasesWe assume that z(x) converges in a mean square sense implying the existenceof the first two moments of z(x) The weaker assumption of convergence inprobability which does not imply existence of the moments could be madeif necessary (see Parzen 1964 1992) for more detailed discussion)

If we think of MXz(x) as a sample observation within one interval of theprocess z(x) then over the set of possible intervals that we might observeMXz(x) becomes a random variable with mean variance and possiblyother moments Consider first the integral of z(x) within intervals of length XParzen (1964) shows that the first two moments of

int X0 z(x)dx are

E

[int X

0z(x)dx

]=int X

0micro(x)dx = microX (256)

Var

[int X

0z(x)dx

]=int X

0

int X

0Cz(xi minus xj)dxidxj = 2

int X

0(X minus r)Cz(r)dr

(257)

and that the autocovariance function of the integralint X

0 z(x)dx as the interval[0 X] is allowed to translate along dimension x is (VanMarcke 1983)

Cint X0 z(x)dx(r) = Cov

[int X

0z(x)dx

int r+X

rz(x)dx

]

=int X

0

int X

0Cz(r + xi minus xj)dxidxj (258)

Spatial variability and geotechnical reliability 119

The corresponding moments of the spatial mean MXz(x) are

E[MXz(x)]= E

[1X

int X

0z(x)dx

]=int X

0

1Xmicro(x)dx = micro (259)

Var[MXz(x)]= Var

[1X

int X

0z(x)dx

]= 2

X2

int X

0(X minus r)Cz(r)dr (260)

CMXz(x)(r) = Cov

[1X

int X

0z(x)dx

1X

int r+X

rz(x)dx

]

= 1X2

int X

0

int X

0Cz(r + xi minus xj)dxidxj (261)

The effect of spatial averaging is to smooth the process The varianceof the averaged process is smaller than that of the original process z(x)and the autocorrelation of the averaged process is wider Indeed averaging issometimes referred to as smoothing (Gelb and Analytic Sciences CorporationTechnical Staff 1974)

The reduction in variance from z(x) to the averaged process MXz(x) canbe represented in a variance reduction function γ (X)

γ (X) = Var[MXz(x)]

var[z(x)] (262)

The variance reduction function is 10 for X = 0 and decays to zero as Xbecomes large γ (X) can be calculated from the autocovariance function ofz(x) as

γ (X) = 2x

xint0

(1 minus r

X

)R2(r)dr (263)

in which Rz(r) is the autocorrelation function of z(x) Note that the squareroot of γ (X) gives the corresponding reduction of the standard deviationof z(x) Table 24 gives one-dimensional variance reduction functions forcommon autocovariance functions It is interesting to note that each of thesefunctions is asymptotically proportional to 1X Based on this observationVanMarcke (1983) proposed a scale of fluctuation θ such that

θ = limXrarrinfin

X γ (X) (264)

or γ (X) = θ X as X rarr infin that is θ X is the asymptote of the variancereduction function as the averaging window expands The function γ (X)

Tabl

e2

4V

aria

nce

redu

ctio

nfu

nctio

nsfo

rco

mm

on1D

auto

cova

rian

ces

(aft

erV

anM

arck

e19

83)

Mod

elAu

toco

rrel

atio

nVa

rianc

ere

duct

ion

func

tion

Scal

eof

fluct

uatio

n

Whi

teno

ise

R x(δ

)= 1

ifδ=

00

othe

rwise

γ(X

)= 1

ifX

=0

0ot

herw

ise0

Line

arR x

(δ)= 1

minus|δ|

δ n

ifδ

leδ 0

0ot

herw

iseγ

(X)= 1

minusX3δ

0if

Xle

δ 0(δ

0X

)[ 1minusδ 03X] ot

herw

iseδ 0

Expo

nent

ial

R x(δ

)=ex

p(minusδ

δ 0

(X)=

2(δ 0X

)2( X δ 0

minus1+

exp2

(minusXδ 0

))4δ

0

Squa

red

expo

nent

ial

(Gau

ssia

n)R x

(δ)=

exp2

(minus|δ|

δ 0

(X)=

(δ0

X)2[ radic π

X δ 0

(minusXδ 0

)+ex

p2(minus

Xδ 0

)minus1]

inw

hich

is

the

erro

rfu

nctio

n

radic πδ 0

Spatial variability and geotechnical reliability 121

converges rapidly to this asymptote as X increases For θ to exist it isnecessary that Rz(r) rarr0 as r rarr infin that is that the autocorrelation func-tion decreases faster than 1r In this case θ can be found from the integralof the autocorrelation function (the moment of Rz(r) about the origin)

θ = 2int infin

0Rz(r)dr =

int infin

minusinfinRz(r)dr (265)

This concept of summarizing the spatial or temporal scale of autocorrelationin a single number typically the first moment of Rz(r) is used by a variety ofother workers and in many fields Taylor (1921) in hydrodynamics calledit the diffusion constant (Papoulis and Pillai 2002) Christakos (1992) ingeoscience calls θ 2 the correlation radius Gelhar (1993) in groundwaterhydrology calls θ the integral scale

In two dimensions the equivalent expressions for the mean and varianceof the planar integral

int X0

int X0 z(x)dx are

E

[int X

0z(x)dx

]=int X

0micro(x)dx = microX (266)

Var

[int X

0z(x)dx

]=int X

0

int X

0Cz(xi minus xj)dxidxj = 2

int X

0(X minus r)Cz(r)dr

(267)

Papoulis and Pillai (2002) discuss averaging in higher dimensions as doElishakoff (1999) and VanMarcke (1983)

245 Stochastic differentiation

The continuity and differentiability of a random field depend on the con-vergence of sequences of random variables z(xa)z(xb) in which xa xb aretwo locations with (vector) separation r = |xa minusxb| The random field is saidto be continuous in mean square at xa if for every sequence z(xa)z(xb)E2[z(xa)minusz(xb)] rarr0 as rrarr0 The random field is said to be continuousin mean square throughout if it is continuous in mean square at every xaGiven this condition the random field z(x) is mean square differentiablewith partial derivative

partz(x)partxi

= lim|r|rarr0

z(x + rδi) minus z(x)r

(268)

in which the delta function is a vector of all zeros except the ith termwhich is unity While stronger or at least different convergence propertiescould be invoked mean square convergence is often the most natural form in

122 Gregory B Baecher and John T Christian

practice because we usually wish to use a second-moment representation ofthe autocovariance function as the vehicle for determining differentiability

A random field is mean square continuous if and only if its autocovariancefunction Cz(r) is continuous at |r| = 0 For this to be true the first derivativesof the autocovariance function at |r|=0 must vanish

partCz(r)partxi

= 0 for all i (269)

If the second derivative of the autocovariance function exists and is finite at|r| = 0 then the field is mean square differentiable and the autocovariancefunction of the derivative field is

Cpartzpartxi(r) = part2Cz(r)partx2

i (270)

The variance of the derivative field can then be found by evaluating the auto-covariance Cpartzpartxi

(r) at |r| = 0 Similarly the autocovariance of the secondderivative field is

Cpart2zpartxipartxj(r) = part4Cz(r)partx2

i partx2j (271)

The cross covariance function of the derivatives with respect to xi and xj inseparate directions is

Cpartzpartxipartzpartxj(r) = minuspart2Cz(r)partxipartxj (272)

Importantly for the case of homogeneous random fields the field itself z(x)and its derivative field are uncorrelated (VanMarcke 1983)

So the behavior of the autocovariance function in the neighborhood ofthe origin is the determining factor for mean-square local properties of thefield such as continuity and differentiability (Crameacuter and Leadbetter 1967)Unfortunately the properties of the derivative fields are sensitive to thisbehavior of Cz(r) near the origin which in turn is sensitive to the choice ofautocovariance model Empirical verification of the behavior of Cz(r) nearthe origin is exceptionally difficult Soong and Grigoriu (1993) discuss themean square calculus of stochastic processes

246 Linear functions of random fields

Assume that the random field z(x) is transformed by a deterministicfunction g() such that

y(x) = g[z(x)] (273)

In this equation g[z(x0)] is a function of z alone that is not of x0 and not ofthe value of z(x) at any x other than x0 Also we assume that the transforma-tion does not depend on the value of x that is the transformation is space- or

Spatial variability and geotechnical reliability 123

time-invariant y(z + δ) = g[z(x + δ)] Thus the random variable y(x) is adeterministic transformation of the random variable z(x) and its probabilitydistribution can be obtained from derived distribution methods Similarlythe joint distribution of the sequence of random variables y(x1) hellip y(xn)can be determined from the joint distribution of the sequence of randomvariables x(x1) hellip x(xn) The mean of y(x) is then

E[y(x)] =infinint

minusinfing(z)fz(z(x))dz (274)

and the autocorrelation function is

Ry(y1y2) = E[y(x1)y(x2)] =infinint

minusinfin

infinintminusinfin

g(z1)g(z2)fz(z(x1)z(x2))dz1dz2

(275)

Papoulis and Pillai(2002) show that the process y(x) is (strictly) stationary ifz(x) is (strictly) stationary Phoon (2006b) discusses limitations and practicalmethods of solving this equation Among the limitations is that such non-Gaussian fields may not have positive definite covariance matrices

The solutions for nonlinear transformations are difficult but for linearfunctions general results are available The mean of y(x) for linear g(z) isfound by transforming the expected value of z(x) through the function

E[y(x)] = g(E[z(x)]) (276)

The autocorrelation of y(x) is found in a two-step process

Ryy(x1x2) = Lx1[Lx2

[Rzz(x1x2)]] (277)

in which Lx1is the transformation applied with respect to the first variable

z(x1) with the second variable treated as a parameter and Lx2is the trans-

formation applied with respect to the second variable z(x2) with the firstvariable treated as a parameter

247 Excursions (level crossings)

A number of applications arise in geotechnical practice for which one isinterested not in the integrals (averages) or differentials of a stochastic pro-cess but in the probability that the process exceeds some threshold eitherpositive or negative For example we might be interested in the probabilitythat a stochastically varying water inflow into a reservoir exceeds some rate

124 Gregory B Baecher and John T Christian

or in the properties of the weakest interval or seam in a spatially varyingsoil mass Such problems are said to involve excursions or level crossings ofa stochastic process The following discussion follows the work of Crameacuter(1967) Parzen (1964) and Papoulis and Pillai (2002)

To begin consider the zero-crossings of a random process the points xi atwhich z(xi) = 0 For the general case this turns out to be a surprisingly diffi-cult problem Yet for the continuous Normal case a number of statementsor approximations are possible Consider a process z(x) with zero mean andvariance σ 2 For the interval [xx+ δ] if the product

z(x)z(x + δ) lt 0 (278)

then there must be an odd number of zero-crossings within the interval forif this product is negative one of the values must lie above zero and the otherbeneath Papoulis and Pillai(2002) demonstrate that if the two (zero-mean)variables z(x) and z(x + δ) are jointly normal with correlation coefficient

r = E [z(x)z(x + δ)]σxσx+δ

(279)

then

p(z(x)z(x + δ) lt 0) = 12

minus arcsin(r)π

= arccos(r)π

p(z(x)z(x + δ) gt 0) = 12

+ arcsin(r)π

= π minus arccos(r)π

(280)

The correlation coefficient of course can be taken from the autocorrelationfunction Rz(δ) Thus

cos[πp(z(x)z(x + δ) lt 0)] = Rz(δ)

Rz(0)(281)

and the probability that the number of zero-crossings is positive is just thecomplement of this result

The probability of exactly one zero-crossing p1(δ) is approximatelyp1(δ) asymp p0(δ) and expanding the cosine in a Fourier series and truncating totwo terms

1 minus π2p21(δ)

2= Rz(δ)

Rz(0)(282)

or

p1(δ) asymp 1π

radic2[Rz(0) minus Rz(δ)]

Rz(0)(283)

Spatial variability and geotechnical reliability 125

In the case of a regular autocorrelation function for which the derivativedRz(0)dδ exists and is zero at the origin the probability of a zero-crossingis approximately

p1(δ) asymp δ

π

radicminusd2Rz(0)dδ2

Rz(0)(284)

The non-regular case for which the derivative at the origin is not zero (egdRz(δ) = exp(δδ0)) is discussed by Parzen (1964) Elishakoff (1999) andVanMarcke (1983) treat higher dimensional results The related probabilityof the process crossing an arbitrary level zlowast can be approximated by notingthat for small δ and thus r rarr1

P[z(x) minus zlowastz(x + δ) minus zlowast lt 0

]asymp P [z(x)z(x + δ) lt 0]eminusarcsin2(r)

2σ2

(285)

For small δ the correlation coefficient Rz(δ) is approximately 1 and thevariances of z(x) and z(x + δ) are approximately Rz(0) thus

p1zlowast (δ) asymp p1zlowast (δ) lt 0]e minusarcsin2(r)2Rz (0) (286)

and for the regular case

p1(δ) asymp δ

π

radicminusd2Rz(0)dδ2

Rz(0)e

minusarcsin2(r)2Rz (0) (287)

Many other results can be found for continuous Normal processes eg theaverage density of the number of crossings within an interval the probabilityof no crossings (ie drought) within an interval and so on A rich literature isavailable of these and related results (Yaglom 1962 Parzen 1964 Crameacuterand Leadbetter 1967 Gelb and Analytic Sciences Corporation TechnicalStaff 1974 Adler 1981 Cliff and Ord 1981 Cressie 1991 Christakos1992 2000 Christakos and Hristopulos 1998)

248 Example New Orleans hurricane protection systemLouisiana (USA)

In the aftermath of Hurricane Katrina reliability analyses were con-ducted on the reconstructed New Orleans hurricane protection system(HPS) to understand the risks faced in future storms A first-excursionor level crossing methodology was used to calculate the probability offailure in long embankment sections following the approach proposed

126 Gregory B Baecher and John T Christian

by VanMarcke (1977) This resulted in fragility curves for a reach oflevee The fragility curve gives the conditional probability of failure forknown hurricane loads (ie surge and wave heights) Uncertainties in thehurricane loads were convolved with these fragility curves in a systems riskmodel to generate unconditional probabilities and subsequently risk whenconsequences were included

As a first approximation engineering performance models and calcu-lations were adapted from the US Army Corps of Engineersrsquo DesignMemoranda describing the original design of individual levee reaches(USACE 1972) Engineering parameter and model uncertainties were prop-agated through those calculations to obtain approximate fragility curvesas a function of surge and wave loads These results were later calibratedagainst analyses which applied more sophisticated stability models and therisk assessments were updated

A typical design profile of the levee system is shown in Figure 224Four categories of uncertainty were included in the reliability analysisgeological and geotechnical uncertainties involving the spatial distribu-tion of soils and soil properties within and beneath the HPS geotechnicalstability modeling of levee performance erosion uncertainties involvingthe performance of levees and fills during overtopping and mechanicalequipment uncertainties including gates pumps and other operating sys-tems and human operator factors affecting the performance of mechanicalequipment

The principal uncertainty contributing to probability of failure of the leveesections in the reliability analysis was soil engineering properties specificallyundrained strength Su measured in Q-tests (UU tests) Uncertainties in soilengineering properties was presumed to be structured as in Figure 216

Figure 224 Typical design section from the USACE Design Memoranda for theNew Orleans Hurricane Protection System (USACE 1972)

Spatial variability and geotechnical reliability 127

and the variance of the uncertainty in soil properties was divided into fourterms

Var(Su) = Var(x) + Var(e) + Var(m) + Var(b) (288)

in which Var() is variance Su is measured undrained strength x is the soilproperty in situ e is measurement error (noise) m is the spatial mean (whichhas some error due to the statistical fluctuations of small sample sizes) andb is a model bias or calibration term caused by systematic errors in mea-suring the soil properties Measured undrained strength for one reach theNew Orleans East lakefront levees are shown as histograms in Figure 225Test values larger than 750 PCF (36 kPa) were assumed to be local effectsand removed from the statistics The spatial pattern of soil variability wascharacterized by autocovariance functions in each region of the system andfor each soil stratum (Figure 226) From the autocovariance analyses twoconclusions were drawn The measurement noise (or fine-scale variation)in the undrained strength data was estimated to be roughly 34 the totalvariance of the data (which was judged not unreasonable given the Q-testmethods) and the autocovariance distance in the horizontal direction forboth the clay and marsh was estimated to be on the order of 500 feetor more

The reliability analysis was based on limiting equilibrium calculationsFor levees the analysis was based on General Design Memorandum (GDM)calculations of factor of safety against wedge instability (USACE 1972)

0

1

2

3

4

5

6

7

8

9

10

0 250 500 750 1000 1250 1500

Undrained Shear Strength Q-test (PCF)

FillMarshDistributary Clay

Figure 225 Histogram of Q-test (UU) undrained soil strengths New Orleans Eastlakefront

128 Gregory B Baecher and John T Christian

C10minus2

29972

19928

9884

minus16

minus10204

minus202480 1025 205 3075 41

Distance h10minus2

5125 615 7175 82

Figure 226 Representative autocovariance function for inter-distributary clay undrainedstrength (Q test) Orleans Parish Louisiana

P(f

|Ele

vatio

n) P

roba

bilit

y of

Fai

lure 10

05

0

Authorization Basis

015 Fractile Level

085 Fractile Level

Median

Elevation (ft)

Range ofthe SystemReliability

Figure 227 Representative fragility curves for unit reach and long reach of levee

using the so-called method of planes The calculations are based onundrained failure conditions Uncertainties in undrained shear strength werepropagated through the calculations to estimate a coefficient of variationin the calculated factor of safety The factor of safety was assumed to beNormally distributed and a fragility curve approximated through threecalculation points (Figure 227)

The larger the failure surface relative to the autocorrelation of the soilproperties the more the variance of the local averages is reduced VanMarcke(1977) has shown that the variance of the spatial average for a unit-widthplain strain cross-section decreases approximately in proportion to (LrL)for Lgt rL in which L is the cross-sectional length of the failure surface and

Spatial variability and geotechnical reliability 129

rL is an equivalent autocovariance distance of the soil properties across thefailure surface weighted for the relative proportion of horizontal and verticalsegments of the surface For the wedge failure modes this is approximatelythe vertical autocovariance distance The variance across the full failure sur-face of width b along the axis of the levee is further reduced by averagingin the horizontal direction by an additional factor (brH) for b gt rH inwhich rH is the horizontal autocovariance distance At the same time thatthe variance of the average strength on the failure surface is reduced bythe averaging process so too the autocovariance function of this averagedprocess stretches out from that of the point-to-point variation

For a failure length of approximately 500 feet along the levee axis and30 feet deep typical of those actually observed with horizontal and ver-tical autocovariance distances of 500 feet and 10 feet respectively thecorresponding variance reduction factors are approximately 075 for aver-aging over the cross-sectional length L and between 073 and 085 foraveraging over the failure length b assuming either an exponential orsquared-exponential (Gaussian) autocovariance The corresponding reduc-tion to the COV of soil strength based on averaging over the failure plane isthe root of the product of these two factors or between 074 and 08

For a long levee the chance of at least one failure is equivalent to the chancethat the variations of the mean soil strength across the failure surface dropbelow that required for stability at least once along the length VanMarckedemonstrated that this can be determined by considering the first crossingsof a random process The approximation to the probability of at least onefailure as provided by VanMarcke was used in the present calculations toobtain probability of failure as a function of levee length

25 Concluding comments

In this chapter we have described the importance of spatial variation ingeotechnical properties and how such variation can be dealt with in aprobabilistic analysis Spatial variation consists essentially of two parts anunderlying trend and a random variation superimposed on it The distri-bution of the variability between the trend and the random variation is adecision made by the analyst and is not an invariant function of nature

The second-moment method is widely used to describe the spatial varia-tion of random variation Although not as commonly used in geotechnicalpractice Bayesian estimation has many advantages over moment-basedestimation One of them is that it yields an estimate of the probabilitiesassociated with the distribution parameters rather than a confidence intervalthat the data would be observed if the process were repeated

Spatially varying properties are generally described by random fieldsAlthough these can become extremely complicated relatively simple modelssuch as Gaussian random field have wide application The last portion of

130 Gregory B Baecher and John T Christian

the chapter demonstrates how they can be manipulated by differentiationby transformation through linear processes and by evaluation of excursionsbeyond specified levels

Notes

1 It is true that not all soil or rock mass behaviors of engineering importance aregoverned by averages for example block failures in rock slopes are governed by theleast favorably positioned and oriented joint They are extreme values processesNevertheless averaging soil or rock properties does reduce variance

2 In early applications of geotechnical reliability a great deal of work was focusedon appropriate distributional forms for soil property variability but this no longerseems a major topic First the number of measurements typical of site characteri-zation programs is usually too few to decide confidently whether one distributionalform provides a better fit than another and second much of practical geotechnicalreliability work uses second-moment characterizations rather than full distribu-tional analysis so distributional assumptions only come into play at the endof the work Second-moment characterizations use only means variances andcovariances to characterize variability not full distributions

3 In addition to aleatory and epistemic uncertainties there are also uncertainties thathave little to do with engineering properties and performance yet which affectdecisions Among these is the set of objectives and attributes considered importantin reaching a decision the value or utility function defined over these attributesand discounting for outcomes distributed in time These are outside the presentscope

References

Adler R J (1981) The Geometry of Random Fields Wiley ChichesterAng A H-S and Tang W H (1975) Probability Concepts in Engineering Planning

and Design Wiley New YorkAzzouz A Baligh M M and Ladd C C (1983) Corrected field vane strength

for embankment design Journal of Geotechnical Engineering ASCE 109(3)730ndash4

Baecher G B(1980) Progressively censored sampling of rock joint traces Journalof the International Association of Mathematical Geologists 12(1) 33ndash40

Baecher G B (1987) Statistical quality control for engineered fills EngineeringGuide US Army Corps of Engineers Waterways Experiment Station GL-87-2

Baecher G B Ladd C C Noiray L and Christian J T (1997) Formal obser-vational approach to staged loading Transportation Research Board AnnualMeeting Washington DC

Barnett V (1982) Comparative Statistical Inference John Wiley and Sons LondonBerger J O (1993) Statistical Decision Theory and Bayesian Analysis Springer

New YorkBerger J O De Oliveira V and Sanso B (2001) Objective Bayesian analysis of

spatially correlated data Journal of the American Statistical Association 96(456)1361ndash74

Bjerrum L (1972) Embankments on soft ground In ASCE Conference on Perfor-mance of Earth and Earth-Supported Structures Purdue pp 1ndash54

Spatial variability and geotechnical reliability 131

Bjerrum L (1973) Problems of soil mechanics and construction on soft clays InEighth International Conference on Soil Mechanics and Foundation EngineeringMoscow Spinger New York pp 111ndash59

Chiasson P Lafleur J Soulie M and Law K T (1995) Characterizing spatialvariability of a clay by geostatistics Canadian Geotechnical Journal 32 1ndash10

Christakos G (1992) Random Field Models in Earth Sciences Academic PressSan Diego

Christakos G (2000) Modern Spatiotemporal Geostatistics Oxford UniversityPress Oxford

Christakos G and Hristopulos D T (1998) Spatiotemporal Environmental HealthModelling A Tractatus Stochasticus Kluwer Academic Boston MA

Christian J T Ladd C C and Baecher G B (1994) Reliability applied toslope stability analysis Journal of Geotechnical Engineering ASCE 120(12)2180ndash207

Cliff A D and Ord J K (1981) Spatial Processes Models amp Applications PionLondon

Crameacuter H and Leadbetter M R (1967) Stationary and Related StochasticProcesses Sample Function Properties and their Applications Wiley New York

Cressie N A C (1991) Statistics for Spatial Data Wiley New YorkDavis J C (1986) Statistics and Data Analysis in Geology Wiley New YorkDeGroot D J and Baecher G B (1993) Estimating autocovariance of in situ soil

properties Journal Geotechnical Engineering Division ASCE 119(1) 147ndash66Elishakoff I (1999) Probabilistic Theory of Structures Dover Publications

Mineola NYFuleihan N F and Ladd C C (1976) Design and performance of Atchafalaya

flood control levees Research Report R76-24 Department of Civil EngineeringMassachusetts Institute of Technology Cambridge MA

Gelb A and Analytic Sciences Corporation Technical Staff (1974) Applied OptimalEstimation MIT Press Cambridge MA

Gelhar L W (1993) Stochastic Subsurface Hydrology Prentice-Hall EnglewoodCliffs NJ

Hartford D N D (1995) How safe is your dam Is it safe enough MEP11-5 BCHydro Burnaby BC

Hartford D N D and Baecher G B (2004) Risk and Uncertainty in Dam SafetyThomas Telford London

Hilldale C (1971) A probabilistic approach to estimating differential settle-ment MS thesis Civil Engineering Massachusetts Institute of TechnologyCambridge MA

Javete D F (1983) A simple statistical approach to differential settlements on clayPh D thesis Civil Engineering University of California Berkeley

Journel A G and Huijbregts C (1978) Mining Geostatistics Academic PressLondon

Jowett G H (1952) The accuracy of systematic simple from conveyer belts AppliedStatistics 1 50ndash9

Kitanidis P K (1985) Parameter uncertainty in estimation of spatial functionsBayesian analysis Water Resources Research 22(4) 499ndash507

Kitanidis P K (1997) Introduction to Geostatistics Applications to HydrogeologyCambridge University Press Cambridge

132 Gregory B Baecher and John T Christian

Kolmogorov A N (1941) The local structure of turbulence in an incompress-ible fluid at very large Reynolds number Doklady Adademii Nauk SSSR 30301ndash5

Lacasse S and Nadim F (1996) Uncertainties in characteristic soil properties InUncertainty in the Geological Environment ASCE specialty conference MadisonWI ASCE Reston VA pp 40ndash75

Ladd CC and Foott R (1974) A new design procedure for stability of soft claysJournal of Geotechnical Engineering 100(7) 763ndash86

Ladd C C (1975) Foundation design of embankments constructed on con-necticut valley varved clays Report R75-7 Department of Civil EngineeringMassachusetts Institute of Technology Cambridge MA

Ladd C C Dascal O Law K T Lefebrve G Lessard G Mesri G andTavenas F (1983) Report of the subcommittee on embankment stability__annexe II Committe of specialists on Sensitive Clays on the NBR Complex SocieteacutedrsquoEnergie de la Baie James Montreal

Lambe TW and Associates (1982) Earthquake risk to patio 4 and site 400 Reportto Tonen Oil Corporation Longboat Key FL

Lambe T W and Whitman R V (1979) Soil Mechanics SI Version WileyNew York

Lee I K White W and Ingles O G (1983) Geotechnical Engineering PitmanBoston

Lumb P (1966) The variability of natural soils Canadian Geotechnical Journal3 74ndash97

Lumb P (1974) Application of statistics in soil mechanics In Soil Mechanics NewHorizons Ed I K Lee Newnes-Butterworth London pp 44ndash112

Mardia K V and Marshall R J (1984) Maximum likelihood estimation of modelsfor residual covariance in spatial regression Biometrika 71(1) 135ndash46

Mateacutern B (1960) Spatial Variation 49(5) Meddelanden fran Statens Skogsforskn-ingsinstitut

Mateacutern B (1986) Spatial Variation Springer BerlinMatheron G (1971) The Theory of Regionalized Variables and Its Application ndash

Spatial Variabilities of Soil and Landforms Les Cahiers du Centre de Morphologiemathematique 8 Fontainebleau

Papoulis A and Pillai S U (2002) Probability Random Variables and StochasticProcesses 4th ed McGraw-Hill Boston MA

Parzen E (1964) Stochastic Processes Holden-Day San Francisco CAParzen E (1992) Modern Probability Theory and its Applications Wiley

New YorkPhoon K K (2006a) Bootstrap estimation of sample autocorrelation functions

In GeoCongress Atlanta ASCEPhoon K K (2006b) Modeling and simulation of stochastic data In GeoCongress

Atlanta ASCEPhoon K K and Fenton G A (2004) Estimating sample autocorrelation func-

tions using bootstrap In Proceedings Ninth ASCE Specialty Conference onProbabilistic Mechanics and Structural Reliability Albuquerque

Phoon K K and Kulhawy F H (1996) On quantifying inherent soil variabilityUncertainty in the Geologic Environment ASCE specialty conference MadisonWI ASCE Reston VA pp 326ndash40

Spatial variability and geotechnical reliability 133

Soong T T and Grigoriu M (1993) Random Vibration of Mechanical andStructural Systems Prentice Hall Englewood Cliffs NJ

Soulie M and Favre M (1983) Analyse geostatistique drsquoun noyau de barrage telque construit Canadian Geotechnical Journal 20 453ndash67

Soulie M Montes P and Silvestri V (1990) Modeling spatial variability of soilparameters Canadian Geotechnical Journal 27 617ndash30

Switzer P (1995) Spatial interpolation errors for monitoring data Journal of theAmerican Statistical Association 90(431) 853ndash61

Tang W H (1979) Probabilistic evaluation of penetration resistance Journal ofthe Geotechnical Engineering Division ASCE 105(10) 1173ndash91

Taylor G I (1921) Diffusion by continuous movements Proceeding of the LondonMathematical Society (2) 20 196ndash211

USACE (1972) New Orleans East Lakefront Levee Paris Road to South Point LakePontchartrain Barrier Plan DM 2 Supplement 5B USACE New Orleans DistrictNew Orleans

VanMarcke E (1983) Random Fields Analysis and Synthesis MIT PressCambridge MA

VanMarcke E H (1977) Reliability of earth slopes Journal of the GeotechnicalEngineering Division ASCE 103(GT11) 1247ndash65

Weinstock H (1963) The description of stationary random rate processes E-1377Massachusetts Institute of Technology Cambridge MA

Wu T H (1974) Uncertainty safety and decision in soil engineering JournalGeotechnical Engineering Division ASCE 100(3) 329ndash48

Yaglom A M (1962) An Introduction to the Theory of Random FunctionsPrentice-Hall Englewood Cliffs NJ

Zellner A (1971) An Introduction to Bayesian Inference in Econometrics WileyNew York

Chapter 3

Practical reliability approachusing spreadsheet

Bak Kong Low

31 Introduction

In a review on first-order second-moment reliability methods USACE (1999)rightly noted that a potential problem with both the Taylorrsquos series methodand the point estimate method is their lack of invariance for nonlinear perfor-mance functions The document suggested the more general HasoferndashLindreliability index (Hasofer and Lind 1974) as a better alternative but con-ceded that ldquomany published analyses of geotechnical problems have notused the HasoferndashLind method probably due to its complexity especiallyfor implicit functions such as those in slope stability analysisrdquo and that ldquothemost common method used in Corps practice is the Taylorrsquos series methodbased on a Taylorrsquos series expansion of the performance function about theexpected valuesrdquo

A survey of recent papers on geotechnical reliability analysis reinforces theabove USACE observation that although the HasoferndashLind index is perceivedto be more consistent than the Taylorrsquos series mean value method the latteris more often used

This chapter aims to overcome the computational and conceptual barriersof the HasoferndashLind index for correlated normal random variables and thefirst-order reliability method (FORM) for correlated nonnormals in thecontext of three conventional geotechnical design problems Specificallythe conventional bearing capacity model involving two random variablesis first illustrated to elucidate the procedures and concepts This is fol-lowed by a reliability-based design of an anchored sheet pile wall involvingsix random variables which are first treated as correlated normals thenas correlated nonnormals Finally reliability analysis with search for crit-ical noncircular slip surface based on a reformulated Spencer method ispresented This probabilistic slope stability example includes testing therobustness of search for noncircular critical slip surface modeling lognor-mal random variables deriving probability density functions from reliabilityindices and comparing results inferred from reliability indices with MonteCarlo simulations

Practical reliability approach 135

The expanding ellipsoidal perspective of the HasoferndashLind reliability indexand the practical reliability approach using object-oriented constrained opti-mization in the ubiquitous spreadsheet platform were described in Low andTang (1997a 1997b) and extended substantially in Low and Tang (2004)by testing robustness for various nonnormal distributions and complicatedperformance functions and by providing enhanced operational convenienceand versatility

Reasonable statistical properties are assumed for the illustrative cases pre-sented in this chapter actual determination of the statistical properties is notcovered Only parametric uncertainty is considered and model uncertaintyis not dealt with Hence this chapter is concerned with reliability methodand perspectives and not reliability in its widest sense The focus is onintroducing an efficient and rational design approach using the ubiquitousspreadsheet platform

The spreadsheet reliability procedures described herein can be appliedto stand-alone numerical (eg finite element) packages via the establishedresponse surface method (which itself is straightforward to implement inthe ubiquitous spreadsheet platform) Hence the applicability of the relia-bility approach is not confined to models which can be formulated in thespreadsheet environment

32 Reliability procedure in spreadsheet andexpanding ellipsoidal perspective

321 A simple hands-on reliability analysis

The proposed spreadsheet reliability evaluation approach will be illustratedfirst for a case with two random variables Readers who want a betterunderstanding of the procedure and deeper appreciation of the ellipsoidalperspective are encouraged to go through the procedure from scratch on ablank Excel worksheet After that some Excel files for hands-on and deeperappreciation can be downloaded from httpalummiteduwwwbklow

The example concerns the bearing capacity of a strip footing sustain-ing non-eccentric vertical load Extensions to higher dimensions and morecomplicated scenarios are straightforward

With respect to bearing capacity failure the performance function (PerFn)for a strip footing in its simplest form is

PerFn = qu minus q (31a)

where qu = cNc + poNq + B2γNγ (31b)

in which qu is the ultimate bearing capacity q the applied bearing pressurec the cohesion of soil po the effective overburden pressure at foundation

136 Bak Kong Low

level B the foundation width γ the unit weight of soil below the base offoundation and Nc Nq and Nγ are bearing capacity factors which areestablished functions of the friction angle (φ) of soil

Nq = eπ tanφ tan2(

45 + φ

2

)(32a)

Nc =(Nq minus 1

)cot (φ) (32b)

Nγ = 2(Nq + 1

)tanφ (32c)

Several expressions for Nγ exist The above Nγ is attributed to Vesic inBowles (1996)

The statistical parameters and correlation matrix of c and φ are shown inFigure 31 The other parameters in Equations (31a) and (31b) are assumedknown with values q = QvB = (200 kNm)B po = 18 kPa B = 12 m andγ = 20 kNm3 The parameters c and φ in Equations (31) and (32) readtheir values from the column labeled xlowast which were initially set equal tothe mean values These xlowast values and the functions dependent on them

qu = cNc + PoNq + B gNγ2

Units m kNm2kNm3 degrees as appropriate

X Qv q

Corr Matrix

PerFn

Solution procedure

nxnx =

xi ndash mi

C

Nq

NC

Ng

B Pogsm

f

b

63391463

3804

1074 1

00

= qu(x) - q

3268

1minus05

minus2732

minus0187

minus05

2507

2015

5 200 1667 12 20

Qv B

182

si

=si

xi ndash mi

si

xi ndash miT

[R ]minus1

1 Initially x values = mean values m

2 Invoke Solver to minimize b by changing x values subject to PerFn = 0 amp x values ge 0

Figure 31 A simple illustration of reliability analysis involving two correlated randomvariables which are normally distributed

Practical reliability approach 137

change during the optimization search for the most probable failure pointSubsequent steps are

1 The formula of the cell labeled β in Figure 31 is Equation (35b)in Section 323 ldquo=sqrt(mmult(transpose(nx) mmult(minverse(crmat)nx)))rdquo The arguments nx and crmat are entered by selecting thecorresponding numerical cells of the column vector

(xi minusmicroi

)σi and

the correlation matrix respectively This array formula is then enteredby pressing ldquoEnterrdquo while holding down the ldquoCtrlrdquoand ldquoShiftrdquo keysMicrosoft Excelrsquos built-in matrix functions mmult transpose andminverse have been used in this step Each of these functions containsprogram codes for matrix operations

2 The formula of the performance function is g (x) = qu minus q wherethe equation for qu is Equation (31b) and depends on the xlowastvalues

3 Microsoft Excelrsquos built-in constrained optimization program Solveris invoked (via ToolsSolver) to Minimize β By Changing the xvalues Subject To PerFn le 0 and x values ge 0 (If used for thefirst time Solver needs to be activated once via ToolsAdd-insSolverAdd-in)

The β value obtained is 3268 The spreadsheet approach is simple andintuitive because it works in the original space of the variables It does notinvolve the orthogonal transformation of the correlation matrix and iter-ative numerical partial derivatives are done automatically on spreadsheetobjects which may be implicit or contain codes

The following paragraphs briefly compares lumped factor of safetyapproach partial factors approach and FORM approach and provideinsights on the meaning of reliability index in the original space of therandom variables More details can be found in Low (1996 2005a) Lowand Tang (1997a 2004) and other documents at httpalummiteduwwwbklow

322 Comparing lumped safety factor and partial factorsapproaches with reliability approach

For the bearing capacity problem of Figure 31 a long-established determin-istic approach evaluates the lumped factor of safety (Fs) as

Fs = qu minus po

q minus po= f (cφ ) (33)

where the symbols are as defined earlier If c = 20 kPa and φ = 15 andwith the values of Qv B γ and po as shown in Figure 31 then the factor of

138 Bak Kong Low

45 Limit state surface boundary between safeand unsafe domains

40

35

30

25

20

Coh

esio

n c

15

10

5

0

SAFE

one-sigmadispersion ellipse

Mean-valuepointDesign point

Friction angle φ (degrees)0 10 20 30

UNSAFER

r

β-ellipse

b = R rsc

mc

Figure 32 The design point the mean-value point and expanding ellipsoidal perspectiveof the reliability index in the original space of the random variables

safety is Fs asymp 20 by Equation (33) In the two-dimensional space of c andφ (Figure 32) one can plot the Fs contours for different combinations ofc andφ including the Fs =10 curve which separates the unsafe combinationsfrom the safe combinations of c and φ The average point (c = 20 kPa andφ = 15) is situated on the contour (not plotted) of Fs = 20 Design by thelumped factor of safety approach is considered satisfactory with respect tobearing capacity failure if the factor of safety by Equation (33) is not smallerthan a certain value (eg when Fs ge 20)

A more recent and logical approach (eg Eurocode 7) applies partial fac-tors to the parameters in the evaluation of resistance and loadings Designis acceptable if

Bearing capacity (based on reduced c and φ) ge Applied pressure

(amplified) (34)

A third approach is reliability-based design where the uncertainties andcorrelation structure of the parameters are represented by a one-standard-deviation dispersion ellipsoid (Figure 32) centered at the mean-value pointand safety is gaged by a reliability index which is the shortest distance(measured in units of directional standard deviations Rr) from the safemean-value point to the most probable failure combination of parame-ters (ldquothe design pointrdquo) on the limit state surface (defined by Fs = 10for the problem in hand) Furthermore the probability of failure (Pf )

Practical reliability approach 139

can be estimated from the reliability index β using the established equa-tion Pf = 1 minus(β) = (minusβ) where is the cumulative distribution (CDF)of the standard normal variate The relationship is exact when the limitstate surface is planar and the parameters follow normal distributions andapproximate otherwise

The merits of a reliability-based design over the lumped factor-of-safetydesign is illustrated in Figure 33a in which case A and case B (with dif-ferent average values of effective shear strength parameters cprime and φprime) showthe same values of lumped factor of safety yet case A is clearly safer thancase B The higher reliability of case A over case B will correctly be revealedwhen the reliability indices are computed On the other hand a slope mayhave a computed lumped factor of safety of 15 and a particular foun-dation (with certain geometry and loadings) in the same soil may have acomputed lumped factor of safety of 25 as in case C of Figure 33(b)Yet a reliability analysis may show that they both have similar levels ofreliability

The design point (Figure 32) is the most probable failure combination ofparametric values The ratios of the respective parametric values at the cen-ter of the dispersion ellipsoid (corresponding to the mean values) to those atthe design point are similar to the partial factors in limit state design exceptthat these factored values at the design point are arrived at automatically(and as by-products) via spreadsheet-based constrained optimization Thereliability-based approach is thus able to reflect varying parametric sensitiv-ities from case to case in the same design problem (Figure 33a) and acrossdifferent design realms (Figure 33b)

one-standard-deviationdispersion ellipsoidFs = 14

Fs = 10

Unsafe Fs lt 10

Fs = 12

Fs = 30

Fs = 10

Fs = 10

Slope

(Slope)Unsafe

(Foundation)

Foundation

Safe

A

B

20 15

12C

cprimecprime

φprime φprime(a) (b)

Figure 33 Schematic scenarios showing possible limitations of lumped factor of safety(a) Cases A and B have the same lumped Fs = 14 but Case A is clearly morereliable than Case B (b) Case C may have Fs = 15 for a slope and Fs = 25 fora foundation and yet have similar levels of reliability

140 Bak Kong Low

323 HasoferndashLind index reinterpreted via expandingellipsoid perspective

The matrix formulation (Veneziano 1974 Ditlevsen 1981) of the HasoferndashLind index β is

β = minxisinF

radic(x minusmicro)T Cminus1 (x minusmicro) (35a)

or equivalently

β = minxisinF

radic[xi minusmicroi

σi

]T

[R]minus1[

xi minusmicroi

σi

](35b)

where x is a vector representing the set of random variables xi micro the vec-tor of mean values microi C the covariance matrix R the correlation matrixσi the standard deviation and F the failure domain Low and Tang (1997b2004) used Equation (35b) in preference to Equation (35a) because the cor-relation matrix R is easier to set up and conveys the correlation structuremore explicitly than the covariance matrix C Equation (35b) was enteredin step (1) above

The ldquoxlowastrdquo values obtained in Figure 31 represent the most probable failurepoint on the limit state surface It is the point of tangency (Figure 32) of theexpanding dispersion ellipsoid with the bearing capacity limit state surfaceThe following may be noted

(a) The xlowast values shown in Figure 31 render Equation (31a) (PerFn) equalto zero Hence the point represented by these xlowast values lies on the bearingcapacity limit state surface which separates the safe domain from theunsafe domain The one-standard-deviation ellipse and the β-ellipse inFigure 32 are tilted because the correlation coefficient between c and φ isminus05 in Figure 31 The design point in Figure 32 is where the expandingdispersion ellipse touches the limit state surface at the point representedby the xlowast values of Figure 31

(b) As a multivariate normal dispersion ellipsoid expands its expandingsurfaces are contours of decreasing probability values according tothe established probability density function of the multivariate normaldistribution

f (x) = 1

(2π )n2 |C|05

exp[minus1

2(x minusmicro)T Cminus1 (x minusmicro)

](36a)

= 1

(2π )n2 |C|05

exp[minus1

2β2]

(36b)

Practical reliability approach 141

where β is defined by Equation (35a) or (35b) without the ldquominrdquoHence to minimize β (or β2 in the above multivariate normal distribu-tion) is to maximize the value of the multivariate normal probabilitydensity function and to find the smallest ellipsoid tangent to thelimit state surface is equivalent to finding the most probable failurepoint (the design point) This intuitive and visual understanding ofthe design point is consistent with the more mathematical approachin Shinozuka (1983 equations 4 41 and associated figure) in whichall variables were transformed into their standardized forms and thelimit state equation had also to be written in terms of the standard-ized variables The differences between the present original space versusShinozukarsquos standardized space of variables will be further discussed in(h) below

(c) Therefore the design point being the first point of contact betweenthe expanding ellipsoid and the limit state surface in Figure 32is the most probable failure point with respect to the safe mean-value point at the centre of the expanding ellipsoid where Fs asymp 20against bearing capacity failure The reliability index β is the axisratio (Rr) of the ellipse that touches the limit state surface and theone-standard-deviation dispersion ellipse By geometrical properties ofellipses this co-directional axis ratio is the same along any ldquoradialrdquodirection

(d) For each parameter the ratio of the mean value to the xlowast value is similarin nature to the partial factors in limit state design (eg Eurocode 7)However in a reliability-based design one does not specify the par-tial factors The design point values (xlowast) are determined automaticallyand reflect sensitivities standard deviations correlation structure andprobability distributions in a way that prescribed partial factors cannotreflect

(e) In Figure 31 the mean value point at 20 kPa and 15 is safe againstbearing capacity failure but bearing capacity failure occurs when thec and φ values are decreased to the values shown (6339 1463) Thedistance from the safe mean-value point to this most probable failurecombination of parameters in units of directional standard deviationsis the reliability index β equal to 3268 in this case

(f) The probability of failure (Pf ) can be estimated from the reliabilityindex β Microsoft Excelrsquos built-in function NormSDist() can be usedto compute () and hence Pf Thus for the bearing capacity problemof Figure 31 Pf = NormSDist(minus3268) = 0054 This value com-pares remarkably well with the range of values 0051minus0060 obtainedfrom several Monte Carlo simulations each with 800000 trials usingthe commercial simulation software RISK (httpwwwpalisadecom)The correlation matrix was accounted for in the simulation The excel-lent agreement between 0054 from reliability index and the range

142 Bak Kong Low

0051minus0060 from Monte Carlo simulation is hardly surprisinggiven the almost linear limit state surface and normal variates shownin Figure 32 However for the anchored wall shown in the nextsection where six random variables are involved and nonnormal dis-tributions are used the six-dimensional equivalent hyperellispoid andthe limit state hypersurface can only be perceived in the mindrsquos eyeNevertheless the probabilities of failure inferred from reliability indicesare again in close agreement with Monte Carlo simulations Com-puting the reliability index and Pf = (minusβ) by the present approachtakes only a few seconds In contrast the time needed to obtain theprobability of failure by Monte Carlo simulation is several ordersof magnitude longer particularly when the probability of failure issmall and many trials are needed It is also a simple matter to inves-tigate sensitivities by re-computing the reliability index β (and Pf ) fordifferent mean values and standard deviations in numerous what-ifscenarios

(g) The probability of failure as used here means the probability that inthe presence of parametric uncertainties in c and φ the factor of safetyEquation (33) will be le 10 or equivalently the probability that theperformance function Equation (31a) will be le 0

(h) Figure 32 defines the reliability index β as the dimensionless ratio Rrin the direction from the mean-value point to the design point This isthe axis ratio of the β-ellipsoid (tangential to the limit state surface) tothe one-standard-deviation dispersion ellipsoid This axis ratio is dimen-sionless and independent of orientation when R and r are co-directionalThis axis-ratio interpretation in the original space of the variables over-comes a drawback in Shinozukarsquos (1983) standardized variable spacethat ldquothe interpretation of β as the shortest distance between the ori-gin (of the standardized space) and the (transformed) limit state surfaceis no longer validrdquo if the random variables are correlated A furtheradvantage of the original space apart from its intuitive transparency isthat it renders feasible and efficient the two computational approachesinvolving nonnormals as presented in Low amp Tang (2004) and (2007)respectively

33 Reliability-based design of an anchored wall

This section illustrates reliability-based design of anchored sheet pile walldrawing material from Low (2005a 2005b) The analytical formulationsin a deterministic anchored wall design are the basis of the performancefunction in a probabilistic-based design Hence it is appropriate to brieflydescribe the deterministic approach prior to extending it to a probabilistic-based design An alternative design approach is described in BS8002(1994)

Practical reliability approach 143

331 Deterministic anchored wall design based on lumpedfactor and partial factors

The deterministic geotechnical design of anchored walls based on the freeearth support analytical model was lucidly presented in Craig (1997) Anexample is the case in Figure 34 where the relevant soil properties are theeffective angle of shearing resistance φprime and the interface friction angle δ

between the retained soil and the wall The characteristic values are cprime = 0φprime = 36 and δ = frac12φprime The water table is the same on both sides of thewall The bulk unit weight of the soil is 17 kNm3 above the water table and20 kNm3 below the water table A surcharge pressure qs = 10 kNm2 actsat the top of the retained soil The tie rods act horizontally at a depth 15 mbelow the top of the wall

In Figure 34 the active earth pressure coefficient Ka is based on theCoulomb-wedge closed-form equation which is practically the same asthe KeriselndashAbsi active earth pressure coefficient (Kerisel and Absi 1990)The passive earth pressure coefficient Kp is based on polynomial equations

17

20

10qs

36 (Characteristic value)

Surcharge qs = 10 kNm2

18

200

M5

M1 4

02

Ka Kah Kp Kph

0225 6966 662 329

Boxed cells contain equations

15 m

Water table

TA

d Fs

d

γsat

γ φprime δ

γ

γsat

64 m

24 m

45

1 -272

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-782

-139

-371

3657

4545

2767

7745

8693

949

-123

-216

-1077

-322

3472

1733

2

3

4

5

3

1

2

δ

φprime

Figure 34 Deterministic design of embedment depth d based on a lumped factor ofsafety of 20

144 Bak Kong Low

(Figure 35) fitted to the values of KeriselndashAbsi (1990) for a wall with avertical back and a horizontal retained soil surface

The required embedment depth d of 329 m in Figure 34 ndash for alumped factor of safety of 20 against rotational failure around anchor pointA ndash agrees with Example 69 of Craig (1997)

If one were to use the limit state design approach with a partial factor of12 for the characteristic shear strength one enters tanminus1(tanφprime12) = 31in the cell of φprime and changes the embedment depth d until the summationof moments is zero A required embedment depth d of 283 m is obtainedin agreement with Craig (1997)

Function KpKeriseIAbsi(phi del)lsquoPassive pressure coefficient Kp for vertical wall back and horizontal retained filllsquoBased on Tables in Kerisel amp Absi (1990) for beta = 0 lamda = 0 andlsquo delphi = 0033 05 066 100x = del phiKp100=000007776 phi ^ 4 - 0006608 phi ^ 3 + 02107 phi ^ 2 - 2714 phi + 1363Kp66=000002611 phi ^ 4 - 0002113 phi ^ 3 + 006843 phi ^ 2 - 08512 phi + 5142Kp50 = 000001559 phi ^ 4 - 0001215 phi ^ 3 + 003886 phi ^ 2 - 04473 phi + 3208Kp33 = 0000007318 phi ^ 4 - 00005195 phi ^ 3 + 00164 phi ^ 2 - 01483 phi + 1798Kp0 = 0000002636 phi ^ 4 - 00002201 phi ^ 3 + 0008267 phi ^ 2 - 00714 phi + 1507Select Case xCase 066 To 1 Kp = Kp66 + (x - 066) 1 - 066) (Kp100 - Kp66)Case 05 To 066 Kp = Kp50 + (x - 05) (066 - 05) (Kp66 - Kp50)Case 033 To 05 Kp = Kp33 + (x - 033) (05 - 033) (Kp50 - Kp33)Case 0 To 033 Kp = Kp0 + x 033 (Kp33 - Kp0)End SelectKpKeriselAbsi = KpEnd Function

Value from Kerisel-Absi tables

Polynomial curves equations as givenin the above Excel VBA code

40

35

30

25

20

Pas

sive

ear

th p

ress

ure

coef

ficie

nt K

p

15

10

5

00 10 20

Friction angle φ30

0

033

050

066

40 50

d f = 10

Figure 35 User-created Excel VBA function for Kp

Practical reliability approach 145

The partial factors in limit state design are applied to the characteristicvalues which are themselves conservative estimates and not the most proba-ble or average values Hence there is a two-tier nested safety first during theconservative estimate of the characteristic values and then when the partialfactors are applied to the characteristic values This is evident in Eurocode 7where Section 243 clause (5) states that the characteristic value of a soil orrock parameter shall be selected as a cautious estimate of the value affectingthe occurrence of the limit state Clause (7) further states that character-istic values may be lower values which are less than the most probablevalues or upper values which are greater and that for each calculation themost unfavorable combination of lower and upper values for independentparameters shall be used

The above Eurocode 7 recommendations imply that the characteristicvalue of φprime (36) in Figure 34 is lower than the mean value of φprime Hence inthe reliability-based design of the next section the mean value of φprime adoptedis higher than the characteristic value of Figure 34

While characteristic values and partial factors are used in limit state designmean values (not characteristic values) are used with standard deviations andcorrelation matrix in a reliability-based design

332 From deterministic to reliability-based anchoredwall design

The anchored sheet pile wall will be designed based on reliability analy-sis (Figure 36) As mentioned earlier the mean value of φprime in Figure 36is larger ndash 38 is assumed ndash than the characteristic value of Figure 34In total there are six normally distributed random variables with meanand standard deviations as shown Some correlations among parametersare assumed as shown in the correlation matrix For example it is judgedlogical that the unit weights γ and γsat should be positively correlatedand that each is also positively correlated to the angle of friction φprime sinceγ prime = γsat minus γw

The analytical formulations based on force and moment equilibrium in thedeterministic analysis of Figure 34 are also required in a reliability analysisbut are expressed as limit state functions or performance functions ldquo= Sum(Moments1rarr5)rdquo

The array formula in cell β of Figure 36 is as described in step 1 of thebearing capacity example earlier in this chapter

Given the uncertainties and correlation structure in Figure 36 wewish to find the required total wall height H so as to achieve a relia-bility index of 30 against rotational failure about point ldquoArdquo Initiallythe column xlowast was given the mean values Microsoft Excelrsquos built-inconstrained optimization tool Solver was then used to minimize β bychanging (automatically) the xlowast column subject to the constraint that

146 Bak Kong Low

Surcharge qs

03

Ka Kah Kp Kph

0249 5904 5637 279 000

d

d = H ndash 64 ndash z

1215

Boxed cells containequations

15 m

Water table

TA

H PerFn

crmatrix (Correlation matrix)

nX

d

γsat

γ φprime δ64 m

300

b

z

45

1 -311

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-828

-149

-356

1892

4575

2767

7775

8733

972

-142

-229

-1156

-311

1839

Normal

Normal

Normal

Normal

Normal

Normal

1844

1028

162

3351

1729

2963

20

10

17

Mean StDev

38

19

24

1

1

05

0

05

0

0

05

1

0

05

0

0

0

0

1

0

0

0

05

05

0

1

08

0

0

0

0

08

1

0

0

0

0

0

0

1

2

085 -09363

-15572

013807

-22431

-17077

187735

2

1

03

γsat

qs

γ

x

φprime

δ

z

γsat

γsat

qs

qs

γ

γ

φprime

φprime

δ

δ

z

z

2

3

4

5 3

1

2

Dredge level

Figure 36 Design total wall height for a reliability index of 30 against rotational failureDredge level and hence z and d are random variables

the cell PerFn be equal to zero The solution (Figure 36) indicates thata total height of 1215 m would give a reliability index of 30 againstrotational failure With this wall height the mean-value point is safeagainst rotational failure but rotational failure occurs when the mean val-ues descendascend to the values indicated under the xlowast column Thesexlowast values denote the design point on the limit state surface and rep-resent the most likely combination of parametric values that will causefailure The distance between the mean-value point and the design pointin units of directional standard deviations is the HasoferndashLind reliabilityindex

Practical reliability approach 147

As noted in Simpson and Driscoll (1998 81 158) clause 8321 ofEurocode 7 requires that an ldquooverdigrdquo allowance shall be made for wallswhich rely on passive resistance This is an allowance ldquofor the unforeseenactivities of nature or humans who have no technical appreciation of thestability requirements of the wallrdquo For the case in hand the reliability anal-ysis in Figure 36 accounts for uncertainty in z requiring only the mean valueand the standard deviation of z (and its distribution type if not normal) to bespecified The expected embedment depth is d = 1215minus64minusmicroz = 335 mAt the failure combination of parametric values the design value of z iszlowast = 29632 and dlowast = 1215 minus 64 minus zlowast = 279 m This corresponds to anldquooverdigrdquo allowance of 056 m Unlike Eurocode 7 this ldquooverdigrdquo is deter-mined automatically and reflects uncertainties and sensitivities from case tocase in a way that specified ldquooverdigrdquo cannot Low (2005a) illustrates anddiscusses this automatic probabilistic overdig allowance in a reliability-baseddesign

The nx column indicates that for the given mean values and uncer-tainties rotational stability is not surprisingly most sensitive to φprime andthe dredge level (which affects z and d and hence the passive resistance)It is least sensitive to uncertainties in the surcharge qs because the aver-age value of surcharge (10 kNm2) is relatively small when compared withthe over 10 m thick retained fill Under a different scenario where thesurcharge is a significant player its sensitivity scale could conceivably bedifferent It is also interesting to note that at the design point where thesix-dimensional dispersion ellipsoid touches the limit state surface bothunit weights γ and γsat (1620 and 1844 respectively) are lower thantheir corresponding mean values contrary to the expectation that higherunit weights will increase active pressure and hence greater instability Thisapparent paradox is resolved if one notes that smaller γsat will (via smaller γ prime)reduce passive resistance smaller φprime will cause greater active pressure andsmaller passive pressure and that γ γsat and φprime are logically positivelycorrelated

In a reliability-based design (such as the case in Figure 36) one doesnot prescribe the ratios meanxlowast ndash such ratios or ratios of (characteristicvalues)xlowast are prescribed in limit state design ndash but leave it to the expand-ing dispersion ellipsoid to seek the most probable failure point on the limitstate surface a process which automatically reflects the sensitivities of theparameters The ability to seek the most-probable design point without pre-suming any partial factors and to automatically reflect sensitivities from caseto case is a desirable feature of the reliability-based design approach Thesensitivity measures of parameters may not always be obvious from a pri-ori reasoning A case in point is the strut with complex supports analyzedin Low and Tang (2004 85) where the mid-span spring stiffness k3 andthe rotational stiffness λ1 at the other end both turn out to have surpris-ingly negligible sensitivity weights this sensitivity conclusion was confirmed

148 Bak Kong Low

by previous elaborate deterministic parametric plots In contrast reliabilityanalysis achieved the same conclusion relatively effortlessly

The spreadsheet-based reliability-based design approach illustrated inFigure 36 is a more practical and relatively transparent intuitive approachthat obtains the same solution as the classical HasoferndashLind method forcorrelated normals and FORM for correlated nonnormals (shown below)Unlike the classical computational approaches the present approach doesnot need to rotate the frame of reference or to transform the coordinatespace

333 Positive reliability index only if mean-value point isin safe domain

In Figure 36 if a trial H value of 10 m is used and the entire ldquoxlowastrdquo columngiven the values equal to the ldquomeanrdquo column values the performance func-tion PerFn exhibits a value of minus4485 meaning that the mean value point isalready inside the unsafe domain Upon Solver optimization with constraintPerFn = 0 a β index of 134 is obtained which should be regarded as anegative index ie minus134 meaning that the unsafe mean value point is atsome distance from the nearest safe point on the limit state surface that sep-arates the safe and unsafe domains In other words the computed β indexcan be regarded as positive only if the PerFn value is positive at the meanvalue point For the case in Figure 36 the mean value point (prior to Solveroptimization) yields a positive PerFn for H gt 106 m The computed β indexincreases from 0 (equivalent to a lumped factor of safety equal to 10 ie onthe verge of failure) when H is 106 m to 30 when H is 1215 m as shownin Figure 37

4

3

2

1

0

8 9 10 11 12 13

Total wall height H

Rel

iabi

lity

inde

x b

minus1

minus2

minus3

minus4

minus5

Figure 37 Reliability index is 300 when H = 1215 m For H smaller than 106 m themean-value point is in the unsafe domain for which the reliability indices arenegative

Practical reliability approach 149

334 Reliability-based design involving correlatednonnormals

The two-parameter normal distribution is symmetrical and theoretically hasa range from minusinfin to +infin For a parameter that admits only positive valuesthe probability of encroaching into the negative realm is extremely remoteif the coefficient of variation (Standard deviationMean) of the parameter is020 or smaller as for the case in hand Alternatively the lognormal dis-tribution has often been suggested in lieu of the normal distribution sinceit excludes negative values and affords some convenience in mathematicalderivations Figure 38 shows an efficient reliability-based design when therandom variables are correlated and follow lognormal distributions Thetwo columns labeled microN and σN contain the formulae ldquo=EqvN(hellip 1)rdquoand ldquoEqvN(hellip 2)rdquo respectively which invoke the user-created functionsshown in Figure 38 to perform the equivalent normal transformation(when variates are lognormals) based on the following RackwitzndashFiesslertwo-parameter equivalent normal transformation (Rackwitz and Fiessler1978)

Equivalent normal standard deviation σN = φminus1 [F (x)]

f (x)

(37a)

Equivalent normal mean microN = x minusσN timesminus1 [F (x)] (37b)

where x is the original nonnormal variateminus1[] is the inverse of the cumula-tive probability (CDF) of a standard normal distribution F(x) is the originalnonnormal CDF evaluated at x φ is the probability density function (PDF)of the standard normal distribution and f (x) is the original nonnormalprobability density ordinates at x

For lognormals closed form equivalent normal transformation is avail-able and has been used in the VBA code of Figure 38 Efficient Excel VBAcodes for equivalent normal transformations of other nonnormal distribu-tions (including Gumbel uniform exponential gamma Weibull triangularand beta) are given in Low and Tang (2004 2007) where it is shown thatreliability analysis can be performed with various distributions merely byentering ldquoNormalrdquo ldquoLognormalrdquo ldquoExponentialrdquo ldquoGammardquo hellip in thefirst column of Figure 38 and distribution parameters in the columns to theleft of the xlowast column In this way the required RackwitzndashFiessler equivalentnormal evaluations (for microN and σN) are conveniently relegated to functionscreated in the VBA programming environment of Microsoft Excel

Therefore the single spreadsheet cell object β in Figure 38 contains severalExcel function objects and substantial program codes

For correlated nonnormals the ellipsoid perspective (Figure 32) and theconstrained optimization approach still apply in the original coordinate

150 Bak Kong Low

Lognormal

Lognormal

Lognormal

Lognormal

Lognormal

Lognormal

03

Ka Kah Kp Kph

0239 6307 601 261 000

d

d = H ndash 64 ndash z

122

Boxed cells containequations

H PerFn

Correlation matrix

300

b

1 -293

Forces Lever arm

(kNm) (m) (kN-mm)

Moments

-802

-145

-36

1835

4595

2767

7795

8760

982

-135

-222

-1131

-311

1802

Mean

17 085 1636 1697

1994

9803

3779

1893

226

0817

0938

1988

1814

0927

0396

-074

-124

0119

-181

-139

2327

1878

1004

345

1763

3182

1

2

2

1

03

20

10

38

19

24

StDev

1

05

0

05

0

0

05

1

0

05

0

0

0

0

1

0

0

0

05

05

0

1

08

0

0

0

0

08

1

0

0

0

0

0

0

1

γsat

qs

γ

x

φprime

δ

z

γsat

γsat

qs

qs

γ

γ

φprime

φprime

δ

δsNmN

z

z

2

3

4

5

nX

The microN and σN column invoke theuser-defined function EqvN_LNbelow to obtain the equivalentnormal mean microN and equivalentnormal standard deviation σN of thelognormal variates

More nonnormal options availablein Low amp Tang (2004 2007)

Function EqvN_LN(mean StDev x code)lsquoReturns the equivalent mean of the lognormal variateif if code is 1lsquoReturns the equivalent standard deviation of the lognormal variates if code is 2del = 00001 lsquovariable lower limitIf x lt del Then x = dellamda = Log(mean) - 05 Log(1 + (StDev mean) ^ 2)If code = 1 Then EqvN_LN = x (1 - Log(x) + lamda)If code = 2 Then EqvN_LN = x Sqr(Log(1 + (StDev mean) ^ 2))End Function

Figure 38 Reliability-based design of anchored wall correlated lognormals

system except that the nonnormal distributions are replaced by an equiv-alent normal ellipsoid centered not at the original mean of the nonnormaldistributions but at an equivalent normal mean microN

β = minxisinF

radicradicradicradic[xi minusmicroNi

σNi

]T

[R]minus1

[xi minusmicroN

i

σNi

](38)

Practical reliability approach 151

as explained in Low and Tang (2004 2007) One Excel file associated withLow (2005a) for reliability analysis of anchored sheet pile wall involving cor-related nonnormal variates is available for download at httpalummiteduwwwbklow

For the case in hand the required total wall height H is practically thesame whether the random variables are normally distributed (Figure 36)or lognormally distributed (Figure 38) Such insensitivity of the design tothe underlying probability distributions may not always be expected par-ticularly when the coefficient of variation (standard deviationmean) or theskewness of the probability distribution is large

If desired the original correlation matrix (ρij) of the nonnormalscan be modified to ρprime

ij in line with the equivalent normal transforma-tion as suggested in Der Kiureghian and Liu (1986) Some tables ofthe ratio ρprime

ijρij are given in Appendix B2 of Melchers (1999) includ-ing a closed-form solution for the special case of lognormals For thecases illustrated herein the correlation matrix thus modified differs onlyslightly from the original correlation matrix Hence for simplicity theexamples of this chapter retain their original unmodified correlationmatrices

This section has illustrated an efficient reliability-based design approachfor an anchored wall The correlation structure of the six variables wasdefined in a correlation matrix Normal distributions and lognormal dis-tributions were considered in turn (Figures 36 and 38) to investigatethe implication of different probability distributions The procedure is ableto incorporate and reflect the uncertainty of the passive soil surface ele-vation Reliability index is the shortest distance between the mean-valuepoint and the limit state surface ndash the boundary separating safe and unsafecombinations of parameters ndash measured in units of directional standarddeviations It is important to check whether the mean-value point is inthe safe domain or unsafe domain before performing reliability analysisThis is done by noting the sign of the performance function (PerFn) inFigures 36 and 38 when the xlowast columns were initially assigned the meanvalues If the mean value point is safe the computed reliability index ispositive if the mean-value point is already in the unsafe domain the com-puted reliability index should be considered a negative entity as illustratedin Figure 37

The differences between reliability-based design and design based on spec-ified partial factors were briefly discussed The merits of reliability-baseddesign are thought to lie in its ability to explicitly reflect correlation struc-ture standard deviations probability distributions and sensitivities and toautomatically seek the most probable failure combination of parametric val-ues case by case without relying on fixed partial factors Corresponding toeach desired value of reliability index there is also a reasonably accuratesimple estimate of the probability of failure

152 Bak Kong Low

34 Practical probabilistic slope stability analysisbased on reformulated Spencer equations

This section presents a practical procedure for implementing Spencer methodreformulated for a computer age first deterministically then probabilisti-cally in the ubiquitous spreadsheet platform The material is drawn fromLow (2001 2003 both available at the authorrsquos website) and includes test-ing the robustness of search for noncircular critical slip surface modelinglognormal random variables deriving probability density functions fromreliability indices and comparing results inferred from reliability indices withMonte Carlo simulations The deterministic modeling is described first as itunderlies the limit state function (ie performance function) of the reliabilityanalysis

341 Deterministic Spencer method reformulated

Using the notations in Nash (1987) the sketch at the top of Figure 39(below columns I and J) shows the forces acting on a slice (slice i) thatforms part of the potential sliding soil mass The notations are weight Wibase length li base inclination angle αi total normal force Pi at the baseof slice i mobilized shearing resistance Ti at the base of slice i horizontaland vertical components (Ei Eiminus1 λiEi λiminus1Eiminus1) of side force resultantsat the left and right vertical interfaces of slice i where λiminus1 and λi are thetangents of the side force inclination angles (with respect to horizontal) atthe vertical interfaces Adopting the same assumptions as Spencer (1973)but reformulated for spreadsheet-based constrained optimization approachone can derive the following from MohrndashCoulomb criterion and equilibriumconsiderations

Ti = [cprimeili +(Pi minus uili

)tanφprime

i

]F (MohrndashCoulomb criteria) (39)

Pi cosαi = Wi minusλiEi +λiminus1Eiminus1 minus Ti sinαi (vertical equilibrium)(310)

Ei = Eiminus1 + Pi sinαi minus Ti cosαi (horizontal equilibrium) (311)

Pi =

[Wi minus

(λi minusλiminus1

)Eiminus1

minus1F

(cprime

ili minus uili tanφprimei

)(sinαi minusλi cosαi

)]

[λi sinαi + cosαi

+1F

tanφprimei

(sinαi minusλi cosαi

)]

(from above three equations) (312)

Undrained shear strength profile of soft clay Embankment

depth

cu

0

40

15

28

3

20

5

20

7

26

100 25 50

Cm

10(kPa) (deg) (kNm3)

30 20

φm γm

γclay

16 (kNm3)

0

2

4

6Dep

th

cu (kPa)

8

10

37

(m)

(kPa)

1 minus10

Xn

γw

Pw

Mw X

cX

minX

max

(XiminusX

0)

(XnminusX

0)

Xn

X0

yc

Rru

X0

minus5 0 5 10

ytop

Ω

Ω H

27

X ybot

ytop

γave C φ W α

rad u l P T E λ σprime Lx

Ly

Units m kNm3 kNm2 kNor other consistent set of units

5 173 10 15 61518 02

hc

ybot

slope angle

Soft clay

10

5

0

minus5

minus10

15 20

A B C D E F G H I J K L M N O P Q R

234567

8

9

10

111213

14

15

1617181920212223242526272829303132333435363738394041424344

454647484950

Embankment

Reformulated Spencer method

λiE

i

λ

λiminus1Eiminus1

αi

Eiminus1

Ei

F

0105

Array formulas

λ

Varying side-force angle λ

000 000

1287 1287

DumEq

Varying λ

λ=λ sin π

TRUE

ΣM ΣForces

Ti

Wi

l

Pi

491 minus589795 1340

Center of Rotation

Framed cells contain quations

17467

0 1747 327 500 15 0000

1 1658 137 500 2000 1000 30 474 1136 1073 210 4972 2850 481 0012 1298 1212 563

2 1570 000 500 2000 1000 30 763 0997 1727 163 8096 3636 963 0025 3249 1124 726

3 1472 minus118 500 1958 3526 0 1074 0879 2189 154 11201 4214 1557 0038 5094 1030 854

4 1374 minus214 500 1900 2715 0 1242 0770 2531 137 13712 2884 2305 0050 7497 932 961

5 1276 minus292 500 1866 2252 0 1378 0673 2809 125 14925 2195 3063 0062 9090 834 1047

6 1178 minus356 500 1843 2000 0 1490 0582 3037 117 15635 1825 3770 0072 10275 735 1119

7 1079 minus410 500 1827 2000 0 1583 0496 3225 112 16090 1734 4384 0082 11192 638 1178

8 981 minus453 500 1815 2000 0 1658 0415 3380 107 16506 1666 4896 0090 12014 540 1226

9 883 minus487 450 1801 2000 0 1670 0336 3404 104 16403 1615 5285 0096 12377 442 1264

10 785 minus513 400 1784 2000 0 1619 0259 3300 102 15816 1578 5538 0101 12279 343 1295

11 687 minus531 350 1767 2066 0 1556 0184 3171 100 15237 1603 5659 0103 12093 245 1317

12 589 minus542 300 1751 2110 0 1481 0110 3018 099 14654 1619 5659 0105 11826 147 1331

13 491 minus546 250 1734 2132 0 1394 0037 2841 098 14044 1627 5548 0104 11461 049 1339

14 393 minus542 200 1717 2132 0 1296 minus0037 2641 098 13380 1627 5337 0101 10985 minus049 1339

15 294 minus531 150 1698 2110 0 1186 minus0110 2418 099 12636 1619 5037 0097 10381 minus147 1331

16 196 minus513 100 1677 2066 0 1065 minus0184 2171 100 11786 1603 4663 0091 9636 minus245 1317

17 098 minus487 050 1652 2000 0 932 minus0259 1900 102 10799 1578 4234 0083 8737 minus343 1295

18 000 minus453 000 1620 2000 0 787 minus0336 1604 104 9674 1615 3763 0074 7703 minus442 1264

19 minus098 minus410 000 1600 2000 0 677 minus0415 1380 107 8912 1666 3251 0064 6932 minus540 1226

20 minus196 minus356 000 1600 2000 0 601 minus0496 1225 112 8534 1734 2692 0053 6422 minus638 1178

21 minus294 minus292 000 1600 2000 0 509 minus0582 1037 117 7974 1825 2101 0040 5752 minus736 1119

22 minus393 minus214 000 1600 2252 0 397 minus0673 809 125 7392 2195 1469 0027 5084 minus834 1047

23 minus491 minus118 000 1600 2715 0 261 minus0770 531 137 6839 2884 786 0014 4470 minus932 961

24 minus589 000 000 1600 3526 0 93 minus0879 189 154 6712 4214 00 0000 4175 minus1030 854

Figure 39 Deterministic analysis of a 5 m high embankment on soft ground with depth-dependent undrained shear strength The limit equilibrium method of slices isbased on reformulated Spencer method with half-sine variation of side forceinclination

154 Bak Kong Low

sum[Ti cosαi minus Pi sinαi

]minus Pw = 0 (overall horizontal equilibrium)

(313)

sum[(Ti sinαi + Pi cosαi minus Wi

)lowast Lxi+(Ti cosαi minus Pi sinαi

)lowast Lyi

]minus Mw = 0

(overall moment equilibrium) (314)

Lxi = 05(xi + ximinus1

)minus xc (horizontal lever arm of slice i) (315)

Lyi = yc minus 05(yi + yiminus1

)(vertical lever arm of slice i) (316)

where ci φi and ui are cohesion friction angle and pore water pressurerespectively at the base of slice i Pw is the water thrust in a water-filledvertical tension crack (at x0) of depth hc and Mw the overturning momentdue to Pw Equations (315) and (316) required for noncircular slip surfacegive the lever arms with respect to an arbitrary center The use of both λiand λiminus1 in Equation (312) allows for the fact that the right-most slice(slice 1) has a side that is adjacent to a water-filled tension crack henceλ0 = 0 (ie the direction of water thrust is horizontal) and for differentλ values (either constant or varying) on the other vertical interfaces

The algebraic manipulation that results in Equation (312) involves open-ing the term (Pi minus uili)tanφprime of Equation (39) an action legitimate only if(Pi minus uili) ge 0 or equivalently if the effective normal stress σ prime

i (= Pili minus ui)at the base of a slice is nonnegative Hence after obtaining the critical slipsurface in the section to follow one needs to check that σ prime

i ge 0 at the baseof all slices and Ei ge 0 at all the slice interfaces Otherwise one should con-sider modeling tension cracks for slices near the upper exit end of the slipsurface

Figure 39 shows the spreadsheet set-up for deterministic stability analysisof a 5 m high embankment on soft ground The undrained shear strength pro-file of the soft ground is defined in rows 44 and 45 The subscript m in cellsP44R44 denotes embankment Formulas need be entered only in the firstor second cell (row 16 or 17) of each column followed by autofilling downto row 40 The columns labeled ytop γave and c invoke the functions shownin Figure 310 created via ToolsMacroVisualBasicEditorInsertModuleon the Excel worksheet menu The dummy equation in cell P2 is equalto Flowast1 This cell unlike cell O2 can be minimized because it contains aformula

Initially xc = 6 yc = 8 R = 12 in cells I11K11 and λprime = 0 F = 1in cells N2O2 Microsoft Excelrsquos built-in Solver was then invoked to settarget and constraints as shown in Figure 311 The Solver option ldquoUseAutomatic Scalingrdquo was also activated The critical slip circle and factor ofsafety F = 1287 shown in Figure 39 were obtained automatically withinseconds by Solver via cell-object oriented constrained optimization

Practical reliability approach 155

Function Slice_c(ybmid dmax dv cuv cm)lsquocomment dv = depth vectorlsquocuv = cu vectorIf ybmid gt 0 Then Slice_c = cm Exit FunctionEnd Ifybmid = Abs(ybmid)If ybmid gt dmax Then lsquoundefined domain Slice_c = 300 lsquohence assume hard stratum Exit FunctionEnd IfFor j = 2 T o dvCount lsquoarray size=dvCount If dv(j) gt= ybmid Then interp = (ybmid - dv(j - 1)) (dv(j) - dv(j - 1)) Slice_c = cuv(j - 1) + (cuv(j) - cuv(j - 1)) interp Exit For End IfNext jEnd Function

Function ytop(x omega H)grad = Tan(omega 314159 180)If x lt 0 Then ytop = 0If x gt=0 And x lt H grad Then ytop = x gradIf x gt=H grad Then ytop = HEnd Function

Function AveGamma(ytmid ybmid gm gclay)If ybmid lt 0 Then Sum = (ytmid gm + Abs(ybmid) gclay) AveGamma = Sum (ytmid - ybmid) Else AveGamma = gmEnd IfEnd Function

Figure 310 User-defined VBA functions called by columns ytop γave and c of Figure 39

Noncircular critical slip surface can also be searched using Solver asin Figure 311 except that ldquoBy Changing Cellsrdquo are N2O2 B16 B18B40 C17 and C19C39 and with the following additional cell constraintsB16 ge B11tan(radians(A11)) B16 ge B18 B40 le 0 C19C39 le D19D39O2 ge 01 and P17P40 ge 0

Figure 312 tests the robustness of the search for noncircular critical sur-face Starting from four arbitrary initial circles the final noncircular criticalsurfaces (solid curves each with 25 degrees of freedom) are close enoughto each other though not identical Perhaps more pertinent their factors ofsafety vary narrowly within 1253 ndash 1257 This compares with the minimumfactor of safety 1287 of the critical circular surface of Figure 39

Figure 311 Excel Solver settings to obtain the solution of Figure 39

8

4

0

-4

-8

-12

-20 -10 0 10

Circular initial

Noncircular critical

20

FS =125-126

Figure 312 Testing the robustness of search for the deterministic critical noncircular slipsurface

Practical reliability approach 157

342 Reformulated Spencer method extendedprobabilistically

Reliability analyses were performed for the embankment of Figure 39as shown in Figure 313 The coupling between Figures 39 and 313 isbrought about simply by entering formulas in cells C45H45 and P45R45of Figure 39 to read values from column vi of Figure 313 The matrixform of the HasoferndashLind index Equation (35b) will be used except thatthe symbol vi is used to denote random variables to distinguish it from thesymbol xi (for x-coordinate values) used in Figure 39

Spatial correlation in the soft ground is modeled by assuming an autocor-relation distance (δ) of 3 m in the following established negative exponentialmodel

ρij = eminus∣∣Depth(i) minus Depth(j)

∣∣δ (317)

mean StDev Correlation matrixldquocrmatrdquo

Dist Name vi microi σi microiN σi

N (vi-microiN)σi

N

lognormal Cm 104815 10 150 9872 1563 0390

lognormal φm 3097643 30 300 29830 3090 0371

lognormal γm 2077315 20 100 19959 1038 0784

lognormal Cu1 3426899 40 600 39187 5112 -0962

lognormal Cu2 2287912 28 420 27246 3413 -1279

lognormal Cu3 1598835 20 300 19390 2385 -1426

lognormal Cu4 1583775 20 300 19357 2362 -1490

lognormal Cu5 2207656 26 390 25442 3293 -1022

lognormal Cu6 3459457 37 555 36535 5160 -0376

nv

EqvN(1)

Array formula Ctrl+Shift then enter 1961PrFailβ

00249 =normsdist(-β)

Probability of failure

EqvN(2)

Function EqvN(Distributions Name v mean StDev code)Select Case UCase(Trim(Distribution Name)) lsquotrim leadingtrailing spaces amp convert to uppercaseCase ldquoNORMALrdquo If code = 1 Then EqvN = mean If code = 2 Then EqvN = StDevCase ldquoLOGNORMALrdquo If vlt0000001 Then v=0000001 lamda = Log(mean) - 05 Log(1+(StDevmean)and2) If code=1 Then EqvN=v(1-Log(v) +lamda) If code=2 Then EqvN=vSqr(Log(1+(StDevmean)and2))End selectEnd Function

1

-03

05

0

0

0

0

0

0

-03

1

05

0

0

0

0

0

0

05

05

1

0

0

0

0

0

0

0

0

0

1

06065

03679

01889

0097

00357

0

0

0

06065

1

06065

03114

01599

00588

0

0

0

03679

06065

1

05134

02636

0097

0

0

0

01889

03114

05134

1

05134

01889

0

0

0

0097

01599

02636

05134

1

03679

0

Cm -φm γm Cu1 Cu2 Cu3 Cu4 Cu4 Cu1

0

0

00357

00588

0097

01889

03679

1

0 15 3 5 7 10

0

15

3

5

7

10

depth

Equation 16 autofilled

=SQRT(MMULT(TRANSPOSE(nv) MMULT(MINVERSE(crmat)nv)))

Figure 313 Reliability analysis of the case in Figure 39 accounting for spatial variation ofundrained shear strength The two templates are easily coupled by replacingthe values cm φm γm and the cu values of Figure 39 with formulas that pointto the values of the vi column of this figure

158 Bak Kong Low

(Reliability analysis involving horizontal spatial variations of undrainedshear strengths and unit weights is illustrated in Low et al 2007 for a clayslope in Norway)

Only normals and lognormals are illustrated Unlike a widely documentedclassical approach there is no need to diagonalize the correlation matrix(which is equivalent to rotating the frame of reference) Also the iterativecomputations of the equivalent normal mean (microN) and equivalent normalstandard deviation (σN) for each trial design point are automatic during theconstrained optimization search

Starting with a deterministic critical noncircular slip surface of Figure 312the λprime and F values of Figure 39 were reset to 0 and 1 respectively The vivalues in Figure 313 were initially assigned their respective mean valuesThe Solver routine in Microsoft Excel was then invoked to

bull Minimize the quadratic form (a 9-dimensional hyperellipsoid in originalspace) ie cell ldquoβrdquo

bull By changing the nine random variables vi the λprime in Figure 39 and the 25coordinate values x0 x2 x24 yb1 yb3 yb23 of the slip surface (F remainsat 1 ie at failure or at limit state)

bull Subject to the constraints minus1 le λprime le 1 x0 ge Htan(radians()) x0 ge x2x24 le 0 yb3 yb23 le yt3 yt23

sumForces = 0

sumM = 0 and σ prime

1 σ prime24 ge 0

The Solver option ldquoUse automatic scalingrdquo was also activated

The β index is 1961 for the case with lognormal variates (Figure 313)The corresponding probability of failure based on the hyperplane assump-tion is 249 The reliability-based noncircular slip surface is shown inFigure 314 The nine vi values in Figure 313 define the most probablefailure point where the equivalent dispersion hyperellipsoid is tangent tothe limit state surface (F = 1) of the reliability-based critical slip surfaceAt this tangent point the values of cu are as expected smaller thantheir mean values but the cm and φm values are slightly higher thantheir respective mean values This peculiarity is attributable to cm and φmbeing positively correlated to the unit weight of the embankment γm andalso reflects the dependency of tension crack depth hc (an adverse effect)on cm since the equation hc = 2cm (γm

radicKa) is part of the model in

Figure 39By replacing ldquolognormalrdquo with ldquonormalrdquo in the first column of

Figure 313 re-initializing column vi to mean values and invoking Solverone obtains a β index of 1857 with a corresponding probability of fail-ure equal to 316 compared with 249 for the case with lognormalvariates The reliability-based noncircular critical slip surface of the casewith normal variates is practically indistinguishable from the case with log-normal variates Both are however somewhat different (Figure 314) fromthe deterministic critical noncircular surface from which they evolved via

Practical reliability approach 159

(m)

(m)

Deterministic critical

64

20

-2 0 5 10 15 20-5-10-4-6-8

Figure 314 Comparison of reliability-based critical noncircular slip surfaces (the two uppercurves for normal variates and lognormal variates) with the deterministiccritical noncircular slip surface (the lower dotted curve)

Table 31 Reliability indices for the example case

[Lognormal variates] [Normal variates]

Reliability index 1961 1857(1971)lowast (1872)lowast

Prob of failure 249 316(244)lowast (306)lowast

lowast at deterministic critical noncircular surface

Table 32 Solverrsquos computing time on a computer

F for specified slip surface asymp 03 sF search for noncircular surface 15 sβ for specified slip surface 2 sβ search for noncircular surface 20 s

25 degrees of freedom during Solverrsquos constrained optimization search Thisdifference in slip surface geometry matters little however for the followingreason If the deterministic critical noncircular slip surface is used for relia-bility analysis the β index obtained is 1971 for the case with lognormalsand 1872 for the case with normals These β values are only slightlyhigher (lt1) than the β values of 1961 and 1857 obtained earlier with aldquofloatingrdquo surface Hence for the case in hand performing reliability anal-ysis based on the fixed deterministic critical noncircular slip surface willyield practically the same reliability index as the reliability-based critical slipsurface Table 31 summarizes the reliability indices for the case in handTable 32 compares the computing time

160 Bak Kong Low

343 Deriving probability density functions fromreliability indices and comparison withMonte Carlo simulations

The reliability index β in Figure 313 has been obtained with respect tothe limit state surface defined by F = 10 The mean value point repre-sented by column microi is located on the 1253 factor-of-safety contour ascalculated in the deterministic noncircular slip surface search earlier A dis-persion hyperellipsoid expanding from the mean-value point will touchthe limit state surface F = 10 at the design point represented by thevalues in the column labeled vi The probability of failure 249 approx-imates the integration of probability density in the failure domain F le 10If one defines another limit state surface F = 08 a higher value of β isobtained with a correspondingly smaller probability that F le 08 reflect-ing the fact that the limit state surface defined by F = 08 is further awayfrom the mean-value point (which is on the factor-of-safety contour 1253)In this way 42 values of reliability indices (from positive to negative) cor-responding to different specified limit states (from F = 08 to F = 18)were obtained promptly using a short VBA code that automatically invokesSolver for each specified limit state F The series of β indices yields thecumulative distribution function (CDF) of the factor of safety based onPr[F le FLimitState] asymp (minusβ) where () is the standard normal cumula-tive distribution function The probability density function (PDF) plots inFigure 315 (solid curves) were then obtained readily by applying cubicspline interpolation (eg Kreyszig 1988) to the CDF during which pro-cess the derivatives (namely the PDF) emerged from the tridiagonal splinematrix The whole process is achieved easily using standard spreadsheetmatrix functions Another alternative was given in Lacasse and Nadim(1996) which examined pile axial capacity and approximated the CDFby appropriate probability functions

For comparison direct Monte Carlo simulations with 20000 realiza-tions were performed using the commercial software RISK with LatinHypercube sampling The random variables were first assumed to be nor-mally distributed then lognormally distributed The mean values standarddeviations and correlation structure were as in Figure 313

The solid PDF curves derived from the β indices agree remarkably wellwith the Monte Carlo PDF curves (the dashed curves in Figure 315)

Using existing state-of-the-art personal computer the time taken by Solverto determine either the factor of safety F or the reliability index β of theembankment in hand is as shown in Table 32

The computation time for 20000 realizations in Monte Carlo simula-tions would be prohibitive (20000times15 s or 83 h) if a search for thecritical noncircular slip surface is carried out for each random set (cm φmγm and the cu values) generated Hence the PDF plots of Monte Carlo

Practical reliability approach 161

3

from β indices Normalvariates

Factor of safety

from Monte Carlosimulation20000 trials

PD

F

25

2

15

1

05

006 08 1 12 14 16 18

3

from β indices Lognormalvariates

Factor of safety

from simulation20000 trials

25

2

15

1

05

006 08 1 12 14 16 18

3

Normal variates

Lognormals

Factor of safety

Both curves basedon β indices

PD

F

25

2

15

1

05

006 08 1 12 14 16 18

3

Normals

Lognormals

Factor of safety

Both curves fromMonte Carlosimulationseach 20000 trials

25

2

15

1

05

006 08 1 12 14 16 18

Figure 315 Comparing the probability density functions (PDF) obtained from reliabilityindices with those obtained from Monte Carlo simulations ldquoNormalsrdquo meansthe nine input variables are correlated normal variates and ldquoLognormalsrdquomeans they are correlated lognormal variates

simulations shown in Figure 315 have been obtained by staying put atthe reliability-based critical noncircular slip surface found in the previoussection In contrast computing 42 values of β indices (with fresh non-circular surface search for each limit state) will take only about 14 minNevertheless for consistency of comparison with the Monte Carlo PDFplots the 42 β values underlying each solid PDF curve of Figure 315have also been computed based on the same reliability-based critical non-circular slip surface shown in Figure 314 Thus it took about 14 min(42times2 s) to produce each reliability indices-based PDF curve but about70 times as long (asymp 20000 times 03 s) to generate each simulation-basedPDF curve What-if scenarios can be investigated more efficiently using thepresent approach of first-order reliability method than using Monte Carlosimulations

The probabilities of failure (F le 1) from Monte Carlo simulations with20000 realizations are 241plusmn for lognormal variates and 328plusmn fornormal variates These compare well with the 249 and 316 shown inTable 31 Note that only one β index value is needed to compute each prob-ability of failure in Table 31 Only for PDF does one need about 30 or 40β values of different limit states in order to define the CDF from which thePDF are obtained

162 Bak Kong Low

35 On the need for a positive definitecorrelation matrix

Although the correlation coefficient between two random variables has arange minus1 le ρij le 1 one is not totally free in assigning any values within thisrange for the correlation matrix This was explained in Ditlevsen (1981 85)where a 3times3 correlation matrix with ρ12 = ρ13 = ρ23 = minus1 implies a contra-diction and is hence inconsistent A full-rank (ie no redundant variables)correlation matrix has to be positive definite One may note that HasoferndashLind index is defined as the square-root of a quadratic form (Equation (35a)or (35b)) and unless the quadratic form and its covariance matrix and cor-relation matrix are positive-definite one is likely to encounter the square rootof a negative entity which is meaningless whether in the dispersion ellipsoidperspective or in the perspective of minimum distance in the reduced variatespace

Mathematically a symmetric matrix R is positive definite if and only ifall the eigenvalues of R are positive For present purposes one can createthe equation QForm = uTRminus1u side by side with β = radic

(QForm) DuringSolverrsquos searches if the cell Qform shows negative value it means that thequadratic form is not positive definite and hence the correlation matrix isinconsistent

The Monte Carlo simulation program RISK (httpwwwpalisadecom)will also display a warning of ldquoInvalid correlation matrixrdquo and offer tocorrect the matrix if simulation is run involving an inconsistent correlationmatrix

That a full-rank correlation or covariance matrix should be positive def-inite is an established theoretical requirement although not widely knownThis positive-definiteness requirement is therefore not a limitation of thepaperrsquos proposed procedure

The joint PDF of an n-dimensional multivariate normal distributionis given by Equation (36a) The PDF has a peak at mean-value pointand decreasing ellipsoidal probability contours surrounding micro only if thequadratic form and hence the covariance matrix and its inverse are positivedefinite (By lemmas of matrix theory C positive definite implies Cminus1 is pos-itive definite and also means the correlation matrix R and its inverse arepositive definite)

Without assuming any particular probability distributions it is docu-mented in books on multivariate statistical analysis that a covariance matrix(and hence a correlation matrix) cannot be allowed to be indefinite becausethis would imply that a certain weighted sum of its variables has a negativevariance which is inadmissible since all real-valued variables must havenonnegative variances (The classical proof can be viewed as reduction adabsurdum ie a method of proving the falsity of a premise by showing thatthe logical consequence is absurd)

Practical reliability approach 163

An internet search using the Google search engine with ldquopositive defi-nite correlation matrixrdquo as search terms also revealed thousands of relevantweb pages showing that many statistical computer programs (in addi-tion to the RISK simulation software) will check for positive definitenessof correlation matrix and offer to generate the closest valid matrix toreplace the entered invalid one For example one such web site titledldquoNot Positive Definite Matrices ndash Causes and Curesrdquo at httpwwwgsuedusimmkteernpdmatrihtml refers to the work of Wothke (1993)

36 Finite element reliability analysis via responsesurface methodology

Programs can be written in spreadsheet to handle implicit limit state func-tions (eg Low et al 1998 Low 2003 and Low and Tang 2004 87)However there are situations where serviceability limit states can only beevaluated using stand-alone finite element or finite difference programs Inthese circumstances reliability analysis and reliability-based design by thepresent approach can still be performed provided one first obtains a responsesurface function (via the established response surface methodology) whichclosely approximates the outcome of the stand-alone finite element or finitedifference programs Once the closed-form response functions have beenobtained performing reliability-based design for a target reliability index isstraightforward and fast Performing Monte-Carlo simulation on the closedform approximate response surface function also takes little time Xu andLow (2006) illustrate the finite element reliability analysis of embankmentson soft ground via the response surface methodology

37 Limitations and flexibilities of object-orientedconstrained optimization in spreadsheet

If equivalent normal transformation is used for first-order reliability methodinvolving nonnormal distributions other than the lognormal it is neces-sary to compute the inverse of the standard normal cumulative distributions(Equations (37a) and (37b)) There is no closed form equation for this pur-pose Numerical procedures such as that incorporated in Excelrsquos NormSInvis necessary In Excel 2000 and earlier versions NormSInv(CDF) returnscorrect values only for CDF between about 000000035 and 099999965ie within about plusmn5 standard deviations from the mean There are unde-sirable and idiosyncratic sharp kinks at both ends of the validity rangeIn EXCEL XP (2002) and later versions the NormSInv (CDF) functionhas been refined and returns correct values for a much broader band ofCDF 10minus16 lt CDF lt (1 minus 10minus16) or about plusmn8 standard deviations fromthe mean In general robustness and computation efficiency will be bet-ter with the broader validity band of NormSInv(CDF) in Excel XP This is

164 Bak Kong Low

because during Solverrsquos trial solutions using the generalized reduced gradientmethod the trial xlowast values may occasionally stray beyond 5 standard devia-tions from the mean as dictated by the directional search before returningto the real design point which could be fewer than 5 standard deviationsfrom the mean

Despite its simple appearance the single spreadsheet cell that contains theformula for reliability index β is in fact a conglomerate of program routinesIt contains Excelrsquos built-in matrix operating routines ldquommultrdquo ldquotransposerdquoand ldquominverserdquo which can operate on up to 50times50 matrices The membersof the ldquonxrdquo vector in the formula for β are each functions of their respec-tive VBA codes in the user-created function EqvN (Figure 313) Similarlythe single spreadsheet cell for the performance function g(x) can also con-tain many nested functions (built-in or user-created) In addition up to 100constraints can be specified in the standard Excel Solver Regardless of theircomplexities and interconnections they (β index g(x) constraints) appear asindividual cell objects on which Excelrsquos built-in Solver optimization programperforms numerical derivatives and iterative directional search using the gen-eralized reduced gradient method as implemented in Lasdon and WarenrsquosGRG2 code (httpwwwsolvercomtechnology4htm)

The examples of Low et al (1998 2001) with implicit performance func-tions and numerical methods coded in Excelrsquos programming environmentinvolved correlated normals only and have no need for the Excel NormSInvfunction and hence are not affected by the limitations of NormSInv in Excel2000 and earlier versions

38 Summary

This chapter has elaborated on a practical object-oriented constrained opti-mization approach in the ubiquitous spreadsheet platform based on thework of Low and Tang (1997a 1997b 2004) Three common geotechni-cal problems have been used as illustrations namely a bearing capacityproblem an anchored sheet pile wall embedment depth design and areformulated Spencer method Slope reliability analyses involving spatiallycorrelated normal and lognormal variates were demonstrated with searchfor the critical noncircular slip surface The HasoferndashLind reliability indexwas re-interpreted using the perspective of an expanding equivalent dis-persion ellipsoid centered at the mean in the original space of the randomvariables When nonnormals are involved the perspective is one of equiva-lent ellipsoids The probabilities of failure inferred from reliability indices arein good agreement with those from Monte Carlo simulations for the exam-ples in hand The probability density functions (of the factor of safety) werealso derived from reliability indices using simple spreadsheet-based cubicspline interpolation and found to agree well with those generated by the farmore time-consuming Monte Carlo simulation method

Practical reliability approach 165

The coupling of spreadsheet-automated cell-object oriented constrainedoptimization and minimal macro programming in the ubiquitous spreadsheetplatform together with the intuitive expanding ellipsoid perspective ren-ders a hitherto complicated reliability analysis problem conceptually moretransparent and operationally more accessible to practicing engineers Thecomputational procedure presented herein achieves the same reliability indexas the classical first order reliability method (FORM) ndash well-documented inAng and Tang (1984) Madsen et al (1986) Haldar and Mahadevan (1999)Baecher and Christian (2003) for example ndash but without involving the con-cepts of eigenvalues eigenvectors and transformed space which are requiredin a classical approach It also affords possibilities yet unexplored or difficulthitherto Although bearing capacity anchored wall and slope stability aredealt with in this chapter the perspectives and techniques presented couldbe useful in many other engineering problems

Although only correlated normals and lognormals are illustrated the VBAcode shown in Figure 313 can be extended (Low and Tang 2004 and 2007)to deal with the triangular the exponential the gamma the Gumbel andthe beta distributions for example

In reliability-based design it is important to note whether the mean-valuepoint is in the safe domain or in the unsafe domain When the mean-valuepoint is in the safe domain the distance from the safe mean-value pointto the most probable failure combination of parametric values (the designpoint) on the limit state surface in units of directional standard deviationsis a positive reliability index When the mean-value point is in the unsafedomain (due to insufficient embedment depth of sheet pile wall for example)the distance from the unsafe mean-value point to the most probable safecombination of parametric values on the limit state surface in units of direc-tional standard deviations is a negative reliability index This was shownin Figure 37 for the anchored sheet pile wall example It is also importantto appreciate that the correlation matrix to be consistent must be positivedefinite

The meaning of the computed reliability index and the inferred probabilityof failure is only as good as the analytical model underlying the performancefunction Nevertheless even this restrictive sense of reliability or failure ismuch more useful than the lumped factor of safety approach or the partialfactors approach both of which are also only as good as their analyticalmodels

In a reliability-based design the design point reflects sensitivities standarddeviations correlation structure and probability distributions in a way thatprescribed partial factors cannot

The spreadsheet-based reliability approach presented in this chaptercan operate on stand-alone numerical packages (eg finite element) viathe response surface method which is itself readily implementable inspreadsheet

166 Bak Kong Low

The present chapter does not constitute a comprehensive risk assessmentapproach but it may contribute component blocks necessary for such afinal edifice It may also help to overcome a language barrier that hamperswider adoption of the more consistent HasoferndashLind reliability index andreliability-based design Among the issues not covered in this chapter aremodel uncertainty human uncertainty and estimation of statistical parame-ters These and other important issues were discussed in VanMarcke (1977)Christian et al (1994) Whitman (1984 1996) Morgenstern (1995) Baecherand Christian (2003) among others

Acknowledgment

This chapter was written at the beginning of the authorrsquos four-monthsabbatical leave at NGI Norway summer 2006

References

Ang H S and Tang W H (1984) Probability Concepts in Engineering Planningand Design Vol 2 ndash Decision Risk and Reliability John Wiley New York

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley Chichester UK

Bowles J E (1996) Foundation Analysis and Design 5th ed McGraw-HillNew York

BS 8002 (1994) Code of Practice for Earth Retaining Structures British StandardsInstitution London

Christian J T Ladd C C and Baecher G B (1994) Reliability applied toslope stability analysis Journal of Geotechnical Engineering ASCE 120(12)2180ndash207

Craig R F (1997) Soil Mechanics 6th ed Chapman amp Hall LondonDer Kiureghian A and Liu P L (1986) Structural reliability under incom-

plete probability information Journal of Engineering Mechanics ASCE 112(1)85ndash104

Ditlevsen O (1981) Uncertainty Modeling With Applications to MultidimensionalCivil Engineering Systems McGraw-Hill New York

ENV 1997-1 (1994) Eurocode 7 Geotechnical Design Part 1 General RulesCENEuropean Committee for Standardization Brussels

Haldar A and Mahadevan S (1999) Probability Reliability and StatisticalMethods in Engineering Design John Wiley New York

Hasofer A M and Lind N C (1974) Exact and invariant second-moment codeformat Journal of Engineering Mechanics ASCE 100 111ndash21

Kerisel J and Absi E (1990) Active and Passive Earth Pressure Tables 3rd edAA Balkema Rotterdam

Kreyszig E (1988) Advanced Engineering Mathematics 6th ed John Wiley amp SonsNew York pp 972ndash3

Lacasse S and Nadim F (1996) Model uncertainty in pile axial capacity cal-culations Proceedings of the 28th Offshore Technology Conference Texaspp 369ndash80

Practical reliability approach 167

Low B K (1996) Practical probabilistic approach using spreadsheet Geotech-nical Special Publication No 58 Proceedings Uncertainty in the GeologicEnvironment From Theory to Practice ASCE USA Vol 2 pp 1284ndash302

Low B K (2001) Probabilistic slope analysis involving generalized slip sur-face in stochastic soil medium Proceedings 14th Southeast Asian Geotech-nical Conference December 2001 Hong Kong AA Balkema Rotterdampp 825ndash30

Low B K (2003) Practical probabilistic slope stability analysis In Proceedings Soiland Rock America MIT Cambridge MA June 2003 Verlag Gluumlckauf GmbHEssen Germany Vol 2 pp 2777ndash84

Low B K (2005a) Reliability-based design applied to retaining walls Geotech-nique 55(1) 63ndash75

Low B K (2005b) Probabilistic design of anchored sheet pile wall Proceedings16th International Conference on Soil Mechanics and Geotechnical Engineering12ndash16 September 2005 Osaka Japan Millpress pp 2825ndash8

Low B K and Tang W H (1997a) Efficient reliability evaluation using spread-sheet Journal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K and Tang W H (1997b) Reliability analysis of reinforced embankmentson soft ground Canadian Geotechnical Journal 34(5) 672ndash85

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Low B K and Tang W H (2007) Efficient spreadsheet algorithm for first-orderreliability method Journal of Engineering Mechanics ASCE 133(12) 1378ndash87

Low B K Lacasse S and Nadim F (2007) Slope reliability analysis accountingfor spatial variation Georisk Assesment and Management of Risk for EngineeringSystems and Geohazards Taylor amp Francis 1(4) 177ndash89

Low B K Gilbert R B and Wright S G (1998) Slope reliability analysis usinggeneralized method of slices Journal of Geotechnical and GeoenvironmentalEngineering ASCE 124(4) 350ndash62

Low B K Teh C I and Tang W H (2001) Stochastic nonlinear pndashy analysisof laterally loaded piles Proceedings of the Eight International Conference onStructural Safety and Reliability ICOSSAR lsquo01 Newport Beach California 17ndash22June 2001 8 pages AA Balkema Rotterdam

Madsen H O Krenk S and Lind N C (1986) Methods of Structural SafetyPrentice Hall Englewood Cliffs NJ

Melchers R E (1999) Structural Reliability Analysis and Prediction 2nd edJohn Wiley New York

Morgenstern N R (1995) Managing risk in geotechnical engineering ProceedingsPan American Conference ISSMFE

Nash D (1987) A comparative review of limit equilibrium methods of stability anal-ysis In Slope Stability Eds M G Anderson and K S Richards Wiley New Yorkpp 11ndash75

Rackwitz R and Fiessler B (1978) Structural reliability under combined randomload sequences Computers and Structures 9 484ndash94

Shinozuka M (1983) Basic analysis of structural safety Journal of StructuralEngineering ASCE 109(3) 721ndash40

Simpson B and Driscoll R (1998) Eurocode 7 A Commentary ARUPBREConstruction Research Communications Ltd London

168 Bak Kong Low

Spencer E (1973) Thrust line criterion in embankment stability analysis Geotech-nique 23 85ndash100

US Army Corps of Engineers (1999) ETL 1110-2-556 Risk-based analysis ingeotechnical engineering for support of planning studies Appendix A pagesA11 and A12 (httpwwwusacearmymilpublicationseng-tech-ltrsetl1110-2-556tochtml)

VanMarcke E H (1977) Reliability of earth slopes Journal of GeotechnicalEngineering ASCE 103(11) 1247ndash66

Veneziano D (1974) Contributions to Second Moment Reliability Research ReportNo R74ndash33 Department of Civil Engineering MIT Cambridge MA

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110(2) 145ndash88

Whitman R V (1996) Organizing and evaluating uncertainnty in geotechnicalengineering Proceedings Uncertainty in Geologic Environment From Theoryto Practice ASCE Geotechnical Special Publication 58 ASCE New York V1pp 1ndash28

Wothke W (1993) Nonpositive definite matrices in structural modeling In TestingStructural Equation Models Eds KA Bollen and J S Long Sage Newbury ParkCA pp 257ndash93

Xu B and Low B K (2006) Probabilistic stability analyses of embankmentsbased on finite-element method Journal of Geotechnical and GeoenvironmentalEngineering ASCE 132(11) 1444ndash54

Chapter 4

Monte Carlo simulationin reliability analysis

Yusuke Honjo

41 Introduction

In this chapter Monte Carlo Simulation (MCS) techniques are described inthe context of reliability analysis of structures Special emphasis is placedon the recent development of MCS for reliability analysis In this contextgeneration of random numbers by low-discrepancy sequences (LDS) andsubset Markov Chain Monte Carlo (MCMC) techniques are explained insome detail The background to the introduction of such new techniques isalso described in order for the readers to make understanding of the contentseasier

42 Random number generation

In Monte Carlo Simulation (MCS) it is necessary to generate random num-bers that follow arbitrary probability density functions (PDF) It is howeverrelatively easy to generate such random numbers once a sequence of uniformrandom numbers in (00 10) is given (this will be described in section 23)

Because of this the generation of a sequence of random numbers in (0010) is discussed first in this section Based on the historical developmentsthe methods can be classified to pseudo random number generation andlow-discrepancy sequences (or quasi random number) generation

421 Pseudo random numbers

Pseudo random numbers are a sequence of numbers that look like randomnumbers by deterministic calculations This is a suitable method for acomputer to generate a large number of reproducible random numbers andis used commonly today (Rubinstein 1981 Tsuda 1995)

170 Yusuke Honjo

Pseudo random numbers are usually assumed to satisfy the followingconditions

1 A great number of random numbers can be generated instantaneously2 If there is a cycle in the generation (which usually exists) it should be

long enough to be able to generate a large number of random numbers3 The random numbers should be reproducible4 The random numbers should have appropriate statistical properties

which can usually be examined by statistical testings for example Chisquare goodness of fit test

The typical methods of pseudo random number generation include themiddle-square method and linear recurrence relations (sometimes called thecongruential method)

The origin of the middle-square method goes back to Von Neumann whoproposed generating a random number of 2a digits from xn of 2a digitsby squaring xn to obtain 4a digits number and then cut the first and lasta digits of this number to obtain xn+1 This method however was foundto have relatively short cycle and also does not satisfy satisfactory statisticalcondition This method is rarely used today

On the other hand the general form of linear recurrence relations can begiven by the following equation

xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b (mod P) (41)

where mod P implies that xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b minus Pknand kn = [(xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b)P] denotes the largestpositive integer in (xn+1 = a0xn + a1xnminus1 + + ajxnminusj + b)P

The simplest form of Equation (41) may be given as

xn+1 = xn + xnminus1 (mod P) (42)

This is called the Fibonacci method If the generated random numbers are2 digits integer and suppose the initial two numbers are x1 = 11 and x2 = 36and P = 100 the generated numbers are 4783301443

The modified versions of Equation (41) are multiplicative congruentialmethod and mixed congruential method which are still popularly used andprograms are easily available (see for example Koyanagi 1989)

422 Low-discrepancy sequences (quasi random numbers)

One of the most important applications of MCS is to solve multi-dimensionalintegration problems which includes reliability analysis The problem can be

Monte Carlo simulation 171

generally described as a multi-dimensional integration in a unit hyper cube

I =int 1

0

int 1

0f (x)dx [x = (x1x2 xk)] (43)

The problem solved by MCS can be described as the following equation

S(N) = 1N

Nsumi=1

f (xi) (44)

where xirsquos are N randomly generated points in the unit hyper cube

The error of |S(N) minus I| is proportional to O(Nminus 12 ) which is related to

the sample number N Therefore if one wants to improve the accuracy ofthe integration by one digit one needs to increase the sample point number100 times

Around 1960 some mathematicians showed that if one can generate thepoints sequences with some special conditions the accuracy can be improvedby O(Nminus1) or even O(Nminus2) Such points sequences were termed quasirandom numbers (Tsuda 1995)

However it was found later that quasi random numbers are not so effec-tive as the dimensions of the integration increase to more than 50 On theother hand there was much demand especially in financial engineeringto solve integrations of more than 1000 dimensions which expeditedresearch in this area in 1990s

One of the triggers that accelerated the research in this area was theKoksmandashHlawka theorem which related the convergence of the integrationand discrepancy of numbers generated in the unit hyper cube According tothis theorem the convergence of integration is faster as the discrepancy ofthe points sequences is smaller Based on this theorem the main objectiveof the research in the 1990s was to generate point sequences in the veryhigh dimensional hyper cube with as low discrepancy as possible In otherwords how to generate point sequences with maximum possible uniformitybecame the main target of the research Such point sequences were termedlow-discrepancy sequences (LDS) In fact LDS are point sequences that allowthe accuracy of the integration in k dimensions to improve in proportion to

O(

(logN)k

N

)or less (Tezuka 1995 2003)

Based on this development the term LDS is more often used than quasirandom numbers in order to avoid confusion between pseudo and quasirandom numbers

The discrepancy is defined as follows (Tezuka 1995 2003)

DN(k) = supyisin[01]k

∣∣∣∣∣∣([0y)N)

Nminus

kprodi=1

yi

∣∣∣∣∣∣ (45)

172 Yusuke Honjo

10

0000

10y1

y2

Figure 41 Concept of discrepancy in 2-dimensional space

where ([0y]N) denotes number of points in [0y) Therefore DN(k)

indicates discrepancy between the distribution of N points xn in thek dimensional hyper cube [01]k from ideally uniform points distributions

In Figure 41 discrepancy in 2-dimensional space is illustrated conceptu-ally where N = 10 If the number of the points in a certain area is always inproportion to the area rate DN

(k) is 0 otherwise DN(k) becomes larger as

points distribution is biasedA points sequences xi is said to be LDS when it satisfies the conditions

below

|S(N) minus I| le VfDN(k) (46)

DN(k) = c(k)

(logN)k

N

where Vf indicates the fluctuation of function f (x) within the domain of theintegration and c(k) is a constant which only depends on dimension k

Halton Faure and Sobolrsquo sequences are known to be LSD Variousalgorithms are developed to generate LSD (Tezuka 1995 2003)

In Figure 42 1000 samples generated by the mixed congruential methodand LSD are compared It is understood intuitively from the figure that pointsgenerated by LSD are more uniformly distributed

423 Random number generation following arbitrary PDF

In this section generation of random numbers following arbitrary PDF isdiscussed The most popular method to carry out this is to employ inversetransformation method

Monte Carlo simulation 173

1

08

06

04

02

00 02 04 06 08 1

1

08

06

04

02

00 02 04 06 08 1

(a) (b)

Figure 42 (a) Pseudo random numbers by mixed congruential method and (b) pointsequences by LDS 1000 generated sample points in 2-dimensional unit space

Fu (u ) Fx (x )1

0 11u xu1 u3 u2 u4

u4

u2

u3

u1

x4 x2x3x1

Figure 43 Concept of generation of random number xi by uniform random number uibased on the inverse transformation

Suppose X is a random variable whose CDF is FX(x) = pFX(x) is a non-decreasing function and 0 le p le 10 Thus the following

inverse transformation always exists for any p as (Rubinstein 1981)

Fminus1X (p) = min

[x FX(x) ge p

]0 le p le 10 (47)

By using this inverse transformation any random variable X that followsCDF FX(x) can be generated based on uniform random numbers in (01)(Figure 43)

X = Fminus1X (U) (48)

174 Yusuke Honjo

Some of the frequently used inverse transformations are summarizedbelow (Rubinstein 1981 Ang and Tang 1985 Reiss and Thomas 1997)Note that U is a uniform random variable in (01)

Exponential distribution

FX(x) = 1 minus exp[minusλx] (x ge 0)

X = Fminus1X (U) = minus1

λln(1 minus U)

The above equation can be practically rewritten as follows

X = Fminus1X (U) = minus1

λln(U)

Cauchy distribution

fX(x) = α

πα2 + (x minusλ)2 α gt 0λ gt 0minusinfin lt x lt infin

FX(x) = 12

+πminus1tanminus1(

x minusλ

α

)

X = Fminus1X (U) = λ+α tan

(U minus 1

2

)]

Gumbel distribution

FX(x) = exp[minusexpminusa(x minus b)] (minusinfin lt x lt infin)

X = Fminus1X (U) = minus1

aln(minusln(U)

)+ b

Frechet distribution

FX(x) = exp

[minus(

ν

x minus ε

)k]

(ε lt x lt infin)

X = Fminus1X (U) = ν

(minusln(U))minus1k + ε

Monte Carlo simulation 175

Weibull distribution

FX(x) = exp

[minus(ωminus xωminus ν

)k]

(minusinfin lt x ltω)

X = Fminus1X (U) = (ωminus ν)

(ln(U)

)1k +ω

Generalized Pareto distribution

FX(x) = 1 minus(

1 + γx minusmicro

σ

)minus1γ

(micro le x)

X = Fminus1X (U) = σ

γ

[(1 minus U)minusγ minus 1

]+micro

The normal random numbers which are considered to be used mostfrequently next to the uniform random numbers do not have inverse trans-formation in an explicit form One of the most common ways to generate thenormal random numbers is to employ the Box and Muller method which isdescribed below (see Rubinstein 1981 86ndash7 for more details)

U1 and U2 are two independent uniform random variables Based on thesevariables two independent standard normal random numbers Z1 and Z2can be generated by using following equations

Z1 = (minus2 ln(U1))12 cos(2πU2) (49)

Z2 = (minus2 ln(U1))12 sin(2πU2)

A program based on this method can be obtained easily from standardsubroutine libraries (eg Koyanagi 1989)

424 Applications of LDS

In Yoshida and Sato (2005a) reliability analyses by MCS are presentedwhere results by LDS and by OMCS (ordinary Monte Carlo simulation) arecompared The performance function they employed is

g(xS) =5sum

i=1

xi minus(1 + x1

2500

)S (410)

where xi are independent random variables which follow identically a lognormal distribution with mean 250 and COV 10

The convergence of the failure probability with the number of simulationruns are presented in Figure 44 for LSD and 5 OMCS runs (which are

176 Yusuke Honjo

10minus2

10minus3

Failu

re P

roba

bilit

y

10minus4

100 101 102

Number103

LDSCrude

Figure 44 Relationship between calculated Pf and number of simulation runs (Yoshida andSato 2005a)

indicated by ldquoCruderdquo in the figure) It is observed that LDS has fasterconvergence than OMCS

43 Some methods to improve the efficiencyof MCS

431 Accuracy of ordinary Monte Carlo Simulation(OMCS)

We consider the integration of the following type in this section

Pf =int

intI [g (x) le 0] fX (x)dx (411)

where fX is the PDF of basic variables X and I is an indicator functiondefined below

I (x) = 1 g(x) le 0

0 g(x) gt 0

The indicator function identifies the domain of integration Function g(x)is a performance function in reliability analysis thus the integration provides

Monte Carlo simulation 177

the failure probability Pf Note that the integration is carried out on all thedomains where basic variables are defined

When this integration is evaluated by MCS Pf can be evaluated as follows

Pf asymp 1N

Nsumj=1

I[g(xj

)le 0]

(412)

where xj is j-th sample point generated based on PDF fx()One of the important aspects of OMCS is to find the necessary number of

simulation runs to evaluate Pf of required accuracy This can be generallydone in the following way (Rubinstein 1981 115ndash18)

Let N be the total number of runs and Nf be the ones which indicatedthe failure Then the failure probability Pf is estimated as

Pf = Nf

N(413)

The mean and variance of Pf can be evaluated as

E[Pf

]= 1

NE[NF

]= NPf

N= Pf (414)

Var[Pf

]= Var

[Nf

N

]= 1

N2 Var[Nf]

= 1N2 NPf

(1 minus Pf

)= 1N

Pf(1 minus Pf

)(415)

Note that here these values are obtained by recognizing the fact that Nffollows binominal distribution of N trials where the mean and the varianceare given as NPf and NPf (1 minus Pf ) respectively

Suppose one wants to obtain a necessary number of simulation runs for

probability∣∣∣Pf minus Pf

∣∣∣le ε to be more than 100α

Prob[∣∣∣Pf minus Pf

∣∣∣ le ε]

ge α (416)

On the other hand the following relationship is given based onChebyshevrsquos inequality formula

Prob[∣∣∣Pf minus Pf

∣∣∣lt ε]

ge 1 minusVar[Pf

]ε2 (417)

178 Yusuke Honjo

From Equations (416) and (417) and using Equation (415) thefollowing relationship is obtained

α le 1 minusVar[Pf

]ε2 = 1 minus 1

Nε2 Pf(1 minus Pf

)1 minusα ge 1

Nε2 Pf(1 minus Pf

)

N ge Pf(1 minus Pf

)(1 minusα)ε2 (418)

Based on Equation (418) the necessary number of simulation runs N canbe obtained for any given ε and α Let ε = δPf and one obtains for N

N ge Pf(1 minus Pf

)(1 minusα)δ2P2

f

= (1Pf ) minus 1(1 minusα)δ2 asymp 1

(1 minusα)δ2Pf(419)

The necessary numbers of simulation runs N are calculated in Table 41for ε = 01Pf ie δ = 01 and α = 095 In other words N obtained inthis table ensures the error of Pf to be within plusmn10 for 95 confidenceprobability

The evaluation shown in Table 41 is considered to be very conservativebecause it is based on the very general Chebyshevrsquos inequality relationshipBroding (Broding et al 1964) gives necessary simulation runs as below(Melchers 1999)

N gtminus ln(1 minusα)

Pf(420)

Table 41 Necessary number of simulation runs to evaluate Pf of required accuracy

δ α Pf N by Chebyshev N by Bording

01 095 100E-02 200000 300100E-04 20000000 29957100E-06 2000000000 2995732

05 095 100E-02 8000 300100E-04 800000 29957100E-06 80000000 2995732

10 095 100E-02 2000 300100E-04 200000 29957100E-06 20000000 2995732

50 095 100E-02 80 300100E-04 8000 29957100E-06 800000 2995732

Monte Carlo simulation 179

where N is necessary number of simulation runs for one variable α is givenconfidence level and Pf is the failure probability For example if α = 095and Pf = 10minus3 the necessary N is by Equation (420) about 3000 In thecase of more than one independent random variable one should multiplythis number by the number of variables N evaluated by Equation (420) isalso presented in Table 41

432 Importance sampling (IMS)

As can be understood from Table 41 the required number of simulation runsin OMCS to obtain Pf of reasonable confidence level is not small especiallyfor cases of smaller failure probability In most practical reliability analysesthe order of Pf is of 10minus4 or less thus this problem becomes quite seriousOne of the MCS methods to overcome this difficulty is the ImportanceSampling (IMS) technique which is discussed in this section

Theory

Equation (411) to calculate Pf can be rewritten as follows

Pf =int

intI[g(x) le 0] fX(x)

hV (x)hV (x)dx (421)

where v is a random variable following PDF hV

Pf asymp 1N

⎧⎨⎩

Nsumj=1

I[g(vj) le 0] fX(vj)

hV (vj)

⎫⎬⎭ (422)

where hV is termed the Importance Sampling function and acts as a con-trolling function for sampling Furthermore the optimum hV is known tobe the function below (Rubinstein 1981 122ndash4)

hV (v) = I [g(v) le 0] fX(v)Pf

(423)

This function hV (v) in the case of a single random variable is illustratedin Figure 45

It can be shown that the estimation variance of Pf is null for the samplingfunction hV (v) given in Equation (423) In other words Pf can be obtained

180 Yusuke Honjo

fx (x ) hv (v )

G(x ) = x minus a

apf

x v

Figure 45 An illustration of the optimum Importance Function in the case of a singlevariable

exactly by using this sample function as demonstrated below

Pf =int int

intI [g(x) le 0]

fX(x)hV (x)

hV (x)dx

=int int

intI [g(x) le 0] fX(x)

[I [g(x) le 0] fX(x)

Pf

]minus1

hV (x)dx

= Pf

int int

inthV (x)dx = Pf

Therefore Pf can be obtained without carrying out MCS if the importancefunction of Equation (423) is employed

However Pf is unknown in practical situations and the result obtainedhere does not have an actual practical implication Nevertheless Equation(423) provides a guideline on how to select effective important samplingfunctions a sampling function that has a closer form to Equation (423) maybe more effective It is also known that if an inappropriate sampling func-tion is chosen the evaluation becomes ineffective and estimation varianceincreases

Selection of importance sampling functions in practice

The important region in reliability analyses is the domain where g(x) le 0The point which has the maximum fX(x) in this domain is known as thedesign point which is denoted by xlowast One of the most popular ways to sethV is to set the mean value of this function at this design point (Melcher1999) Figure 46 conceptually illustrates this situation

Monte Carlo simulation 181

Failure

Z (x)=0

Limit State Surface

Safe

0

hv (x )fx (x )

x2

x1

Figure 46 A conceptual illustration of importance sampling function set at the design point

If g(x) is a linear function and the space is standardized normal space thesample points generated by this hV fall in the failure region by about 50which is considered to give much higher convergence compared to OMCSwith a smaller number of sample points

However if the characteristic of a performance function becomes morecomplex and also if the number of basic variables increases it is not a sim-ple task to select an appropriate sample function Various so-called adaptivemethods have been proposed but most of them are problem-dependentIt is true to say that there is no comprehensive way to select an appro-priate sample function at present that can guarantee better results in allcases

433 Subset MCMC (Markov Chain Monte Carlo) method

Introduction to the subset MCMC method

The subset MCMC method is a reliability analysis method by MCS proposedby Au and Beck (2003) They combined the subset concept with the MCMCmethod which had been known as a very flexible MCS technique to generaterandom numbers following any arbitrary PDF (see for example Gilks et al1996) They combined these two concepts to develop a very effective MCStechnique for structural reliability analysis

The basic concept of this technique is explained in this subsection whereasa more detailed calculation procedure is presented in the next subsectionSome complementary mathematical proofs are presented in the appendix ofthis chapter and some simple numerical examples in subsection 433

182 Yusuke Honjo

Let F be a set indicating the failure region Also let F0 be the total set andFi(I = 1 middot middot middotm) be subsets that satisfy the relationship below

F0 sup F1 sup F2 sup sup Fm sup F (424)

where Fm is the last subset assumed in the Pf calculation Note that thecondition Fm sup F is always satisfied

The failure probability can be calculated by using these subsets as follows

Pf = P(F) = P(F | Fm)P(Fm | Fmminus1) P(F1 | F0) (425)

Procedure to calculate Pf by subset MCMC method

(1) Renewal of subsets

The actual procedure to set a subset is as follows where zi = g(xi) is thecalculated value of a given performance function at sample point xi Nt isthe total number of generated points in each subset Ns are number of pointsselected among Nt points from the smaller zi values and Nf is number offailure points (ie zi is negative) in Nt Also note that x used in this sectioncan be a random variable or a random vector that follows a specified PDF

Step 1 In the first cycle Nt points are generated by MCS that follow thegiven PDF

Step 2 Order the generated samples in ascending order by the value of ziand select smaller Ns(lt Nt) points A new subset Fk+1 is defined bythe equation below

Fk+1 =

x | z(x) le zs + zs+1

2

(426)

Note that zirsquos in this equation is assembled in ascending order Theprobability that a point generated in subset Fk+1 for points in subsetFk is calculated to be P(Fk+1 | Fk) = NsNt

Step 3 By repeating Step 2 for a range of subsets Equation (425) can beevaluatedNote that xi (i = 1 Ns) are used as seed points in MCMC togenerate the next Nt samples which implies that NtNs samplesare generated from each of these seed points In other words NtNspoints are generated from each seed point by MCMCFurthermore the calculation is ceased when Nf reaches certainnumber with respect to Nt

Monte Carlo simulation 183

(2) Samples generation by MCMC

By MCMC method Nt samples are generated in subset Fk+1 This method-ology is described in this subsection

Let π (x) be a PDF of x that follows PDF fX(x) and yet conditioned to bewithin the subset Fk PDF π (x) is defined as follows

π (x) = c middot fX(x) middot ID(x) (427)

where

ID(x) =

1 if (x sub Fk)0 otherwise

where c is a constant for PDF π to satisfy the condition of a PDFPoints following this PDF π (x) can be generated by MCMC by the

following procedure (Gilks et al 1996 Yoshida and Sato 2005)

Step 1 Determine any arbitrary PDF q(xprime | xi) which is termed proposaldensity In this research a uniform distribution having mean xiand appropriate variance is employed (The author tends to usethe variance of the original PDF to this uniform distribution Thesmaller variance tends to give higher acceptance rate (ie Equation(429)) but requires more runs due to slow movement of the subsetboundary)

Step 2 The acceptance probability α(xprimexi) is calculated Then set xi+1 = xprimewith probability α or xi+1 = xi with probability 1 minus α Theacceptance probability α is defined as the following equation

α(xprimexi) = min

10q(xi | xprime) middotπ (xprime)q(xprime | xi)π (xi)

= min

10q(xi | xprime) middot fX(xprime) middot ID(xprime)

q(xprime | xi)fX(xi)

(428)

Equation (428) can be rewritten as below considering the fact thatq(xprime | xi) is a uniform distribution and point xi is certainly in thesubset under consideration

α = min

10fX(xprime) middot ID(xprime)

fX(xi)

(429)

Step 3 Repeat Step 2 for necessary cycles

184 Yusuke Honjo

s

Failure Region

Performance Function

Stable Region

R

Z = R minus S

Design Point

Figure 47 A conceptual illustration of the subset MCMC method

(3) Evaluation of the failure probability Pf

In this study the calculation is stopped when Nf reaches certain number withrespect to Nt otherwise the calculation in Step 2 is repeated for a renewedsubset which is smaller than the previous one

Some of more application examples of the subset MCMC to geotechnicalreliability problems can be seen in Yoshida and Sato (2005b)

The failure probability can be calculated as below

Pf = P (z lt 0) =

Ns

Nt

mminus1 Nf

Nt(430)

The mathematical proof of this algorithm is presented in the nextsection

The conceptual illustration of this procedure is presented in Figure 47It is understood that by setting the subsets more samples are generated tothe closer region to the performance function which is supposed to improvethe efficiency of MCS considerably compared to OMCS

Examples of subset MCMC

Two simple reliability analyses are presented here to demonstrate the natureof subset MCMC method

Monte Carlo simulation 185

8

6

4S

R

2

00 2 4 6 8 10

k = 1

k = 2

k = 3

Z = 0

Zsub_1

Zsub_2

Figure 48 A calculation example of subset MCMC method when Z = R minus S

(1) An example on a linear performance function

The first example employs the simplest performance function Z = R ndash SThe resistance R follows a normal distribution N(7010) whereas theexternal force S a normal distribution N(3010) The true value of thefailure probability of this problem is Pf = 234E minus 03

Figure 48 exhibits a calculation example of this reliability analysis In thiscalculation Nt = 100 and two subsets are formed before the calculation isceased It is observed that more samples are generated closer to the limitstate line as the calculation proceeds

In order to examine the performance of subset MCMC method the num-ber of selected sample points to proceed to the next subset Ns and stoppingcriteria of the calculation are altered Actually Ns is set to 251020 or 50The stopping criteria are set based on the rate of Nf to Nt as Nf ge050020010 or 005Nt Also a criterion Nf ge Ns is added Nt is setto 100 in all cases

Table 42 lists the results of the calculation 1000 calculations are madefor each case where mean and COV of log10(calculated Pf ) log10(truePf )are presented

In almost all cases the means are close to 10 which exhibit very littlebias in the present calculation for evaluating Pf COV seems to be somewhatsmaller for case Ns is larger

186 Yusuke Honjo

Table 42 Accuracy of Pf estimation for Z = R minus S

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 0992 0995 0997 0976 0943Cov 0426 0448 0443 0390 0351

➁Nf 005N Mean 0995 0993 0989 0989 0971Cov 0428 0378 0349 0371 0292

➂Nf 010N Mean 1033 0994 0991 0985 0985Cov 0508 0384 0367 0312 0242

➃Nf 020N Mean 1056 1074 1006 0993 0990Cov 0587 0489 0348 0305 0165

➄Nf 050N Mean 1268 1111 1056 1020 0996Cov 0948 0243 0462 0319 0162

Table 43 Number of performance function calls for Z = R minus S example

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 1719 1881 2077 2329 3432sd 251 371 438 561 1092

➁Nf 005N Mean 1858 2090 2291 2716 4400sd 313 562 445 540 882

➂Nf 010N Mean 2029 2218 2518 2990 5066sd 324 616 396 532 754

➃Nf 020N Mean 2129 2406 2706 3265 5694sd 312 486 389 480 681

➄Nf 050N Mean 2260 2580 2930 3611 6547sd 257 371 426 484 627

Table 43 presents a number of performance function calls in each caseThe mean and sd (standard deviation) of these numbers are shown It isobvious from this table that the numbers of the function calls increase as Nsincreases It is understandable because as Ns increases the speed of subsetsconverging to the limit state line decreases which makes the number of callslarger

As far as the present calculation is concerned the better combinationof Ns and the stopping criterion seems to be around Ns = 20sim50 andNf ge 020sim050 Nt However the number of performance function callsalso increases considerably for these cases

(2) An example on a parabolic performance function

The second example employs a parabolic performance function Z =(R minus 110)2 minus (S minus 6) The resistance R follows a normal distribution

Monte Carlo simulation 187

10

8

6

k = 1

k = 3

k = 4

k = 5

Z = 0

k = 2

Zsub_1

Zsub_2

Zsub_3

Zsub_4S

4

24 6 8 10

R12 14

Figure 49 A calculation example of subset MCMC method when Z = (Rminus110)2 minus (Sminus6)

N(8507072) whereas the external force S follows a normal distributionN(5007072) The true value of the failure probability of this problemwhich is obtained by one million runs of OMCS is Pf = 320E minus 04

Figure 49 exhibits a calculation example of this reliability analysis In thiscalculation Nt = 100 and four subsets were formed before the calculationwas ceased It is observed that more samples are generated closer to thelimit state line as the calculation proceeds which is the same as for the firstexample

All the cases that have been calculated in the previous example are repeatedin this example as well where Nt = 100

Table 44 exhibits the same information as in the previous example Themean value is practically 10 in most of the cases which suggests there islittle bias in the evaluation On the other hand COV tends to be smaller forlarger Ns

Table 45 also provides the same information as the previous examplesThe number of function calls increases as Ns is set larger It is difficult toidentify the optimum Ns and the stopping criterion

44 Final remarks

Some of the recent developments in Monte Carlo simulation (MCS) tech-niques namely low-discrepancy sequences (LDS) and the subset MCMC

188 Yusuke Honjo

Table 44 Accuracy of Pf estimation for Z = (R minus 110)2 minus (S minus 6)

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 1097 1101 1078 1069 0997Cov 1027 0984 0839 0847 0755

➁Nf 005N Mean 1090 1092 1071 1056 1002Cov 0975 0924 0865 0844 0622

➂Nf 010N Mean 1135 1083 1055 1051 1024Cov 1056 0888 0857 0737 0520

➃Nf 020N Mean 1172 1077 1089 1054 1013Cov 1129 0706 0930 0777 0387

➄Nf 050N Mean 1309 1149 1160 1100 0997Cov 1217 0862 1015 0786 0366

Table 45 Number of performance function calls for Z = (R minus 110)2 minus (S minus 6) example

Cutoff criterion Ns = 2 Ns = 5 Ns = 10 Ns = 20 Ns = 50

➀Nf 002N Mean 2173 2501 2825 3416 5515sd 709 735 789 830 1254

➁Nf 005N Mean 2343 2703 3091 3707 6388sd 519 628 794 678 1113

➂Nf 010N Mean 2457 2866 3235 3999 7124sd 361 775 696 656 926

➃Nf 020N Mean 2627 2997 3475 4308 7727sd 621 848 732 908 880

➄Nf 050N Mean 2679 3187 3703 4673 8427sd 392 816 716 851 923

method that are considered to be useful in structural reliability problemsare highlighted in this chapter

There are many other topics that need to be introduced for MCS in struc-tural reliability analysis These include generation of a three-dimensionalrandom field MCS technique for code calibration and partial factorsdetermination

It is the authorrsquos observation that classic reliability analysis tools such asFORM are now facing difficulties when applied to recent design methodswhere the calculations become more and more complex and sophisticatedOn the other hand our computational capabilities have progressed tremen-dously compared to the time when these classic methods were developedWe need to develop more flexible and more user-friendly reliability analysistools and MCMS is one of the strong candidate methods to be used forthis purpose More development however is still necessary to fulfill theserequirements

Monte Carlo simulation 189

Appendix Mathematical proof of MCMC byMetropolisndashHastings algorithm

(1) Generation of random numbers by MCMC basedon MetropolisndashHastings algorithm

Let us generate x that follows PDF π (x) Note that x used in this sectioncan be a random variable or a random vector that follows the specifiedPDF π (x)

Let us consider a Markov chain process x(t) rarr x(t+1) The procedure togenerate this chain is described as follows (Gilks et al 1996)

Step 1 Fix an appropriate proposal density which can generally bedescribed as

q(

xprime∣∣x(t))

(431)

Step 2 Calculate the acceptance probability which is defined by the equa-tion below

α(xprimex(t)

)= min

1

q(x(t)∣∣xprime)π (xprime)

q(xprime|x(t)

)π(x(t))

(432)

α is the acceptance probability in (0010)Step 3 x(t) is advanced to x(t+1) by the condition below based on the

acceptance probability calculated in the previous step

x(t+1) =

xprime with probability α

x(t) with probability 1 minusα

x(t+1) generated by this procedure surely depends on the previous stepvalue x(t)

(2) Proof of the MetropolisndashHastings algorithm

It is proved in this subsection that x generated following the proceduredescribed above actually follows PDF π (x) The proof is given in the threesteps

1 First the transition density which is to show the probability to moveform x to xprime in this Markov chain is defined

p(xprime∣∣x)= q

(xprime∣∣x)α (xprimex

)(433)

190 Yusuke Honjo

2 It is shown next that the transition density used in the MetropolisndashHastings algorithm satisfies so-called detailed balance This implies thatp(xprime | x) is reversible with respect to time

p(xprime∣∣x)π (x) = p

(x|xprime)π (xprime) (434)

3 Finally it is proved that generated sequences of numbers by this Markovchain follow a stationary distribution π (x) which is expressed by thefollowing relationshipint

p(xprime∣∣x)π (x)dx = π

(xprime) (435)

It is proved here that the transition density used in MetropolisndashHastingsalgorism satisfies the detailed balance

It is obvious that Equation (434) is satisfied when x = xprimeWhen x = xprime

p(xprime∣∣x)π (x) = q

(xprime∣∣x)α (xprimex

)π (x)

= q(xprime∣∣x)min

1

q (x|xprime)π (xprime)q (xprime|x)π (x)

π (x)

= minq(xprime∣∣x)π (x) q

(x|xprime)π (xprime) (436)

Therefore p (xprime|x)π (x) is symmetric with respect to x and xprime Thus

p(xprime∣∣x)π (x) = q

(xprime∣∣x)π (xprime) (437)

From this relationship the detailed balance of Equation (434) is provedFinally the stationarity of generated x and xprime are proved based on the

detailed balance of Equation (434) This can be proved if the results beloware obtained

x sim π (x)

xprime sim π(xprime) (438)

This is equivalent to prove the stationarity of the Markov chainintp(xprime | x

)π (x)dx = π

(xprime) (439)

which is obtained as followsintp(xprime∣∣x)π (x)dx =

intp(x|xprime)π (xprime)dx

= π(xprime)int p

(x|xprime)dx

= π(xprime)

Monte Carlo simulation 191

Thus a sequence of numbers generated by the MetropolisndashHastings algo-rithm follow PDF π (x)

References

Ang A and Tang W H (1984) Probability Concepts in Engineering Planning andDesign Vol II Decision Risk and Reliability John Wiley amp Sons

Au S-K and Beck J L (2003) Subset Simulation and its Appplication to SeismicRisk Based Dynamic Analysis Journal of Engineering Mechanics ASCE 129 (8)901ndash17

Broding W C Diederich F W and Parker P S (1964) Structural optimizationand design based on a reliability design criterion J Spacecraft 1(1) 56ndash61

Gilks W R Richardson S and Spiegelhalter D J (1996) Markov Chain MonteCarlo in Practice Chapman amp HallCRC London

Koyanagi Y (1989) Random Numbers Numerical Calculation Softwares byFORTRAN 77 Eds T Watabe M Natori and T Oguni Maruzen Co Tokyopp 313ndash22 (in Japanese)

Melcher R E (1999) Structure Reliability Analysis and Prediction 2nd ed JohnWiley amp Sons Ltd England

Reiss R D and Thomas M (1997) Statistical Analysis of Extreme ValuesBirkhauser Basel

Rubinstein R Y (1981) Simulation and the Monte Carlo Method John Wiley ampSons Ltd New York

Tezuka S (1995) Uniform Random Numbers Theory and Practice Kluwer BostonTezuka S (2003) Mathematics in Low Discrepancy Sequences In Computational

Statistics I Iwanami Publisher Tokyo pp 65ndash120 (in Japanese)Tsuda T (1995) Monte Carlo Methods and Simulation 3rd ed Baifukan Tokyo

(in Japanese)Yoshida I and Sato T (2005a) A Fragility Analysis by Low Discrepancy Sequences

Journal of Structural Engineering 51A 351ndash6 (in Japanese)Yoshida I and Sato T (2005b) Effective Estimation Method of Low Failure Prob-

ability by using Markov Chain Monte Carlo Journal of Structural EngineeringNo794I-72m 43ndash53 (in Japanese)

Chapter 5

Practical application ofreliability-based designin decision-making

Robert B Gilbert Shadi S Najjar Young-Jae Choi andSamuel J Gambino

51 Introduction

A significant advantage of reliability-based design (RBD) approaches is tofacilitate and improve decision-making While the attention in RBD hasgenerally been focused on the format and calibration of design-checkingequations the basic premise of a reliability-based design is that the tar-get of the design should achieve an acceptable level of reliability With thispremise in mind designers and stakeholders can utilize a range of possiblealternatives in order to achieve the desired reliability The objective of thischapter is to present practical methods and information that can be used tothis end

This chapter begins with a discussion of decision trees and decisioncriteria Next the methods for conducting reliability analyses and the resultsfrom reliability analyses are considered in the context of decision-makingFinally the calibration of mathematical models with data is addressed forthe purposes of reliability analyses and ultimately decision-making

The emphasis in presenting the material in this chapter is on illustrat-ing basic concepts with practical examples These examples are all basedon actual applications where the concepts were used in solving real-worldproblems to achieve more reliable andor more cost-effective designs

In order to provide consistency throughout the chapter the examples areintentionally narrowed in scope to the design of foundations for offshorestructures For context two types of structures are considered herein fixedjacket platforms and floating production systems Fixed jackets consist ofa steel frame with legs supported at the sea floor with driven pipe piles(Figure 51) Fixed jackets are used in water depths up to several hundredmeters with driven piles that are approximately 1 m in diameter and 100 mlong Floating production systems consist of a steel hull moored to the seafloor with 8ndash16 mooring lines anchored by steel caissons that are jackedinto the soil using under-pressure and are called suction caissons Floatingsystems are used in water depths of 1000 m or more with suction caissonsthat are approximately 5 m in diameter and 30 m long Both structures

Practical application of RBD 193

Ocean Surface

Mudline

Mudline

Driven SteelPipe Pile

Foundation

SuctionCaisson

Foundation

Jacket

MooringLine

TopsidesFacilities

TopsidesFacilities

Hull

FixedJacket

Platform

FloatingProduction

System

Figure 51 Schematic of typical offshore structures

provide a platform for the production and processing of oil and gas offshoreThe structures typically cost between $100 million for jackets up to morethan $1 billion for floating systems and the foundation can cost anywherefrom 5 to 50 of the cost of the structure

52 Decision trees

Decision trees are a useful tool for structuring and making decisions(Benjamin and Cornell 1970 Ang and Tang 1984) They organize theinformation that is needed in making a decision provide for a repeatableand consistent process allow for an explicit consideration of uncertainty andrisk and facilitate communication both in eliciting input from stakeholdersto the decision and in presenting and explaining the basis for a decision

There are two basic components in a decision tree alternatives andoutcomes A basic decision tree in reliability-based design is shown inFigure 52 to illustrate these components There are two alternatives eg twodifferent pile lengths For each alternative there are two possible outcomesthe design functions adequately eg the settlements in the foundation donot distress the structure or it functions inadequately eg the settlementsare excessive In a decision tree the alternatives are represented by limbsthat are joined by a decision node while the outcomes are represented by

194 R B Gilbert et al

limbs that are joined by an outcome node (Figure 52) The sequence of thelimbs from left to right indicates the order of events in the decision processIn the example in Figure 52 the decision about the design alternative willbe made before knowing whether or not the foundation will function ade-quately Finally the outcomes are associated with consequences which areexpressed as costs in Figure 52

The following examples taken from actual projects illustrate a variety ofapplications for decision trees in RBD

Design B

Design A

FunctionsInadequately

Outcome Node

Decision Node

Inadequately

Adequately

Adequately

Functions

Functions

Functions

Consequences

Cost forConstruction

and Corrective Action

Cost forConstruction

and CorrectiveAction

Cost forConstruction

Cost forConstruction

Figure 52 Basic decision tree

Box 51 Example I ndash Decision tree for design of new facility

The combination of load and resistance factors to be used for the design ofa caisson foundation for a floating offshore structure was called into ques-tion due to the unique nature of the facility the loads on the foundation weredominated by the sustained buoyant load of the structure versus transientenvironmental loads and the capacity of the foundation was dominated byits weight versus the shear resistance of the soil The design-build contractorproposed to use a relatively small safety margin defined as the ratio of theload factor divided by the resistance factor due to the relatively small uncer-tainty in both the load and the capacity The owner wanted to consider thisproposed safety margin together with a typical value for more conventionaloffshore structures which would be higher and an intermediate value Thetwo considerations in this decision were the cost of the foundation whichwas primarily affected by needing to use larger vessels for installation as theweight and size of the caisson increased and the cost associated with a pull-out failure of the caisson if its capacity was not adequate The decision treein Figure 53 shows the structure of decision alternatives and outcomes thatwere considered

Practical application of RBD 195

Box 51 contrsquod

Relative Costs

Use Contractorrsquos ProposedSafety Margin (Small)

No Caisson Failure

Caisson Failure

Use Intermediate SafetyMargin (Medium)

No Caisson Failure

Caisson Failure

No Caisson FailureUse Conventional SafetyMargin (High)

None(Base Case)

Facility Damage

Larger Foundation

Larger Foundation + Facility Damage

Largest Foundation

Largest Foundation +Facility Damage Caisson Failure

Figure 53 Decision tree for Example I ndash Design of new facility

53 Decision criteria

Once the potential alternatives have been established through use of deci-sion trees or by some other means the criterion for making a decision amongpotential alternatives should logically depend on the consequences for possi-ble outcomes and their associated probabilities of happening A rational anddefensible criterion to compare alternatives when the outcomes are uncer-tain is the value of the consequence that is expected if that alternative isselected (Benjamin and Cornell 1970 Ang and Tang 1984) The expectedconsequence E(C) is obtained mathematically as follows

E (C) =sumallci

ciPC

(ci

)or

intallc

cfC (c)dc (51)

where C is a random variable representing the consequence and PC

(ci

)or

fC (c) is the probability distribution for c in discrete or continuous formrespectively

The consequences of a decision outcome generally include a variety ofattributes such as monetary value human health and safety environmentalimpact social and cultural impact and public perception Within thecontext of expected consequence (Equation (51)) a simple approach toaccommodate multiple attributes is to assign a single value such as a

Box 52 Example II ndash Decision tree for site investigation in mature field

The need for drilling geotechnical borings in order to design new structures ina mature offshore field where considerable geologic and geotechnical infor-mation was already available was questioned The trade-off for not drillinga site-specific boring was that an increased level of conservatism would beneeded in design to account for the additional uncertainty The decision tree inFigure 54 shows the structure of decision alternatives and outcomes that wereconsidered In this case the outcome of drilling the boring ie the geotech-nical design properties derived from the boring data is not known beforedrilling the boring In addition there is a range of possible design propertiesthat could be obtained from the boring and therefore a range of possible piledesigns that would be needed for a given set of geotechnical properties Thesemi-circles in Figure 54 indicate that there is a continuous versus a discreteset of possibilities at the outcome and decision nodes

Relative Costs

No Pile Failure New BoringDrill New Site-SpecificBoring

GeotechnicalProperties

DevelopConventional

Design

Pile Failure New Boring + Facility Damage

No Pile Failure Added Conservatism in Design

AddConservatism

to Design

Pile Failure

Use AvailableGeotechnicalInformation Added Conservatism in Design

+ Facility Damage

Figure 54 Decision tree for Example II ndash Site investigation in mature field

Box 53 Example III ndash Decision tree for pile monitoring in frontier field

The pile foundation for an offshore structure in a frontier area was designedbased on a preliminary analysis of the site investigation data The geotechnicalproperties of the site were treated in design as if the soil conditions were similarto other offshore areas where the experience base was large The steel wasthen ordered Subsequently a more detailed analysis of the geotechnical prop-erties showed that the soil conditions were rather unusual calling into question

Box 53 contrsquod

how the properties should be used in design and leading to relatively largeuncertainty in the estimated pile capacity The owner was faced with a seriesof decisions First should they stay with the original pile design or change itgiven the uncertainty in the pile capacity considering that changing the piledesign after the steel had been ordered would substantially impact the costand schedule of the project Second if they decided to stay with the originalpile design should they monitor the installation to confirm the capacity wasacceptable considering that this approach required a flexible contract wherethe pile design may need to be updated after the pile is installed The decisiontree in Figure 55 shows the structure of decision alternatives and outcomesthat were considered all of the possibilities are shown for completeness eventhough some such as keeping the design in spite of evidence that the capacityis unacceptable could intuitively be ruled out

Relative CostsNone (Base Case)No Pile Failure

Make Design More Conservative

Keep OriginalDesign

AcceptableCapacity

Do Not Monitor Pile Driving

MonitorPile Driving

UnacceptableCapacity

Facility Damage Pile Failure Keep

DesignNo Pile Failure Monitoring + Contract Flexibility

Monitoring + Contract Flexibility + Facility Damage

Pile Failure

Monitoring + Contract Flexibility+ Design Change

No Pile Failure

ChangeDesign Monitoring + Contract Flexibility +

Design Change + Facility DamagePile Failure No Pile Failure Monitoring + Contract FlexibilityKeep

Design Monitoring + ContractFlexibility + Facility Damage

Pile Failure

Monitoring + Contract Flexibility+ Design Change

No Pile FailureChangeDesign Monitoring + Contract Flexibility +

Design Change + Facility DamageDesign Change

Pile Failure No Pile Failure

Pile Failure Design Change + Facility Damage

Figure 55 Decision tree for Example III ndash Pile monitoring in frontier field

Box 54 Decision criterion for Example I ndash Design of new facility

The decision tree for the design example described in Box 51 (Figure 53)is completed in Figure 56 with information for the values and probabilitiesof the consequences The probabilities and costs for caisson failure corre-spond to the event that the caisson will pull out of the mudline at some timeduring the 20-year design life for the facility The costs are reported as negative

Continued

198 R B Gilbert et al

Box 54 contrsquod

values in order to denote monetary loss It is assumed that a caisson failurewill result in a loss of $500 million The cost of redesigning and installingthe caisson to achieve an intermediate safety margin is $1 million while thecost of redesigning and installing the caisson to achieve a high safety margin is$5 million The cost of the caisson increases significantly if the highest safetymargin is used because a more costly installation vessel will be required Inthis example the maximum expected consequence (or the minimum expectedcost) is obtained for the intermediate safety margin Note that the contributionof the expected cost of failure to the total expected cost becomes relativelyinsignificant for small probabilities of failure

Relative Costs (Millions $)

Use Contractorrsquos ProposedSafety Margin (Small)

No Caisson Failure 0(099)

(001)E(C) = 0(099) ndash 500(001) = minus$5 Millionminus500Caisson Failure

minus1No Caisson Failure(0999)

Use Intermediate SafetyMargin (Medium)

(0001)E(C) = minus1 ndash 500(0001) = minus$15 Million Caisson Failure minus(1 + 500)

No Caisson Failure(09999)

minus5Use Conventional SafetyMargin (High)

(00001)E(C) = minus5 ndash 500(00001) = minus$505 MillionCaisson Failure minus(5 + 500)

Figure 56 Completed decision tree for Example I ndash Design of new facility

monetary value that implicitly considers all of the possible attributes for anoutcome

A second approach to consider factors that cannot necessarily be directlyrelated to monetary value such as human fatalities is to establish tolerableprobabilities of occurrence for an event as a function of the consequencesThese tolerable probabilities implicitly account for all of the attributesassociated with the consequences A common method of expressing thisinformation is on a risk tolerance chart which depicts the annual probabil-ity of occurrence for an event versus the consequences expressed as humanfatalities These charts are sometimes referred to as FndashN charts where Fstands for probability or frequency of exceedance typically expressed on anannual basis and N stands for number of fatalities (Figure 57) These chartsestablish a level of risk expressed by an envelope or line which will betolerated by society in exchange for benefits such as economical energy

Practical application of RBD 199

1E-08

1E-07

1E-06

1E-05

1E-04

1E-03

1E-02

1E-01

1 10 100 1000

Number of Fatalities (N)

Lower Bound(Nuclear Power

Plants)

Target for OffshoreStructures

Ann

ual P

roba

bilit

y of

Inci

dent

s w

ith N

or

Mor

e F

atal

ities

Upper Bound(IndustrialFacilities)

Figure 57 Risk tolerance chart for an engineered facility

The risk is considered acceptable if the combination of probability and conse-quences falls below the envelope Excellent discussions concerning the basisfor applications of and limitations with these types of charts are provided inFischhoff et al (1981) Whitman (1984) Whipple (1985) ANCOLD (1998)Bowles (2001) USBR (2003) and Christian (2004)

The chart on Figure 57 includes an upper and lower bound for risktolerance based on a range of FndashN curves that have been published Thelower bound (most stringent criterion) on Figure 57 corresponds to a pub-lished curve estimating the risk tolerated by society for nuclear power plants(USNRC 1975) For comparison purposes government agencies in both theUnited States and Australia have established risk tolerance curves for damsthat are one to two orders of magnitude above the lower bound on Figure 57(ANCOLD 1998 and USBR 2003) The upper bound (least stringent crite-rion) on Figure 57 envelopes the risks that are being tolerated for a varietyof industrial applications such as refineries and chemical plants based onpublished data (eg Whitman 1984) As an example the target levels of riskthat are used by the oil industry for offshore structures are shown as a shadedrectangular box on Figure 57 (Bea 1991 Stahl et al 1998 Goodwin et al2000)

The level of risk that is deemed acceptable for the same consequences onFigure 57 is not a constant and depends on the type of facility If expectedconsequences are calculated for an event which results in a given number

200 R B Gilbert et al

of fatalities the points along the upper bound on Figure 57 correspondto 01 fatalities per year and those along the lower bound correspond to1 times 10minus5 fatalities per year Facilities where the consequences affect thesurrounding population versus on-site workers ie the risks are imposedinvoluntarily versus voluntarily and where there is a potential for catas-trophic consequences such as nuclear power plants and dams are generallyheld to higher standards than other engineered facilities

The risk tolerance curve for an individual facility may have a steeper slopethan one to one which is the slope of the bounding curves on Figure 57A steeper slope indicates that the tolerable expected consequence in fatalitiesper year decreases as the consequence increases Steeper curves reflect anaversion to more catastrophic events

Expressing tolerable risk in terms of costs is helpful for facilities wherefatalities are not necessarily possible due to a failure The Economist (2004)puts the relationship between costs and fatalities at approximately in orderof magnitude terms $10000000 US per fatality For example a typicaltolerable risk for an offshore structure is 0001 to 01 fatalities per year(shaded box Figure 57) which corresponds approximately to a tolerableexpected consequence of failure between $10000 and $1000000 per yearA reasonable starting point for a target value in a preliminary evaluation ofa facility is $100000 per year

The effect of time is not shown explicitly in a typical risk tolerance curvesince the probability of failure is expressed on an annual basis Howevertime can be included if the annual probabilities on Figure 57 are assumed toapply for failure events that occur randomly with time such as explosionshurricanes and earthquakes Based on the applications for which these curveswere developed this assumption is reasonable In this way the tolerabilitycurve can be expressed in terms of probability of failure in the lifetime ofthe facility meaning that failure events that may or may not occur randomlywith time can be considered on a consistent basis

Box 55 Tolerable risk for Example I ndash Design of new facility

Revisiting the example for the design of a floating system in Boxes 51and 54 (Figures 53 and 56) the event of ldquocaisson failurerdquo does not leadto fatalities The primary consequences for the failure of this very large andexpensive facility are the economic damage to the operator and the associatednegative perception from the public In addition the event of ldquocaissonfailurerdquo is not an event that occurs randomly with time over the 20-yearlifetime of the facility The maximum load on the foundation occurs whena maintenance operation is performed which happens once every five yearsof operation The probability of ldquocaisson failurerdquo in Figure 56 corresponds

Practical application of RBD 201

Box 55 contrsquod

to the probability that the caisson will be overloaded in at least one of theseoperations over its 20-year life The information on Figure 57 has beenre-plotted on Figure 58 for this example by assuming that incidents occurrandomly according to a Poisson process eg an incident with an annualprobability of 0001 per year on Figure 57 corresponds to probability ofoccurrence of 1 minus eminus(0001year)(20 years) = 002 in 20 years Figure 58 showsthat an acceptable probability of failure in the 20-year lifetime for this facilityis between 0002 and 002 Therefore the first design alternative on Figure 56(use contractorrsquos proposed factor of safety (small) with a probability of caissonfailure equal to 001) would provide a level of risk that is marginal concerningwhat would generally be tolerated

1E-07

1E-06

1E-05

1E-04

1E-03

1E-02

1E-01

1E+00

10 100 1000 10000

Cost of Failure C ($ Millions)

Lower Bound(Nuclear Power

Plants)

Upper Bound(IndustrialFacilities)

Target for OffshoreStructures

Pro

babi

lity

of In

cide

nt w

ith C

or

Mor

e D

amag

e in

20

Yea

rs

Figure 58 Risk tolerance chart for Example I ndash Design of floating system

A third approach to consider factors in decision making that cannot nec-essarily be directly related to monetary value is to use multi-attribute utilitytheory (Kenney and Raiffa 1976) This approach provides a rational andsystematic means of combining consequences expressed in different mea-sures to be combined together into a single scale of measure a utility valueWhile flexible and general implementation of multi-attribute utility theoryis cumbersome and not commonly used in engineering practice

202 R B Gilbert et al

54 Reliability analysis

Reliability analyses are an integral part of the decision-making processbecause they establish the probabilities for outcomes in the decision trees(eg the probability of caisson failure in Figure 56) The following sectionsillustrate practical applications for relating reliability analyses to decisionmaking

541 Simplified first-order analyses

The probability of failure for a design can generally be expressed as the prob-ability that a load exceeds a capacity A useful starting point for a reliabilityanalysis is to assume that the load on the foundation and the capacity ofthe foundation are independent random variables with lognormal distribu-tions In this way the probability of failure can be analytically related tothe first two moments of the probability distributions for the load and thecapacity This simplified first-order analysis can be expressed in a convenientmathematical form as follows (eg Wu et al 1989)

P(Load gt Capacity

)sim= Φ

⎛⎜⎝minus ln

(FSmedian

)radicδ2

load + δ2capacity

⎞⎟⎠ (52)

where P(Load gt Capacity) is the probability that the load exceeds the capac-ity in the design life which is also referred to as the lifetime probability offailure FSmedian is the median factor of safety which is defined as the ratioof the median capacity to the median load δ is the coefficient of variation(cov) which is defined as the standard deviation divided by the mean valuefor that variable and Φ () is the cumulative distribution function for a stan-dard normal variable The median factor of safety in Equation (52) can berelated to the factor of safety used in design as follows

FSmedian = FSdesign times

(capacitymedian

capacitydesign

)(

loadmedian

loaddesign

) (53)

where the subscript ldquodesignrdquo indicates the value used to design thefoundation The ratios of the median to design values represent biasesbetween the median value (or the most likely value since the mode equals themedian for a lognormal distribution) in the design life and the value that isused in the design check with the factor of safety or with load and resistancefactors The coefficients of variation in Equation (52) represent uncertainty

Practical application of RBD 203

in the load and the capacity The denominator in Equation (52) is referredto as the total coefficient of variation

δtotal =radicδ2

load + δ2capacity (54)

An exact solution for Equation (52) is obtained if the denominator iethe total cov in Equation (54) is replaced by the following expressionradic

ln(1 + δ2

load

)+ ln(1 + δ2

capacity

) the approximation in Equation (52) is

reasonable for values of the individual coefficients of variation that are lessthan about 03

The relationship between the probability of failure and the median factorof safety and total cov is shown in Figure 59 An increase in the medianfactor of safety and a decrease in the total cov both reduce the probabilityof failure

542 Physical bounds

In many practical applications of reliability analyses there are physicalbounds on the maximum load that can be applied to the foundation orthe minimum capacity that will be available in response to the load It is

0

01

02

03

04

05

06

07

08

1 2 3 4 5 6 7 8 9 10

Tot

al c

ov

Median FS

0001

000001

00001

001Failure Probability = 01

0000001

FixedJackets

Floating Systems

FixedJackets

Floating Systems

Figure 59 Graphical solutions for simplified first-order reliability analysis

204 R B Gilbert et al

Box 56 Reliability benchmarks for Example I ndash Design of new facility

In evaluating the appropriate level of conservatism for the design of a newfloating system as described in Box 51 (Figure 53) it is important to con-sider the reliability levels achieved for similar types of structures Fixed steeljacket structures have been used for more than 50 years to produce oil and gasoffshore in relatively shallow water depths up to about 200 m In recent yearsfloating systems have been used in water depths ranging from 1000 to 3000 mTo a large extent the foundation design methods developed for driven pilesin fixed structures have been applied directly to the suction caissons that areused for floating systems

For a pile in a fixed jacket with a design life of 20 years the median factorof safety is typically between three and five and the total cov is typicallybetween 05 and 07 (Tang and Gilbert 1993) Hence the resulting probabil-ity of failure in the lifetime is on the order of 001 based on Figure 59 Notethat the event of foundation failure ie axial overload of a single pile in thefoundation does not necessarily lead to collapse of a jacket failure probabil-ities for the foundation system are ten to 100 times smaller than those for asingle pile (Tang and Gilbert 1993)

For comparison typical values were determined for the median factor ofsafety and the total cov for a suction caisson foundation in a floating pro-duction system with a 20-year design life (Gilbert et al 2005a) The medianfactor of safety ranges from three to eight The median factor of safety tendsto be higher for the floating versus fixed systems for two reasons First a newsource of conservatism was introduced for floating systems in that the foun-dations are checked for a design case where the structure is damaged (ieone line is removed from the mooring system) Second the factors of safetyfor suction caissons were generally increased above those for driven piles dueto the relatively small experience base with suction caissons In addition to ahigher median factor of safety the total cov value for foundations in floatingsystems tends to be smaller with values between 03 and 05 This decreasecompared to fixed jackets reflects that there is generally less uncertainty inthe load applied to a mooring system foundation compared to that applied toa jacket foundation The resulting probabilities of foundation failure tend tobe smaller for floating systems compared to fixed jackets by several orders ofmagnitude (Figure 59) From the perspective of decision making this simpli-fied reliability analysis highlights a potential lack of consistency in how thedesign code is applied to offshore foundations and provides insight into whysuch an inconsistency exists and how it might be addressed in practice

important to consider these physical bounds in decision making applicationbecause they can have a significant effect on the reliability

One implication of the significant role that a lower-bound capacity canhave on reliability is that information about the estimated lower-boundcapacity should possibly be included in design-checking equations for

Box 57 Lower-bound capacity for Example I ndash Design of new facility

In evaluating the appropriate level of conservatism for the design of a newfloating system as described in Box 51 (Figure 53) it is important to con-sider the range of possible values for the capacity of the caisson foundationsA reasonable estimate of a lower-bound on the capacity for a suction caissonin normally consolidated clay can be obtained using the remolded strengthof the clay to calculate side friction and end bearing Based on an analysisof load-test data for suction caissons Najjar (2005) found strong statisticalevidence for the existence of this physical lower bound and that the ratio oflower-bound capacities to measured capacities ranged from 025 to 10 withan average value of 06

The effect of a lower-bound capacity on the reliability of a foundationfor a floating production system is shown in Figure 510 The structure isanchored with suction caisson foundations in a water depth of 2000 m detailsof the analysis are provided in Gilbert et al (2005a) Design informationfor the caissons is as follows loadmedianloaddesign = 07 δload = 014capacitymediancapacitydesign = 13 and δcapacity = 03 The lognormaldistribution for capacity is assumed to be truncated at the lower-bound valueusing a mixed lognormal distribution where the probability that the capac-ity is equal to the lower bound is set equal to the probability that the capacity is

100E-07

100E-06

100E-05

100E-04

100E-03

100E-02

100E-01

100E+00

0 02 04 06 08

Ratio of Lower-Bound to Median Capacity

Pro

babi

lity

of F

ailu

re in

Life

time

FSdesign = 15

FSdesign = 20

FSdesign = 25

Figure 510 Effect of lower-bound capacity on foundation reliability for Example I ndashDesign of floating system

Continued

206 R B Gilbert et al

Box 57 contrsquod

less than or equal to the lower bound for the non-truncated distribution Theprobability of failure is calculated through numerical integration

The results on Figure 510 show the significant role that a lower-boundcapacity can have on the reliability For a lower-bound capacity that is 06times the median capacity the probability of failure is more than 1000 timessmaller with the lower-bound than without it (when the ratio of the lower-bound to median capacity is zero) for a design factor of safety of 15

RBD codes For example an alternative code format would be to have twodesign-checking equations

φcapacitycapacitydesign ge γloadloaddesign

or

φcapacitylower boundcapacitylower bound ge γloadloaddesign

(55)

where φcapacity and γload are the conventional resistance and load factorsrespectively and φcapacitylower bound

is an added resistance factor that is appliedto the lower-bound capacity Provided that one of the two equations is satis-fied the design would provide an acceptable level of reliability Gilbert et al(2005b) show that a conservative value of 075 for φcapacitylower bound

wouldcover a variety of typical conditions in foundation design

Box 58 Lower-bound capacity for Example III ndash Pile monitoring in frontier field

The example described in Box 53 (Figure 55) illustrates a practical applica-tion where a lower-bound value for the foundation capacity provides valuableinformation for decision-making Due to the relatively large uncertainty inthe pile capacity at this frontier location the reliability for this foundationwas marginal using the conventional design check even though a relativelylarge value had been used for the design factor of safety FSdesign = 225 Theprobability of foundation failure in the design life of 20 years was slightlygreater than the target value of 1 times 10minus3 therefore the alternative of usingthe existing design without additional information (the top design alternativeon Figure 55) was not acceptable However the alternative of supplementingthe existing design with information from pile installation monitoring wouldonly be economical if the probability that the design would need to be changedafter installation was small Otherwise the preferred alternative for the ownerwould be to modify the design before installation

Box 58 contrsquod

In general pile monitoring information for piles driven into normal toslightly overconsolidated marine clays cannot easily be related to the ultimatepile capacity due to the effects of set-up following installation The capacitymeasured at the time of installation may only be 20ndash30 of the capacity afterset-up However the pile capacity during installation does arguably providea lower-bound on the ultimate pile capacity In order to establish the proba-bility of needing to change the design after installation the reliability of thefoundation was related to the estimated value for the lower-bound capacitybased on pile driving as shown on Figure 511 In this analysis the pile capac-ity estimated at the time of driving was considered to be uncertain it wasmodeled as a normally distributed random variable with a mean equal to theestimated value and a coefficient of variation of 02 to account for errors inthe estimated value The probability of foundation failure in the design lifewas then calculated by integrating the lower-bound value over the range ofpossible values for a given estimate The result is shown in Figure 511 Notethat even though there is uncertainty in the lower-bound value it still can havea large effect on the reliability

The expected value of driving resistance to be encountered and mea-sured during installation is indicated by the arrow labeled ldquoinstallationrdquo in

1E-05

1E-04

1E-03

1E-020 01 02 03 04 05 06

Estimated Lower Bound of Pile Capacity based on DrivingResistance

(Expressed as Ratio to Median Capacity)

Installation

Pro

babi

lity

of F

ound

atio

n F

ailu

re in

Des

ign

Life

Re-Tap

Target

Figure 511 Reliability versus lower-bound capacity for Example III ndash Pile monitoringin frontier field

Continued

208 R B Gilbert et al

Box 58 contrsquod

Figure 511 While it was expected that the installation information would justbarely provide for an acceptable level of reliability (Figure 511) the conse-quence of having to change the design at that point was very costly In orderto provide greater assurance the owner decided to conduct a re-tap analysis 5days after driving The arrow labeled ldquore-taprdquo in Figure 511 shows that thisinformation was expected to provide the owner with significant confidencethat the pile design would be acceptable The benefits of conducting the re-tapanalysis were considered to justify the added cost

543 Systems versus components

Nearly all design codes whether reliability-based or not treat foundationdesign on the basis of individual components However from a decision-making perspective it is insightful to consider how the reliability of anindividual foundation component is related to the reliability of the overallfoundation and structural system

Box 59 System reliability analysis for Example I ndash Design of new facility

The mooring system for a floating offshore facility such as that describedin Box 51 (Figure 53) includes multiple lines and foundation anchors Theresults from a component reliability analysis are shown in Figure 512 fora facility moored in three different water depths (Choi et al 2006) Theseresults correspond to the most heavily loaded line in the system where theprimary source of loading is from hurricanes Each mooring line consists ofsegments of steel chain and wire rope or polyester The points labeled ldquorope ampchainrdquo in Figure 512 correspond to a failure anywhere within the segmentsof the mooring line the points labeled ldquoanchorrdquo correspond to a failure at thefoundation and the points labeled ldquototalrdquo correspond to a failure anywherewithin the line and foundation The probability that the foundation fails isabout three orders of magnitude smaller than that for the line

The reliability of the system of lines was also related to that for the mostheavily loaded line (Choi et al 2006) In this analysis the event that thesystem fails is related to that for an individual line in two ways First the loadswill be re-distributed to the remaining lines when a single line fails Secondthe occurrence of a single line failure indicates information about the loads onthe system if a line has failed it is more likely that the system is being exposedto a severe hurricane (although it is still possible that the loads are relativelysmall and the line capacity for that particular line was small) The results forthis analysis are shown in Figure 513 where the probability that the mooringsystem fails is expressed as a conditional probability for the event that a

Box 59 contrsquod

single line in the system fails during a hurricane For the mooring systems in2000 and 3000 m water depths the redundancy is significant in that there isless than a 10 chance the system will fail even if the most heavily loaded linefails during a hurricane Also the mooring system in the 1000 m water depthhas substantially less redundancy than those in deeper water

10E-08

10E-07

10E-06

10E-05

10E-04

10E-03

10E-02

Water Depth (m)

1000 2000 3000

Rope ampChain

Anchor

Total

Pro

babi

lity

of F

ailu

re in

Des

ign

Life

Figure 512 Comparison of component reliabilities for most heavily loaded line inoffshore mooring system

70

Pro

babi

lity

of S

yste

m F

ailu

regi

ven

Fai

lure

of S

ingl

e Li

ne

0

10

20

50

60

40

30

1000 2000 3000Water Depth (m)

Figure 513 System redundancy for offshore mooring system

Continued

210 R B Gilbert et al

Box 59 contrsquod

The results in Figures 512 and 513 raise a series of questions forconsideration by the writers of design codes

(1) Is it preferable to have a system fail in the line or at the foundation Thebenefit of a failure in the foundation is that the weight of the foundationattached to the line still provides a restoring force to the entire mooringsystem even after it has pulled out from the seafloor The cost of failurein the foundation is that the foundation may cause collateral damage toother facilities if it is dragged across the seafloor

(2) Would the design be more effective if the reliability of the foundationwere brought closer to that of the line components It is costly to designfoundations that are significantly more conservative than the line Inaddition to the cost a foundation design that is excessively conserva-tive may pose problems in constructability and installation due to itslarge size

(3) Should the reliability for individual lines and foundations be consistentfor different water depths

(4) Should the system reliability be consistent for the different waterdepths

(5) How much redundancy should be achieved in the mooring system

55 Model calibration

The results from a reliability analysis need to be realistic to be of practicalvalue in decision-making Therefore the calibration of mathematical modelswith real-world data is very important

A general framework for calibrating a model with a set of data is obtainedfrom the following assumptions

bull The variation for an individual measurement about its mean value isdescribed by a Hermite Polynomial transformation of a standard normaldistribution which can theoretically take on any possible shape as theorder of the transformation approaches infinity (Journel and Huijbregts1978 Wang 2002)

bull The relationship between an individual data point with other data pointsis described by a linear correlation

bull The shape of the Hermite Polynomial transformation is consistentbetween data points ie affine correlation as described in Journel andHuijbregts (1978)

Practical application of RBD 211

In mathematical terms the probability distribution to describe variationsbetween a single measurement yi and a model of that measurement Yi isdescribed by a mean value a standard deviation correlation coefficientsand a conditional probability distribution

microYi= gmicro

(xYi

θ1 θnmicro

)(56)

σYi= gσ

(xYi

microYiθnmicro+1 θnmicro+nσ

)(57)

ρYiYj= gρ

(xYi

xYjθnmicro+nσ+1

θnmicro+nσ+nρ

)(58)

FYi|y1yiminus1

(yi

∣∣y1 yiminus1)= gF

(xYi

microYi|y1yiminus1σYi|y1yiminus1

θnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

)(59)

where microYiis the mean value and gmicro () is a model with nmicro model param-

eters θ1 θnmicro that relate the mean to attributes of the measurementxYi

σYiis the standard deviation and gσ () is a model with nσ model

parameters θnmicro+1 θnmicro+nσ that relate the standard deviation to attributes

and the mean value of the measurement ρYiYjis the correlation coeffi-

cient between measurements i and j and gρ () is a model with nρ modelparameters θnmicro+nσ+1 θnmicro+nσ+nρ

that relate the correlation coefficient to

the attributes for measurements i and j and FYi|y1yiminus1

(yi

∣∣y1 yiminus1)

isthe cumulative distribution function for measurement i conditioned on themeasurements y1 to yiminus1 and gF () is a model with nF model parametersθnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

that relate the cumulative distribution func-tion to the data attributes and the conditional mean value microYi|y1yiminus1

andstandard deviation σYi|y1yiminus1

which are obtained from the mean valuesstandard deviations and correlations coefficients for measurements 1 to iand the measurements y1 to yiminus1 The nF model parameters in gF () are thecoefficients in the Hermite Polynomial transformation function where theorder of the transformation is nF + 1

gF

(xYi

microYi|y1yiminus1σYi|y1yiminus1

θnmicro+nσ+nρ+1 θnmicro+nσ+nρ+nF

)

=int

ϕ(u)leyi minusmicroYi|y1yiminus1

σYi|y1yiminus1

1radic2π

eminus 12 u2

du (510)

212 R B Gilbert et al

where

ϕ (u) =

[H1 (u) +

nF+1summ=2

ψmm Hm (u)

]

minusradic

1 +nF+1summ=2

ψ2m

m

=y minusmicroYi|y1yiminus1

σYi|y1yiminus1

(511)

in which ψm are polynomial coefficients that are expressed as modelparameters ψm = θnmicro+nσ+nρ+mminus1 and Hm (u) are Hermite polynomialsHm+1 (u) = minusuHm (u)minusmHmminus1 (u) with H0 (u) = 1 and H1 (u) = minusu Thepractical implementation of Equation (510) involves first finding all of

the intervals of u in Equation (511) where ϕ (u) le yiminusmicroYi|y1yiminus1σYi|y1yiminus1

and then

integrating the standard normal distribution over those intervals If thedata points are assumed to follow a normal distribution then the order

of the polynomial transformation is one nF = 0 ϕ (u) = u = yminusmicroYi|y1yiminus1σYi|y1yiminus1

and FYi|y1yiminus1

(yi

∣∣y1 yiminus1)= (u) where (u) is the standard normal

functionThe framework described above contains nθ = nmicro + nσ + nρ + nF model

parameters θ1 θnmicro+nσ+nρ+nF that need to be estimated or calibrated

based on available information It is important to bear in mind that thesemodel parameters have probability distributions of their own since they arenot known with certainty For a given set of measurements the calibrationof the various model parameters follows a Bayesian approach

f1nθ |y1yn

(θ1 θnθ

∣∣y1 yn

)(512)

=P(y1 yn

∣∣∣θ1 θnθ

)f1nθ

(θ1 θnθ

)P(y1 yn

)where f1nθ |y1yn

(θ1 θnθ

∣∣y1 yn

)is the calibrated or updated

probability distribution for the model parameters P(y1 yn

∣∣∣θ1 θnθ

)is

the probability of obtaining this specific set of measurements for a given set

of model parameters and f1nθ

(θ1 θnθ

)is the probability distribu-

tion for the model parameters based on any additional information that isindependent of the measurements The probability of obtaining the mea-

surements P(y1 yn

∣∣∣θ1 θnθ

) can include both point measurements

as well as interval measurements such as proof loads or censored values

Practical application of RBD 213

(Finley 2004) While approximations exist for solving Equation (512) (egGilbert 1999) some form of numerical integration is generally required

The information from model calibration can be used in decision makingin two ways First the calibrated distribution from Equation (512) providesmodels that are based on all available information at the time of making adecision uncertainty in the calibrated parameters can be included directlyin the decision since the model parameters are represented by a probabilitydistribution and not deterministic point estimates Second the effect andtherefore value of obtaining additional information before making a decision(eg Figure 54) can be assessed

Box 510 Probability distribution of capacity for Example I ndash Design of new facility

The probability distribution for axial pile capacity plays an important role inreliability-based design such as described in Box 51 because it dictates theprobability of foundation failure due to overloading A database of pile loadtests with 45 driven piles in clay was analyzed by Najjar (2005) to investi-gate the shape of the probability distribution First a conventional analysiswas conducted on the ratio of measured to predicted capacity where the dis-tribution for this ratio was assumed to be lognormal Individual data pointswere assumed to be statistically independent In terms of the framework pre-sented in Equations (56) through (511) the attribute for each data point xiis the predicted capacity the function gmicro () is microInYi

= ln(θ1xi) the function

gσ () is σlnYi=radic

ln(1 + θ2

2

) the data points are statistically independent ie

the correlation coefficients between data points are zero and the cumulativedistribution function for lnYi gF () is a normal distribution with mean microlnYiand standard deviation σlnYi

Therefore there are two model parameters tobe calibrated with the data θ1 and θ2

In calibrating the model parameters with Equation (512) the only infor-mation used was the measured data points in the load test database ief12

(θ1θ2

)is taken as a non-informative diffuse prior distribution The cal-

ibrated expected values for θ1 and θ2 from the updated probability distributionobtained with Equation (512) are 096 and 024 respectively The resultingprobability distribution is shown on Figure 514 and labeled ldquoconventionallognormalrdquo

In Boxes 57 and 58 the effect of a lower bound on the pile capacity wasshown to be significant In order to investigate the existence of a lower-boundvalue an estimate for a lower-bound capacity was established for each datapoint based on the site-specific soil properties and pile geometry Specificallyremolded undrained shear strengths were used to calculate a lower-boundcapacity for driven piles in normally consolidated to slightly overconsolidated

Continued

Box 510 contrsquod

00001

0001

001

01

1

050 151

Actual CapacityPredicted Capacity

Cum

ulat

ive

Dis

tribu

tion

Fun

ctio

n

Hermite PolynomialTransformation

ConventionalnormalLog

Lower Bound

Figure 514 Calibrated probability distributions for axial pile capacity

clays and residual drained shear strengths and at-rest conditions were used tocalculate a lower-bound capacity for driven piles in highly overconsolidatedclays (Najjar 2005) For all 45 data points the measured capacity was abovethe calculated value for the lower-bound capacity To put this result in per-spective the probability of obtaining this result if the distribution of capacityreally is a lognormal distribution (with a lower bound of zero) is essentiallyzero meaning that a conventional lognormal distribution is not plausible

The model for the probability distribution of the ratio of measured to pre-dicted capacity was therefore refined to account for a more complicated shapethan a simple lognormal distribution The modifications are the following anadditional attribute the predicted lower-bound capacity is included for eachdata point and a 7th-order Hermite Polynomial transformation is used as ageneral model for the shape of the probability distribution In terms of Equa-tions (56) through (511) the attributes for each data point are the predictedcapacity x1i and the calculated lower-bound capacity x2i the function gmicro ()is microYi

= θ1x1i the function gσ () is σYi= θ2microYi

the correlation coefficientsbetween data points were zero and the cumulative distribution function forlnYi gF () is obtained by combining Equations (511) and (512) as follows

FY(yi)=

⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩

0 yi lt x2i

Φ(ui)where

H1(ui)+ 7sum

m=2

θm+1m Hm

(ui)

minusradicradicradicradic1 +

7summ=2

θ2m+1m

= yi minusmicroYi

σYi

yi ge x2i

(513)

Box 510 contrsquod

Therefore there are now eight model parameters to be calibrated with thedata θ1 to θ8

The calibrated expected values for the model parameters from the updatedprobability distribution obtained with Equation (512) are θ1 = 096θ2 = 026 θ3 = minus0289 θ4 = 0049 θ5 = minus0049 θ6 = 0005 θ7 = 005 andθ8 = 0169 The resulting probability distribution is shown on Figure 514 andlabeled ldquoHermite polynomial transformationrdquo

There are two practical conclusions from this calibrated model First theleft-hand tail of the calibrated distribution with the 7th-order Hermite Poly-nomial transformation is substantially different than that for the conventionallognormal distribution (Figure 514) This difference is significant because itis the lower percentiles of the distribution for capacity say less than 10which govern the probability of failure Second truncating the conventionallognormal distribution at the calculated value for the lower-bound capacityprovides a reasonable and practical fit to the more complicated 7th-orderHermite Polynomial transformation

Box 511 Value of new soil boring for Example II ndash Site investigation in mature field

A necessary input to the decision about whether or not to drill an additional soilboring in a mature field as described in Box 52 and illustrated in Figure 54is a model describing spatial variability in pile capacity across the field Thegeology for the field in this example is relatively uniform marine clays thatare normally to slightly overconsolidated The available data are soil boringswhere the geotechnical properties were measured and a design capacity wasdetermined Two types of soil borings are available modern borings wherehigh-quality soil samples were obtained and older borings where soil sampleswere of lower quality Pile capacity for these types of piles is governed by sideshear capacity

The model for the spatial variability in design pile capacity expressed interms of the average unit side shear over a length of pile penetration is givenby Equations (514) through (517)

microYi=(θ1 + θ2x1i + θ3x2

1i

)ex2i θ4 (514)

where Yi is the average unit side shear which is the total capacity due to sideshear divided by the circumference of the pile multiplied by its length x1i isthe penetration of the pile and x2i is 0 for modern borings and 1 for older

Continued

Box 511 contrsquod

borings (the term ex2i θ4 accounts for biases due to the quality of soil samples)

σYi= eθ4ex2i θ5 (515)

where the term eθ4 accounts for a standard deviation that does not dependon pile penetration and that is non-negative and the term ex2i θ5 accounts forany effects on the spatial variability caused by the quality of the soil samplesincluding greater or smaller variability

ρYiYj=⎛⎝e

minusradic(

x3iminusx3j)2+(x4iminusx4j

)2(eθ6+x1ieθ7)⎞⎠e

minus∣∣x2iminusx2j∣∣eθ8

(516)

where x3i and x4i are the coordinates describing the horizontal position

of boring i the term eminusradic(

x3iminusx3j)2+(x4iminusx4j

)2(eθ6+x1ieθ7)

accounts for apositive correlation between nearby borings that is described by a positive cor-

relation distance of eθ6 +x1ieθ7 and the term eminus∣∣x2iminusx2j

∣∣eθ8 accounts for asmaller positive correlation between borings with different qualities of samplesthan those with the same qualities of samples and the probability distributionfor the average side friction is modeled by a normal distribution

FYi|y1yiminus1

(yi∣∣y1 yiminus1

)= Φ

(yi minusmicroYi|y1yiminus1

σYi|y1yiminus1

)(517)

Therefore there are eight parameters to be calibrated with the data θ1 to θ8The detailed results for this calibration are provided in Gambino and Gilbert(1999)

Before considering the decision at hand there are several practicalconclusions that can be obtained from the calibrated model in its own rightThe expected values for the mean and standard deviation of average unitside shear from modern borings are 98 kPa and 72 kPa respectively Thecoefficient of variation for pile capacity is then 7298 or 007 meaning thatthe magnitude of spatial variability in pile capacity across this field is relativelysmall The expected horizontal correlation distance is shown with respect topile penetration in Figure 515 it is on the order of thousands of meters andit increases with pile penetration The expected bias due to older soil boringsis equal to 091 (ex2iθ4 in Equation (514)) meaning that designs based onolder borings tend to underestimate the pile capacity that would be obtainedfrom a modern boring Also the expected effect on the spatial variability dueto older soil borings is to reduce the standard deviation by 086 (ex2iθ5 ) in

Box 511 contrsquod

0

20

40

60

80

100

120

140

160

0 1000 2000 3000 4000

Horizontal Correlation Distance (m)

Pile

Pen

etra

tion

(m)

Figure 515 Calibrated horizontal correlation distance for pile capacities betweenborings

Equation (515) meaning that the data from the older soil borings tends tomask some of the naturally occurring spatial variations that are picked upwith modern borings thereby reducing the apparent standard deviation Fur-thermore the expected correlation coefficient between an older boring and a

modern boring at the exact same location is 058 (eminus∣∣x2iminusx2j∣∣eθ8 in Equa-

tion (516) and notably less than the ideal value of 10 meaning that it is notpossible to deterministically predict the pile capacity obtained from a modernboring by using information from an older boring

The results for this calibrated model are illustrated in Figures 516a and516b for an example 4000 m times 4000 m block in this field Within this blockthere are two soil borings available a modern one and an older one at therespective locations shown in Figures 516a and 516b The three-dimensionalsurface shown in Figure 1516a denotes the expected value for the calculateddesign capacity at different locations throughout the block The actual designpile capacities at the two boring locations are both above the average for thefield (Figure 516a) Therefore the expected values for the design pile capac-ity at other locations of an offshore platform within this block are aboveaverage however as the location moves further away from the existing bor-ings the expected value tends toward the unconditional mean for the fieldapproximately 30 MN

The uncertainty in the pile capacity due to not having a site-specificmodern soil boring is expressed as a coefficient of variation on Figure 516b

Continued

Box 511 contrsquod0

500

1000

2000

2500

3500

4000

1500

3000 0

1500

2000

2500

3000

3500

4000

500

1000

26

27

28

29

30

31

32

33

34

x Coordinate (m)

Exp

ecte

d P

ile C

apac

ity (

MN

)

y Coordinate (m)

Unconditional Mean

Older BoringModern Boring

Figure 516a Expected value for estimated pile capacity versus location for a 100 mlong 1 m diameter steel pipe pile

At the location of the modern boring this cov is zero since a modern boringis available at that location and the design capacity is known However at thelocation of the older boring the cov is greater than zero since the correlationbetween older and modern borings is not perfect (Figure 516b) therefore eventhough the design capacity is known at this location the design capacity thatwould have been obtained based on a modern soil boring is not known Asthe platform location moves away from both borings the cov approachesthe unconditional value for the field approximately 0075

The final step is to put this information from the calibrated model for spatialvariability into the decision tree in Figure 54 The added uncertainty in nothaving a site-specific modern boring is included in the reliability analysis byadding uncertainty to the capacity the greater the uncertainty the lower thereliability for the same set of load and resistance factors in a reliability-baseddesign code In order to achieve the same reliability as if a modern soil boring

Box 511 contrsquod

0

500

1000

1500

2000

2500

3000

3500

4000

0500

1000

1500

2000

2500

3000

3500

40000000

0020

0040

0060

0080

x Coordinate (m) y Coordinate (m)

co

v in

Pile

Cap

acity

Older Boring

Modern Boring

Unconditional cov

Figure 516b Coefficient of variation for estimated pile capacity versus location fora 100 m long 1 m diameter steel pipe pile

were available the resistance factor needs to be smaller as expressed in thefollowing design check format

φspatial

(φcapacitycapacitydesign

)ge γloadloaddesign (517)

where φspatial is a partial resistance factor that depends on the magnitudeof spatial variability obtained from Figure 516b and the design capacityis obtained from Figure 516a Figure 517 shows the relationship betweenφspatial and the coefficient of variation due to spatial variability for thefollowing assumptions design capacity is normally distributed with a cov of03 when a site-specific boring is available load is normally distributed witha cov of 05 and the target probability of failure for the pile foundationis 6 times 10minus3 (Gilbert et al 1999) To use this chart a designer would first

Continued

Box 511 contrsquod

075

080

085

090

095

100

0 002 004 006 008 01

Coefficient of Variation due to Spatial Variability

Partial ResistanceFactor

due to SpatialVariabilityφspatial

Figure 517 Partial resistance factor versus coefficient of variation for spatialvariability

quantify the spatial variability as a cov value using the approach describedabove as shown in Figure 516b The designer would then read the corre-sponding partial resistance factor directly from Figure 517 as a functionof this cov due to spatial variability When there is not additional uncer-tainty because a modern soil boring is available at the platform location(ie the cov due to spatial variability is 0) the partial resistance factoris 10 and does not affect the design As the cov due to spatial variabilityincreases the partial resistance factor decreases and has a larger effect on thedesign

The information in Figures 516a 516b and 517 can be combined togetherto support the decision-making process For example consider a location in theblock where the cov due to spatial variability is 006 from Figure 516b Therequired partial resistance factor to account for this spatial variability is 095or 95 of the design capacity could be relied upon in this case comparedto what is expected if a site-specific boring is drilled Since pile capacity isapproximately proportional to pile length over a small change in length thisreduction in design capacity is roughly equivalent to a required increase in pilelength of about 5 Therefore the cost of drilling a site-specific boring couldbe compared to the cost associated with increasing the pile lengths by 5 inthe decision tree in Figure 54 to decide whether or not to drill an additionalsoil boring

Practical application of RBD 221

56 Summary

There can be considerable value in using the reliability-based designapproach to facilitate and improve decision-making This chapter hasdemonstrated the practical potential of this concept Several different levelsof decisions were addressed

bull project-specific decisions such as how long to make a pile foundationor how many soil borings to drill

bull design code decisions such as what resistance factors should be usedand

bull policy decisions such as what level of reliability should be achieved forcomponents and systems in different types of facilities

An emphasis has been placed on first establishing what is important in thecontext of the decisions that need to be made before developing and applyingmathematical tools An emphasis has also been placed on practical meth-ods to capture the realistic features that will influence decision-making It ishoped that the real-world examples described in this chapter motivate thereader to consider how the role of decision making can be utilized in thecontext of reliability-based design for a variety of different applications

Acknowledgments

We wish to acknowledge gratefully the following organizations for support-ing the research and consulting upon which this paper is based AmericanPetroleum Institute American Society of Civil Engineers BP ExxonMobilNational Science Foundation Offshore Technology Research Center ShellState of Texas Advanced Research and Technology Programs Unocal andUnited States Minerals Management Service In many cases these organiza-tions provided real-world problems and data as well as financial supportWe would also like to acknowledge the support of our colleagues andstudents at The University of Texas at Austin The views and opinionsexpressed herein are our own and do not reflect any of the organizationsparties or individuals with whom we have worked

References

ANCOLD (1998) Guidelines on Risk Assessment Working Group on Risk Assess-ment Australian National Committee on Large Dams Sydney New South WalesAustralia

Ang A A-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Volume II ndash Decision Risk and Reliability John Wiley amp SonsNew York

222 R B Gilbert et al

Bea R G (1991) Offshore platform reliability acceptance criteria DrillingEngineering Society of Petroleum Engineers June 131ndash6

Benjamin J R and Cornell C A (1970) Probability Statistics and Decision forCivil Engineers McGraw-Hill New York

Bowles D S (2001) Evaluation and use of risk estimates in dam safetydecisionmaking In Proceedings Risk-Based Decision-Making in Water ResourcesASCE Santa Barbara California 17 pp

Choi Y J Gilbert R B Ding Y and Zhang J (2006) Reliability ofmooring systems for floating production systems Final Report for MineralsManagement Service Offshore Technology Research Center College StationTexas 90 pp

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental EngineeringASCE 130 (10) 985ndash1003

Finley C A (2004) Designing and analyzing test programs with censored datafor civil engineering applications PhD dissertation The University of Texas atAustin

Fischhoff B Lichtenstein S Slovic P Derby S L and Keeney R L (1981)Acceptable Risk Cambridge University Press Cambridge

Gambino S J and Gilbert RB (1999) Modeling spatial variability in pile capacityfor reliability-based design Analysis Design Construction and Testing of DeepFoundations ASCE Geotechnical Special Publication No 88 135ndash49

Gilbert R B (1999) First-order second-moment bayesian method for data analysisin decision making Geotechnical Engineering Center Report Department of CivilEngineering The University of Texas at Austin Austin Texas 50 pp

Gilbert R B Gambino S J and Dupin R M (1999) Reliability-based approachfor foundation design without with-specific soil borings In Proceedings Off-shore Technology Conference Houston Texas OTC 10927 Society of PetroleumEngineers Richardson pp 631ndash40

Gilbert R B Choi Y J Dangyach S and Najjar S S (2005a) Reliability-baseddesign considerations for deepwater mooring system foundations In Proceed-ings ISFOG 2005 Frontiers in Offshore Geotechnics Perth Western AustraliaTaylor amp Francis London pp 317ndash24

Gilbert R B Najjar S S and Choi Y J (2005b) Incorporating lower-boundcapacities into LRFD codes for pile foundations In Proceedings Geo-FrontiersAustin TX ASCE Virginia pp 361ndash77

Goodwin P Ahilan R V Kavanagh K and Connaire A (2000) Integratedmooring and riser design target reliabilities and safety factors In ProceedingsConference on Offshore Mechanics and Arctic Engineering New Orleans TheAmerican Society of Mechanical Engineers (ASME) New York 785ndash92

Journel A G and Huijbregts Ch J (1978) Mining Geostatistics Academic PressSan Diego

Kenney R L and Raiffa H (1976) Decision with Multiple Objectives Preferencesand Value Tradeoffs John Wiley and Sons New York

Najjar S S (2005) The importance of lower-bound capacities in geotech-nical reliability assessments PhD dissertation The University of Texas atAustin

Practical application of RBD 223

Stahl B Aune S Gebara J M and Cornell C A (1998) Acceptance criteria foroffshore platforms In Proceedings Conference on Offshore Mechanics and ArcticEngineering OMAE98-1463

Tang W H and Gilbert R B (1993) Case study of offshore pile system reliabilityIn Proceedings Offshore Technology Conference OTC 7196 Houston Societyof Petroleum Engineers pp 677ndash83

The Economist (2004) The price of prudence A Survey of Risk 24 January 6ndash8USBR (2003) Guidelines for Achieving Public Protection in Dam Safety Decision

Making Dam Safety Office United States Bureau of Reclamation Denver COUSNRC (1975) Reactor safety study an assessment of accident risks in US com-

mercial nuclear power plants United States Nuclear Regulatory CommissionNUREG-75014 Washington D C

Wang D (2002) Development of a method for model calibration with non-normaldata PhD dissertation The University of Texas at Austin

Whipple C (1985) Approaches to acceptable risk In Proceedings Risk-Based Deci-sion Making in Water Resources Ed Y Y Haimes and EZ Stakhiv ASCENew York CA pp 31ndash45

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110(2) 145ndash88

Wu T H Tang W H Sangrey D A and Baecher G (1989) Reliability of offshorefoundations Journal of Geotechnical Engineering ASCE 115(2) 157ndash78

Chapter 6

Randomly heterogeneous soilsunder static and dynamicloads

Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

61 Introduction

The values of soil properties used in geotechnical engineering and geome-chanics involve a significant level of uncertainty arising from several sourcessuch as inherent random heterogeneity (also referred to as spatial variabil-ity) measurement errors statistical errors (due to small sample sizes) anduncertainty in transforming the index soil properties obtained from soil testsinto desired geomechanical properties (eg Phoon and Kulhawy 1999) Inthis chapter only the first source of uncertainty will be examined (inher-ent random heterogeneity) as it can have a dramatic effect on soil responseunder loading (in particular soil failure) as will be demonstrated in the fol-lowing As opposed to lithological heterogeneity (which can be describeddeterministically) the aforementioned random soil heterogeneity refers tothe natural spatial variability of soil properties within geologically distinctlayers Spatially varying soil properties are treated probabilistically and areusually modeled as random fields (also referred to as stochastic fields) In thelast decade or so the probabilistic characteristics of the spatial variabilityof soil properties have been quantified using results of various sets of in-situtests

The first effort in accounting for uncertainties in the soil mass in geotech-nical engineering and geomechanics problems involved the random variableapproach According to this approach each soil property is modeled bya random variable following a prescribed probability distribution function(PDF) Consequently the soil property is constant over the analysis domainThis approach allowed the use of established deterministic analysis meth-ods developed for uniform soils but neglected any effects of their spatialvariability It therefore reduced the natural spatial variation of soil prop-erties to an uncertainty in their mean values only This rather simplisticapproach was quickly followed by a more sophisticated one modeling thespatial heterogeneity of various soil properties as random fields This becamepossible from an abundance of data from in-situ tests that allowed the

Soils under static and dynamic loads 225

establishment of various probabilistic characteristics of the spatial variabilityof soil properties

In the last decade or so a series of papers have appeared in the litera-ture dealing with the effect of inherent random soil heterogeneity on themechanical behavior of various problems in geomechanics and geotechnicalengineering using random field theory A few representative papers are men-tioned here grouped by the problem examined (1) foundation settlementsBrzakala and Pula (1996) Paice et al (1996) Houy et al (2005) (2) soilliquefaction Popescu et al (1997 2005ab) Fenton and Vanmarke (1998)Koutsourelakis et al (2002) Elkateb et al (2003) Hicks and Onisiphorou(2005) (3) slope stability El Ramly et al (2002 2005) Griffiths and Fenton(2004) and (4) bearing capacity of shallow foundations Cherubini (2000)Nobahar and Popescu (2000) Griffiths et al (2002) Fenton and Griffiths(2003) Popescu et al (2005c) The methodology used in essentially all ofthese studies was Monte Carlo Simulation (MCS)

First-order reliability analysis approaches (see eg Christian (2004) fora review) as well as perturbationexpansion techniques postulate the exis-tence of an ldquoaverage responserdquo that depends on the average values of thesoil properties This ldquoaverage responserdquo is similar to that obtained froma corresponding uniform soil having properties equal to the average prop-erties of the randomly variable soil The inherent soil variability (randomheterogeneity) is considered to induce uncertainty in the computed responseonly as a random fluctuation around the ldquoaverage responserdquo For exam-ple in problems involving a failure surface it is quite common to assumethat the failure mechanism is deterministic depending only on the averagevalues of the soil properties More specifically in most reliability analysesof slopes involving spatially variable soil deterministic analysis methodsare first used to estimate the critical slip surface (corresponding to a mini-mum factor of safety and calculated assuming uniform soil properties) Afterthat the random variability of soil properties along this pre-determinedslip surface is used in the stochastic analysis (eg El Ramly et al 20022005)

In contrast to the type of work mentioned in the previous paragrapha number of recent MCS-based studies that did not impose any restric-tions on the type and geometry of the failure mechanism (eg Paice et al1996 for foundation settlements Popescu et al 1997 2005ab for soilliquefaction Griffiths and Fenton 2004 for slope stability and Popescuet al 2005c for bearing capacity of shallow foundations) observed thatthe inherent spatial variability of soil properties can significantly modifythe failure mechanism of spatially variable soils compared to the corre-sponding mechanism of uniform (deterministic) soils For example Fochtand Focht (2001) state that ldquothe actual failure surface can deviate fromits theoretical position to pass through weaker material so that the averagemobilized strength is less than the apparent average strengthrdquo The deviation

226 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

of the failure surface from its deterministic configuration can be dramaticas there can be a complete change in the topology of the surface Further-more the average response of spatially variable soils (obtained from MCS)was observed to be significantly different from the response of the corre-sponding uniform soil For example Paice et al (1996) predicted an up to12 increase in average settlements for an elastic heterogeneous soil withcoefficient of variation CV = 42 compared to corresponding settlementsof a uniform soil deposit with the same mean soil properties Nobahar andPopescu (2000) and Griffiths et al (2002) found a 20ndash30 reduction in themean bearing capacity of heterogeneous soils with CV = 50 comparedto the corresponding bearing capacity of a uniform soil with the same aver-age properties Popescu et al (1997) predicted an increase of about 20 inthe amount of pore-water pressure build-up for a heterogeneous soil depositwith CV = 40 compared to the corresponding results of uniform soilwith the same mean properties It should be noted that the effects of spatialvariability were generally stronger for phenomena governed by highly non-linear constitutive laws (such as bearing capacity and soil liquefaction) thanfor phenomena governed by linear laws (eg settlements of foundations onelastic soil)

This chapter discusses the effects of inherent spatial variability (randomheterogeneity) of soil properties on two very different phenomena bearingcapacity failure of shallow foundations under static loads and seismicallyinduced soil liquefaction The first implies a limit equilibrium type of failureinvolving a large volume of soil while the second is based on a micro-scale(soil grain level) mechanism Based on results of numerical work previouslypublished by the authors it is shown that the end effects of spatial variabilityon limit loads are qualitatively similar for both phenomena

The probabilistic characteristics of the spatial variability of soils are brieflydiscussed in the first part of this chapter A general MCS approach forgeotechnical systems exhibiting random variation of their properties is alsopresented The second part focuses on the effects of inherent soil spatialvariability Based on examples dealing with the bearing capacity of shallowfoundations and seismically induced soil liquefaction it is shown that thespatial variability of soil properties can change the soil behavior from thewell known theoretical results obtained for uniform (deterministic) soils dra-matically The mechanisms of this intriguing behavior are analyzed in somedetail and interpreted using experimental and numerical evidence It is alsoshown that in some situations the resulting factors of safety and failureloads for randomly heterogeneous soils can be considerably lower than thecorresponding ones determined from some current reliability approachesused in geotechnical engineering with potentially significant implicationsfor design The concept of characteristic values or ldquoequivalent uniformrdquo soilproperties is discussed in the last part of the chapter as a means of accountingfor the effects of soil variability in practical applications

Soils under static and dynamic loads 227

62 Representing the random spatial variability ofsoil properties

The natural heterogeneity in a supposedly homogeneous soil layer may bedue to small-scale variations in mineral composition environmental condi-tions during deposition past stress history variations in moisture contentetc (eg Tang 1984 Lacasse and Nadim 1996) From a geotechnical designviewpoint this small-scale heterogeneity is manifested as spatial variation ofrelevant soil properties (such as relative density shear strength hydraulicconductivity etc)

An example of soil spatial variability is presented in Figure 61 in terms ofrecorded cone tip resistance values in a number of piezocone (CPTu) profilesperformed for core verification at one of the artificial islands (namely TarsiutP-45) constructed in the Canadian Beaufort Shelf and used as drilling plat-forms for oil exploration in shallow waters (Gulf Canada Resources 1984)The cone tip resistance qc is directly related to shear strength and to liq-uefaction potential of sandy soils The profiles shown in Figure 61 werelocated on a straight line at 9 m center-to-center distances Those recordsare part of a more extended soil investigation program consisting of 32piezocone tests providing an almost uniform coverage of the area of inter-est (72 mtimes72 m) The soil deposit at Tarsuit P-45 consists of two distinct

9m

MAC31

dept

h -

z(m

)

0minus5

minus10

minus15

minus20

0 10 20 0 10 20 0 10 20cone tip resistance (MPa)

averagevalues

example of loosesand pocket

example of densesand pocket

0 10 20 0 10 20 0 10 20

MAC04 MAC05 MAC06 MAC07 MAC08

9m 9m 9m 9m

Figure 61 Cone tip resistance recorded in the sand core at Tarsiut P-45 (after Popescuet al 1997)

228 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

a50m

P3

P2 P5 P8

P6

P9

P12

clay

sandclay

sandclay

sand

clay

sandclay

sand

clay

sand

clay

sandclay

sandclay

sand

0 25 50 0 25

Stress normalized standard penetration resistance - (N1)60recorded in the dense sand layer

50 0 25 50 0 25 50

clay

sand

clay 25 50

25 50

250 50

b

P11

sand profile P10

finesandwithsilt

siltyclay

densesand

clay

sandde

pth

(m)

dept

h (m

)55

m

55 m

50m 50m

minus45

minus40

minus35

minus30

minus25

P1 P4

P10P7

minus45

minus40

minus35

minus30

minus25

dept

h (m

)

minus45

minus40

minus35

minus30

minus25

minus20

minus15

minus10

minus5

dept

h (m

)

minus45

minus40

minus35

minus30

minus25

(N1)60

Figure 62 Standard Penetration Test results from a 2D measurement array in the TokyoBay area Japan (after Popescu et al 1998a)

layers an upper layer termed the ldquocorerdquo and a base layer termed the ldquobermrdquo(see eg Popescu et al 1998a for more details) Selected qc records fromthe core are shown in Figure 61 Records from both layers are shown inFigure 63a

A second example is presented in Figure 62 in terms of StandardPenetration Test (SPT) results recorded at a site in the Tokyo Bay areaJapan where an extended in situ soil test program had been performed forliquefaction risk assessment A two-dimensional measurement array consist-ing of 24 standard penetration test profiles was performed in a soil depositformed of three distinct soil layers (Figure 62b) a fine sand with silt inclu-sions a silty clay layer and a dense to very dense sand layer The resultsshown in Figure 62a represent stress normalized SPT blowcounts (N1)60recorded in the dense sand layer in 12 of the profiles Results from all soillayers are shown in Figure 63b

Soils under static and dynamic loads 229

c Probability density functions fitted to normalized fluctuations of recorded data

Gaussian distribution(for comparison)

Beta distribution (Tarsiut - core)Beta distribution (Tarsiut - berm)

Lognormal distrib(Tokyo Bay - Fine sand) Gamma distribution(Tokyo Bay - Dense sand)

a CPT data from Tarsiut P-45 (hydraulic fill) b SPT data from Tokyo Bay (natural deposit)

0 5 10Cone tip resistance - qc (MPa) Normalized SPT blowcount - (N1)60

15 20 25 0

minus50

minus30

05

04

03

02

01

0

minus4 minus3 minus2 minus1 0Normalized data (zero-mean unit standard deviation)

1 2 3 4

minus25

minus20

minus15

minus10

minus50

minus40

minus30

SILTY CLAY

Dep

th -

z(m

)

Dep

th -

z(m

)

FINE SAND

minus20mminus16m dry

DENSESAND

minus20

saturated

CORE

BERM

minus10

0

20 40 60 80 100

Figure 63 In-situ penetration test results at two sites (after Popescu et al 1998a) and cor-responding fitted probability distribution functions for the normalized data (zeromean unit standard deviation) (a) cone penetration resistance from 8 CPTprofiles taken at an artificial island in the Beaufort Sea (Gulf Canada Resources1984) (b) normalized standard penetration data from 24 borings at a site in theTokyo Bay area involving a natural soil deposit (c) fitted PDFs to normalizedfluctuations of field data Thick straight lines in (a) and (b) represent averagevalues (spatial trends)

It can be easily observed from Figures 61 and 62 that soil shear strength ndashwhich is proportional to qc and (N1)60 ndash is not uniform over the test area butvaries from one location to another (both in the horizontal and vertical direc-tions) As deterministic descriptions of this spatial variability are practicallyimpossible ndash owing to the prohibitive cost of sampling and to uncertaintiesinduced by measurement errors (eg VanMarcke 1989) ndash it has becomestandard practice today in the academic community to model this smallscale random heterogeneity using stochastic field theory The probabilistic

230 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

characteristics of a stochastic field describing spatial variability of soilproperties are briefly discussed in the following

621 Mean value

For analysis purposes the inherent spatial variability of soil propertiesis usually separated into an average value and fluctuations about it (egVanMarcke 1977 DeGroot and Baecher 1993) The average values of cer-tain index soil properties (eg qc) are usually assumed to vary linearly withdepth (eg Rackwitz 2000) reflecting the dependence of shear strength onthe effective confining stress They are usually called ldquospatial trendsrdquo andcan be estimated from a reasonable amount of in-situ soil data (using forexample a least square fit) In a 2D representation denoting the horizontaland vertical coordinates by x and z respectively the recorded qc values areexpressed as

qc(xz) = qavc (z) + uf (xz) (61)

where uf(xz) are the random fluctuations around the spatial trend qavc (z)

Assuming isotropy in the horizontal plane extension to 3D is straightfor-ward The spatial trends qav

c (z) are shown in Figures 61 and 63ab bythick straight lines representing a linear increase with depth of the aver-age recorded index values qc and (N1)60 It is mentioned that the thick linesin all plots in Figure 61 correspond to the same trend for all profiles (oneaverage calculated from all records) Several authors (eg Fenton 1999bEl-Ramly et al 2002) note the importance of correct identification of thespatial trends Phoon and Kulhawy (2001) state that ldquodetrending is as muchan art as a sciencerdquo and recommend using engineering judgment

622 Degree of variability

The degree of variability ndash or equivalently the magnitude of the randomfluctuations around the mean values ndash is quantified by the variance or thestandard deviation As the systematic trends can be identified and expresseddeterministically they are usually removed from the subsequent analysis anditrsquos only the local fluctuations that are modeled using random field theoryFor mathematical convenience the fluctuations uf(xz) in Equation (61) arenormalized by the standard deviation σ (z)

u(xz) = [qc(xz) minus qavc (z)]σ (z) (62)

so that the resulting normalized fluctuations u(xz) can be described by azero-mean unit standard deviation random field

As implied by Equation (62) the standard deviation σ (z) may be a func-tion of the depth z A depth-dependent standard deviation can be inferred

Soils under static and dynamic loads 231

by evaluating it in smaller intervals over which it is assumed to be constantrather than using the entire soil profile at one time For example the standarddeviation of the field data shown in Figure 61 was calculated over 1 m longmoving intervals and was found to vary between 1 and 4 MPa (Popescu1995)

The most commonly used parameter for quantifying the magnitude offluctuations in soil properties around their mean values is the coefficient ofvariation (CV) For the data in Figure 61 CV(z) = σ (z)qav

c (z) was found tovary between 20 and 60 The average CV of SPT data from the densesand layer in Figure 62 was about 40

It should be mentioned at this point that the CPT and SPT records shown inFigures 61 and 62 include measurement errors and therefore the degreesof variability discussed here may be larger than the actual variability ofsoil properties The measurement errors are relatively small for the caseof CPT records (eg for the electrical cone the average standard devia-tion of measurement errors is about 5 American Society for Testing andMaterials 1989) but they can be significant for SPT records which areaffected by many sources of errors (discussed by Schmertmann 1978 amongothers)

623 Correlation structure

The fluctuations of soil properties around their spatial trends exhibit in gen-eral some degree of coherencecorrelation as a function of depth as can beobserved in Figures 61 and 62 For example areas with qc values consis-tently larger than average indicate the presence of dense (stronger) pocketsin the soil mass while areas with qc values consistently smaller than aver-age indicate the presence of loose (less-resistant) soil Examples of denseand loose soil zones inferred from the fluctuations of qc are identified inFigure 61 Similarly the presence of a stronger soil zone at a depth of about35 m and spanning profiles P5 and P8ndashP12 can be easily observed from theSPT results presented in Figure 62

The similarity between fluctuations recorded at two points as a function ofthe distance between those two points is quantified by the correlation struc-ture The correlation between values of the same material property measuredat different locations is described by the auto-correlation function A signif-icant parameter associated with the auto-correlation function is called thescale of fluctuation (or correlation distance) and represents a length overwhich significant coherence is still manifested Owing to the geological soilformation processes for most natural soil deposits the correlation distancein the horizontal direction is significantly larger than the one in the verticaldirection This can be observed in Figure 61 by the shape of the dense andloose soil pockets In this respect separable correlation structure models (egVanMarcke 1983) seem appropriate to model different spatial variability

232 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

characteristics in the vertical direction (normal to soil strata) and in thehorizontal one

There are several one-dimensional theoretical auto-correlation functionmodels that can be combined into separable correlation structures and usedto describe natural soil variability (eg VanMarcke 1983 Shinozuka andDeodatis 1988 Popescu 1995 Rackwitz 2000) Inferring the parame-ters of a theoretical correlation model from field records is straightfor-ward for the vertical (depth) direction where a large number of closelyspaced data are usually available (eg from CPT records) However it ismuch more challenging for the horizontal direction where sampling dis-tances are much larger DeGroot and Baecher (1993) and Fenton (1999a)review the most common methods for estimating the auto-correlationfunction of soil variability in the vertical direction Przewlocki (2000)and Jaksa (2007) present methods for estimating the two-dimensionalcorrelation of soil spatial variability based on close-spaced field measure-ments in both vertical and horizontal directions As close-spaced recordsin horizontal direction are seldom available in practice Popescu (1995)and Popescu et al (1998a) introduce a method for estimating the hor-izontal auto-correlation function based on a limited number of verticalprofiles

As a note of caution it is mentioned here that various different definitionsfor the correlation distance exist in the literature yielding different values forthis parameter (see eg Popescu et al 2005c for a brief discussion) Anotherproblem stems from the fact that when estimating correlation distances fromdiscrete data the resulting values are dependent on the sampling distance(eg Der Kiureghian and Ke 1988) and the length scale or the distance overwhich the measurements are taken (eg Fenton 1999b)

When two or more different soil properties can be recorded simultane-ously it is interesting to assess the spatial interdependence between themand how it affects soil behavior For example from piezocone test resultsone can infer another index property ndash besides qc ndash called soil classifica-tion index which is related to grain size and soil type (see eg Jefferies andDavies 1993 Robertson and Wride 1998) The spatial dependence betweentwo different properties measured at different locations is described by thecross-correlation function In this case the variability of multiple soil prop-erties is modeled by a multivariate random field (also known as a vectorfield) and the ensemble of auto-correlation and cross-correlation functionsform the cross-correlation matrix

As the field data u(xz) employed to estimate the correlation structurehave been detrended and normalized (refer to Equation (62)) the result-ing field is stationary It is always possible to model directly non-stationarycharacteristics in spatial correlation but this is very challenging because oflimited data and modeling difficulties (eg Rackwitz 2000) Consequentlystationarity is almost always assumed (at least in a piecewise manner)

Soils under static and dynamic loads 233

624 Probability distribution

Based on several studies reported in the literature soil properties can followdifferent probability distribution functions (PDFs) for different types of soilsand sites but due to physical reasons they always have to follow distribu-tions defined only for nonnegative values of the soil properties (excludingthus the Gaussian distribution) Unlike Gaussian stochastic fields wherethe first two moments provide complete probabilistic information non-Gaussian fields require knowledge of moments of all orders As it is extremelydifficult to estimate moments higher than order two from actual data (egLumb 1966 Phoon 2006) the modeling and subsequent simulation of soilproperties that are represented as homogeneous non-Gaussian stochasticfields are usually done using their cross-correlation matrix (or equiva-lently cross-spectral density matrix) and non-Gaussian marginal probabilitydistribution functions

While there is no clear evidence pointing to any specific non-Gaussianmodel for the PDF of soil properties one condition that has to be satisfiedis for the PDF to have a lower (nonnegative) bound The beta gamma andlognormal PDFs are commonly used for this purpose as they all satisfy thiscondition For prescribed values of the mean and standard deviation thegamma and lognormal PDFs are one-parameter distributions (eg the valueof the lower bound can be considered as this parameter) They are bothskewed to the left Similarly for prescribed values of the mean and standarddeviation the beta PDF is a two-parameter distribution and consequentlymore flexible in fitting in-situ data Moreover it can model data that aresymmetrically distributed or skewed to the right Based on penetration datafrom an artificial and a natural deposit Popescu et al (1998a) observed thatPDFs of soil strength in shallow layers are skewed to the left while for deepersoils the corresponding PDFrsquos tend to follow more symmetric distributions(refer to Figure 63)

As will be discussed later the degree of spatial variability (expressed bythe coefficient of variation) is a key factor influencing the behavior of het-erogeneous soils as compared to uniform ones It has also been determinedthat both the correlation structure and the marginal probability distributionfunctions of soil properties affect the response behavior of heterogeneoussoils to significant degrees (eg Popescu et al 1996 2005a Fenton andGriffiths 2003)

63 Analysis method

631 Monte Carlo Simulation approach

Though expensive computationally Monte Carlo Simulation (MCS) isthe only currently available universal methodology for accurately solving

234 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

problems in stochastic mechanics involving strong nonlinearities and largevariations of non-Gaussian uncertain system parameters as is the case ofspatially variable soils As it will be shown later in this chapter failuremechanisms of heterogeneous soils may be different from the theoreticalones (derived for assumed deterministic uniform soil) and can change signif-icantly from one realization (sample function) of the random soil propertiesto another This type of behavior prevents the use of stochastic analysismethods such as the perturbationexpansion ones that are based on theassumption of an average failure mechanism and variations (fluctuations)around it

MCS is a means of solving numerically problems in mathematics physicsand other sciences through sampling experiments and basically consists ofthree steps (eg Elishakoff 1983) (1) simulation of the random quanti-ties (variables fields) (2) solution of the resulting deterministic problemfor a large number of realizations in step 1 and (3) statistical analysis ofresults For the case of spatially variable soils the realizations of the ran-dom quantities in step 1 consist of simulated sample functions of stochasticfields with probabilistic characteristics estimated from field data as discussedin Section 62 Each such sample function represents a possible realizationof the relevant soil index properties over the analysis domain The deter-ministic problem in step 2 is usually solved by finite element analysis Onesuch analysis is performed for each realization of the (spatially variable) soilproperties

632 Simulation of homogeneous non-Gaussian stochasticfields

Stochastic fields modeling spatially variable soil properties are non-Gaussianmulti-dimensional (2D or 3D) and can be univariate (describing the variabil-ity of a single soil property) or multivariate (referring to the variability of sev-eral soil properties and their interdependence) Among the various methodsthat have been developed to simulate homogeneous non-Gaussian stochas-tic fields the following representative ones are mentioned here Yamazakiand Shinozuka (1988) Grigoriu (1995 1998) Gurley and Kareem (1998)Deodatis and Micaletti (2001) Puig et al (2002) Sakamoto and Ghanem(2002) Masters and Gurley (2003) Phoon et al (2005)

As discussed in Section 624 it is practically impossible to estimate non-Gaussian joint PDFs from actual soil data Therefore a full descriptionof the random field representing the spatial variability of soil propertiesat a given site is not achievable Under these circumstances the simu-lation methods considered generate sample functions matching a targetcross-correlation structure (CCS) ndash or equivalently cross-spectral densitymatrix (CSDM) in the wave number domain ndash and target marginal PDFsthat can be inferred from the available field information It is mentioned

Soils under static and dynamic loads 235

here that certain simulation methods match only lower order moments(such as mean variance skewness and kurtosis) instead of marginal PDFs(the reader is referred to Deodatis and Micaletti 2001 for a review anddiscussion)

In most methods for non-Gaussian random field simulation a Gaussiansample function is first generated starting from the target CCS (or targetCSDM) and then mapped into the desired non-Gaussian sample functionusing a transformation procedure that involves nonlinear mapping (egGrigoriu 1995 Phoon 2006) While the target marginal PDFs are matchedafter such a mapping the nonlinearity of this transformation modifies theCCS and the resulting non-Gaussian sample function is no longer compati-ble with the target CCS Cases involving small CVs and non-Gaussian PDFsthat are not too far from Gaussianity result in general in changes in the CCSthat are small compared to the uncertainties involved in estimating the tar-get CCS from field data However for highly skewed marginal PDFs thedifferences between target and resulting CCSs may become significant Forexample Popescu (2004) discussed such an example where mapping from aGaussian to a lognormal random field for very large CVs of soil variabilityproduced differences of up to 150 between target and resulting correlationdistances

There are several procedures for correcting this problem and generat-ing sample functions that are compatible with both the target CCS andmarginal PDFs These methods range from iterative correction of the tar-get CCS (or target CSDM) used to generate the Gaussian sample function(eg Yamazaki and Shinozuka 1988) to analytical transformation methodsthat are applicable to certain combinations of target CCS ndash target PDF (egGrigoriu 1995 1998) A review of these methods is presented by Deodatisand Micaletti (2001) together with an improved algorithm for simulatinghighly skewed non-Gaussian fields

The methodology used for the numerical examples presented in this studycombines work done on the spectral representation method by Yamazaki andShinozuka (1988) Shinozuka and Deodatis (1996) and Deodatis (1996) andextends it to simulation of multi-variate multi-dimensional (mVndashnD) homo-geneous non-Gaussian stochastic fields According to this methodology asample function of an mVndashnD Gaussian vector field is first generated usingthe classic spectral representation method Then this Gaussian vector fieldis transformed into a non-Gaussian one that is compatible with a prescribedcross-spectral density matrix and with prescribed (non-Gaussian) marginalPDFs assumed for the soil properties This is achieved through the clas-sic memoryless nonlinear transformation of ldquotranslation fieldsrdquo (Grigoriu1995) and the iterative scheme proposed by Yamazaki and Shinozuka(1988) For a detailed presentation of this simulation algorithm and a dis-cussion on the convergence of the iterative scheme the reader is referredto Popescu et al (1998b) The reason that a more advanced methodology

236 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

such as that of Deodatis and Micaletti (2001) is not used in this chapteris the fact that the random fields describing various soil properties areusually only slightly skewed and consequently the iterative correctionproposed by Yamazaki and Shinozuka (1988) gives sufficiently accurateresults

633 Deterministic finite element analyses with generatedsoil properties as input

Solutions of boundary value problems can be readily obtained through finiteelement analysis (FEA) Along the lines of the MCS approach a deterministicFEA is performed for every generated sample function representing a pos-sible realization of soil index properties over the analysis domain In eachsuch analysis the relevant material (soil) properties are obtained at eachspatial location (finite element centroid) as a function of the simulated soilindex properties Two important issues related to such analyses are worthmentioning

1 The size of finite elements should be selected to accurately reproducethe simulated boundary value problem and at the same time to ade-quately capture the essential features of the stochastic spatial variabilityof soil properties Regarding modeling the spatial variability the opti-mum mesh refinement depends on both correlation distance and type ofcorrelation structure For example Der Kireghian and Ke (1988) rec-ommend the following upper bounds for the size of finite elements tobe used with an exponential correlation structure xk le (025minus05)θkwhere xk is the finite element size in the spatial direction ldquokrdquo and θkis the correlation distance in the same spatial direction

2 Quite often the mesh used for generating sample functions of the ran-dom field representing the soil properties is different from the finiteelement mesh Therefore the generated random soil properties have tobe transferred from one mesh to another There are several methods foraccomplishing this data transfer (see eg Brenner 1991 for a review)Two of them are mentioned here (see Popescu 1995 for a compari-son study) (1) the spatial averaging method proposed by VanMarcke(1977) which assigns to each element a value obtained as an averageof stochastic field values over the element domain and (2) the midpointmethod (eg Shinozuka and Dasgupta 1986 Der Kiureghian and Ke1988 Deodatis 1989) in which the random field is represented by itsvalues at the centroid of each finite element The midpoint method isused in all numerical examples presented here as it better preserves thenon-Gaussian characteristics describing the variability of different soilproperties

Soils under static and dynamic loads 237

64 Understanding mechanisms and effects

641 A simple example

To provide a first insight on how spatial variability of material propertiescan modify the mechanical response and which are the main factors affectingstructural behavior a simple example is presented in Figure 64 The responseof an axially loaded linear elastic bar with initial length L0 unit area A0 = 1and uniform Youngrsquos modulus E = 1 is compared to that of another linearelastic bar with the same L0 and A0 but made of two different materialswith Youngrsquos moduli equal to E1 = 12 and E2 = 08 The uniform barand the non-uniform one are shown in Figures 64a and 64b respectivelyNote that the non-uniform bar in Figure 64b has an ldquoaveragerdquo modulus(E1 + E2)2 = 1 equal to the Youngrsquos modulus of the uniform bar Thelinear stressndashstrain relationships of the three materials E E1 and E2 areshown in Figure 64c The two bars are now subjected to an increasingtensile axial stress σ The ratio of the resulting axial strains ε-variableε-uniform (variable denoting the non-uniform bar) is plotted versus a rangeof values for σ in Figure 64e In this case involving linear elastic materialsonly a 4 difference is observed between ε-variable and ε-uniform for allvalues of σ (continuous line in Figure 64e) This small difference is mainly

L02

L02

uniform(E)

stiffer(E1=12E)

softer(E2=08E)

s s

E=10

E1=12

E2=08

s

e

c Linear material

E1=12E=10E2=08

s

e

d Bi-linear material

Eacute=01

Eacute1=012

Eacute2=008

ep = 1

a

0 05 1 15 20

05

1

15

2

25

Axial stress ndash s

104

sp2

sp2

sp1

sp1

LinearBi-linear

e

b

ε-va

riabl

e ε

-uni

form

Figure 64 Simple example illustrating the effects of material heterogeneity on mechanicalresponse (a) uniform bar (b) non-uniform bar (c) linear stressndashstrain relation-ships (d) bi-linear stressndashstrain relationships (e) resulting strain ratios for arange of axial stresses

238 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

induced by the type of averaging considered for Youngrsquos modulus and itwould vanish if geometric mean were used for the elastic moduli instead ofarithmetic mean

The situation is significantly different for the same bars when the mate-rial behavior becomes nonlinear Assuming bilinear material behavior for allthree materials as shown in Figure 64d the resulting differences betweenthe responses in terms of axial strains are now significant As indicated bythe dotted line in Figure 64e the ratio ε-variableε-uniform now exceeds200 for some values of σ The reason for this different behavior is thatfor a certain range of axial stresses the response of the non-uniform bar iscontrolled by the softer material that reaches the plastic state (with resultingvery large strains) before the material of the uniform bar From this simpleexample it can be concluded that (1) mechanical effects induced by mate-rial heterogeneity are more pronounced for phenomena governed by highlynonlinear laws (2) loose zones control the deformation mechanisms and(3) the effects of material heterogeneity are stronger for certain values of theload intensity All these observations will be confirmed later based on morerealistic examples of heterogeneous soil behavior

642 Problems involving a failure surface

In a small number of recent MCS-based studies that did not impose anyrestrictions on the geometry of the failure mechanism it has been observedthat the inherent spatial variability of soil properties could significantlymodify the failure mechanism of spatially variable soils as compared tothe corresponding mechanism of uniform (deterministic) soils

Along these lines Figure 65 presents results of finite element analyses(FEA) of bearing capacity (BC) for a purely cohesive soil with elastic-plasticbehavior (the analysis details are presented in Popescu et al 2005c) Theresults of a so-called ldquodeterministic analysisrdquo assuming uniform soil strengthover the analysis domain are shown in Figure 65a in terms of maximumshear strain contours Under increasing uniform vertical pressure the foun-dation settles with no rotations and induces bearing capacity failure in asymmetrical pattern essentially identical to that predicted by Prandtlrsquos the-ory The symmetric pattern for the maximum shear strain can be clearly seenin Figure 65a The corresponding pressurendashsettlement curve is shown witha dotted line in Figure 65d

Figure 65b displays one sample function of the spatially variable soil shearstrength having the same mean value as the value used in the deterministic(uniform soil) analysis (cav

u =100 kPa) and a coefficient of variation of 40The undrained shear strength is modeled here as a homogeneous randomfield with a symmetric beta probability distribution function and a separablecorrelation structure with correlation distances θH =5 m in the horizontaldirection and θV =1 m in the vertical direction Lighter areas in Figure 65b

Soils under static and dynamic loads 239

Initial soillevel

δB =01

B =4m

Max shearstrain ()

011020

a

δB =01

Max shearstrain ()

011020

c

b

15010050

cu (kPa)

d

Settlement δ(m)

Spatially varying soil shown in Fig 65bUniform soil (deterministic)

Nor

mal

ized

pre

ssur

e q

c uav

0 01 02 03 04 050

20

40

60

Figure 65 Comparison between finite element computations of bearing capacity for a uni-form vs a heterogeneous soil deposit (a) contours of maximum shear strain fora uniform soil deposit with undrained shear strength cu =100 kPa (b) contoursof undrained shear strength (cu) obtained from a generated sample function ofa random field modeling the spatial variability of soil (c) contours of maximumshear strain for the variable soil shown in Figure 5(b) (d) computed normalizedpressure vs settlement curves Note that the settlement δ is measured at themidpoint of the width B of the foundation (after Popescu et al 2005c)

indicate weaker zones of soil while darker areas indicate stronger zonesFigure 65c displays results for the maximum shear strain corresponding tothe spatially variable soil deposit shown in Figure 65b An unsymmetric fail-ure surface develops passing mainly through weaker soil zones as illustratedby the dotted line in Figure 65b The resulting pressurendashsettlement curve(continuous line in Figure 65d) indicates a lower ultimate BC for the spa-tially variable soil than that for the uniform one (dotted line in Figure 65d)It is important to emphasize here that the failure surface for the spatially vari-able soil passes mainly through weak soil zones indicated by lighter patchesin Figure 65b It should be noted that other sample functions of the spatiallyvariable soil shear strength different from the one shown in Figure 65b willproduce different failure surfaces from the one shown in Figure 65c (anddifferent from the deterministic failure pattern shown in Figure 65a)

Figure 66 also taken from Popescu et al (2005c) presents results involv-ing 100 sample functions in a Monte Carlo simulation type of analysis Theproblem configuration here is essentially identical to that in Figure 65 the

240 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

20 10 0 10 200

25

50

75

Foundation slope ()

b

Nor

mal

ized

pre

ssur

e q

c uav

Settlements (m)

0 01 02 03 04 050

20

40

60

Monte Carlo simulation resultsDeterministic analysis results

Average from Monte Carlo simulations

a

Nor

mal

ized

pre

ssur

e q

c uav

Settlement at ultimate bearing capacity(δB =001 B = 4m)

Figure 66 Monte Carlo simulation results involving 100 sample functions for a strip founda-tion on an overconsolidated clay deposit with variable undrained shear strengthProblem configuration identical to that in Figure 65 except marginal PDF of soilproperties (a) normalized bearing pressures vs settlements (b) normalizedbearing pressures vs footing rotations (after Popescu et al 2005c)

only difference being that the marginal PDF of soil properties is now modeledby a Gamma distribution (compared to a beta distribution in Figure 65)The significant variation between different sample functions becomes imme-diately obvious as well as the fact that on the average the ultimate BC ofspatially variable soils is considerably lower than the corresponding valueof uniform (deterministic) soils Figure 66b indicates also that the spatialvariability of soils induces footing rotations (differential settlements) forcentrally loaded symmetric foundations

The aforementioned results suggest the following behavior for problemsinvolving the presence of a failure surface (1) the consideration of the spatialvariability of soil properties leads to different failure mechanisms (surfaces)for different realizations of the soil properties These failure surfaces canbecome dramatically different from the classic ones predicted by existingtheories for uniform (deterministic) soils (eg PrandtlndashReisner solution forBC critical slip surface resulting from limit equilibrium analysis for slopestability) (2) an immediate consequence is that the failure loadsfactors ofsafety for the case of spatially variable soils can become significantly lower(on the average) than the corresponding values for uniform (deterministic)soils

643 Problems involving seismically induced soilliquefaction

Marcuson (1978) defines liquefaction as the transformation of a granularmaterial from a solid to a liquefied state as a consequence of increased pore

Soils under static and dynamic loads 241

water pressure and reduced effective stress This phenomenon occurs mostreadily in loose to medium dense granular soils that have a tendency tocompact when sheared In saturated soils pore water pressure drainagemay be prevented due to the presence of silty or clayey seam inclusionsor may not have time to occur due to rapid loading ndash such as in the caseof seismic events In this situation the tendency to compact is translatedinto an increase in pore water pressure This leads to a reduction in effec-tive stress and a corresponding decrease of the frictional shear strengthIf the excess pore water pressure (EPWP) generated at a certain locationin a purely frictional soil (eg sand) reaches the initial value of the effec-tive vertical stress then theoretically all shear strength is lost at thatlocation and the soil liquefies and behaves like a viscous fluid Liquefaction-induced large ground deformations are a leading cause of disasters duringearthquakes

Regarding the effects of soil spatial variability on seismically induced lique-faction both experimental (eg Budiman et al 1995 Konrad and Dubeau2002) and numerical results indicate that more EPWP is generated in a het-erogeneous soil than in the corresponding uniform soil having geomechanicalproperties equal to the average properties of the variable soil

To illustrate the effects of soil heterogeneity on seismically induced EPWPbuild-up some of the results obtained by Popescu et al (1997) for a loose tomedium dense saturated soil deposit subjected to seismic loading are repro-duced in Figure 67 The geomechanical properties and spatial variabilitycharacteristics were estimated based on the piezocone test results shown inFigure 61 The results in Figure 67b show the computed contours of theEPWP ratio with respect to the initial effective vertical stress for six samplefunctions of a stochastic field representing six possible realizations of soilproperties over the analysis domain (see Popescu et al 1997 for more detailson the soil properties) Soil liquefaction (EPWP ratio larger than approxi-mately 09) was predicted for most sample functions shown in Figure 67bAnalysis of an assumed uniform (deterministic) soil deposit with strengthcharacteristics corresponding to the average strength of the soil samples usedin MCS resulted in no soil liquefaction (the maximum predicted EPWP ratiowas 044 as shown in the upper-left plot of Figure 67c) It can be concludedfrom these results that both the pattern and the amount of dynamicallyinduced EPWP build-up are strongly affected by the spatial variability ofsoil properties For the same average values of the soil parameters moreEPWP build-up was predicted in the stochastic analysis (MCS) accountingfor spatial variability than in the deterministic analysis considering uniformsoil properties

It was postulated by Popescu et al (1997) that the presence of loosesoil pockets leads to earlier initiation of EPWP and to local liquefactioncompared to the corresponding uniform soil After that the pressure gra-dient between loose and dense zones would induce water migration into

242 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

20 one-phase finiteelements (2 dofnode)

Water table

Vertical scale is twotimes larger thanhorizontal scale

090100070090050070000050

Grey scale rangefor the excess porepressure ratio (usrsquovo)

480 two- phase finite elementsfor porous media (4 dofnode)with dimensions 3m x 05mfree field boundary

conditions

Sample function 1 Sample function 2 Sample function 3

Sample function 4 Sample function 5 Sample function 6

Deterministic - average 50 - percentile 60 - percentile

70 - percentile 80 - percentile 90 - percentile

a Finite element analysis set- up

600m

b Excess pore pressure ratio predicated using six sample functions of a stochastic vector field withcross- correlation structure and probability distribution functions estimated from piezocone test results

c Excess pore pressure ratio predicted using deterministic input soil parameters

120

m1

6m

(u srsquov0)max = 044

Figure 67 Monte Carlo simulations of seismically induced liquefaction in a saturated soildeposit accounting for natural variability of the soil properties (after Popescuet al 1997) (a) finite element analysis setup (b) contours of EPWP ratio for sixsample functions used in the Monte Carlo simulations (c) contours of EPWPratio using deterministic (ie uniform) soil parameters and various percentilesof soil strength

neighboring denser soil zones followed by softening and liquefaction ofthe dense sand This assumption ndash mainly that the presence of loose soilpockets is responsible for lower liquefaction resistance ndash was further ver-ified by two sets of numerical results First for a given average value ofthe soil strength and a given earthquake intensity the resulting amount ofEPWP build-up in a variable soil increased with the degree of variability ofsoil properties (expressed by the coefficient of variation) This degree of soilvariability directly controls the amount of loose pockets in the soil mass (egPopescu et al 1998c) Second for the same coefficient of variation of spatial

Soils under static and dynamic loads 243

variability more EPWP was predicted when the soil strength fluctuationsfollowed a probability distribution function (PDF) with a fatter left tail thatyields directly a larger amount of loose soil pockets (eg Popescu et al1996)

There are very few experimental studies dealing with liquefaction of spa-tially variable soil Particularly interesting are the results obtained by Konradand Dubeau (2002) They performed a series of undrained cyclic triaxialtests with various cyclic stress ratios on uniform dense sand uniform siltand layered soil (a silt layer sandwiched between two sand layers) The soilsin layered samples were prepared at the same void ratios as the correspond-ing soils in the uniform samples It was concluded from the results that thecyclic strength (expressed as number of cycles to liquefaction) of the layered(non-homogeneous) samples was considerably lower than the one of theuniform silt and the uniform sand samples For example at a cyclic stressratio (CSR) = 0166 the numbers of cycles to liquefaction were NL = 150for uniform dense sand at relative density 77 NL = 90 for silt preparedat void ratio 078 and NL = 42 for the layered soil Chakrabortty et al(2004) reproduced the cyclic undrained triaxial tests on uniform and lay-ered samples made of dense sand and silt layers described by Konrad andDubeau (2002) and studied the mechanism by which a sample made of twodifferent soils liquefies faster than each of the soils tested separately in uni-form samples Their explanation ndash resulting from a detailed analysis of thenumerical results ndash was that water was squeezed from the more deformablesilt layer and injected into the neighboring sand leading to liquefaction ofthe dense sand

Regarding liquefaction mechanisms of soil deposits involving the samematerial but with spatially variable strength Ghosh and Madabhushi (2003)performed a series of centrifuge experiments to analyze the effects of local-ized loose patches in a dense sand deposit subjected to seismic loads Theyobserved that EPWP is first generated in the loose sand patches and thenwater migrates into the neighboring dense sand reducing the effective stressand loosening the dense soil that can subsequently liquefy As discussedbefore it is believed that a similar phenomenon is responsible for the lowerliquefaction resistance of continuously heterogeneous soils compared to thatof corresponding uniform soils To further verify this assumed mechanismPopescu et al (2006) calibrated a numerical finite element model for liquefac-tion analysis (Preacutevost 1985 2002) to reproduce the centrifuge experimentalresults of Ghosh and Madabhushi (2003) and then used it for a detailedanalysis of a structure founded on a hypothetical chess board-like heteroge-neous soil deposit subjected to earthquake loading From careful analysis ofthe EPWP calculated at the border between loose and dense zones it wasclearly observed how pore water pressures built up first in the loose areasand then transferred into the dense sand zones that eventually experiencedthe same increase in EPWP Water ldquoinjectionrdquo into dense sand loosened the

244 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

strong pockets and the overall liquefaction resistance of the heterogeneoussoil deposit became much lower than that of a corresponding uniform soilalmost as low as the liquefaction resistance of a deposit made entirely ofloose sand

65 Quantifying the effects of random soilheterogeneity

651 General considerations

Analyzing the effects of inherent soil variability by MCS involves the solu-tion of a large number of nonlinear boundary value problems (BVPs) aswas described earlier in this chapter Such an approach is too expensivecomputationally and too complex conceptually to be used in current designapplications The role of academic research is to eventually provide thegeotechnical practice with easy to understandeasy to use guidelines in theform of charts and characteristic values that account for the effects of soilvariability In this respect MCS results contain a wealth of information thatcan be processed in various ways to provide insight into the practical aspectsof the problem at hand Two such design guidelines estimated from MCSresults are presented here

1 ldquoEquivalent uniformrdquo soil properties representing values of certain soilproperties which ndash when used in a deterministic analysis assuming uni-form soil properties over the analysis domain ndash would provide a responseldquoequivalentrdquo to the average response resulting from computationallyexpensive MCS accounting for soil spatial variability The responseresulting from deterministic analysis is termed ldquoequivalentrdquo to the MCSresponse since only certain components of it can be similar For exam-ple when studying BC failure of symmetrically loaded foundations onecan obtain in a deterministic analysis an ultimate BC equal to the aver-age one from MCS but cannot simulate footing rotations Similarlyfor the case of soil liquefaction a deterministic analysis can reproducethe average amount of EPWP resulting from MCS but cannot predictany kind of spatially variable liquefaction pattern (see eg Figures 67band c) In conclusion an equivalent uniform analysis would only matchthe average response provided by a set of MCS accounting for spatialvariability of soil properties and nothing more The equivalent uniformproperties are usually expressed as percentiles of field test recorded soilindex properties and are termed ldquocharacteristic percentilesrdquo

2 Fragility curves which are an illustrative and practical way of express-ing the probability of exceeding a prescribed threshold in the response(or damage level) as a function of load intensity They can be useddirectly by practicing engineers without having to perform any complex

Soils under static and dynamic loads 245

computations and form the basis for all risk analysisloss estimationriskreduction calculations of civil infrastructure systems performed by emer-gency management agencies and insurance companies Fragility curvescan include effects of multiple sources of uncertainty related to materialresistance or load characteristics The fragility curves are usually rep-resented as shifted lognormal cumulative distribution functions For aspecific threshold in the response each MCS case analyzed is treatedas a realization of a Bernoulli experiment The Bernoulli random vari-able resulting from each MCS case is assigned to the unity probabilitylevel if the selected threshold is exceeded or to zero if the computedresponse is less than the threshold Next the two parameters of theshifted lognormal distribution are estimated using the maximum like-lihood method and the results of all the MCS cases For a detaileddescription of this methodology the reader is referred to Shinozukaet al (2000) and Deodatis et al (2000)

Such guidelines have obvious qualitative value indicating the most impor-tant effects of soil heterogeneity for a particular BVP and type of responsewhich probabilistic characteristics of spatial variability have major effectsand which are the directions where more investigations would provide maxi-mum benefit However several other aspects have to be carefully consideredas will be demonstrated in the following when such guidelines are used inquantitative analyses

652 Liquefaction of level ground randomheterogeneous soil

Regarding liquefaction analysis of natural soil deposits exhibiting a certaindegree of spatial variability in their properties Popescu et al (1997) sug-gested that in order to predict more accurate values of EPWP build-up ina deterministic analysis assuming uniform soil properties one has to use amodified (or equivalent uniform) soil strength The results of deterministicanalyses presented in Figure 67c illustrate this idea of an equivalent uniformsoil strength for liquefaction analysis of randomly heterogeneous soils Forevery such analysis in Figure 67c the uniform (deterministic) soil propertiesare determined based on a certain percentile of the in-situ recorded qc (asliquefaction strength is proportional to qc) From visual examination andcomparison of Figures 67b and 67c one can select a percentile value some-where between 70 and 80 that would lead to an equivalent amountof EPWP in a deterministic analysis to the one predicted by multiple MCSThis value is termed ldquocharacteristic percentilerdquo A comparison between theresults presented in Figures 67b and 67c also shows clearly the differencesin the predicted liquefaction pattern of natural heterogeneous soil versusthat of assumed uniform soil This fact somehow invalidates the use of an

246 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

equivalent uniform soil strength estimated solely on the basis of averageEPWP build-up as liquefaction effects may be controlled also by the pres-ence of a so called ldquomaximal planerdquo defined by Fenton and VanMarcke(1988) as the horizontal plane having the highest value of average EPWPratio This aspect is discussed in detail by Popescu et al (2005a)

It should be mentioned that establishing the characteristic percentile byldquovisual examination and comparisonrdquo of results is exemplified here onlyfor illustrative purposes Popescu et al (1997) compared the range of MCSpredictions with deterministic analysis results in terms of several indicescharacterizing the severity of liquefaction and in terms of horizontal dis-placements Furthermore these comparisons were made for seismic inputswith various ranges of maximum spectral amplitudes For the type of soildeposit analyzed and for the probabilistic characteristics of spatial variabil-ity considered in that study it was determined that the 80-percentile of soilstrength was a good equivalent uniform value to be used in deterministicanalyses

The computed characteristic percentile of soil strength is valid only for thespecific situation analyzed and can be affected by a series of factors such assoil type probabilistic characteristics of spatial variability seismic motionintensity and frequency content etc

Popescu et al (1998c) studied the effect of the ldquointensityrdquo of spatial vari-ability on the characteristic percentile of soil strength A fine sand depositwith probabilistic characteristics of its spatial variability derived from thefield data from Tokyo Bay area (upper soil layer in Figures 62b and 63b)has been analyzed in a parametric study involving a range of values for theCV of soil strength Some of these results are shown in Figure 68 in theform of range of average EPWP ratios predicted by MCS as a function ofCV (shaded area) The EPWP ratio was averaged over the entire analysisdomain (12 m deep times 60 m long) As only 10 sample functions of spatialvariability have been used for each value of CV these results have onlyqualitative value It can be observed that for the situation analyzed in thisexample the effects of spatial variability are insignificant for CVlt02 andare strongly dependent on the degree of soil variability for CV between 02and 08 It can be therefore concluded that for the soil type and seismicintensity considered in this example CV=02 is a threshold beyond whichspatial variability has an important effect on liquefaction The range of aver-age EPWP ratios predicted by MCS is compared in Figure 68 with averageEPWP ratios computed from deterministic analyses assuming uniform soilproperties and using various percentiles of in-situ recorded soil strength Thiscomparison offers some insight about the relation between the characteristicpercentile of soil strength and the CV

Intensity of seismic ground motion is another important factor Popescuet al (2005a) studied the effect of soil heterogeneity on liquefaction fora soil deposit subjected to a series of earthquake ground accelerations

Soils under static and dynamic loads 247

05

04

03

02

0 04 08

Coefficient of variation of soil strength - CV

range from Monte Carlo simulations

90-percentile

80-percentile

70-percentile

60-percentile

averageAve

rage

EP

WP

rat

io

12 16

Figure 68 Range of average excess pore water pressure (EPWP) ratio from Monte Carlosimulations as a function of the coefficient of variation of soil strength (afterPopescu et al 1998c) Horizontal lines represent average EPWP ratios com-puted from deterministic analyses assuming uniform soil strength equal to theaverage and four percentiles of the values used in MCS

corresponding to a wide range of seismic intensities A saturated soil depositwith randomly varying soil strength was considered in a range of full 3D anal-yses (accounting for soil variability in all three spatial directions) as well asin a set of corresponding 2D analyses under a plane strain assumption (andtherefore assuming infinite correlation distance in the third direction) Thesoil variability was modeled by a three-dimensional two-variate randomfield having as components the cone tip resistance qc and the soil classifica-tion index A value of CV=05 was assumed for qc For all other probabilisticcharacteristics considered in that study the reader is referred to Popescu et al(2005a) Deterministic analyses assuming average uniform soil propertieswere also performed for comparison

The resulting EPWP ratios were compared in terms of their averages overthe entire analysis domain (Figure 69a) and averages over the ldquomaximalplanerdquo (Figure 69b) A point on each line in Figure 69 represents the com-puted average EPWP ratio for a specific input earthquake intensity expressedby the Arias Intensity (eg Arias 1970) which is a measure of the totalenergy delivered per unit mass of soil during an earthquake The corre-sponding approximate peak ground accelerations (PGA) are also indicatedon Figure 69 Similar to results of previous studies it can be observed thatgenerally higher EPWP ratios were predicted (on average) by MCS than bydeterministic analysis It is important to remark however that for the type ofsoil and input accelerations used in that study significant differences between

01 015 02 03025

Approx PGA (grsquos)

0 05 1 15

0 05 1 15

0

02

04

06

08

1

0

02

04

06

08

1

Arias Intensity (ms)

Arias Intensity (ms)

Ave

rage

EP

WP

rat

ioA

vera

ge E

PW

P r

atio

Deterministic

Stochastic - 2D

Stochastic - 3D

a

0 05 1 150

2

4

6

8

Arias Intensity (ms)

Max

imum

set

tlem

ent (

cm) Deterministic

Stochastic - 2D

Stochastic - 3D

c

Deterministic

Stochastic - 2D

Stochastic - 3D

b

Figure 69 Comparison between stochastic (2D and 3D) and deterministic analysis resultsin terms of predicted (a) EPWP ratios averaged over the entire analysis domain(b) EPWP ratios averaged over the maximal plane (c) maximum settlements(after Popescu et al 2005a)

Soils under static and dynamic loads 249

MCS and deterministic analyses occurred only for seismic inputs with AriasIntensity less than 1 ms (or PGA less than 025 g) while for strongerearthquakes the results were very similar It is obvious therefore that theldquoequivalent uniformrdquo soil strengths or characteristic percentiles developedfor liquefaction of heterogeneous soils are dependent on the seismic loadintensity

As discussed in Section 62 another important probabilistic characteris-tic of the spatial variability is the correlation distance Figure 69 indicatesthat MCS using full 3D analysis yielded practically the same results interms of average EPWP ratios as MCS using 2D analysis in conjunctionwith the plane strain assumption While the 3D analyses account for soilspatial variability in both horizontal directions in 2D plane strain calcula-tions the soil is assumed uniform in one horizontal direction (normal to theplane of analysis) and the correlation distance in that direction is there-fore considered to be infinite Therefore it can be concluded from theseresults that the value of horizontal correlation distance does not affect theaverage amount of EPWP build-up in this problem In contrast the hori-zontal correlation distance affects other factors of the response in this sameproblem as shown in Figure 69c displaying settlements Maximum set-tlements predicted by MCS using 3D analyses are about 40 larger thanthose resulting from MCS using 2D analyses A similar conclusion was alsoreached by Popescu (1995) based on a limited study on the effects of hori-zontal correlation distance on liquefaction of heterogeneous soils a fivefoldincrease in horizontal correlation distance did not change the predicted aver-age EPWP build-up but resulted in significant changes in the pattern ofliquefaction

653 Seismic response of structures on liquefiable randomheterogeneous soil

Some of the effects of inherently spatially variable soil properties on theseismic response of structures founded on potentially liquefiable soils havebeen studied by Popescu et al (2005b) who considered a tall structure ona saturated sand deposit The structure was modeled as a single degree-of-freedom oscillator with a characteristic frequency of 14 Hz corresponding toa seven-storey building The surrounding soil (and consequently the structuretoo) was subjected to a series of 100 earthquake acceleration time historiesscaled according to their Arias Intensities with the objective of establishingfragility curves for two response thresholds exceeding average settlementsof 20 cm and differential settlements of 5 cm These fragility curves areplotted in Figures 610a and 610b for two different assumptions related tosoil properties (1) variable soils with properties modeled by a random fieldwith coefficient of variation CV = 05 correlation distances θH = 8 m in thehorizontal direction and θV = 2 m in the vertical direction and gamma PDF

250 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

0 05 1 15 20

02

04

06

08

1

Arias Intensity (ms)

Pro

b th

at s

ettle

men

t exc

eeds

20

cm

Uniform soilVariable soil

a

0 05 1 15 20

02

04

06

08

1

Arias Intensity (ms)

Pro

bth

at d

iffs

ettle

men

t exc

eeds

5cm

b

Uniform soilVariable soil

Figure 610 Fragility curves for a tall structure on a saturated sand deposit Comparisonbetween results for variable soil and equivalent uniform soil in terms of (a)average settlements (b) differential settlements

(see Popescu et al 2005b for more details) and (2) corresponding uniformsoils

As can be easily observed in Figure 610 the fragility curves calculatedfor variable soil are shifted to the left compared to the ones for uniformsoil indicating that ndash for a given seismic intensity ndash there is a significantlyhigher probability of exceeding the response threshold when soil variabilityis accounted for than for an assumed corresponding uniform soil For exam-ple for a seismic ground motion with Arias Intensity of 1 ms (or PGA ofabout 025 g) a 62 exceedance probability of a 5 cm differential settle-ment is estimated for spatially variable soils versus only a 22 exceedanceprobability for corresponding uniform soils (Figure 610b)

The behavior at very large seismic intensities is very interesting As faras average settlements are concerned (Figure 610a) both fragility curvesconverge to the same probability (approximately unity) indicating that thedegree of liquefaction of the soil below structure is about the same for bothuniform and variable soils This is in agreement with the results presentedin Figures 69a and 69b for ground level soil In contrast the two fragilitycurves for differential settlements in Figure 610b are still far apart at verylarge Arias Intensities This suggets that ndash even if the computed degree ofliquefaction is about the same at the end of the seismic event for both uniformand variable soils ndash the initiation of liquefaction in variable soils takes placein isolated patches that strongly affect subsequent local soil deformationsand therefore differential settlements of foundations This is believed to bedue to lateral migration of pore water from loose soil zones that liquefy firsttowards denser soil pockets (as discussed in Section 644) leading eventuallyto local settlements that cannot be captured in an analysis assuming uniformsoil properties

Soils under static and dynamic loads 251

654 Static bearing capacity of shallow foundations onrandom heterogeneous soil

The effects of various probabilistic characteristics of soil variability on theBC of shallow foundations were studied by Popescu et al (2005c) in aparametric study involving the CV the PDF and the horizontal correla-tion distance of the random field modeling the spatial variability of soilstrength The layout of the foundation under consideration is the one shownin Figure 65 and the main assumptions made are mentioned in Section642 The finite element analyses involved in the MCS scheme were per-formed using ABAQUSStandard (Hibbitt et al 1998) Some of the resultsare presented in Figure 611 in terms of fragility curves expressing theprobability of BC failure of the strip foundation on randomly heteroge-neous soil as a function of the bearing pressure For comparison withresults of corresponding deterministic analyses the bearing pressures q inFigure 611 are normalized with respect to the ultimate BC obtained for

04 06 08 1 120

02

04

06

08

1

prob

abili

ty o

f BC

failu

re

Reference case CV=40 θHB=1Gamma distribution

Beta distribution

CV=10

normalized bearing pressure (qqudet)

qq u

det =

082

PF=5051

054

068 094

PF=50

qHB=4

Figure 611 Fragility curves illustrating the probability of bearing capacity failure of astrip foundation of width B on randomly heterogeneous soil as a functionof the bearing pressure q normalized with respect to the ultimate bearingcapacity qdet

u corresponding to uniform soil with undrained shear strengthcu = 100 kPa

252 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

the corresponding uniform soil qdetu Each fragility curve represents the

results of MCS involving 100 realizations of the spatial variability of soilstrength generated using a set of probabilistic characteristics describing soilvariability One of the sets ndash corresponding to CV = 40 θHB = 1 andgamma-distributed soil strength ndash is labeled ldquoreference caserdquo The other threecases shown in the figure are obtained by varying one parameter of the refer-ence case at a time It is mentioned that the method for building the fragilitycurves shown in Figure 611 is different from the traditional procedure usingBernoulli trials (eg Shinozuka et al 2000 Deodatis et al 2000) and isdescribed in detail in Popescu et al (2005c)

Figure 611 indicates that the two most important probabilistic character-istics of inherent soil heterogeneity ndash with respect to their effects on BCfailure ndash are the coefficient of variation (CV) and marginal PDF of soilstrength Both the CV and the PDF control the amount of loose pocketsin the soil mass Figure 612 demonstrates that the symmetrical beta distri-bution used in this study has a thicker left tail than the gamma distributionthat is positively skewed Therefore for the same CV the soil with beta-distributed shear strength exhibits a larger amount of soft pockets resultingin lower BC than the soil with gamma-distributed shear strength For therange considered in this study it was found that the horizontal correlationdistance θH does not affect significantly the average ultimate BC Howeverit appears that θH has a significant effect on the slope of the fragility curvesThe explanation of this behavior is believed to be related to averaging of the

0 50 100 150 200 250 300

Shear strength cu(kPa)

Mean value

cuav=100kPa

Beta

Gamma

Figure 612 The two models selected for the marginal PDF of the undrained shear strengthof soil Both PDFs have the same mean value (cav

u = 100 kPa) The coefficientof variation used for this plot is CV = 40 for both PDFs (after Popescu et al2005c)

Soils under static and dynamic loads 253

shear strength over the length of the failure surface larger horizontal corre-lation distances tend to diminish the averaging effect on the bearing capacityfailure mechanism and therefore yield larger variability of the results interms of ultimate BC

An equivalent undrained shear strength cu for uniform soils can be inferredfrom the fragility curves presented in Figure 611 in the following way Basedon the linear relation between ultimate BC and cu it is possible to define anapproximate equivalent uniform cu from the normalized pressures qqdet

ucorresponding to a probability of BC failure PF = 50 For example forthe type of soils and BVP analyzed in this example the equivalent uniformcu for the reference case is cEQ

u = 082cavu = 82 kPa (refer to Figure 611 and

remember that cavu = 100 kPa) Similar cEQ

u values can be inferred for theother cases illustrated in Figure 611 as listed in Table 61

Instead of an equivalent uniform cu corresponding to a failure probabil-ity of 50 most modern codes use nominal values of material strengthcorresponding to much lower failure probabilities (usually 5 or 10) Thedetermination of such nominal values for the BC of soil ndash denoted by quN ndashis demonstrated graphically in Figure 611 for a probability of BC fail-ure PF = 5 The resulting normalized nominal values are displayed inTable 61 Regarding the earlier discussion about the effect of the horizontalcorrelation distance θH Table 61 indicates that even if θH has little effect onthe average behavior of spatially variable soils (less than 4 change in cEQ

uwhen θHB varies from 1 to 4) it affects significantly the resulting nominalvalues (21 change in quN when θHB varies from 1 to 4)

More results such as those presented in Figure 611 and Table 61 includ-ing the effects of soil deformability on the BC and also the effects ofvarious probabilistic characteristics on differential settlements are presentedin Popescu et al (2005c)

66 Conclusions

Some distinctive features of spatially variable random heterogeneous soil andthe main geomechanical aspects governing its behavior under load have been

Table 61 Normalized equivalent uniform undrained shear strength and normalizednominal values for the cases illustrated in Figure 611

CV = 40 θHB = 1gamma PDF(reference case)

CV = 10θHB = 1gamma PDF

CV = 40θHB = 1beta PDF

CV = 40θHB = 4gamma PDF

cEQu cav

u 082 098 071 079

quNqdetu 068 094 051 054

254 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

analyzed and discussed for two types of soil mechanics problems seismicallyinduced soil liquefaction and bearing capacity of shallow foundations Forthe case of liquefaction it was shown that pore water pressures build up firstin the loose soil areas and then due to pressure gradients water is ldquoinjectedrdquointo neighboring dense sand In this way the strong pockets are softened andthe overall liquefaction resistance is reduced becoming considerably lowerthan that of the corresponding uniform soil For the case of geotechnicalproblems involving presence of a failure surface the spatial variability ofsoil properties can change dramatically the failure mechanism from the wellknown theoretical result obtained for uniform soils (eg the Prandtl ndash Reisnersolution for bearing capacity) to a failure surface that seeks the weakest pathfollowing predominantly the weak zones (pockets) in the soil mass Such asurface will have a more or less irregular shape and could differ significantlyfrom one possible realization of the spatial distribution of soil strength toanother

It was demonstrated that random soil heterogeneity has similar overalleffects (specifically inducing lower mechanical resistance) on phenomenathat are fundamentally different limit equilibrium type of failures involvinga large volume of soil ndash for BC and micro-scale pore pressure build-upmechanisms ndash for liquefaction Moreover for both phenomena addressedhere the loose zones (pockets) in the soil mass have a crucial and criticaleffect on the failure mechanisms (eg for soil liquefaction they are the initialsources of EPWP build-up while for BC the failure surface seeks the weakestpath) It also appears that random soil heterogeneity has a more pronouncedeffect for phenomena governed by highly nonlinear laws

An outcome of major importance of both experimental investigations andnumerical simulations discussed in this chapter is that the inherent spatialvariability of soil properties affects the fundamental nature of soil behav-ior leading to lower failure loads as compared to those for correspondinguniform soils Therefore besides variability in the computed response spa-tial variability of soil properties will also produce a response (eg failuremechanism or liquefaction pattern) that is in general different from thetheoretical response of the corresponding uniform soil On the other handother uncertainties considered in reliability analyses such as random mea-surement errors and transformation errors only induce variability in thecomputed response Consequently it is imperative that the effects of inherentsoil heterogeneity be estimated separately from those of other uncertaintiesand only after that combined for reliability assessments

For both problems discussed here it was found that the CV and marginalPDF of soil strength are the two most important parameters of spatial vari-ability in reducing liquefaction resistance or bearing capacity and producingsubstantial differential settlements This is due to the fact that both CV andPDF (more specifically the shape of its left tail) control the amount of loosepockets in the soil mass For the range of correlation distances considered

Soils under static and dynamic loads 255

in this study the horizontal correlation distance was found to have only aminor influence on overall liquefaction resistance or bearing capacity It wasfound however to significantly affect the variability of the response andtherefore the low fractiles and the nominal values

References

American Society for Testing and Materials (1989) Standard test method for deepquasi-static cone and friction-cone penetration tests of soil (D3441ndash86) In AnnualBook of Standards Vol 408 ASTM Philadelphia pp 414ndash19

Arias A (1970) A measure of earthquake intensity In Seismic Design for NuclearPower Plants Ed R J Hansen MIT Press Cambridge MA pp 438ndash83

Brenner C E (1991) Stochastic finite element methods literature review Techni-cal Report 35ndash91 Institute of Engineering Mechanics University of InnsbruckAustria

Brzakala W and Pula W (1996) A probabilistic analysis of foundation settlementsComputers and Geotechnics 18(4) 291ndash309

Budiman J S Mohammadi J and Bandi S (1995) Effect of large inclusionson liquefaction of sand In Proceedings Conference on Geotechnical Engr DivGeotechnical Special Publication 56 ASCE pp 48ndash63

Chakrabortty P Jafari-Mehrabadi A and Popescu R (2004) Effects of low per-meability soil layers on seismic stability of submarine slopes In Proceedings of the57th Canadian Geotechnical Conference Quebec City PQ on CD-ROM

Cherubini C (2000) Reliability evaluation of shallow foundation bearing capacityon cprime φprime soils Canadian Geotechnical Journal 37 264ndash9

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental EngineeringASCE 130(10) 985ndash1003

DeGroot D J and Baecher G B (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering ASCE 119(1) 147ndash66

Deodatis G (1989) Stochastic FEM sensitivity analysis of nonlinear dynamicproblems Probabilistic Engineering Mechanics 4(3) 135ndash41

Deodatis G (1996) Simulation of ergodic multi-variate stochastic processes Journalof Engineering Mechanics ASCE 122(8) 778ndash87

Deodatis G and Micaletti R (2001) Simulation of highly skewed non-Gaussian stochastic processes Journal of Engineering Mechanics ASCE 127(12)1284ndash95

Deodatis G Saxena V and Shinozuka M (2000) Effect of spatial variability ofground motion on bridge fragility curves In Proceedings of the 8th ASCE SpecialtyConference on Probabilistic Mechanics and Structural Reliability University ofNotre Dame IL on CD-ROM

Der Kiureghian A and Ke J B (1988) The stochastic finite element method instructural reliability Probabilistic Engineering Mechanics 3(2) 83ndash91

Elishakoff I (1983) Probabilistic Methods in the Theory of Structures WilleyElkateb T Chalaturnyk R and Robertson P K (2003) Simplified geostatistical

analysis of earthquake-induced ground response at the Wildlife Site CaliforniaUSA Canadian Geotechnical Journal 40 16ndash35

256 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

El-Ramly H Morgenstern N R and Cruden D (2002) Probabilistic slope stabilityanalysis for practice Canadian Geotechnical Journal 39 665ndash83

El-Ramly H Morgenstern N R and Cruden D (2005) Probabilistic assessmentof a cut slope in residual soil Geotechnique 55(2) 77ndash84

Fenton G A (1999a) Estimation for stochastic soil models Journal of Geotechnicaland Geoenvironmental Engineering 125(6) 470ndash85

Fenton G A (1999b) Random field modeling of CPT data Journal of Geotechnicaland Geoenvironmental Engineering 125(6) 486ndash98

Fenton G A and Griffiths D V (2003) Bearing capacity prediction of spatiallyrandom c ndash φ soils Canadian Geotechnical Journal 40 54ndash65

Fenton G A and VanMarcke E H (1998) Spatial variation in liquefaction riskGeotechnique 48(6) 819ndash31

Focht J A and Focht III J A (2001) Factor of safety and reliability in geotech-nical engineering (Discussion) Journal of Geotechnical and GeoenvironmentalEngineering ASCE 127(8) 704ndash6

Ghosh B and Madabhushi S P G (2003) Effects of localized soil inhomogeneityin modifying seismic soilndashstructure interaction In Proceeding of the 16th ASCEEngineering Mechanics Conference Seattle WA July

Griffths D V and Fenton G A (2004) Probabilistic slope stability analysis by finiteelements Journal of Geotechnical and Geoenvironmental Engineering ASCE130(5) 507ndash18

Griffths D V Fenton G A and Manoharan N (2002) Bearing capacity of roughrigid strip footing on cohesive soil probabilistic study Journal of Geotechnicaland Geoenvironmental Engineering ASCE 128(9) 743ndash55

Grigoriu M (1995) Applied non-Gaussian Processes Prentice-Hall EnglewoodCliffs NJ

Grigoriu M (1998) Simulation of stationary non-Gaussian translation processesJournal of Engineering Mechanics ASCE 124(2) 121ndash6

Gulf Canada Resources Inc (1984) Frontier development Molikpaq Tarsiutdelineation 1984ndash85 season Technical Report 84F012

Gurley K and Kareem A (1998) Simulation of correlated non-Gaussian pres-sure fields Meccanica-International the Journal of the Italian Association ofTheoretical and Applied Mechanics 33 309ndash17

Hibbitt Karlsson amp Sorensen Inc (1998) ABAQUS version 58 ndash TheoryManual Hibbitt Karlsson amp Sorensen Inc Pawtucket RI

Hicks M A and Onisiphorou C (2005) Stochastic evaluation of static liquefactionin a predominantly dilative sand fill Geotechnique 55(2) 123ndash33

Huoy L Breysse D and Denis A (2005) Influence of soil heterogeneity on loadredistribution and settlement of a hyperstatic three-support frame Geotechnique55(2) 163ndash70

Jaksa M B (2007) Modeling the natural variability of over-consolidated clay inAdelaide South Australia In Characterisation and Natural Properties of Nat-ural Soils Eds Tan Phoon Hight and Leroueil Taylor amp Francis Londonpp 2721ndash51

Jefferies M G and Davies M P (1993) Use of CPTu to estimate equivalent SPTN60 Geotechnical Testing Journal 16(4) 458ndash68

Konrad J-M and Dubeau S (2002) Cyclic strength of stratified soil samplesIn Proceedings of the 55th Canadian Geotechnical Conference Ground and

Soils under static and dynamic loads 257

Water Theory to Practice Canadian Geotechnical Society Alliston ON Octoberpp 89ndash94

Koutsourelakis S Prevost J H and Deodatis G (2002) Risk assessment of aninteracting structurendashsoil system due to liquefaction Earthquake Engineering andStructural Dynamics 31 851ndash79

Lacasse S and Nadim F (1996) Uncertainties in characteristic soil properties InProceedings Uncertainty in the Geologic Environment From Theory to PracticeASCE New York NY pp 49ndash75

Lumb P (1966) The variability of natural soils Canadian Geotechnical Journal3 74ndash97

Marcuson W F III (1978) Definition of terms related to liquefaction Journal ofthe Geotechnical Engineering Division ASCE 104(9) 1197ndash200

Masters F and Gurley K (2003) Non-Gaussian simulation CDF map-based spec-tral correction Journal of Engineering Mechanics ASCE 129(12) 1418ndash1428

Nobahar A and Popescu R (2000) Spatial variability of soil properties ndash effects onfoundation design In Proceedings of the 53rd Canadian Geotechnical ConferenceMontreal Queacutebec 2 pp 1139ndash44

Paice G M Griffiths G V and Fenton G A (1996) Finite element modeling ofsettlement on spatially random soil Journal of Geotechnical Engineering ASCE122 777ndash80

Phoon K-K (2006) Modeling and simulation of stochastic data In ProceedingsGeoCongress 2006 Geotechnical Engineering in the Information Technology Ageon CD-ROM

Phoon K-K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanadian Geotechnical Journal 36 612ndash24

Phoon K-K and Kulhawy F H (2001) Characterization of geotechnical vari-ability and Evaluation of geotechnical property variability (Reply) CanadianGeotechnical Journal 38 214ndash15

Phoon K K Huang H W and Quek S T (2005) Simulation of strongly non-Gaussian processes using KarhunenndashLoeve expansion Probabilistic EngineeringMechanics 20(2) 188ndash98

Popescu R (1995) Stochastic variability of soil properties data analysis digital sim-ulation effects on system behavior PhD Thesis Princeton University PrincetonNJ httpceeprincetonedusimradupapersphd

Popescu R (2004) Bearing capacity prediction of spatially random c ndash φ soils(Discussion) Canadian Geotechnical Journal 41 366ndash7

Popescu R Chakrabortty P and Prevost J H (2005b) Fragility curvesfor tall structures on stochastically variable soil In Proceedings of the 9thInternational Conference on Structural Safety and Reliability (ICOSSAR)Eds G Augusti G I Schueller and M Ciampoli Millpress Rotterdampp 977ndash84

Popescu R Deodatis R and Nobahar A (2005c) Effects of soil heterogeneity onbearing capacity Probabilistic Engineering Mechanics 20(4) 324ndash41

Popescu R Deodatis G and Prevost J H (1998b) Simulation of non-Gaussianhomogeneous stochastic vector fields Probabilistic Engineering Mechanics13(1) 1ndash13

Popescu R Prevost J H and Deodatis G (1996) Influence of spatial variabil-ity of soil properties on seismically induced soil liquefaction In Proceedings

258 Radu Popescu George Deodatis and Jean-Herveacute Preacutevost

Uncertainty in the Geologic Environment From Theory to Practice ASCEMadison Wisconsin pp 1098ndash112

Popescu R Prevost J H and Deodatis G (1997) Effects of spatial variability onsoil liquefaction some design recommendations Geotechnique 47(5) 1019ndash36

Popescu R Prevost J H and Deodatis G (1998a) Spatial variability of soil proper-ties two case studies In Geotechnical Earthquake Engineering and Soil DynamicsGeotechnical Special Publication No 75 ASCE Seattle pp 568ndash79

Popescu R Prevost J H and Deodatis G (1998c) Characteristic percentileof soil strength for dynamic analyses In Geotechnical Earthquake Engineer-ing and Soil Dynamics Geotechnical Special Publication No 75 ASCE Seattlepp 1461ndash71

Popescu R Prevost J H and Deodatis G (2005a) 3D effects in seismic liquefactionof stochastically variable soil deposits Geotechnique 55(1) 21ndash32

Popescu R Prevost J H Deodatis G and Chakrabortty P (2006) Dynamics ofporous media with applications to soil liquefaction Soil Dynamics and EarthquakeEngineering 26(6ndash7) 648ndash65

Prevost J H (1985) A simple plasticity theory for frictional cohesionless soils SoilDynamics and Earthquake Engineering 4(1) 9ndash17

Prevost J H (2002) DYNAFLOW ndash A nonlinear transient finite element analysisprogram Version 02 Technical Report Department of Civil and Environmen-tal Engineering Princeton University Princeton NJ httpwwwprincetonedusimdynaflow

Przewlocki J (2000) Two-dimensional random field of mechanical propertiesJournal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)373ndash7

Puig B Poirion F and Soize C (2002) Non-Gaussian simulation using hermitepolynomial expansion convergences and algorithms Probabilistic EngineeringMechanics 17(3) 253ndash64

Rackwitz R (2000) Reviewing probabilistic soil modeling Computers andGeotechnics 26 199ndash223

Robertson P K and Wride C E (1998) Evaluating cyclic liquefaction potentialusing the cone penetration test Canadian Geotechnical Journal 35(3) 442ndash59

Sakamoto S and Ghanem R (2002) Polynomial chaos decomposition for the simu-lation of non-Gaussian non-stationary stochastic processes Journal of EngineeringMechanics ASCE 128(2) 190ndash201

Schmertmann J H (1978) Use the SPT to measure soil properties ndash Yes buthellipIn Dynamic Geotechnical Testing (STP 654) ASTM Philadelphia pp 341ndash355

Shinozuka M and Dasgupta G (1986) Stochastic finite element methods in dynam-ics In Proceedings of the 3rd Conference on Dynamic Response of StructuresASCE UCLA CA pp 44ndash54

Shinozuka M and Deodatis G (1988) Simulation of multi-dimensional Gaussianstochastic fields by spectral representation Applied Mechanics Reviews ASME49(1) 29ndash53

Shinozuka M and Deodatis G (1996) Response variability of stochastic finiteelement systems Journal Engineering Mechanics ASCE 114(3) 499ndash519

Shinozuka M Feng M Q Lee J and Naganuma T (2000) Statistical analysisof fragility curves Journal of Engineering Mechanics ASCE 126(12) 1224ndash31

Soils under static and dynamic loads 259

Tang W H (1984) Principles of probabilistic characterization of soil propertiesIn Probabilistic Characterization of Soil Properties Bridge Between Theory andPractice ASCE Atlanta pp 74ndash89

VanMarcke E H (1977) Probabilistic modeling of soil profiles Journal ofGeotechnical Engineering Division ASCE 109(5) 1203ndash14

VanMarcke E H (1983) Random Fields Analysis and Synthesis The MIT PressCambridge MA

VanMarcke E H (1989) Stochastic finite elements and experimental measure-ments In International Conference on Computational Methods and ExperimentalMeasurements Capri Italy pp 137ndash155 Springer

Yamazaki F and Shinozuka M (1988) Digital generation of non-Gaussianstochastic fields Journal of Engineering Mechanics ASCE 114(7) 1183ndash97

Chapter 7

Stochastic finite elementmethods in geotechnicalengineering

Bruno Sudret and Marc Berveiller

71 Introduction

Soil and rock masses naturally present heterogeneity at various scales ofdescription This heterogeneity may be of two kinds

bull the soil properties can be considered piecewise homogeneous onceregions (eg layers) have been identified

bull no specific regions can be identified meaning that the spatial variabilityof the properties is smooth

In both cases the use of deterministic values for representing the soil char-acteristics is poor since it ignores the natural randomness of the mediumAlternatively this randomness may be modeled properly using probabilitytheory

In the first of the two cases identified above the material properties may bemodeled in each region as random variables whose distribution (and possiblymutual correlation) have to be specified In the second case the introductionof random fields is necessary Probabilistic soil modeling is a long-term storysee for example VanMarcke (1977) DeGroot and Baecher (1993) Fenton(1999ab) Rackwitz (2000) Popescu et al (2005)

Usually soil characteristics are investigated in order to feed models ofgeotechnical structures that are in project Examples of such structures aredams embankments pile or raft foundations tunnels etc The design thenconsists in choosing characterictics of the structure (dimensions materialproperties) so that it fulfills some requirements (eg retain water support abuilding etc) under a given set of environmental actions that we will callldquoloadingrdquo The design is carried out practically by satisfying some design cri-teria which usually apply onto model response quantities (eg displacementssettlements strains stresses etc) The conservatism of the design accordingto codes of practice is ensured first by introducing safety coefficients andsecond by using penalized values of the model parameters In this approachthe natural spatial variability of the soil is completely hidden

Stochastic finite element methods 261

From another point of view when the uncertainties and variability ofthe soil properties have been identified methods that allow propagationof these uncertainties throughout the model have to be used Classicallythe methods can be classified according to the type of information on the(random) response quantities they provide

bull The perturbation method allows computation of the mean value andvariance of the mechanical response of the system (Baecher and Ingra1981 Phoon et al 1990) This gives a feeling on the central part of theresponse probability density function (PDF)

bull Structural reliability methods allow investigation of the tails of theresponse PDF by computing the probability of exceedance of a pre-scribed threshold (Ditlevsen and Madsen 1996) Among these methodsFOSM (first-order second-moment methods) have been used in geotech-nical engineering (Phoon et al 1990 Mellah et al 2000 Eloseily et al2002) FORMSORM and importance sampling are applied less in thiscontext and have proven successful in engineering mechanics both inacademia and more recently in industry

bull Stochastic finite element (SFE) methods named after the pioneeringwork by Ghanem and Spanos (1991) aim at representing the completeresponse PDF in an intrinsic way This is done by expanding the response(which after proper discretization of the problem is a random vectorof unknown joint PDF) onto a particular basis of the space of randomvectors of finite variance called the polynomial chaos (PC) Applica-tions to geotechnical problems can be found in Ghanem and Brzkala(1996) Sudret and Der Kiureghian (2000) Ghiocel and Ghanem (2002)Clouteau and Lafargue (2003) Sudret et al (2004 2006) Berveiller et al(2006)

In the following we will concentrate on this last class of methods Indeedonce the coefficients of the expansion have been computed a straightfor-ward post-processing of these quantities gives the statistical moments of theresponse under consideration the probability of exceeding a threshold orthe full PDF

The chapter is organized as follows Section 72 presents methods forrepresenting random fields that are applicable for describing the spatial vari-ability of soils Section 73 presents the principles of polynomial chaos expan-sions for representing both the (random) model response and possibly thenon-Gaussian input Section 74 and (respectively 75) reviews the so-calledintrusive (respectively non-intrusive) solving scheme in stochastic finite ele-ment problems Section 76 is devoted to the practical post-processing ofpolynomial chaos expansions Finally Section 77 presents some applicationexamples

262 Bruno Sudret and Marc Berveiller

72 Representation of spatial variability

721 Basics of probability theory and notation

Probability theory gives a sound mathematical framework to the repre-sention of uncertainty and randomness When a random phenomenon isobserved the set of all possible outcomes defines the sample space denotedby An event E is defined as a subset of containing outcomes θ isin The set of events defines the σ -algebra F associated with A probabil-ity measure allows one to associate numbers to events ie their probabilityof occurrence Finally the probability space constructed by means of thesenotions is denoted by ( F P)

A real random variable X is a mapping X ( F P)minusrarrR For continuousrandom variables the PDF and cumulative distribution function (CDF) aredenoted by fX(x) and FX(x) respectively

FX(x) = P(X le x) fX(x) = dFX(x)dx

(71)

The mathematical expectation will be denoted by E[middot] The mean varianceand nth moment of X are

micro equiv E[X] =int infin

minusinfinxfX(x)dx (72)

σ 2 = E[(X minusmicro)2

]=int infin

minusinfin(x minusmicro)2 fX(x)dx (73)

microprimen = E

[Xn]= int infin

minusinfinxn fX(x)dx (74)

A random vector X is a collection of random variables whose probabilisticdescription is contained in its joint PDF denoted by fX(x) The covari-ance of two random variables X and Y (eg two components of a randomvector) is

Cov[X Y] = E[(X minusmicroX)(Y minusmicroY )

](75)

Introducing the joint distribution fXY (x y) of these variables Equation (75)can be rewritten as

Cov[X Y] =int infin

minusinfin

int infin

minusinfin(x minusmicroX)(y minusmicroY ) fXY (x y)dxdy (76)

The vectorial space of real random variables with finite second moment(E[X2]lt infin) is denoted by L2 ( F P) The expectation operator defines

Stochastic finite element methods 263

an inner product on this space

〈X Y〉 = E[XY] (77)

This allows in particuler definition of orthogonal random variables (whentheir inner product is zero)

722 Random fields

A unidimensional random field H(x θ ) is a collection of random variablesassociated with a continuous index x isin Ω sub Rn where θ isin is the coordi-nate in the outcome space Using this notation H(x θo) denotes a particularrealization of the field (ie a usual function mapping Ω into R) whereasH(xo θ ) is the random variable associated with point xo Gaussian ran-dom fields are of practical interest because they are completely describedby a mean function micro(x) a variance function σ 2(x) and an autocovariancefunction CHH(x xprime)

CHH(x xprime) = Cov[H(x) H(xprime)

](78)

Alternatively the correlation structure of the field may be prescribed throughthe autocorrelation coefficient function ρ(x xprime) defined as

ρ(x xprime) = CHH(x xprime)σ (x)σ (xprime)

(79)

Random fields are non-numerable infinite sets of correlated random vari-ables which is computationally intractable Discretizing the random fieldH(x) consists in approximating it by H(x) which is defined by means of afinite set of random variables χi i = 1 n gathered in a random vectordenoted by χ

H(x θ )Discretizationminusrarr H(x θ ) = G[x χ (θ )] (710)

Several methods have been developed since the 1980s to carry out this tasksuch as the spatial average method (VanMarcke and Grigoriu 1983) themidpoint method (Der Kiureghian and Ke 1988) and the shape functionmethod (W Liu et al 1986ab) A comprehensive review and compari-son of these methods is presented in Li and Der Kiureghian (1993) Theseearly methods are relatively inefficient in the sense that a large numberof random variables is required to achieve a good approximation of thefield

More efficient approaches for discretization of random fields using seriesexpansion methods have been introduced in the past 15 years includ-ing the KarhunenndashLoegraveve Expansion (KL) (Ghanem and Spanos 1991)

264 Bruno Sudret and Marc Berveiller

the Expansion Optimal Linear Estimation (EOLE) method (Li and DerKiureghian 1993) and the Orthogonal Series Expansion (OSE) (Zhang andEllingwood 1994) Reviews of these methods have been presented in Sudretand Der Kiureghian (2000) see also Grigoriu (2006) The KL and EOLEmethods are now briefly presented

723 KarhunenndashLoegraveve expansion

Let us consider a Gaussian random field H(x) defined by its mean valuemicro(x) and autocovariance function CHH(x xprime) = σ (x)σ (xprime)ρ(x xprime) TheKarhunenndashLoegraveve expansion of H(x) reads

H(x θ ) = micro(x) +infinsum

i=1

radicλi ξi(θ )ϕi(x) (711)

where ξi(θ ) i = 1 are zero-mean orthogonal variables andλi ϕi(x)

are solutions of the eigenvalue problemint

Ω

CHH(x xprime)ϕi(xprime)dΩxprime = λi ϕi(x) (712)

Equation (712) is a Fredholm integral equation Since kernel CHH(middot middot) isan autocovariance function it is bounded symmetric and positive definiteThus the set of ϕi forms a complete orthogonal basis The set of eigenvalues(spectrum) is moreover real positive and numerable In a sense Equation(711) corresponds to a separation of the space and randomness variables inH(x θ )

The KarhunenndashLoegraveve expansion possesses other interesting properties(Ghanem and Spanos 1991)

bull It is possible to order the eigenvalues λi in a descending series convergingto zero Truncating the ordered series (711) after the Mth term givesthe KL approximated field

H(x θ ) = micro(x) +Msum

i=1

radicλi ξi(θ )ϕi(x) (713)

bull The covariance eigenfunction basis ϕi(x) is optimal in the sense thatthe mean square error (integrated over Ω) resulting from a truncationafter the Mth term is minimized (with respect to the value it would takewhen any other complete basis hi(x) is chosen)

bull The set of random variables appearing in (711) is orthonormal if andonly if the basis functions hi(x) and the constants λi are solutions ofthe eigenvalue problem (712)

Stochastic finite element methods 265

bull As the random field is Gaussian the set of ξi are independent standardnormal variables Furthermore it can be shown (Loegraveve 1977) that theKarhunenndashLoegraveve expansion of Gaussian fields is almost surely conver-gent For non-Gaussian fields the KL expansion also exists howeverthe random variables appearing in the series are of unknown law andmay be correlated (Phoon et al 2002b 2005 Li et al 2007)

bull From Equation (713) the error variance obtained when truncating theexpansion after M terms turns out to be after basic algebra

Var[H(x) minus H(x)

]= σ 2(x) minus

Msumi=1

λi ϕ2i (x)

= Var[H(x)] minus Var[H(x)

]ge 0 (714)

The right-hand side of the above equation is always positive because itis the variance of some quantity This means that the KarhunenndashLoegraveveexpansion always underrepresents the true variance of the field Theaccuracy of the truncated expansion has been investigated in details inHuang et al (2001)

Equation (712) can be solved analytically only for few autocovari-ance functions and geometries of Ω Detailed closed form solutions fortriangular and exponential covariance functions for one-dimensionalhomogeneous fields can be found in Ghanem and Spanos (1991)Otherwise a numerical solution to the eigenvalue problem (712) canbe obtained (same reference chapter 2) Wavelet techniques have beenrecently applied for this purpose in Phoon et al (2002a) leading to afairly efficient approximation scheme

724 The EOLE method

The expansion optimal linear estimation method (EOLE) was proposed byLi and Der Kiureghian (1993) It is based on the pointwise regression ofthe original random field with respect to selected values of the field and acompaction of the data by spectral analysis

Let us consider a Gaussian random field as defined above and a grid ofpoints x1 xN in the domain Ω Let us denote by χ the random vectorH(x1) H(xN) By construction χ is a Gaussian vector whose mean valuemicroχ and covariance matrix χ χ read

microiχ = micro(xi) (715)(

χ χ

)ij

= Cov[H(xi) H(xj)

]= σ (xi)σ (xj)ρ(xi xj) (716)

266 Bruno Sudret and Marc Berveiller

The optimal linear estimation (OLE) of random variable H(x) onto therandom vector χ reads

H(x) asymp micro(x) +primeHχ (x) middot minus1

χ χ middot(χ minusmicroχ

)(717)

where ()prime denotes the transposed matrix and Hχ (x) is a vector whosecomponents are given by

jHχ

(x) = Cov[H(x)χj

]= Cov

[H(x) H(xj)

](718)

Let us now consider the spectral decomposition of the covariancematrix χχ

χχ φi = λi φi i = 1 N (719)

This allows one to linearly transform the original vector χ

χ (θ ) = microχ +Nsum

i=1

radicλi ξi(θ )φi (720)

where ξi i = 1 N are independent standard normal variables Substitut-ing for (720) in (717) and using (719) yields the EOLE representation ofthe field

H(x θ ) = micro(x) +Nsum

i=1

ξi(θ )radicλi

φiTH(x)χ (721)

As in the KarhunenndashLoegraveve expansion the series can be truncated after r le Nterms the eigenvalues λi being sorted first in descending order The varianceof the error for EOLE is

Var[H(x) minus H(x)

]= σ 2(x) minus

rsumi=1

1λi

(φT

i H(x)χ

)2(722)

As in KL the second term in the above equation is identical to the varianceof H(x) Thus EOLE also always underrepresents the true variance Due tothe form of (722) the error decreases monotonically with r the minimalerror being obtained when no truncation is made (r = N) This allows one todefine automatically the cut-off value r for a given tolerance in the varianceerror

Stochastic finite element methods 267

73 Polynomial chaos expansions

731 Expansion of the model response

Having recognized that the input parameters such as the soil propertiescan be modeled as random fields (which are discretized using standardnormal random variables) it is clear that the response of the system isa nonlinear function of these variables After a discretization procedure(eg finite element or finite difference scheme) the response may be con-sidered as a random vector S whose probabilistic properties are yet to bedetermined

Due to the above representation of the input it is possible to expand theresponse S onto the so-called polynomial chaos basis which is a basis of thespace of random variables with finite variance (Malliavin 1997)

S =infinsum

j=0

Sj Ψj

(ξn

infinn=1

)(723)

In this expression the Ψjrsquos are the multivariate Hermite polynomials definedby means of the ξnrsquos This basis is orthogonal with respect to the Gaussianmeasure ie the expectation of products of two different such polynomialsis zero (see details in Appendix A)

Computationnally speaking the input parameters are represented usingM independent standard normal variables see Equations (713) and (721)Considering all M-dimensional Hermite polynomials of degree not exceedingp the response may be approximated as follows

S asympPminus1sumj=0

Sj Ψj (ξ ) ξ = ξ1 ξM (724)

The number of unknown (vector) coefficients in this summation is

P =(

M + pp

)= (M + p)

M p (725)

The practical construction of a polynomial chaos of order M and degree pis described in Appendix A The problem is now recast as computing theexpansion coefficients Sj j = 0 P minus 1 Two classes of methods arepresented below in Sections 74 and 75

732 Representation of non-Gaussian input

In Section 72 the representation of the spatial variability through Gaussianrandom fields has been shown It is important to note that many soil

268 Bruno Sudret and Marc Berveiller

properties should not be modeled as Gaussian random variables or fieldsFor instance the Poisson ratio is a bounded quantity whereas Gaussianvariables are defined on R Parameters such as the Youngrsquos modulus orthe cohesion are positive in nature modeling them as Gaussian intro-duces an approximation that should be monitored carefully Indeed whena large dispersion of the parameter is considered choosing a Gaussianrepresentation can easily lead to negative realizations of the parameterwhich have no physical meaning (lognormal variables or fields are oftenappropriate)

As a consequence if the parameter under consideration is modeled bya non-Gaussian random field it is not possible to expand it as a linearexpression in standard normal variables as in Equations (713) and (721)Easy-to-define non-Gaussian random fields H(x) are obtained by translationof a Gaussian field N(x) using a nonlinear transform h()

H(x) = h(N(x)) (726)

The discretization of this kind of field is straightforward the nonlinear trans-form h() is directly applied to the discretized underlying Gaussian field N(x)(see eg Ghanem 1999 for lognormal fields)

H(x) = h(N(x)) (727)

From another point of view the description of the spatial variability ofparameters is in some cases beyond the scope of the analysis For instancesoil properties may be considered homogeneous in some domains Theseparameters are not well known though and it may be relevant to modelthem as (usually non-Gaussian) random variables

It is possible to transform any continuous random variable with finite vari-ance into a standard normal variable using the iso-probabilistic transformdenoting by FX(x) (respectively (x)) the CDF of X (respectively a standardnormal variable ξ ) the direct and inverse transform read

ξ = minus1 FX(x) X = Fminus1X (ξ ) (728)

If the input parameters are modeled by a random vector with independentcomponents it is possible to represent it using a standard normal randomvector of the same size by applying the above transform componentwiseIf the input random vector has a prescribed joint PDF it is generally not pos-sible to transform it exactly into a standard normal random vector Howeverwhen only marginal PDF and correlations are known an approximate repre-sentation may be obtained by the Nataf transform (Liu and Der Kiureghian1986)

As a conclusion the input parameters of the model which do ordo not exhibit spatial variability may always be represented after some

Stochastic finite element methods 269

discretization process mapping or combination thereof as functionals ofstandard normal random variables

bull for non-Gaussian independent random variables see Equation (728)bull for Gaussian random fields see Equations(713) and (721)bull for non-Gaussian random fields obtained by translation see

Equation (727)

Note that Equation (728) is an exact representation whereas the fielddiscretization techniques provide only approximations (which converge tothe original field if the number of standard normal variables tends toinfinity)

In the sequel we consider that the discretized input fields and non Gaussianrandom variables are represented through a set of independent standardnormal variables ξ of size M and we denote by X(ξ ) the functional thatyields the original variables and fields

74 Intrusive SFE method for static problems

The historical SFE approach consists in computing the polynomial chaoscoefficients of the vector of nodal displacements U(θ ) (Equation (724)) Itis based on the minimization of the residual in the balance equation in theGalerkin sense (Ghanem and Spanos 1991) To illustrate this method letus consider a linear mechanical problem whose finite element discretizationleads to the following linear system (in the deterministic case)

K middot U = F (729)

Let us denote by Nddl the number of degrees of freedom of the structure iethe size of the above linear system If the material parameters are describedby random variables and fields the stiffness matrix K in the above equa-tion becomes random Similarly the load vector F may be random Thesequantities can be expanded onto the polynomial chaos basis

K =infinsum

j=0

KjΨj (730)

F =infinsum

j=0

FjΨj (731)

In these equations Kj are deterministic matrices whose complete descriptioncan be found elsewhere (eg Ghanem and Spanos (1991) in the case when theinput Youngrsquos modulus is a random field and Sudret et al (2004) when theYoungrsquos modulus and the Poisson ratio are non-Gaussian random variables)

270 Bruno Sudret and Marc Berveiller

In the same manner Fj are deterministic load vectors obtained from the data(Sudret et al 2004 Sudret et al 2006)

As a consequence the vector of nodal displacements U is random and maybe represented on the same basis

U =infinsum

j=0

UjΨj (732)

When the three expansions (730)ndash(732) are truncated after P terms andsubstituted for in Equation (729) the residual εP in the stochastic balanceequation reads

εP =(

Pminus1sumi=0

KiΨi

)middot⎛⎝Pminus1sum

j=0

UjΨj

⎞⎠minus

Pminus1sumj=0

FjΨj (733)

Coefficients U0 UPminus1 are obtained by minimizing the residual using aGalerkin technique This minimization is equivalent to requiring the residualbe orthogonal to the subspace of L2(FP) spanned by ΨjPminus1

j=0

E[εPΨk

]= 0 k = 0 P minus 1 (734)

After some algebra this leads to the following linear system whose size isequal to Nddl times P

⎛⎜⎜⎜⎝

K00 middot middot middot K0Pminus1K10 middot middot middot K1Pminus1

KPminus10 middot middot middot KPminus1Pminus1

⎞⎟⎟⎟⎠ middot

⎛⎜⎜⎜⎝

U0U1

UPminus1

⎞⎟⎟⎟⎠=

⎛⎜⎜⎜⎝

F0F1

FPminus1

⎞⎟⎟⎟⎠ (735)

where Kjk =Pminus1sumi=0

dijk Ki and dijk = E[ΨiΨjΨk]Once the system has been solved the coefficients Uj may be post-processed

in order to represent the response PDF (eg by Monte Carlo simulation)to compute the mean value standard deviation and higher moments or toevaluate the probability of exceeding a given threshold The post-processingtechniques are detailed in Section 76 It is important to note already thatthe set of Ujrsquos contains all the probabilistic information on the responsemeaning that post-processing is carried out without additional computationon the mechanical model

Stochastic finite element methods 271

The above approach is easy to apply when the mechanical model is linearAlthough nonlinear problems have been recently addressed (Matthies andKeese 2005 Nouy 2007) their treatment is still not completely matureMoreover this approach naturally yields the expansion of the basic responsequantities (such as the nodal displacements in mechanics) When derivedquantities such as strains or stresses are of interest additional work (andapproximations) is needed Note that in the case of non-Gaussian inputrandom variables expansion of these variables onto the PC basis is neededin order to apply the method which introduces an approximation of theinput Finally the implementation of the historical method as described inthis section has to be carried out for each class of problem this is why it hasbeen qualified as intrusive in the literature All these reasons have led to thedevelopment of so-called non-intrusive methods that in some sense providean answer to the above drawbacks

75 Non-intrusive SFE methods

751 Introduction

Let us consider a scalar response quantity S of the model under considerationeg a nodal displacement strain or stress component in a finite elementmodel

S = h(X) (736)

Contrary to Section 74 each response quantity of interest is directlyexpanded onto the polynomial chaos as follows

S =infinsum

j=0

Sj Ψj (737)

The P-term approximation reads

S =Pminus1sumj=0

Sj Ψj (738)

Two methods are now proposed to compute the coefficients in this expansionfrom a series of deterministic finite element analysis

752 Projection method

The projection method is based on the orthogonality of the polynomial chaos(Le Maicirctre et al 2002 Ghiocel and Ghanem 2002) By pre-multiplying

272 Bruno Sudret and Marc Berveiller

Equation (738) by Ψi and taking the expectation of both members it comes

E[SΨj

]= E

[ infinsumi=0

Sj Ψi Ψj

](739)

Due to the orthogonality of the basis E[Ψi Ψj

]= 0 for any i = j Thus

Sj =E[SΨj

]E[Ψ 2

j

] (740)

In this expression the denominator is known analytically (see Appendix A)and the numerator may be cast as a multidimensional integral

E[SΨj

]=int

RMh(X(ξ ))Ψj(ξ )ϕM(ξ )dξ (741)

where ϕM is the M-dimensional multinormal PDF and where the dependencyof S in ξ through the iso-probabilistic transform of the input parameters X(ξ )has been given for the sake of clarity

This integral may be computed by crude Monte Carlo simulation (Field2002) or Latin Hypercube Sampling (Le Maicirctre et al 2002) Howeverthe number of samples required in this case should be large enough say10000ndash100000 to obtain a sufficient accuracy In cases when the responseS is obtained by a computationally demanding finite element model thisapproach is practically not applicable Alternatively the use of quasi-randomnumbers instead of Monte Carlo (Niederreiter 1992) simulation has beenrecently investigated in Sudret et al (2007) and appears promising

An alternative approach presented in Berveiller et al (2004) and Matthiesand Keese (2005) is the use of a Gaussian quadrature scheme to evaluatethe integral Equation (741) is computed as a weighted summation of theintegrands evaluated at selected points (the so-called integration points)

E[SΨj

]asymp

Ksumi1=1

middot middot middotKsum

iM=1

ωi1 ωiM

h(X(ξi1

ξiM

))Ψj

(ξi1

ξiM

)(742)

In this expression the integration points ξij 1 le i1 le middotmiddot middot le iM le K and

weights ωij 1 le i1 le middotmiddot middot le iM le K in each dimension are computed using

the theory of orthogonal polynomials with respect to the Gaussian measureFor a Kth order scheme the integration points are the roots of the Kth orderHermite polynomial (Abramowitz and Stegun 1970)

Stochastic finite element methods 273

The proper order of the integration scheme K is selected as follows ifthe response S in Equation (737) was polynomial of order p (ie Sj = 0 forj ge P) the terms in the integral (741) would be of degree less than or equalto 2p Thus an integration scheme of order K = p + 1 would give the exactvalue of the expansion coefficients We take this as a rule in the general casewhere the result now is only an approximation of the true value of Sj

As seen from Equations (740)(742) the projection method allows one tocompute the expansion coefficients from selected evaluations of the modelThus the method is qualified as non-intrusive since the deterministic com-putation scheme (ie a finite element code) is used without any additionalimplementation or modification

Note that in finite element analysis the response is usually a vector (eg ofnodal displacements nodal stresses etc) The above derivations are strictlyvalid for a vector response S the expectation in Equation (742) beingcomputed component by component

753 Regression method

The regression method is another approach for computing the responseexpansion coefficients It is nothing but the regression of the exact solu-tion S with respect to the polynomial chaos basis Ψj(ξ ) j = 1 P minus 1Let us assume the following expression for a scalar response quantity S

S = h(X) = S(ξ ) + ε S(ξ ) =Pminus1sumj=0

Sj Ψj(ξ ) (743)

where the residual ε is supposed to be a zero-mean random variable andS = Sj j = 0 P minus 1 are the unknown coefficients The minimizationof the variance of the residual with respect to the unknown coefficientsleads to

S = ArgminE[(h(X (ξ )) minus S(ξ ))2

](744)

In order to solve Equation (744) we choose a set of Q regression points inthe standard normal space say ξ1 ξQ From these points the isoprob-abilistic transform (728) gives a set of Q realizations of the input vector Xsay x1 xQ The mean-square minimization (744) leads to solve thefollowing problem

S = Argmin1Q

Qsumi=1

⎧⎨⎩h(xi) minus

Pminus1sumj=0

Sj Ψj(ξi)

⎫⎬⎭

2

(745)

274 Bruno Sudret and Marc Berveiller

Denoting by Ψ the matrix whose coefficients are given by Ψ ij = Ψj(ξi) i =

1 Q j = 0 Pminus1 and by Sex the vector containing the exact responsevalues computed by the model Sex = h(xi) i = 1 Q the solution toEquation (745) reads

S = (Ψ T middotΨ )minus1 middotΨ T middotSex (746)

The regression approach detailed above is comparable with the so-calledresponse surface method used in many domains of science and engineeringIn this context the set of x1 xQ is the so-called experimental design InEquation (746) Ψ T middotΨ is the information matrix Computationally speak-ing it may be ill-conditioned Thus a particular solver such as the SingularValue Decomposition method should be employed (Press et al 2001)

It is now necessary to specify the choice of the experimental design Start-ing from the early work by Isukapalli (1999) it has been shown in Berveiller(2005) and Sudret (2005) that an efficient design can be built from the rootsof the Hermite polynomials as follows

bull If p denotes the maximal degree of the polynomials in the truncated PCexpansion then the p + 1 roots of the Hermite polynomial of degreep + 1 (denoted by Hep+1) are computed say r1 rp+1

bull From this set M-uplets are built using all possible combinations of theroots rk = (ri1

riM) 1 le i1 le middotmiddot middot le iM le p+1 k = 1 (p+1)M

bull The Q points in the experimental design ξ1 ξQ are selected amongthe r jrsquos by retaining those which are closest to the origin of the spaceie those with the smallest norm or equivalently those leading to thelargest values of the PDF ϕM(ξ j)

To choose the size Q of the experimental design the following empiricalrule was proposed by Berveiller (2005) based on a large number of numericalexperiments

Q = (M minus 1)P (747)

A slightly more efficient rule leading to a smaller value of Q has beenrecently proposed by Sudret (2006 2008) based on the invertibility of theinformation matrix

76 Post-processing of the SFE results

761 Representation of response PDF

Once the coefficients Sj of the expansion of a response quantity areknown the polynomial approximation can be simulated using Monte

Stochastic finite element methods 275

Carlo simulation A sample of standard normal random vector is generatedsay ξ (1) ξ (n) Then the PDF can be plotted using a histogram rep-resentation or better kernel smoothing techniques (Wand and Jones1995)

762 Computation of the statistical moments

From Equation (738) due to the orthogonality of the polynomial chaosbasis it is easy to see that the mean and variance of S is given by

E[S] = S0 (748)

Var[S] = σ 2S =

Pminus1sumj=1

S2j E[Ψ 2

j

](749)

where the expectation of Ψ 2j is given in Appendix A Moments of higher

order are obtained in a similar manner Namely the skewness and kurtosiscoefficients of response variable S (denoted by δS and κS respectively) areobtained as follows

δS equiv 1

σ 3S

E[(S minus E[S])3

]= 1

σ 3S

Pminus1sumi=1

Pminus1sumj=1

Pminus1sumk=1

E[ΨiΨjΨk]Si Sj Sk (750)

κS equiv 1

σ 4S

E[(S minus E[S])4

]= 1

σ 4S

Pminus1sumi=1

Pminus1sumj=1

Pminus1sumk=1

Pminus1suml=1

E[ΨiΨjΨkΨl]Si Sj Sk Sl (751)

Here again expectation of products of three (respectively four) Ψjrsquos areknown analytically see for example Sudret et al (2006)

763 Sensitivity analysis selection of important variables

The problem of selecting the most ldquoimportantrdquo input variables of a modelis usually known as sensitivity analysis In a probabilistic context meth-ods of global sensitivity analysis aim at quantifying which input variable(or combination of input variables) influences most the response variabil-ity A state-of-the-art of such techniques is available in Saltelli et al (2000)They include regression-based methods such as the computation of standard-ized regression coefficients (SRC) or partial correlation coefficients (PCC)and variance-based methods also called ANOVA techniques for ldquoANalysisOf VAriancerdquo In this respect the Sobolrsquo indices (Sobolrsquo 1993 Sobolrsquo andKucherenko 2005) are known as the most efficient tool to find out theimportant variables of a model

The computation of Sobolrsquo indices is traditionnally carried out by MonteCarlo simulation (Saltelli et al 2000) which may be computationally

276 Bruno Sudret and Marc Berveiller

unaffordable in case of time-consuming models In the context of stochasticfinite element methods it has been recently shown in Sudret (2006 2008)that the Sobolrsquo indices can be derived analytically from the coefficients of thepolynomial chaos expansion of the response once the latter have been com-puted by one of the techniques detailed in Sections 74 and 75 For instancethe first order Sobolrsquo indices δi i = 1 M which quantify what fractionof the response variance is due to each input variable i = 1 M

δi = VarXi

[E[S|Xi

]]Var[S]

(752)

can be evaluated from the coefficients of the PC expansion (Equation (738))as follows

δPCi =

sumαisinIi

S2α E[Ψ 2

α

]σ 2

S (753)

In this equation σ 2S is the variance of the model response computed from

the PC coefficients (Equation (749)) and the summation set (defined usingthe multi-index notation detailed in Appendix) reads

Ii =α αi gt 0αj =i = 0

(754)

Higher-order Sobolrsquo indices which correspond to interactions of theinput parameters can also be computed using this approach see Sudret(2006) for a detailed presentation and an application to geotechnicalengineering

764 Reliability analysis

Structural reliability analysis aims at computing the probability of failure of amechanical system with respect to a prescribed failure criterion by account-ing for uncertainties arising in the model description (geometry materialproperties) or the environment (loading) It is a general theory whose devel-opment began in the mid 1970s The research on this field is still active ndash seeRackwitz (2001) for a review

Surprisingly the link between structural reliability and the stochastic finiteelement methods based on polynomial chaos expansions is relatively new(Sudret and Der Kiureghian 2000 2002 Berveiller 2005) For the sake ofcompleteness three essential techniques for solving reliability problems arereviewed in this section Then their application together with (a) a deter-minitic finite element model and (b) a PC expansion of the model responseis detailed

Stochastic finite element methods 277

Problem statement

Let us denote by X = X1X2 XM

the set of random variables describing

the randomness in the geometry material properties and loading This setalso includes the variables used in the discretization of random fields if anyThe failure criterion under consideration is mathematically represented by alimit state function g(X) defined in the space of parameters as follows

bull g(X) gt 0 defines the safe state of the structurebull g(X) le 0 defines the failure statebull g(X) = 0 defines the limit state surface

Denoting by fX(x) the joint PDF of random vector X the probability offailure of the structure is

Pf =int

g(x)le0

fX(x) dx (755)

In all but academic cases this integral cannot be computed analyticallyIndeed the failure domain is often defined by means of response quantities(eg displacements strains stresses etc) which are computed by means ofcomputer codes (eg finite element code) in industrial applications mean-ing that the failure domain is implicitly defined as a function of X Thusnumerical methods have to be employed

Monte Carlo simulation

Monte Carlo simulation (MCS) is a universal method for evaluating integralssuch as Equation (755) Denoting by 1[g(x)le0](x) the indicator function ofthe failure domain (ie the function that takes the value 0 in the safe domainand 1 in the failure domain) Equation (755) rewrites

Pf =int

RM

1[g(x)le0](x) fX(x) dx = E[1[g(x)le0](x)

](756)

where E[] denotes the mathematical expectation Practically Equation(756) can be evaluated by simulating Nsim realizations of the random

vector X sayX (1) X(Nsim)

For each sample g

(X(i))

is evaluated

An estimation of Pf is given by the empirical mean

Pf = 1Nsim

Nsimsumi=1

1[g(x)le0](X(i)) = Nfail

Nsim(757)

278 Bruno Sudret and Marc Berveiller

where Nfail denotes the number of samples that are in the failure domainAs mentioned above MCS is applicable whatever the complexity of thedeterministic model However the number of samples Nsim required to get anaccurate estimation of Pf may be dissuasive especially when the value of Pf issmall Indeed if the order of magnitude of Pf is about 10minusk a total numberNsim asymp 410k+2 is necessary to get accurate results when using Equation(757) This number corresponds approximately to a coefficient of variationCV equal to 5 for the estimator Pf Thus crude MCS is not applicablewhen small values of Pf are sought andor when the CPU cost of each runof the model is non-negligible

FORM method

The first-order reliability method (FORM) has been introduced to get anapproximation of the probability of failure at a low cost (in terms of numberof evaluations of the limit state function)

The first step consists in recasting the problem in the standard nor-mal space by using a iso-probabilistic transformation X rarr ξ = T (X) TheRosenblatt or Nataf transformations may be used for this purpose ThusEquation (756) rewrites

Pf =int

g(x)le0

fX(x) dx =int

g(Tminus1(ξ ))le0

ϕM(ξ ) dξ (758)

where ϕM (ξ ) stands for the standard multinormal PDF

ϕM(ξ ) = 1(radic2π)n exp

(minus1

2

(ξ2

1 +middotmiddot middot+ ξ2M

))(759)

This PDF is maximal at the origin and decreases exponentially with ξ2Thus the points that contribute at most to the integral in Equation (758)are those of the failure domain that are closest to the origin of the space

The second step in FORM thus consists in determining the so-called designpoint ie the point of the failure domain closest to the origin in the stan-dard normal space This point Plowast is obtained by solving an optimizationproblem

Plowast = ξlowast = Argminξ2 g

(Tminus1(ξ )

)le 0

(760)

Several algorithms are available to solve the above optimisation problemeg the AbdondashRackwitz (Abdo and Rackwitz 1990) or the SQP (sequential

Stochastic finite element methods 279

quadratic programming) algorithm The corresponding reliability index isdefined as

β = sign[g(Tminus1(0))

]middot∥∥ξlowast∥∥ (761)

It corresponds to the algebraic distance of the design point to the origincounted as positive if the origin is in the safe domain or negative in theother case

The third step of FORM consists in replacing the failure domain by thehalf space HS(Plowast) defined by means of the hyperplane which is tangent tothe limit state surface at the design point (see Figure 71) This leads to

Pf =int

g(Tminus1(ξ ))le0

ϕM(ξ ) dξ asympint

HS(Plowast)

ϕM(ξ ) dξ (762)

The latter integral can be evaluated in a closed form and gives the firstorder approximation of the probability of failure

Pf asymp Pf FORM = (minusβ) (763)

where (x) denotes the standard normal CDF The unit normal vectorα = ξlowastβ allows definition of the sensitivity of the reliability index withrespect to each variable Precisely the squared components α2

i of α (whichsum to one) are a measure of the importance of each variable in the computedreliability index

g(ξ)=0

Failure Domain

x2

x2 P

x1 x1

HS(P)

β

α

Figure 71 Principle of the first-order reliability method (FORM)

280 Bruno Sudret and Marc Berveiller

Importance sampling

FORM as described above gives an approximation of the probability offailure without any measure of its accuracy contrary to Monte Carlo sim-ulation which provides an estimator of Pf together with the coefficient ofvariation thereof Importance sampling (IS) is a technique that allows tocombine both approaches First the expression of the probability of failureis modified as follows

Pf =int

RMIDf

(X(ξ ))ϕM (ξ )dξ (764)

where IDf(X(ξ )) is the indicator function of the failure domain Let us intro-

duce the sampling density (ξ ) in the above equation which may be anyvalid M-dimensional PDF

Pf =int

RMIDf

(X(ξ ))ϕM (ξ ) (ξ )

(ξ )dξ = E

[IDf

(X(ξ ))ϕM (ξ ) (ξ )

](765)

where E [] denotes the expectation with respect to the sampling density (ξ )To smartly apply IS after a FORM analysis the following sampling densityis chosen

(ξ ) = (2π )minusM2 exp(

minus12

∥∥ξ minus ξlowast∥∥2)

(766)

This allows one to concentrate the sampling around the design point ξlowastThen the following estimator of the probability of failure is computed

Pf IS = 1Nsim

Nsimsumi=1

1Df

(X(ξ (i))

)ϕM

(ξ (i))

(ξ (i)) (767)

which may be rewritten as

Pf IS = exp[minusβ22]Nsim

Nsimsumi=1

1Df

(ξ (i))

exp[minusξ (i) middot ξlowast] (768)

As in any simulation method the coefficient of variation CV of this estimatorcan be monitored all along the simulation Thus the process can be stoppedas soon as a small value of CV say less than 5 is obtained As the samplesare concentrated around the design point a limited number of samples say100ndash1000 is necessary to obtain this accuracy whatever the value of theprobability of failure

Stochastic finite element methods 281

765 Reliability methods coupled with FESFE models

The reliability methods (MCS FORM and IS) described in the section aboveare general ie not limited to stochastic finite element methods They canactually be applied whatever the nature of the model may it be analyticalor algorithmic

bull When the model is a finite element model a coupling between the reli-ability algorithm and the finite element code is necessary Each time thealgorithm requires the evaluation of the limit state function the finiteelement code is called with the current set of input parameters Then thelimit state function is evaluated This technique is called direct couplingin the next section dealing with application examples

bull When an SFE model has been computed first the response is approxi-mately represented as a polynomial series in standard normal randomvariables (Equation (737)) This is an analytical function that can nowbe used together with any of the reliability methods mentioned above

In the next section several examples are presented In each case when areliability problem is addressed the direct coupling and the post-processingof a PC expansion are compared

77 Application examples

The application examples presented in the sequel have been originally pub-lished elsewhere namely in Sudret and Der Kiureghian (2000 2002) for thefirst example Berveiller et al (2006) for the second example and Berveilleret al (2004) for the third example

771 Example 1 Foundation problem ndash spatialvariability

Description of the deterministic problem

Consider an elastic soil layer of thickness t lying on a rigid substratumA superstructure to be founded on this soil mass is idealized as a uniformpressure P applied over a length 2B of the free surface (see Figure 72) Thesoil is modeled as an elastic linear isotropic material A plane strain analysisis carried out

Due to the symmetry half of the structure is modeled by finite elementsStrictly speaking there is no symmetry in the system when random fields ofmaterial properties are introduced However it is believed that this simpli-fication does not significantly influence the results The parameters selectedfor the deterministic model are listed in Table 71

282 Bruno Sudret and Marc Berveiller

Table 71 Example 1 ndash Parameters of the deterministic model

Parameter Symbol Value

Soil layer thickness t 30 mFoundation width 2B 10 mApplied pressure P 02 MPaSoil Youngrsquos modulus E 50 MPaSoil Poissonrsquos ratio ν 03Mesh width L 60 m

A

2B

tE n

Figure 72 Settlement of a foundation ndash problem definition

(a) Mesh (b) Deformed Shape

Figure 73 Finite element mesh and deformed shape for mean values of the parameters bya deterministic analysis

A refined mesh was first used to obtain the ldquoexactrdquo maximum displacementunder the foundation (point A in Figure 72) Less-refined meshes were thentried in order to design a mesh with as few elements as possible that yieldedno more than 1 error in the computed maximum settlement The meshdisplayed in Figure 73(a) was eventually chosen It contains 99 nodes and80 elements The maximum settlement computed with this mesh is equalto 542 cm

Stochastic finite element methods 283

Description of the probabilistic data

The assessment of the serviceability of the foundation described in theabove paragraph is now investigated under the assumption that the Youngrsquosmodulus of the soil mass is spatially varying

The Youngrsquos modulus of the soil is considered to vary only in the verticaldirection so that it is modeled as a one-dimensional random field along thedepth This is a reasonable model for a layered soil medium The field isassumed to be lognormal and homogeneous Its second-moment propertiesare considered to be the mean microE = 50 MPa the coefficient of variationδE = σEmicroE = 02 The autocorrelation coefficient function of the underlyingGaussian field (see Equation (726)) is ρEE(z zprime) = exp(minus|z minus zprime| ) where zis the depth coordinate and = 30 m is the correlation length

The accuracy of the discretization of the underlying Gaussian field N(x)depends on the number of terms M retained in the expansion For eachvalue of M a global indicator of the accuracy of the discretization ε iscomputed from

ε = 1|Ω|intΩ

Var[N(x) minus N(x)

]Var[N(x)]

dΩ (769)

A relative accuracy in the variance of 12 (respectively 8 6) is obtainedwhen using M = 2 (respectively 3 4) terms in the KL expansion of N(x)Of course these values are closely related to the parameters defining the ran-dom field particularly the correlation length As is comparable here to thesize of the domain Ω an accurate discretization is obtained using few terms

Reliability analysis

The limit state function is defined in terms of the maximum settlement uA atthe center of the foundation

g(ξ ) = u0 minus uA(ξ ) (770)

where u0 is an admissible threshold initially set equal to 10 cm and ξ is thevector used for the random field discretization

Table 72 reports the results of the reliability analysis carried out either bydirect coupling between the finite element model and the FORM algorithm(column 2) or by the application of FORM after solving the SFE problem(column 6 for various values of p) Both results have been validated usingimportance sampling (columns 3 and 7 respectively) In the direct cou-pling approach 1000 samples (corresponding to 1000 deterministic FE runs)were used leading to a coefficient of variation of the simulation less than 6In the SFE approach the polynomial chaos expansion of the response is used

284 Bruno Sudret and Marc Berveiller

Table 72 Example 1 ndash Reliability index β ndash Influence of the orders of expansion M and p(u0 = 10cm)

M βFORMdirect β IS

direct p P βFORMSFE β IS

SFE

2 3452 3433 2 6 3617 36133 10 3474 3467

3 3447 3421 2 10 3606 35973 20 3461 3461

4 3447 3449 2 15 3603 35923 35 3458 3459

for importance sampling around the design point obtained by FORM (ie noadditional finite element run is required) and thus 50000 samples can beused leading to a coefficient of variation of the simulation less than 1

It appears that the solution is not much sensitive to the order of expansionof the input field (when comparing the results for M = 2 with respect to thoseobtained for M = 4) This can be understood easily by the fact that the maxi-mum settlement of the foundation is related to the global (ie homogenized)behavior of the soil mass Modeling in a refined manner the spatial variabil-ity of the stiffness of the soil mass by adding terms in the KL expansion doesnot significantly influence the results

In contrary it appears that a PC expansion of third degree (p = 3) isrequired in order to get a satisfactory accuracy on the reliability index

Parametric study

A comprehensive comparison of the two approaches is presented in Sudretand Der Kiureghian (2000) where the influences of various parameters areinvestigated Selected results are reported in this section More precisely theaccuracy of the SFE method combined with FORM is investigated whenvarying the value of the admissible settlement from 6 to 20 cm whichleads to an increasing reliability index A two-term (M = 2) KL expan-sion of the underlying Gaussian field is used The results are reported inTable 73 Column 2 shows the values obtained by direct coupling betweenFORM and the deterministic finite element model Column 4 shows the val-ues obtained using FORM after the SFE solution of the problem using anintrusive approach

The results in Table 73 show that the ldquoSFE+FORMrdquo procedure obviouslyconverges to the direct coupling results when p is increased It appears that athird-order expansion is accurate enough to predict reliability indices up to 5For larger values of β a fourth-order expansion should be used

Note that a single SFE analysis is carried out to get the reliability indicesassociated with the various values of the threshold u0 (once p is chosen)

Stochastic finite element methods 285

Table 73 Example 1 ndash Influence of the thresholdin the limit state function

u0 (cm) βdirect p βSFE

2 04776 0473 3 0488

4 04882 2195

8 2152 3 21654 21662 3617

10 3452 3 34744 34672 4858

12 4514 3 45594 45342 6494

15 5810 3 59184 58462 8830

20 7480 3 77374 7561

In contrary a FORM analysis has to be restarted for each value of u0when direct coupling is used As a conclusion if a single value of β (andrelated Pf asymp (minusβ)) is of interest direct coupling using FORM is proba-bly the most efficient method When the evolution of β with respect to athreshold is investigated the ldquoSFE+FORMrdquo approach may become moreefficient

772 Example 2 Foundation problem ndash non Gaussianvariables

Deterministic problem statement

Let us consider now an elastic soil mass made of two layers of differentisotropic linear elastic materials lying on a rigid substratum A foundationon this soil mass is modeled by a uniform pressure P1 applied over a length2B1 = 10 m of the free surface An additional load P2 is applied over a length2B2 = 5 m (Figure 74)

Due to the symmetry half of the structure is modeled by finite element(Figure 74) The mesh comprises 80 QUAD4 elements as in the previoussection The finite element code used in this analysis is the open source codeCode_Aster1 The geometry is considered as deterministic The elastic mate-rial properties of both layers and the applied loads are modeled by random

286 Bruno Sudret and Marc Berveiller

A

2B1 = 10m

t1 = 775m

t2 = 2225m E2 n2

E1 n1

P1

P2

2B2 = 5m

Figure 74 Example 2 Foundation on a two-layer soil mass

Table 74 Example 2 Two-layer soil layer mass ndash Parameters of the model

Parameter Notation Type of PDF Mean value Coef ofvariation

Upper layer soil thickness t1 Deterministic 775 m ndashLower layer soil thickness t2 Deterministic 2225 m ndashUpper layer Youngrsquos modulus E1 Lognormal 50 MPa 20Lower layer Youngrsquos modulus E2 Lognormal 100 MPa 20Upper layer Poisson ratio ν1 Uniform 03 15Lower layer Poisson ratio ν2 Uniform 03 15Load 1 P1 Gamma 02 MPa 20 Load 2 P2 Weibull 04 MPa 20

variables whose PDF are specified in Table 74 All six random variables aresupposed to be independent

Again the model response under consideration is the maximum verti-cal displacement at point A (Figure 74) The finite element model is thusconsidered as an algorithmic function h() that computes the vertical nodaldisplacement uA as a function of the six input parameters

uA = h(E1E2ν1ν2P1P2) (771)

Reliability analysis

The serviceability of this foundation on a layered soil mass vis-agrave-vis anadmissible settlement is studied Again two stategies are compared

bull A direct coupling between the finite element model and the probabilisticcode PROBAN (Det Norske Veritas 2000) The limit state function

Stochastic finite element methods 287

given in Equation (770) is rewritten in this case as

g(X ) = u0 minush(E1 E2 ν1 ν2 P1 P2) (772)

where u0 is the admissible settlement The failure probability is com-puted using FORM analysis followed by importance sampling Onethousand samples are used in IS allowing a coefficient of variation ofthe simulation less than 5

bull A SFE analysis using the regression method is carried out leading to anapproximation of the maximal vertical settlement

uA =Psum

j=0

ujΨj(ξk6k=1) (773)

For this purpose the six input variables E1 E2 ν1 ν2 P1 P2 are firsttransformed into a six-dimensional standard normal gaussian vectorξ equiv ξk6

k=1 Then a third-order (p = 3) PC expansion of the response

is performed which requires the computation of P =(

6 + 33

)= 84

coefficients An approximate limit state function is then considered

g(X) = u0 minusPsum

j=0

ujΨj(ξk6k=1) (774)

Then FORM analysis followed by importance sampling is applied (onethousand samples coefficient of variation less than 1 for the simula-tion) Note that in this case FORM as well as IS are performed usingthe analytical limit state function Equation (774) This computationis almost costless compared to the computation of the PC expansioncoefficients ujPminus1

j=0 in Equation (773)

Table 75 shows the probability of failure obtained by direct couplingand by SFEregression using various numbers of points in the experimentaldesign (see Section 753) Figure 75 shows the evolution of the ratio betweenthe logarithm of the probability of failure (divided by the logarithm of theconverged probability of failure) versus the number of regression points forseveral values of the maximum admissible settlement u0 Accurate results areobtained when using 420 regression points or more for different values of thefailure probability (from 10minus1 to 10minus4) When taking less than 420 pointsresults are inaccurate When taking more than 420 points the accuracy isnot improved Thus this number seems to be the best compromise betweenaccuracy and efficiency Note that it corresponds to 5times84 points as pointedout in Equation (747)

288 Bruno Sudret and Marc Berveiller

Table 75 Example 2 Foundation on a two-layered soil ndash probability of failure Pf

Thresholdu0 (cm)

Directcoupling

Non-intrusive SFEregression approach

84 pts 168 pts 336 pts 420 pts 4096 pts

12 30910minus1 16210minus1 27110minus1 33110minus1 32310minus1 33210minus1

15 68310minus2 67710minus2 69010minus2 84310minus2 67310minus2 69310minus2

20 21310minus3 ndash 99510minus5 82210minus4 20110minus3 19810minus3

22 46110minus4 ndash 74710minus7 13110minus4 38010minus4 42410minus4

Number of FE runsrequired

84 168 336 420 4096

09

1

11

12

13

14

15

16

17

18

19

0 200 400 600 800 1000Number of points

12 cm15 cm20 cm22 cm

Figure 75 Example 2 Evolution of the logarithm of the failure probability divided by theconverged value vs the number of regression points

773 Example 3 Deep tunnel problem

Deterministic problem statement and probabilistic model

Let us consider a deep tunnel in an elastic isotropic homogeneous soil massLet us consider a homogeneous initial stress field The coefficient of earth

pressure at rest is defined as K0 = σ0xx

σ0yy

Parameters describing geometry

Stochastic finite element methods 289

material properties and loads are given in Table 76 The analysis is carriedout under plane strain conditions Due to the symmetry of the problemonly a quarter of the problem is modeled by finite element using appropri-ate boundary conditions (Figure 76) The mesh contains 462 nodes and420 4-node linear elements which allow a 14-accuracy evaluation of theradial displacement of the tunnel wall compared to a reference solution

Moment analysis

One is interested in the radial displacement (convergence) of the tunnelwall ie the vertical displacement of point E denoted by uE The valueum

E obtained for the mean values of the random parameters (see Table 76)is 624 mm A third-order (p = 3) PC expansion of this nodal displace-

ment is computed This requires P =(

4 + 33

)= 35 coefficients Various SFE

Table 76 Example 3 ndash Parameters of the model

Parameter Notation Type Mean Coef of Var

Tunnel depth L Deterministic 20 m ndashTunnel radius R Deterministic 1 m ndashVertical initial stress minusσ 0

yy Lognormal 02 MPa 30Coefficient of earth

pressure at restK0 Lognormal 05 10

Youngrsquos modulus E Lognormal 50 MPa 20Poisson ratio ν Uniform [01ndash03] 02 29

E n

o

L

L

BA

CD

E

x

yR

dy = 0

dx = 0

dx = 0dy = 0

dx = 0dy= 0

sYY0

sXX0

Figure 76 Scheme of the tunnel Mesh of the tunnel

290 Bruno Sudret and Marc Berveiller

methods are applied to solve the SFE problem namely the intrusive method(Section 74) and the non intrusive regression method (Section 75) usingvarious experimental designs The statistical moments of uE (reduced meanvalue E[uE]um

E coefficient of variation skewness and kurtosis coefficients)are reported in Table 77

The reference solution is obtained by Monte Carlo simulation of thedeterministic finite element model using 30000 samples (column 2) Thecoefficient of variation of this simulation is 025 The regression methodgives good results when there are at least 105 regression points (note thatthis corresponds again to Equation (747)) These results are slightly betterthan those obtained by the intrusive approach (column 3) especially for theskewness and kurtosis coefficients This is due to the fact that input variablesare expanded (ie approximated) onto the PC basis when applying the intru-sive approach while they are exactly represented through the isoprobabilistictransform in the non intrusive approaches

Reliability analysis

Let us now consider the reliability of the tunnel with respect to an admissibleradial displacement u0 The deterministic finite element model is consideredas an algorithmic function h() that computes the radial nodal displacementuE as a function of the four input parameters

uE = h(Eνσ 0

yyK0

)(775)

Two solving stategies are compared

bull A direct coupling between the finite element model PROBAN The limitstate function reads in this case

g(X) = u0 minush(Eνσ 0

yyK0

)(776)

Table 77 Example 3 ndash Moments of the radial displacement at point E

ReferenceMonte Carlo

IntrusiveSFE (p = 3)

Non-intrusive SFERegression

35 pts 70 pts 105 pts 256 pts

uEumE 1017 1031 1311 1021 1019 1018

Coeff of var 0426 0427 1157 0431 0431 0433Skewness minus1182 minus0807 minus0919 minus1133 minus1134 minus1179Kurtosis 5670 4209 13410 5312 5334 5460Number of FE runs required minus 35 70 105 256

Stochastic finite element methods 291

where u0 is the admissible radial displacement The failure probabilityis computed using FORM analysis followed by importance samplingOne thousand samples are used in IS allowing a coefficient of variationof the simulation less than 5

bull A SFE analysis using the regression method is carried out leading to anapproximation of the radial displacement

uE =Psum

j=0

ujΨj(ξk4k=1) (777)

and the associated limit state function reads

g() = u0 minusPsum

j=0

ujΨj(ξk4k=1) (778)

The generalized reliability indices β = minusminus1(Pf IS) associated to limitstate functions (776) and (778) for various values of u0 are reported inTable 78

The above results show that at least 105 points of regression should be usedwhen a third-order PC expansion is used Additional points do not improvethe accuracy of the results The intrusive and non-intrusive approaches givevery similar results They are close to the direct coupling results when theobtained reliability index is not too large For larger values the third-orderPC expansion may not be accurate enough to solve the reliability problemAnyway the non-intrusive approach (which does not introduce any approxi-mation of the input variables) is slightly more accurate than the non intrusivemethod as explained in Example 2

Table 78 Example 3 Generalized reliability indices β = minusminus1(Pf IS) vs admissibleradial displacement

Thresholdu0 (cm)

Direct coupling Intrusive SFE(p = 3P = 35)

Non-intrusive SFEregression

35 pts 70 pts 105 pts 256 pts

7 0427 0251 minus0072 0227 0413 04058 0759 0571 0038 0631 0734 07529 1046 1006 0215 1034 0994 1034

10 1309 1309 0215 1350 1327 128612 1766 1920 0538 1977 1769 174715 2328 2907 0857 2766 2346 232217 2627 3425 1004 3222 2663 265320 3342 4213 1244 3823 3114 3192

292 Bruno Sudret and Marc Berveiller

78 Conclusion

Modeling soil material properties properly is of crucial importance ingeotechnical engineering The natural heterogeneity of soil can be fruitfullymodeled using probability theory

bull If an accurate description of the spatial variability is required randomfields may be employed Their use in engineering problems requires theirdiscretization Two efficient methods have been presented for this pur-pose namely the KarhunenndashLoegraveve expansion and the EOLE methodThese methods should be better known both by researchers and engi-neers since they provide a much better accuracy than older methodssuch as point discretization or local averaging techniques

bull If an homogenized behavior of the soil is sufficient with respect to thegeotechnical problem under consideration the soil characteristics maybe modeled as random variables that are usually non Gaussian

In both cases identification of the parameters of the probabilistic model isnecessary This is beyond the scope of this chapter

Various methods have been reviewed that predict the impact of input ran-dom parameters onto the response of the geotechnical model Attention hasbeen focused on a class of stochastic finite element methods based on poly-nomial chaos expansions It has been shown how the input variablesfieldsshould be first represented using functions of standard normal variables

Two classes of methods for computing the expansion coefficients havebeen presented namely the intrusive and non-intrusive methods The histor-ical intrusive approach is well-suited to solve linear problems It has beenextended to some particular non linear problems but proves delicate to applyin these cases In contrast the projection and regression methods are easyto apply whatever the physics since they make use only of the deterministicmodel as available in the finite element code Several runs of the model forselected values of the input parameters are required The computed responsesare processed in order to get the PC expansion coefficients of the responseNote that the implementation of these non-intrusive methods is done onceand for all and can be applied thereafter with any finite element software athand and more generally with any model (possibly analytical) However thenon-intrusive methods may become computationnally expensive when thenumber of input variables is large which may be the case when discretizedrandom fields are considered

Based on a large number of application examples the authors suggest theuse of second-order (p = 2) PC expansions for estimating mean and standarddeviation of response quantities When reliability problems are consideredat least a third-order expansion is necessary to catch the true shape of theresponse PDF tail

Stochastic finite element methods 293

Acknowledgments

The work of the first author on spatial variability and stochastic finiteelement methods has been initiated during his post-doctoral stay in theresearch group of Professor Armen Der Kiureghian (Departement of Civiland Environmental Engineering University of California at Berkeley) whois gratefully acknowledged The work on non-intrusive methods (as well asmost application examples presented here) is part of the PhD thesis of thesecond author This work was co-supervised by Professor Maurice Lemaire(Institut Franccedilais de Meacutecanique Avanceacutee Clermont-Ferrand France) whois gratefully acknowledged

Appendix A

A1 Hermite polynomials

The Hermite polynomials Hen(x) are solutions of the following differentialequation

yprimeprime minus xyprime + ny = 0 n isin N (779)

They may be generated in practice by the following recurrence relationship

He0(x) = 1 (780)

Hen+1(x) = xHen(x) minus nHenminus1(x) (781)

They are orthogonal with respect to the Gaussian probability measure

int infin

minusinfinHem(x)Hen(x)ϕ(x)dx = nδmn (782)

where ϕ(x) = 1radic

2π eminusx22 is the standard normal PDF If ξ is a standardnormal random variable the following relationship holds

E[Hem(ξ )Hen(ξ )

]= nδmn (783)

The first three Hermite polynomials are

He1(x) = x He2(x) = x2 minus 1 He3(x) = x3 minus 3x (784)

A2 Construction of the polynomial chaos

The Hermite polynomial chaos of order M and degree p is the set of multi-variate polynomials obtained by products of univariate polynomials so that

294 Bruno Sudret and Marc Berveiller

the maximal degree is less than or equal to p Let us define the followinginteger sequence α

α = αi i = 1 M αi ge 0Msum

i=1

αi le p (785)

The multivariate polynomial Ψα is defined by

Ψα(x1 xM) =Mprod

i=1

Heαi(xi) (786)

The number of such polynomials of degree not exceeding p is

P = (M + p)Mp (787)

An original algorithm to determine the set of αrsquos is detailed in Sudret and DerKiureghian (2000) Let Z be a standard normal random vector of size M Itis clear that

E[Ψα(Z)Ψβ (Z)

]=

Mprodi=1

E[Heαi

(Zi)Heβi(Zi)]

= δαβ

Mprodi=1

E[He2

αi(Zi)](788)

The latter equation shows that the polynomial chaos basis is orthogonal

Note

1 This is an open source finite element code developed by Electriciteacute de France RampDDivision see httpwwwcode-asterorg

References

Abdo T and Rackwitz R (1990) A new β-point algorithm for large timeinvariant and time-variant reliability problems In A Der Kiureghian and P Thoft-Christensen Editors Reliability and Optimization of Structural Systems rsquo90 Proc3rd WG 75 IFIP Conf Berkeley 26ndash28 March 1990 Springer Verlag Berlin (1991)pp 1ndash12

Abramowitz M and Stegun I A Eds (1970) Handbook of Mathematical FunctionsDover Publications Inc New York

Baecher G-B and Ingra T-S (1981) Stochastic finite element method in set-tlement predictions Journal of the Geotechnical Engineering Division ASCE107(4) 449ndash63

Stochastic finite element methods 295

Berveiller M (2005) Stochastic finite elements intrusive and non intrusive methodsfor reliability analysis PhD thesis Universiteacute Blaise Pascal Clermont-Ferrand

Berveiller M Sudret B and Lemaire M (2004) Presentation of two methodsfor computing the response coefficients in stochastic finite element analysis InProceedings of the 9th ASCE Specialty Conference on Probabilistic Mechanicsand Structural Reliability Albuquerque USA

Berveiller M Sudret B and Lemaire M (2006) Stochastic finite element a nonintrusive approach by regression Revue Europeacuteenne de Meacutecanique Numeacuterique15(1ndash3) 81ndash92

Choi S Grandhi R Canfield R and Pettit C (2004) Polynomial chaos expansionwith latin hypercube sampling for estimating response variability AIAA Journal45 1191ndash8

Clouteau D and Lafargue R (2003) An iterative solver for stochastic soil-structureinteraction In Computational Stochastic Mechanics (CSM-4) Eds P Spanos andG Deodatis Millpress Rotterdam pp 119ndash24

DeGroot D and Baecher G (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering ASCE 119(1) 147ndash66

Der Kiureghian A and Ke J-B (1988) The stochastic finite element method instructural reliability Probabilistic Engineering Mechanics 3(2) 83ndash91

Det Norske Veritas (2000) PROBAN userrsquos manual V43Ditlevsen O and Madsen H (1996) Structural Reliability Methods John Wiley

and Sons ChichesterEloseily K Ayyub B and Patev R (2002) Reliability assessment of pile groups in

sands Journal of Structural Engineering ASCE 128(10) 1346ndash53Fenton G-A (1999a) Estimation for stochastic soil models Journal of Geotechnical

Engineering ASCE 125(6) 470ndash85Fenton G-A (1999b) Random field modeling of CPT data Journal of Geotechnical

Engineering ASCE 125(6) 486ndash98Field R (2002) Numerical methods to estimate the coefficients of the polyno-

mial chaos expansion In Proceedings of the 15th ASCE Engineering MechanicsConference

Ghanem R (1999) The nonlinear Gaussian spectrum of log-normal stochasticprocesses and variables Journal of Applied Mechanic ASME 66 964ndash73

Ghanem R and Brzkala V (1996) Stochastic finite element analysis of randomlylayered media Journal of Engineering Mechanics 122(4) 361ndash9

Ghanem R and Spanos P (1991) Stochastic Finite Elements ndash A Spectral ApproachSpringer New York

Ghiocel D and Ghanem R (2002) Stochastic finite element analysis of seis-mic soil-structure interaction Journal of Engineering Mechanics (ASCE) 12866ndash77

Grigoriu M (2006) Evaluation of KarhunenndashLoegraveve spectral and sampling rep-resentations for stochastic processes Journal of Engineering Mechanics (ASCE)132(2) 179ndash89

Huang S Quek S and Phoon K (2001) Convergence study of the truncatedKarhunenndashLoegraveve expansion for simulation of stochastic processes InternationalJournal of Numerical Methods in Engineering 52(9) 1029ndash43

Isukapalli S S (1999) Uncertainty Analysis of transport-transportation modelsPhD thesis The State University of New Jersey NJ

296 Bruno Sudret and Marc Berveiller

Le Maicirctre O Reagen M Najm H Ghanem R and Knio D (2002) A stochasticprojection method for fluid flow-II Random Process Journal of ComputationalPhysics 181 9ndash44

Li C and Der Kiureghian A (1993) Optimal discretization of random fieldsJournal of Engineering Mechanics 119(6) 1136ndash54

Li L Phoon K and Quek S (2007) Simulation of non-translation processes usingnon Gaussian KarhunenndashLoegraveve expansion Computers and Structures 85(5ndash6)pp 264ndash276

Liu P-L and Der Kiureghian A (1986) Multivariate distribution models withprescribed marginals and covariances Probabilistic Engineering Mechanics 1(2)105ndash12

Liu W Belytschko T and Mani A (1986a) Probabilistic finite elements fornon linear structural dynamics Computer Methods in Applied Mechanics andEngineering 56 61ndash86

Liu W Belytschko T and Mani A (1986b) Random field finite elementsInternational Journal of Numerical Methods in Engineering 23(10) 1831ndash45

Loegraveve M (1977) Probability Theory Springer New-YorkMalliavin P (1997) Stochastic Analysis Springer BerlinMatthies H and Keese A (2005) Galerkin methods for linear and nonlinear elliptic

stochastic partial differential equations Computer Methods in Applied Mechanicsand Engineering 194 1295ndash331

Mellah R Auvinet G and Masrouri F (2000) Stochastic finite elementmethod applied to non-linear analysis of embankments Probabilistic EngineeringMechanics 15(3) 251ndash9

Niederreiter H (1992) Random Number Generation and Quasi-Monte CarloMethods Society for Industrial and Applied Mathematics Philadelphia PA

Nouy A (2007) A generalized spectral decomposition technique to solve a classof linear stochastic partial differential equations Computer Methods in AppliedMechanics and Engineering 196(45ndash48) 4521ndash37

Phoon K Huang H and Quek S (2005) Simulation of strongly non Gaussianprocesses using Karhunen-Loegraveve expansion Probabilistic Engineering Mechanics20(2) 188ndash98

Phoon K Huang S and Quek S (2002a) Implementation of KarhunenndashLoegraveveexpansion for simulation using a waveletndashGalerkin scheme Probabilistic Engi-neering Mechanics 17(3) 293ndash303

Phoon K Huang S and Quek S (2002b) Simulation of second-order processesusing KarhunenndashLoegraveve expansion Computers and Structures 80(12) 1049ndash60

Phoon K Quek S Chow Y and Lee S (1990) Reliability analysis of pilesettlements Journal of Geotechnical Engineering ASCE 116(11) 1717ndash35

Popescu R Deodatis G and Nobahar A (2005) Effects of random heterogene-ity of soil properties on bearing capacity Probabilistic Engineering Mechanics20 324ndash41

Press W Vetterling W Teukolsky S-A and Flannery B-P (2001) NumericalRecipes Cambridge University Press Cambridge

Rackwitz R (2000) Reviewing probabilistic soil modeling Computers andGeotechnics 26 199ndash223

Rackwitz R (2001) Reliability analysis ndash a review and some perspectives StructuralSafety 23 365ndash95

Stochastic finite element methods 297

Saltelli A Chan K and Scott E Eds (2000) Sensitivity Analysis John Wiley andSons New York

Sobolrsquo I (1993) Sensitivity estimates for nonlinear mathematical models Mathe-matical Modeling and Computational Experiment 1 407ndash14

Sobolrsquo I and Kucherenko S (2005) Global sensitivity indices for nonlinearmathematical models Review Wilmott Magazine 1 56ndash61

Sudret B (2005) Des eacuteleacutements finis stochastiques spectraux aux surfaces de reacuteponsestochastiques une approche unifieacutee In Proceedings 17egraveme Congregraves Franccedilais deMeacutecanique ndash Troyes (in French)

Sudret B (2006) Global sensitivity analysis using polynomial chaos expansionsIn Procedings of the 5th International Conference on Comparative StochasticMechanics (CSM5) Eds P Spanos and G Deodatis Rhodos

Sudret B (2008) Global sensitivity analysis using polynomial chaos expansionsReliability Engineering and System Safety In press

Sudret B Berveiller M and Lemaire M (2004) A stochastic finite element methodin linear mechanics Comptes Rendus Meacutecanique 332 531ndash7 (in French)

Sudret B Berveiller M and Lemaire M (2006) A stochastic finite element pro-cedure for moment and reliability analysis Revue Europeacuteenne de MeacutecaniqueNumeacuterique 15(7ndash8) 1819ndash35

Sudret B Blatman G and Berveiller M (2007) Quasi random numbers in stochas-tic finite element analysis ndash application to global sensitivity analysis In Proceedingsof the 10th International Conference on Applications of Statistics and Probabilityin Civil Engineering (ICASP10) Tokyo

Sudret B and Der Kiureghian A (2000) Stochastic finite elements and reliabilitya state-of-the-art report Technical Report n˚ UCBSEMM-200008 University ofCalifornia Berkeley

Sudret B and Der Kiureghian A (2002) Comparison of finite element reliabilitymethods Probabilistic Engineering Mechanics 17 337ndash48

VanMarcke E (1977) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103(GT11) 1227ndash46

VanMarcke E-H and Grigoriu M (1983) Stochastic finite element analysis ofsimple beams Journal of Engineering Mechanics ASCE 109(5) 1203ndash14

Wand M and Jones M (1995) Kernel Smoothing Chapman and Hall LondonZhang J and Ellingwood B (1994) Orthogonal series expansions of random

fields in reliability analysis Journal of Engineering Mechanics ASCE 120(12)2660ndash77

Chapter 8

Eurocode 7 andreliability-based design

Trevor L L Orr and Denys Breysse

81 Introduction

This chapter outlines how reliability-based geotechnical design is being intro-duced in Europe through the adoption of Eurocode 7 the new Europeanstandard for geotechnical design Eurocode 7 is based on the limit statedesign concept with partial factors and characteristic values This chaptertraces the development of Eurocode 7 it explains how the overall relia-bility of geotechnical structures is ensured in Eurocode 7 it shows howthe limit state concept and partial factor method are implemented inEurocode 7 for geotechnical designs it explains how characteristic val-ues are selected and design values obtained it presents the partial factorsgiven in Eurocode 7 to obtain the appropriate levels of reliability and itexamines the use of probability-based reliability methods such as first-order reliability method (FORM) analyses and Monte Carlo simulations forgeotechnical designs and the use of these methods for calibrating the par-tial factor values An example is presented of a spread foundation designedto Eurocode 7 using the partial factor method and this is followed bysome examples of the use of probabilistic methods to investigate howuncertainty in the parameter values affects the reliability of geotechnicaldesigns

82 Eurocode program

In the 1975 the Commission of the European Community (CEC) decidedto initiate work on the preparation of a program of harmonized codesof practice known as the Eurocodes for the design of structures Thepurpose and intended benefits of this program were that by providingcommon design criteria it would remove the barriers to trade due to theexistence of different codes of practice in the member states of what was

Eurocode 7 and RBD 299

then the European Economic Community (EEC) and is now the EuropeanUnion (EU) The Eurocodes would also serve as reference documents forfulfilling the requirements for mechanical resistance stability and resis-tance to fire including aspects of durability and economy specified in theConstruction Products Directive which have been adopted in each EUmember statersquos building regulations A further objective of the Eurocodesis to improve the competitiveness of the European construction industryinternationally

The set of Eurocodes consists of 10 codes which are European stan-dards ie Europaumlische Norms (ENs) The first Eurocode EN 1990 sets outthe basis of design adopted in the set of Eurocodes the second EN 1991provides the loads on structures referred to as actions the codes EN 1992 ndashEN 1997 and EN 1999 provide the rules for designs involving the differentmaterials and the code EN 1998 provides the rules for seismic design ofstructures Part 1 of EN 1997 called Eurocode 7 and referred to as thisthroughout this chapter provides the general rules for geotechnical designAs explained in the next section EN 1997 is published by CEN (ComiteacuteEacuteuropeacuteen de Normalisation or European Commitee for Standardization) asare the other Eurocodes

83 Development of Eurocode 7

The development of Eurocode 7 started in 1980 when Professor KevinNash Secretary General of the International Society for Soil Mechanics andFoundation Engineering invited Niels Krebs Ovesen to form a committeeconsisting of representatives from eight of the nine EEC member states atthat time which were Belgium Denmark France Germany Ireland ItalyNetherlands and the UK (there was no representative from Luxembourg andGreece joined the committee from the first meeting in 1981 when it joinedthe EEC) to prepare for the CEC a draft model limit state design code forEurocode 7 which was published in 1987 Subsequently the work on allthe Eurocodes was transferred from the CEC to CEN and the pre-standardversion of Eurocode 7 which was based on partial material factors waspublished in 1994 as ENV 1997ndash1 Eurocode 7 Geotechnical design Part 1General rules Then taking account of comments received on the ENV ver-sion and including partial resistance factors as well as partial material factorsthe full standard version of Eurocode 7 ndash Part 1 EN 1997ndash1 was publishedby CEN in November 2004 Since each member state is responsible for thesafety levels of structures within its jurisdiction there was as specified inGuidance Document Paper L (EC 2002) a two-year period following pub-lication of the EN ie until November 2006 for each country to preparea National Annex giving the values of the partial factors and other safetyelements so that Eurocode 7 could be used for geotechnical designs in thatcountry

300 Trevor L L Orr and Denys Breysse

84 Eurocode design requirements and the limitstate design concept

The basic Eurocode design requirements given in EN 1990 are thata structure shall be designed and executed in such a way that it willduring its intended life with appropriate degrees of reliability and in aneconomical way

bull sustain all actions and influences likely to occur during execution anduse and

bull remain fit for the use for which it is required

To achieve these basic design requirements the limit state design concept isadopted in all the Eurocodes and hence in Eurocode 7 The limit state designconcept involves ensuring that for each design situation the occurrence ofall limit states is sufficiently unlikely where a limit state is a state beyondwhich the structure no longer fulfills the design criteria The limit states thatare considered are ultimate limit states (ULSs) and serviceability limit states(SLSs) which are defined as follows

1 Ultimate limit states are those situations involving safety such as thecollapse of a structure or other forms of failure including excessivedeformation in the ground prior to failure causing failure in the sup-ported structure or where there is a risk of danger to people or severeeconomic loss Ultimate limit states have a low probability of occurrencefor well-designed structures as noted in Section 812

2 Serviceability limit states correspond to those conditions beyond whichthe specified requirements of the structure or structural element are nolonger met Examples include excessive deformations settlements vibra-tions and local damage of the structure in normal use under workingloads such that it ceases to function as intended Serviceability limitstates have a higher probability of occurrence than ultimate limit statesas noted in Section 812

The calculation models used in geotechnical designs to check that theoccurrence of a limit state is sufficiently unlikely should describe the behav-ior of the ground at the limit state under consideration Thus separateand different calculations should be carried out to check the ultimate andserviceability limit states In practice however it is often known from expe-rience which limit state will govern the design and hence having designedfor this limit state the avoidance of the other limit states may be veri-fied by a control check ULS calculations will normally involve analyzinga failure mechanism and using ground strength properties while SLS calcu-lations will normally involve a deformation analysis and ground stiffness or

Eurocode 7 and RBD 301

compressibility properties The particular limit states to be considered in thecase of common geotechnical design situations are listed in the appropriatesections of Eurocode 7

Geotechnical design differs from structural design since it involves anatural material (soil) which is variable and whose properties need tobe determined from geotechnical investigations rather than a manufac-tured material (such as concrete or steel) which is made to meet certainspecifications Hence in geotechnical design the geotechnical investiga-tions to identify and characterize the relevant ground mass and determinethe characteristic values of the ground properties are an important partof the design process This is reflected in the fact that Eurocode 7 hastwo parts Part 1 ndash General rules and Part 2 ndash Ground investigation andtesting

85 Reliability-based designs and Eurocode designmethods

As noted in the previous section the aim of designs to the Eurocodes isto provide structures with appropriate degrees of reliability thus designsto the Eurocodes are reliability-based designs (RBDs) Reliability is definedin EN 1990 as the ability of a structure or a structural member to fulfillthe specified requirements including the design working life for which ithas been designed and it is noted that reliability is usually expressed inprobabilistic terms EN 1990 states that the required reliability for a struc-ture shall be achieved by designing in accordance with the appropriateEurocodes and by the use of appropriate execution and quality managementmeasures

EN 1990 allows for different levels of reliability which take account of

bull the cause andor mode of obtaining a limit state (ie failure)bull the possible consequences of failure in terms of risk to life injury or

potential economic lossbull public aversion to failure andbull the expense and procedures necessary to reduce the risk of failure

Examples of the use of different levels of reliability are the adoption of ahigh level of reliability in a situation where a structure poses a high risk tohuman life or where the economic social or environmental consequencesof failure are great as in the case of a nuclear power station and a low levelof reliability where the risk to human life is low and where the economicsocial and environmental consequences of failure are small or negligible asin the case of a farm building

In geotechnical designs the required level of reliability is obtained inpart through ensuring that the appropriate design measures and control

302 Trevor L L Orr and Denys Breysse

checks are applied to all aspects and at all stages of the design process fromconception through the ground investigation design calculations and con-struction to maintenance For this purpose Eurocode 7 provides lists ofrelevant items and aspects to be considered at each stage Also a risk assess-ment system known as the Geotechnical Categories is provided to takeaccount of the different levels of complexity and risk in geotechnical designsThis system is discussed in Section 86

According to EN 1990 designs to ensure that the occurrence of limit stateis sufficiently unlikely may be carried out using either of the following designmethods

bull the partial factor method orbull an alternative method based directly on probabilistic methods

The partial factor method is the normal Eurocode design method and is themethod presented in Eurocode 7 This method involves applying appropri-ate partial factor values specified in the National Annexes to Eurocode 7 tostatistically based characteristic parameter values to obtain the design val-ues for use in relevant calculation models to check that a structure has therequired probability that neither an ultimate nor a serviceability limit statewill be exceeded during a specified reference period This design methodis referred to as a semi-probabilistic reliability method in EN 1990 asexplained in Section 812 The selection of characteristic values and theappropriate partial factor values to achieve the required reliability level arediscussed in Sections 88ndash811 As explained in Section 812 there is no pro-cedure in Eurocode 7 to allow for reliability differentiation by modifyingthe specified partial factor values and hence the calculated reliability levelin order to take account of the consequences of failure Instead reliabilitydifferentiation may be achieved through using the system of GeotechnicalCategories

Eurocode 7 does not provide any guidance on the direct use of fully proba-bilistic reliability methods for geotechnical design However EN 1990 statesthat the information provided in Annex C of EN 1990 which includes guid-ance on the reliability index β value may be used as a basis for probabilisticdesign methods

86 Geotechnical risk and GeotechnicalCategories

In Eurocode 7 three Geotechnical Categories referred to as Geotechni-cal Categories 1 2 and 3 have been introduced to take account of thedifferent levels of complexity of a geotechnical design and to establishthe minimum requirements for the extent and content of the geotechnicalinvestigations calculations and construction control checks to achieve the

Eurocode 7 and RBD 303

required reliability The factors affecting the complexity of a geotechnicaldesign include

1 the nature and size of the structure2 the conditions with regard to the surroundings for example neighboring

structures utilities traffic etc3 the ground conditions4 the groundwater situation5 regional seismicity and6 the influence of the environment eg hydrology surface water subsi-

dence seasonal changes of moisture

The use of the Geotechnical Categories is not a code requirement asthey are presented as an application rule rather than a principle andso are optional rather than mandatory The advantage of the Geotech-nical Categories is that they provide a framework for categorizing thedifferent levels of risk in a geotechnical design and for selecting appro-priate levels of reliability to account for the different levels of riskGeotechnical risk is a function of two factors the geotechnical haz-ards (ie dangers) and the vulnerability of people and the structureto specific hazards With regard to the design complexity factors listedabove factors 1 and 2 (the structure and its surroundings) relate tovulnerability and factors 3ndash6 (ground conditions groundwater seismic-ity and environment) are geotechnical hazards The various levels ofcomplexity of these factors in relation to the different GeotechnicalCategories and the associated geotechnical risks are shown in Table 81taken from Orr and Farrell (1999) It is the geotechnical designerrsquosresponsibility to ensure through applying the Eurocode 7 requirementsappropriately and by the use of appropriate execution and qualitymanagement measures that structures have the required reliability iehave sufficient safety against failure as a result of any of the potentialhazards

In geotechnical designs to Eurocode 7 the distinction between theGeotechnical Categories lies in the degree of expertise required and inthe nature and extent of the geotechnical investigations and calculationsto be carried out as shown in Table 82 taken from Orr and Farrell(1999) Some examples of structures in each Geotechnical Category arealso shown in this table As noted in the previous section Eurocode 7does not provide for any differentiation in the calculated reliability forthe different Geotechnical Categories by allowing variation in the par-tial factors values Instead while satisfying the basic design requirementsand using the specified partial factor values the required reliability isachieved in the higher Geotechnical Categories by greater attention tothe quality of the geotechnical investigations the design calculations the

Table 81 Geotechnical Categories related to geotechnical hazards and vulnerability levels

Factors to beconsidered

Geotechnical Categories

GC1 GC2 GC3

Geotechnicalhazards

Low Moderate High

Groundconditions

Known fromcomparableexperience to bestraightforwardNot involving softloose orcompressible soilloose fill orsloping ground

Ground conditionsand propertiescan be determinedfrom routineinvestigations andtests

Unusual orexceptionally difficultground conditionsrequiring non-routineinvestigations andtests

Groundwatersituation

No excavationsbelow water tableexcept whereexperienceindicates this willnot causeproblems

No risk of damagewithout priorwarning tostructures due togroundwaterlowering or drainageNo exceptionalwater tightnessrequirements

High groundwaterpressures andexceptionalgroundwaterconditions egmulti-layered stratawith variablepermeability

Regionalseismicity

Areas with no orvery lowearthquake hazard

Moderate earthquakehazard whereseismic design code(EC8) may be used

Areas of highearthquake hazard

Influence of theenvironment

Negligible risk ofproblems due tosurface watersubsidencehazardouschemicals etc

Environmentalfactors covered byroutine designmethods

Complex or difficultenvironmental factorsrequiring specialdesign methods

Vulnerability Low Moderate HighNature and

size of thestructure andits elements

Small and relativelysimple structuresor constructionInsensitivestructures inseismic areas

Conventional typesof structures withno abnormal risks

Very large or unusualstructures andstructures involvingabnormal risks Verysensitive structures inseismic areas

Surroundings Negligible risk ofdamage to orfrom neighboringstructures orservices andnegligible riskfor life

Possible risk ofdamage toneighboringstructures orservices due forexampleto excavationsor piling

High risk of damage toneighboringstructures or services

Geotechnical risk Low Moderate High

Eurocode 7 and RBD 305

monitoring during construction and the maintenance of the completedstructure

The reliability of a geotechnical design is influenced by the expertise ofthe designer No specific guidelines are given in Eurocode 7 with regard tothe level of expertise required by designers for particular Geotechnical Cate-gories except that as stated in both EN 1990 and EN 1997ndash1 it is assumedthat structures are designed by appropriately qualified and experienced per-sonnel Table 82 provides an indication of the levels of expertise required bythose involved in the different Geotechnical Categories The main featuresof the different Geotechnical Categories are summarized in the followingparagraphs

861 Geotechnical Category 1

Geotechnical Category 1 includes only small and relatively simple struc-tures for which the basic design requirements may be satisfied on the basisof experience and qualitative geotechnical investigations and where there isnegligible risk for property and life due to the ground or loading conditionsGeotechnical Category 1 designs involve empirical procedures to ensure therequired reliability without the use of any probability-based analyses ordesign methods Apart from the examples listed in Table 82 Eurocode 7does not and without local knowledge cannot provide detailed guidanceor specific requirements for Geotechnical Category 1 these must be foundelsewhere for example in local building regulations national guidancedocuments and textbooks According to Eurocode 7 the design of Geotech-nical Category 1 structures requires someone with appropriate comparableexperience

862 Geotechnical Category 2

Geotechnical Category 2 includes conventional types of structures and foun-dations with no abnormal risk or unusual or exceptionally difficult groundor loading conditions Structures in Geotechnical Category 2 require quan-titative geotechnical data and analyses to ensure that the basic requirementswill be satisfied and require a suitably qualified person normally a civilengineer with appropriate geotechnical knowledge and experience Routineprocedures may be used for field and laboratory testing and for designand construction The partial factor method presented in Eurocode 7 is themethod normally used for Geotechnical Category 2 designs

863 Geotechnical Category 3

Geotechnical Category 3 includes structures or parts of structures thatdo not fall within the limits of Geotechnical Categories 1 or 2

Table 82 Investigations designs and structural types related to Geotechnical Categories

Geotechnical Categories

GC1 GC2 GC3

Expertiserequired

Person withappropriatecomparableexperience

Experienced qualifiedperson

Experiencedgeotechnicalspecialist

Geotechnicalinvestigations

Qualitativeinvestigationsincluding trial pits

Routineinvestigationsinvolving boringsfield andlaboratory tests

Additional moresophisticatedinvestigations andlaboratory tests

Designprocedures

Prescriptivemeasures andsimplified designprocedures egdesign bearingpressures basedon experience orpublishedpresumed bearingpressures Stabilityor deformationcalculations maynot be necessary

Routine calculationsfor stability anddeformationsbased on designproceduresin EC7

More sophisticatedanalyses

Examples ofstructures bull Simple 1 and 2

storey structuresand agriculturalbuildings havingmaximum designcolumn load of250 kN andmaximum designwall load of100 kNm

bull Retaining walls andexcavationsupports whereground leveldifference doesnot exceed 2 m

bull Small excavationsfor drainage andpipes

Conventionalbull Spread and pile

foundationsbull Walls and other

retainingstructures

bull Bridge piers andabutments

bull Embankments andearthworks

bull Ground anchorsand other supportsystems

bull Tunnels in hardnon-fractured rock

bull Very largebuildings

bull Large bridgesbull Deep excavationsbull Embankments on

soft groundbull Tunnels in soft or

highly permeableground

Eurocode 7 and RBD 307

Geotechnical Category 3 includes very large or unusual structures struc-tures involving abnormal risks or unusual or exceptionally difficult groundor loading conditions and structures in highly seismic areas Geotechni-cal Category 3 structures will require the involvement of a specialist suchas a geotechnical engineer While the requirements in Eurocode 7 are theminimum requirements for Geotechnical Category 3 Eurocode 7 does notprovide any special requirements for Geotechnical Category 3 Eurocode 7states that Geotechnical Category 3 normally includes alternative provisionsand rules to those given in Eurocode 7 Hence in order to take account of theabnormal risk associated with Geotechnical Category 3 structures it wouldbe appropriate to use probability-based reliability analyses when designingthese to Eurocode 7

864 Classification into a particular GeotechnicalCategory

If the Geotechnical Categories are used the preliminary classification ofa structure into a particular category is normally performed prior to anyinvestigation or calculation being carried out However this classificationmay need to be changed during or following the investigation or designas additional information becomes available Also when using this sys-tem all parts of a structure do not have to be treated according to thehighest Geotechnical Category Only some parts of a structure may needto be classified in a higher category and only those parts will need to betreated differently for example with regard to the level of investigation orthe degree of sophistication of the design A higher Geotechnical Categorymay be used to achieve a higher level of reliability or a more economicaldesign

87 The partial factor method and geotechnicaldesign calculations

Geotechnical design calculations involve the following components

bull imposed loads or displacements referred to as actions F in theEurocodes

bull properties of soil rock and other materials X or ground resistances Rbull geometrical data abull partial factors γ or some other safety elementsbull action effects E for example resulting forces or calculated settlementsbull limiting or acceptable values C of deformations crack widths vibra-

tions etc andbull a calculation model describing the limit state under consideration

308 Trevor L L Orr and Denys Breysse

ULS design calculations in accordance with Eurocode 7 involve ensuringthat the design effect of the actions or action effect Ed does not exceed thedesign resistance Rd where the subscript d indicates a design value

Ed le Rd (81)

while SLS calculations involve ensuring that the design action effect Ed (egsettlement) is less than the limiting value of the deformation of the structureCd at the SLS

Ed le Cd (82)

In geotechnical design the action effects and resistances may be functionsof loads and material strengths as well as being functions of geometricalparameters For example in the design of a cantilever retaining wall againstsliding due to the frictional nature of soil both the action effect E whichis the earth pressure force on the retaining wall and the sliding resistanceR are functions of the applied loads as well as being functions of the soilstrength and the height of the wall ie in symbolic form they are EFXaand RFXa

When using the partial factor method in a ULS calculation and assum-ing the geometrical parameters are not factored the Ed and Rd may beobtained either by applying partial action and partial material factors γFand γM to representative loads Frep and characteristic soil strengths Xk orelse partial action effect and partial resistance factors γE and γR may beapplied to the action effects and the resistances calculated using unfactoredrepresentative loads and characteristic soil strengths This is indicated in thefollowing equations where the subscripts rep and k indicate representativeand characteristic values respectively

Ed = E[γFFrepXkγMad] (83)

and Rd = R[γFFrepXkγMad] (84)

or Ed = γEE[FrepXkad] (85)

and Rd = R[FrepXkad]γR (86)

The difference between the Design Approaches presented in Section 811 iswhether Equations (83) and (84) or Equations (85) and (86) are usedThe definition of representative and characteristic values and how they areselected and the choice of partial factor values to obtain Ed and Rd arediscussed in the following sections

Eurocode 7 and RBD 309

88 Characteristic and representative valuesof actions

EN 1990 defines the characteristic value of an action Fk as its main repre-sentative value and its value is specified as a mean value an upper or lowervalue or a nominal value EN 1990 states that the variability of perma-nent loads G may be neglected if G does not vary significantly during thedesign working life of the structure and its coefficient of variation V is smallGk should then be taken equal to the mean value EN 1990 notes that thevalue of V for Gk can be in the range 005 to 010 depending on the typeof structure For variable loads Q the characteristic value Qk may eitherbe an upper value with an intended probability of not being exceeded ora nominal value when a statistical distribution is not known In the caseof climatic loads a probability of 002 is quoted in EN 1990 for referenceperiod of 1 year corresponding to a return period of 50 years for the char-acteristic value No V value for variable loads is given in EN 1990 but atypical value would be 015 which is the value used in the example in Section814 If a load F is a normally distributed random variable with a meanvalue micro(F) standard deviation σ (F) and coefficient of variation V(F) thecharacteristic value corresponding to the 95 fractile is given by

Fk = micro(F) + 1645σ (F) = microF(1 + 1645V(F)) (87)

The representative values of actions Frep are obtained from the equation

Frep = ψFk (88)

where ψ = either 10 or ψ0 ψ1 or ψ2The factors ψ0 ψ1 or ψ2 are factors that are applied to the characteristic

values of the actions to obtain the representative values for different designsituations The factor ψ0 is the combination factor used to obtain the funda-mental combination ψ0Qk of a variable action for persistent and transientdesign situations and is applied only to the non-leading variable actions Thefactor ψ0 is chosen so that the probability that the effects caused by the com-bination will be exceeded is approximately the same as the probability of theeffects caused by the characteristic value of an individual action The factorψ1 is used to obtain the frequent value ψ1Qk of a variable action and thefactor ψ2 is used to obtain the quasi-permanent value ψ2Qk of a variableaction for accidental or seismic design situations The factor ψ1 is chosenso that either the total time within the reference period during which it isexceeded is only a small given part of the reference period or so that theprobability of it being exceeded is limited to a given value while the factorψ2 is the value determined so that the total period of time for which it willbe exceeded is a large fraction of the reference period Both ψ1 and ψ2 areapplied to all the variable actions

310 Trevor L L Orr and Denys Breysse

The equations for combining the actions for different design situationsusing the factors ψ0 ψ1 and ψ2 and the values of these factors in thecase of imposed snow and wind loads on buildings are given in EN 1990An example of a ψ value is ψ0 = 07 for an imposed load on a buildingthat is not a load in a storage area Because the different variable loads areoften much less significant than the permanent loads in geotechnical designscompared with the significance of the variable loads to the permanent loadsin many structural designs (for example comparing the permanent and vari-able loads in the designs of a slope and a roof truss) the combination factorsψ0 are used much less in geotechnical design than in structural designConsequently in geotechnical design the representative actions are oftenequal to the characteristic actions and hence Fk is normally used for Frep indesign calculations

89 Characteristic values of material properties

EN 1990 defines the characteristic value of a material or product propertyXk as ldquothe value having a prescribed probability of not being attained in ahypothetical unlimited test seriesrdquo In structural design this value generallycorresponds to a specified fractile of the assumed statistical distribution ofthe particular property of the material or product Where a low value ofa material property is unfavorable EN 1990 states that the characteristicvalue should be defined as the 5 fractile For this situation and assuminga normal distribution the characteristic value is given by the equation

Xk = micro(X) minus 1645σ (X) = micro(X)(1 minus 1645V(X)) (89)

where micro(X) is the mean value σ (X) is the standard deviation and V(X) isthe coefficient of variation of the unlimited test series X and the coefficient1645 provides the 5 fractile of the test results

The above definition of the characteristic value and Equation (89) areapplicable in the case of test results that are normally distributed and whenthe volume of material in the actual structural element being designed issimilar to the volume in the test element This is normally the situation instructural design when for example the failure of a beam is being analyzedand the strength is based on the results of tests on concrete cubes Howeverin geotechnical design the volume of soil involved in a geotechnical failureis usually much greater than the volume of soil involved in a single test theconsequence of this is examined in the following paragraphs

In geotechnical design Equation (89) may not be applicable because thedistribution of soil properties may not be normal and if V(X) is large thecalculated Xk value may violate a physical lower bound for example a crit-ical state value of φprime while if V(X) is greater than 06(= 11645) then thecalculated Xk value is negative This problem with Equation (89) does not

Eurocode 7 and RBD 311

occur in the case of structural materials such as steel for which the V(X) val-ues are normally about 01 normally distributed and do not have physicallower bounds

As noted above Equation (89) is not normally applicable in geotechni-cal design because the volume of soil involved in a failure is usually muchlarger than the volume of soil involved in a single field test or in a teston a laboratory specimen Since soil even homogeneous soil is inherentlyvariable the value affecting the occurrence of a limit state is the meanie average value over the relevant slip surface or volume of ground nota locally measured low value Hence in geotechnical designs the charac-teristic strength is the 5 fractile of the mean strengths measured alongthe slip surface or in the volume of soil affecting the occurrence of thatlimit state which is not normally the 5 fractile of the test results How-ever a particular situation when Equation (89) is applicable in geotechnicaldesign is when centrifuge tests are used to obtain the mean soil strengthof the soil on the entire failure surface because then the characteristicvalue is the 5 fractile of the soil strengths obtained from the centrifugetests

How the volume of soil involved in a failure affects the characteristicvalue is demonstrated by considering the failures of a slope and a foun-dation The strength value governing the stability of the slope is the meanvalue of a large volume of soil involving the entire slip surface while thefailure mechanism of a spread foundation involves a much smaller volumeof ground and if the spread foundation is in a weak zone of the sameground as in the slope the mean value governing the stability of the foun-dation will be lower than the mean value governing the stability of theslope Hence the characteristic value chosen to design the spread founda-tion should be a more cautious (ie a lower) estimate of the mean valuecorresponding to a local mean value than the less cautious estimate of themean value corresponding to a global mean value chosen to design theslope in the same soil Refined slope stability analyses have shown thatthe existence of weaker zones can change the shape of slip surface cross-ing these zones Thus careful attention needs to be paid to the influence ofweaker zones even if some simplified practical methods have been proposedto account for their influence when deriving characteristic values (Breysseet al 1999)

A further difference between geotechnical and structural design is that nor-mally only a very limited number of test results are available in geotechnicaldesign Arising from this the mean and standard deviation values obtainedfrom the test results may not be the same as the mean and standard deviationvalues of the soil volume affecting the occurrence of the limit state

Because of the nature of soil and soil tests leading to the problems outlinedabove when using the EN 1990 definition of the characteristic value for soilproperties the definition of the characteristic value of a soil property is given

312 Trevor L L Orr and Denys Breysse

in Eurocode 7 as ldquoa cautious estimate of the value affecting the occurrenceof the limit staterdquo There is no mention in the Eurocode 7 definition of theprescribed probability or the unlimited test series that are in the EN 1990definition Each of the terms ldquocautiousrdquo ldquoestimaterdquo and ldquovalue affecting theoccurrence of the limit staterdquo in the Eurocode 7 definition of the characteristicvalue is important and how they should be interpreted is explained in thefollowing paragraphs

The characteristic value is an estimated value and the selection of thisvalue from the very limited number of test results available in geotechni-cal designs involves taking account of spatial variability and experience ofthe soil and hence using judgment and caution The problem in selectingthe characteristic value of a ground property is deciding how cautious theestimate of the mean value should be In an application rule to explain thecharacteristic value in geotechnical design Eurocode 7 states that ldquoif sta-tistical methods are used the characteristic value should be derived suchthat the calculated probability of a worst value governing the occurrence ofthe limit state under consideration is not greater than 5rdquo It also statesthat such methods should differentiate between local and regional samplingand should allow the use of a priori knowledge regarding the variabilityof ground properties However Eurocode 7 provides no guidance for thedesigner on how this ldquocautious estimaterdquo should be chosen instead it reliesmainly on the designerrsquos professional expertise and relevant experience ofthe ground conditions

The following equation for the characteristic value corresponding to a95 confidence level that the actual mean value is greater than this valueis given by

Xk = m(X) minus (tradic

N)s(X) (810)

where m(X) and s(X) are the mean and the standard deviation of the sampleof test values and t is known as the Student t value (Student 1908) the valueof which depends on the actual number N of test values considered and onthe required confidence level

The following simpler equation for the characteristic value which hasbeen found to be useful in practice has been proposed by Schneider (1997)

Xk = m(X) minus 05s(X) (811)

The selection of the characteristic value is illustrated by the followingexample Ten undrained triaxial tests were carried out on samples obtainedat various depths from different boreholes through soil in which 10 m longpiles are to be founded and the measured undrained shear strength cu valuesare plotted in Figure 81 It is assumed that the measured cu values are thecorrect values at the locations where the samples were taken so that there is

Eurocode 7 and RBD 313

0

2

4

6

8

10

12

14

20 40 60 80 1000

Undrained shear strength cu kPa

Dep

th b

elow

gro

und

leve

l (m

)

Test values

Mean shaft and toe values

Eqn 811 char valuesover shaft and toe

Eqn 810 char valuesover shaft and toe

5 fractile char values overshaft and toe (Eqn 89)

Design values based onEqn 811

Figure 81 Test results and characteristic values obtained using different equations

no bias or variation in the measured values due to the testing method Themeans of the 7 measured values along the shaft and the 4 measured values inthe zone 1 m above and 2 m below the toe were calculated as m(cus) = 61 kPaand m(cut) = 50 kPa respectively and these values are plotted in Figure 81The standard deviations of the measured cu values along the shaft and aroundthe toe were calculated as s(cus) = 143 kPa and s(cut) = 66 kPa givingcoefficients of variation V(cus) = 023 and V(cut) = 013

If Equation (89) is used and the means and standard deviations of thetwo sets of test results are assumed to be the same as the means andstandard deviations of all the soil along the shaft and around the toethe characteristic cu along the shaft is cusk = m(cus) minus 1645 s(cus) =610minus1645times143 = 375 kPa while the characteristic cu around the toe iscutk = m(cut) minus 164 s(cut) = 500 minus 164 times 66 = 392 kPa These resultsare clearly incorrect as they are both less than the lowest measured valuesand the calculated characteristic value around the toe is greater than thecharacteristic value along the shaft They provide the 5 fractiles of the cuvalues measured on the relatively small volume of soil in the samples testedand not the 5 estimates of the mean cu values of all the soil over the eachfailure zone

Using Equation (810) and choosing t =1943 for N =7 the characteristiccu value along the shaft is

cusk =m(cus)minus(1943radic

N)s(cus)=610minus(1943radic

7)143=505 kPa

314 Trevor L L Orr and Denys Breysse

and choosing t = 2353 for N = 4 the characteristic cu value around thetoe is

cut k = m(cut) minus (2353radic

N)s(cut) = 500 minus (2353radic

4)66 = 423 kPa

Using Schneiderrsquos Equation (811) the characteristic cu value along theshaft is

cusk = m(cus) minus 05s(cus) = 610 minus 05 times 143 = 539 kPa

and the characteristic cu value around the toe is

cutk = m(cut) minus 05s(cut) = 500 minus 05 times 66 = 467 kPa

These characteristic values are all plotted in Figure 81 and show that forthe test results in this example the values obtained using Equations (810)and (811) are similar with the values obtained using Equation (810) beingslightly more conservative than those obtained using Equation (811) Fromthe results obtained in this example it can be seen that the characteristicvalue of a ground property is similar to the value that has conven-tionally been selected for use in geotechnical designs as noted by Orr(1994)

When only very few test results are available interpretation of the char-acteristic value using the classical statistical approach outlined above is notpossible However using prior knowledge in the form of local or regionalexperience of the particular soil conditions the characteristic value may beestimated using a Bayesian statistical procedure as shown for example byOvesen and Denver (1994) and Cherubini and Orr (1999) An example ofprior knowledge is an estimate of coefficient of variation of the strength fora particular soil deposit Typical ranges of V for soil parameters are givenin Table 83 The value V = 010 for the coefficient of variation of tanφprime isused in the example in Section 815

Table 83 Typical range of V for soil parameters

Soil parameter Typical range of V values RecommendedV value if limitedtest resultsavailable

tanφprime 005ndash015 010cprime 020ndash040 040cu 020ndash040 030mv 020ndash040 040γ (weight density) 001ndash010 0

Eurocode 7 and RBD 315

Duncan (2000) has proposed a practical rule to help the engineer in assess-ing V This rule is based on the ldquo3-sigma assumptionrdquo which is equivalentto stating that nearly all the values of a random property lie within the[Xmin Xmax] interval where Xmin and Xmax are equal to Xmean minus 3σ andXmean +3σ respectively Thus if the engineer is able to estimate the ldquoworstrdquovalue Xmin and the ldquobestrdquo value Xmax that X can take then σ can be cal-culated as σ = [Xmax minus Xmin]6 and Equation (89) used to determine theXk value However Duncan added that ldquoa conscious effort should be madeto make the range between Xmin and Xmax as wide as seemingly possibleor even wider to overcome the natural tendency to make the range toosmallrdquo

810 Characteristic values of pile resistances andξ values

When the compressive resistance of a pile is measured in a series of staticpile load tests these tests provide the values of the measured mean com-pressive resistance Rcm controlling the settlement and ultimate resistanceof the pile at the locations of the test piles In this situation and whenthe structure does not have the capacity to transfer load from ldquoweakrdquo toldquostrongrdquo piles Eurocode 7 states that the characteristic compressive pileresistance Rck should be obtained by applying the correlation factors ξ inTable 84 to the mean and lowest Rcm values so as to satisfy the followingequation

Rck = Min (Rcm)mean

ξ1

(Rcm)min

ξ2

(812)

Using Equation (812) and the ξ values in Table 84 it is found that whenonly one static pile load test is carried out the characteristic pile resistanceis the measured resistance divided by 14 whereas if five or more tests arecarried out the characteristic resistance is equal to lowest measured valueWhen pile load tests are carried out the tests measure the mean strengthof the soil over the relevant volume of ground affecting the failure of thepile Hence when assessing the characteristic value from pile load tests the

Table 84 Correlation factors to derive characteristic values from static pile loadtests

ξ for nlowast = 1 2 3 4 ge 5

ξ1 14 13 12 11 10ξ2 14 12 105 10 10

lowastn = number of tested piles

316 Trevor L L Orr and Denys Breysse

ξ factors take account of the variability of the soil and the uncertainty in thesoil strength across the test site

811 Design Approaches and partial factors

For ULSs involving failure in the ground referred to as GEO ultimatelimit states Eurocode 7 provides three Design Approaches termed DesignApproaches 1 2 and 3 (DA1 DA2 and DA3) and recommended valuesof the partial factors γF = γE γM and γR for each Design Approachthat are applied to the representative or characteristic values of theactions soil parameters and resistances as appropriate in accordance withEquations (83)ndash(86) to obtain the design values of these parameters Thesets of recommended partial factor values are summarized in Table 85 Thistable shows that there are two sets of partial factors for Design Approach 1Combination 1 (DA1C1) when the γF values are greater than unity and

Table 85 Eurocode 7 partial factors on actions soil parameters and resistances forGEOSTR ultimate limit states in persistent and transient design situations

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Partial factors onactions (γF) orthe effects ofactions (γE)

Set A1 A2 A1 A1Structural

actions

A2Geotechnical

actions

Permanentunfavorableaction

γG 135 10 135 135 10

Permanentfavorable action

γG 10 10 10 10 10

Variableunfavorableaction

γQ 15 13 15 15 13

Variable favorableaction

γQ 0 0 0 0 0

Accidental action γA 10 10 10 10 10

Partial factors forsoil parameters(γM)

Set M1 M2 M1 M2

Angle of shearingresistance (thisfactor is appliedto tanφprime)

γtanφprime 10 125 10 125

Continued

Table 85 Contrsquod

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Effectivecohesion cprime

γcprime 10 125 10 125

Undrained shearstrength cu

γcu 10 14 10 14

Unconfinedstrength qu

γqu 10 14 10 14

Weight density ofground γ

γγ

10 10 10 10

Partial resistancefactors (γR)

Spreadfoundationsretainingstructures andslopes

Set R1 R1 R2 R3

Bearing resistance γRv 10 10 14 10Sliding resistance

incl slopesγRh 10 10 11 10

Earth resistanc γRh 10 10 14 10

Pile foundations ndashdriven piles

Set R1 R4 R2 R3

Base resistance γb 10 13 11 10Shaft resistance

(compression)γs 10 13 11 10

Total combined(compression)

γst 10 13 11 10

Shaft in tension γt 125 16 115 11

Pile foundations ndashbored piles

Set R1 R4 R2 R3

Base resistance γRe 125 16 115 10Shaft resistance

(compression)γb 10 13 11 10

Total combined(compression)

γs 115 15 11 10

Shaft in tension γt 125 16 115 11

Continued

318 Trevor L L Orr and Denys Breysse

Table 85 Contrsquod

Parameter Factor Design Approaches

DA1 DA2 DA3

DA1C1 DA1C2

Pile foundations ndashCFA piles

Set R1 R4 R2 R3

Base resistance γRe 11 145 11 10Shaft resistance

(compression)γb 10 13 11 10

Total combined(compression)

γs 11 14 11 10

Shaft in tension γt 125 16 115 11

Prestressedanchorages

Set R1 R4 R2 R3

Temporaryresistance

γat 11 11 11 10

Permanentresistance

γap 11 11 11 10

lowastIn the design of piles set M1 is used for calculating the resistance of piles or anchors

the γM and γR values are equal to unity except for the design of piles andanchors and Combination 2 (DA1C2) when the γF value on permanentactions and the γR values are equal to unity and the γM values are greaterthan unity again except for the design of piles and anchorages when theγM values are equal to unity and the γR values are greater than unity InDA2 the γF and γR values are greater than unity while the γM values areequal to unity DA3 is similar to DA1C2 except that a separate set of γFfactors is provided for structural loads and the γR values for the design of acompression pile are all equal to unity so that this Design Approach shouldnot be used to design a compression pile using the compressive resistancesobtained from pile load tests

When on the resistance side partial material factors γM greater than unityare applied to soil parameter values the design is referred to as a materi-als factor approach (MFA) whereas if partial resistance factors γR greaterthan unity are applied to resistances the design is referred to as a resistancefactor approach (RFA) Thus DA1C2 and DA3 are both materials factorapproaches while DA2 is a resistance factor approach

The recommended values of the partial factors given in Eurocode 7 forthe three Design Approaches are based on experience from a number ofcountries in Europe and have been chosen to give designs similar to thoseobtained using existing design standards The fact that Eurocode 7 has threeDesign Approaches with different sets of partial factors means that use of

Eurocode 7 and RBD 319

the different Design Approaches produces designs with different overallfactors of safety as shown by the results of the spread foundation exam-ple in Section 813 presented in Table 87 and hence different reliabilitylevels

In the case of SLS calculations the design values of Ed and Cd inEquation (82) are equal to the characteristic values since Eurocode 7 statesthat the partial factors for SLS design are normally taken as being equal to 10

8111 Model uncertainty

It should be noted that as well as accounting for uncertainty in the loadsmaterial properties and the resistances the partial factors γF = γE γM and γRalso account for uncertainty in the calculation models for the actions and theresistances The influence of model uncertainty on the reliability of designsis examined in Section 818

812 Calibration of partial factors andlevels of reliability

Figure 82 which is an adaptation of a figure in EN 1990 presents anoverview of the various methods available for calibrating the numericalvalues of the γ factors for use with the limit state design equations in theEurocodes According to this figure the factors may be calibrated using eitherof two procedures

Deterministic methodsProcedure 1

Historical methodsEmpirical methods

Probabilistic methodsProcedure 2

Full probabilistic(Level III)

FORM(Level II)

Calibration Calibration Calibration

Semi-probabilisticmethods(Level I)

Partial factordesign

Method c

Method bMethod a

Figure 82 Overview of the use of reliability methods in the Eurocodes (from EN 1990)

320 Trevor L L Orr and Denys Breysse

bull Procedure 1 On the basis of calibration with long experience oftraditional design This is the deterministic method referred to asMethod a in Figure 82 which includes historical and empiricalmethods

bull Procedure 2 On the basis of a statistical evaluation of experimental dataand field observations which should be carried out within the frame-work of a probabilistic reliability theory Two probabilistic methods areshown in Figure 82 Method b which is a full probabilistic or Level IIImethod involving for example Monte Carlo simulations and Method cthe first-order reliability method (FORM) or Level II method In theLevel II method rather than considering failure as a continuous func-tion as in the Level III method the reliability is checked by determiningthe safety index β or probability of failure Pf at only a certain point onthe failure surface

As pointed out by Gulvanessian et al (2002) the term ldquosemi-probabilisticmethods (Level I)rdquo used in Figure 82 which is taken from EN 1990 is notnow generally accepted and may be confusing as it is not defined in EN 1990It relates to the partial factor design method used in the Eurocodes wherethe required target reliability is achieved by applying specified partial factorvalues to the characteristic values of the basic variables

Commenting on the different calibration design methods EN 1990 statesthat calibration of the Eurocodes and selection of the γ values has beenbased primarily on Method a ie on calibration to a long experience ofbuilding tradition The reason for this is to ensure that the safety levels ofstructures designed to the Eurocodes are acceptable and similar to existingdesigns and because Method c which involves full probabilistic analysesis seldom used to calibrate design codes due to the lack of statistical dataHowever EN 1990 states that Method c is used for further developmentof the Eurocodes Examples of this in the case of Eurocode 7 include therecent publication of some reliability analyses to calibrate the γ values andto assess the reliability of serviceability limit state designs eg Honjo et al(2005)

The partial factor values chosen in the ENV version of Eurocode 7 wereoriginally based on the partial factor values in the Danish Code of Practicefor Foundation Engineering (DGI 1978) because when work started onEurocode 7 in 1981 Denmark was the only western European country thathad a limit state geotechnical design code with partial factors It is inter-esting to note that Brinch Hansen (1956) of Denmark was the first to theuse the words ldquolimit designrdquo in a geotechnical context He was also thefirst to link the limit design concept closely to the concept of partial fac-tors and to introduce these two concepts in Danish foundation engineeringpractice (Ovesen 1995) before they were adopted in structural design inEurope

Eurocode 7 and RBD 321

Table 86 Target reliability index β and target probability of failure pf values

Limit state Target Reliability Index β Target Probability of Failure Pf

1 year 50 years 1 year 50 years

ULS 47 38 1 times 10minus6 72 times 10minus5

SLS 29 15 2 times 10minus3 67 times 10minus2

EN 1990 states that if Procedure 2 or a combination of Procedures 1and 2 is used to calibrate the partial factors then for ULS designs theγ values should be chosen such that the reliability levels for representativestructures are as close as possible to the target reliability index β valuegiven in Table 86 which is 38 for 50 years corresponding to the lowfailure rate or low probability of failure Pf = 72 times 10minus5 For SLS designsthe target β value is 15 corresponding to a higher probability of failurePf = 67 times 10minus2 than for ULS designs The ULS and SLS β values are 47and 29 for 1 year corresponding to Pf values of 1 times 10minus6 and 2 times 10minus3respectively It should be noted that the β values of 38 and 47 provide thesame reliability the only difference being the different lengths of the refer-ence periods Structures with higher reliability levels may be uneconomicalwhile those with lower reliability levels may be unsafe Examples of the useof reliability analyses in geotechnical designs are provided in Sections 814and 816

As mentioned by Gulvanessian et al (2002) the estimated reliabilityindices depend on many factors including the type of geotechnical structurethe loading and the ground conditions and their variability and conse-quently they have a wide scatter as shown in Section 817 Also the resultsof reliability analyses depend on the assumed theoretical models used todescribe the variables and the limit state as shown by the example inSection 818 Furthermore as noted in EN 1990 the actual frequency offailure is significantly dependent on human error which is not consid-ered in partial factor design nor are many unforeseen conditions suchas an unexpected nearby excavation and hence the estimated β valuedoes not necessarily provide an indication of the actual frequency offailure

EN 1990 provides for reliability differentiation through the introduc-tion of three consequences classes high medium and low with differentβ values to take account of the consequences of failure or malfunction of thestructure The partial factors in Eurocode 7 correspond to the medium conse-quences class In Eurocode 7 rather than modifying the partial factor valuesreliability differentiation is achieved through the use of the GeotechnicalCategories described in Section 86

322 Trevor L L Orr and Denys Breysse

813 Design of a spread foundation using thepartial factor method

The square pad foundation shown in Figure 83 based on Orr (2005) ischosen as an example of a spread foundation designed to Eurocode 7 usingthe partial factor method For the ULS design the consequence of usingthe different Design Approaches is investigated The foundation supports acharacteristic permanent vertical central load Gk of 900 kN which includesthe self weight of the foundation plus a characteristic variable vertical cen-tral load Qk of 600 kN and is founded at a depth of 08 m in soil withcuk = 200 kPa cprime

k = 0 φprimek = 35 and mvk = 0015m2MN The width of

the foundation is required to support the given loads without the settlementof the foundation exceeding 25 mm assuming the groundwater cannot riseabove the foundation level

The ULS design is carried out by checking that the requirement Ed le Rdis satisfied The design action effect Ed is equal to the design loadFd = γGGk + γQQk and obtained using the partial factors given in Table 85for Design Approaches 1 2 and 3 as appropriate For drained conditionsthe design resistance Rd is calculated using the following equations for thebearing resistance and bearing factors for drained conditions obtained fromAnnex D of Eurocode 7

r = RA = (12)Bγ primeNγ sγ + qprimeNqsq (813)

where

Nq = eπ tanφprimetan(45 +φprime2)

Nγ = 2(Nq minus 1)tanφprime

sq = 1 + (BL)sinφprime

sγ = 1 minus 03(BL)

Gk = 900kN Qk = 600kN

GWLB =

d = 08 m

Soil Stiff tillcuk = 200 kPack = 0 kPaφk = 35degγ= 22 kNm3

mvk = 0015 m2MN

Maximum allowable settlement = 25mm

Figure 83 Square pad foundation design example

Eurocode 7 and RBD 323

Table 87 Design foundation widths actions bearing resistances and overall factors ofsafety for pad foundation design example for drained conditions

B (m) Fk (kN) Rk (kN) Fd = Rd OFS = RkFk

DA1C1 (134)lowast 1500 21150 21150 (141)lowastDA1C2 171 1500 34749 16800 232DA2 156 1500 29610 21150 197DA3 190 1500 43726 21150 292

lowast Since B for DA1C1 is less than B for DA1C2 the B for DA1C1 is not the DA1 design width

and applying the partial factors for Design Approaches 1 2 and 3 given inTable 85 either to tanφprime

k or to the characteristic resistance Rk calculatedusing the φprime

k value as appropriate The calculated ULS design widths B forthe different Design Approaches for drained conditions are presented inTable 87 together with the design resistances Rd which are equal to thedesign loads Fd For Design Approach 1 DA1C2 is the relevant combina-tion as the design width of 171 m is greater than the DA1C1 design widthof 134 m For undrained conditions the DA1C2 design width is 137 mcompared to 171 m for drained conditions hence the design is controlledby the drained conditions

The overall factors of safety OFS using each Design Approach obtainedby dividing Rk by Fk are also given in Table 87 The OFS values in Table 87show that for the φprime value and design conditions in this example DesignApproach 3 is the most conservative giving the highest OFS value of 292Design Approach 2 is the least conservative giving the lowest OFS value of197 and Design Approach 1 gives an OFS value of 232 which is betweenthese values The relationships between the characteristic and the designvalues of the resistances and the total loads for each Design Approach areplotted in Figure 84 The lines joining the characteristic and design valuesshow how for each Design Approach starting from the same total charac-teristic load Fk = Gk +Qk = 900+600 = 1500 kN the partial factors causethe design resistances to decrease andor the design loads to increase to reachthe design values where Rd = Fd Since this problem is linear with respectto the total load when the ratio between the permanent and variable loadsis constant and when φprime is constant lines of constant OFS correspond-ing to lines through the characteristic values for each Design Approachplot as straight lines on Figure 84 These lines show the different safetylevels and hence the different reliability levels obtained using the differ-ent Design Approaches for this square pad foundation design exampleIt should be noted that the design values given in Table 87 and plottedin Figure 84 are for ULS design and do not take account of the settlementof the foundation

324 Trevor L L Orr and Denys Breysse

0

1000

2000

3000

4000

5000

0 1000 2000 3000 4000 5000

Resistance R (kN)

Tot

al fo

unda

tion

load

(G

+ Q

) kN

Design values

DA3DA1C1

DA2

Characteristic(unfactored)

values

Failure criterionRd = Fd

Safe region

OFS = (141)

OFS = 197

OFS = 232

OFS = 292

DA1C2

Figure 84 OFS values using the different Design Approaches for spread foundationexample and relationship between characteristic and design values

The settlement needs be checked by carrying out an SLS design calcula-tion to ensure that the settlement of the foundation due to the characteristicloading Ed does not exceed the allowable settlement Cd of 25 mm ieEd le Cd The SLS loads are calculated using the characteristic loads sincethe SLS partial factors are equal to unity Two settlement components areconsidered the immediate settlement si and the consolidation settlement scThe immediate settlement is evaluated using elasticity theory and the follow-ing equation which has the form of that given in Annex F2 of Eurocode 7

si = p(1 minus ν2u)Bf Eu (814)

where p is the SLS net bearing pressure induced at the base level of thefoundation B is the foundation width Eu is the undrained Youngrsquos modulusνu is undrained Poissonrsquos ratio (equal to 05) and f is a settlement coefficientwhose value depends on the nature of the foundation and its stiffness (forthe settlement at the center of a flexible square foundation in this example

Eurocode 7 and RBD 325

f = 112 is used) For the stiff till it is assumed that Eu = 750cu = 750times200 =150 MPa The consolidation settlement is calculated by dividing the groundbelow the foundation into layers and summing the settlement of each layerusing the following equation

sc = micromvhσ primei = micromvhαip (815)

where mv is the coefficient of volume compressibility = 0015m2MN(assumed constant in each layer for the relevant stress level) h is thethickness of the layer σi

prime is the increment in vertical effective stressin the ith layer due to p and is given by αip where αi is the coeffi-cient for the increase in vertical effective stress in the ith layer obtainedfrom the Fadum chart and micro is the settlement reduction coefficient toaccount for the fact that the consolidation settlement is not one-dimensional(Craig 2004) and its value is chosen as 055 for the square foundation onstiff soil

The SLS is checked for the DA1C2 design foundation width B = 171 mThe SLS bearing pressure is

p = (Gk + Qk)A = (900 + 600)1712 = 5130 kPa

The immediate settlement

si = p(1 minus ν2u)Bf Eu = 5130 times (1 minus 052)171 times 112150 = 49 mm

To calculate the consolidation settlement the soil below foundation isdivided into four layers 10 m thick The increases in vertical stressσ1

prime = αip at the centers of the layers due to the foundation are estimatedfrom the αi factors which are 090 040 018 and 01 giving stress increasesof 463 206 93 and 51 kPa Hence the consolidation settlement

sc =micromvhαip=055times0015times10times(090+040+018+010)5146

=055times0015times10times158times5146=61 mm

Total settlement s = si + sc = 49 + 61 = 110 mm lt 25 mmTherefore the SLS requirement is satisfied for the DA1C2 foundation

width of 171 m

814 First-order reliability method (FORM)analyses

In EN 1990 a performance function g which is referred to as the safetymargin SM in this section is defined as

g = SM = R minus E (816)

where R is the resistance and E is the action effect SM is used in LevelII FORM analyses (see Figure 82) to investigate the reliability of a design

326 Trevor L L Orr and Denys Breysse

Assuming E R and SM are all random variables with Gaussian distributionsthe reliability index is given by

β = micro(SM)σ (SM) (817)

where micro(SM) is the mean value and σ (SM) is the standard deviation of SMgiven by

micro(SM) = micro(R) minusmicro(E) (818)

σ (SM) =radicσ (R)2 +σ (E)2 (819)

The probability of failure is the probability that SM lt 0 and is obtainedfrom

Pf = P[SM lt 0] = P[R lt E] = (minusβ) = 1 minus(β) (820)

where denotes the standardized normal cumulative distribution functionThe example chosen to illustrate the use of the FORM analysis is

the design for undrained conditions of the square foundation shown inFigure 83 Performing a FORM analysis for drained conditions is morecomplex due to the nonlinear expression for the drained bearing resistancefactors Nq and Nγ (see Section 813) The action effect and the bearingresistance for undrained conditions are

E = Fu = G + Q (821)

R = Ru = B2(Nccusc + γD) = B2((π + 2)12cu + γD) (822)

If these variables are assumed to be independent then the means andstandard deviations of Fu and Ru are

micro(Fu)= micro (G) +micro (Q) (823)

σ(Fu)=radicσ (G)2 +σ (Q)2 (824)

micro(Ru) = B2((π + 2)12micro(cu) + γD) (825)

σ (Ru) = B2((π + 2)12)σ (cu) (826)

In order to ensure the relevance of the comparisons between the partial factordesign method and the FORM analysis the means of all the three randomvariables are selected so that the characteristic values are equal to the givencharacteristic values of Gk = 900 kN Qk = 600 kN and cuk = 200 kPa Themean values of these parameters needed in the FORM analysis to determine

Eurocode 7 and RBD 327

Table 88 Parameter values for reliability analyses of the spread foundation example

Coefficient of variation V Mean micro Standard deviation σ

ActionsG 0 900 kN 0 kNQ 015 4587 kN 688 kNSoil parameterstanφprime 010 0737 (equiv 364˚) 0074 (equiv +minus 3˚)cu 030 2353 kPa 706 kPamv 040 001875 m2MN 00075 m2MN

the reliability are calculated from these characteristic values as follows usingthe appropriate V values given in Table 88

bull for the permanent load in this example it is assumed that V(G) = 0in accordance with EN 1990 as noted in Section 88 so that the meanpermanent load is equal to the characteristic value since reorganizingEquation (87) gives

micro(G) = Gk(1 + 1645V(G)) = Gk = 900 kN (827)

bull for the variable load it is assumed to be normally distributed as notedin Section 88 so that for an occurrence probability of 002 or 2 themultiplication factor in Equation (89) is 2054 rather than 1654 for5 and hence for V(Q) = 015 the mean value of the variable load isgiven by

micro(Qm) = Qk(1 + 2054V(Q)) (828)

micro(Q) = 600(1 + 2054 times 015) = 4587 kN

bull for material parameters using Equation (89) by Schneider (1997)

micro(X) = Xk(1 minus 05V(X)) (829)

micro(cu) = cuk(1 minus 05 times V(X)) = 200(1 minus 05 times 030) = 2353 kPa

Although not required for the undrained FORM analysis of the squarefoundation the mean values of tanφprime and mv which are used in the MonteCarlo simulations in Section 816 are also calculated using Equation (829)and are given in Table 88 together with the standard deviation valuescalculated by multiplying the mean and V values Hence assumingGaussian distributions and substituting the mean G Q and cu values inEquations (823) (825) and (818) for micro(Fu) and micro(Ru) and micro(SM) and

328 Trevor L L Orr and Denys Breysse

using the DA1C1 foundation width of B = 137 m reported in Section 813for undrained conditions gives

micro(Fu) = micro(G) +micro(Q) = 9000 + 4587 = 13587 kN

micro(Ru) = B2((π + 2)12micro(cu) + γD)

= 1372((π + 2)12 times 2353 + 22 times 08) = 27578 kN

micro(SM) = micro(Ru) minusmicro(Fu) = 27578 minus 13587 = 13991 kN

Substituting the standard deviation values for G Q and cu in Equations(824) (826) and (819) for σ (Fu) σ (Ru) and σ (SM) gives

σ(Fu)=radicσ (G)2 +σ (Q)2 =

radic02 + 6882 = 688 kN

σ (Ru) = B2(π + 2)12σ (cu) = 1372(π + 2)12 times 706 = 7629 kN

σ (SM) =radicσ(Ru)2 +σ

(Fu)2 =

radic76292 + 6882 = 7660 kN

Hence substituting the values for micro(SM) and σ (SM) in Equation (817) thereliability index is

β = micro(SM)σ (SM) = 139917660 = 183

and from Equation (820) the probability of failure is

PfULS = (minusβ) = (minus183) = 0034 = 34 times 10minus2

The calculated β value of 183 for undrained conditions for a foundationwidth of 137 m is much less than the target ULS β value of 38 and hencethe calculated probability of failure of 34 times 10minus2 is much greater than thetarget value of 72 times 10minus5

It has been seen in Table 87 that the design width for drained conditionsis B = 171 m for DA1C2 and hence this width controls the design It istherefore appropriate to check the reliability of this design foundation widthfor drained conditions Substituting B = 171 m in Equations (825) and(826) gives β = 230 and PfULS = 106 times 10minus2 Although this foundationwidth has a greater reliability both values are still far from and do notsatisfy the target values The reliability indices calculated in this examplemay indirectly demonstrate that in general geotechnical design equationsare usually conservative ie because of model errors they underpredict thereliability index and overpredict the probability of failure The low calculatedreliability indices shown above can arise because the conservative modelerrors are not included The reliability index is also affected by uncertaintiesin the calculation model as discussed in Section 818 However Phoon and

Eurocode 7 and RBD 329

Kulhawy (2005) found that the mean model factor for undrained bearingcapacity at the pile tip is close to unity If this model factor is applicable tospread foundations it will not account for the low β values reported above

The overall factor of safety OFS as defined in Section 813 is obtainedby dividing the characteristic resistance by the characteristic load ForB = 171 m Rk = 1712((π + 2) times 12 times 200 + 22 times 08) = 36598 kN andFk = 900 + 600 = 1500 kN so that OFS = RkFk = 241821500 = 244Thus using the combination of the recommended partial factors of 14 oncu and 13 on Q for undrained conditions with B = 171 m gives the OFSvalue of 244 which is similar in magnitude to the safety factor often used intraditional foundation design whereas the calculated probability of failureof 106 times 10minus2 for this design is far from negligible

815 Effect of variability in cu on design safety andreliability of a foundation in undrainedconditions

To investigate how the variability of the ground properties affects the designsafety and reliability of a foundation in undrained conditions the influenceof the variability of cu on MR where MR is ratio of the mean resistance tothe mean load micro(Ru)micro(Fu) is first examined It should be noted that theMR value is not the traditional factor of safety used in deterministic designswhich is normally a cautious estimate of the resistance divided by the loadnor is it the overall factor of safety for Eurocode 7 designs defined in Section813 as OFS = RkFk The influence of the variability of cu on MR is investi-gated by varying V(cu) in the range 0 to 040 As in the previous calculationsthe characteristic values of the variables G Q and cu are kept equal to thegiven characteristic values so that the following analyses are consistent withthe previous partial factor design and the FORM analysis An increase inV(cu) causes micro(Ru) and hence MR to increase as shown by the graphs ofMR against V(cu) in Figure 85 for the two foundation sizes B = 137 mand 171 m The increase in MR with increase in V(cu) is due to the greaterdifference between the characteristic and mean cu values as the V(cu) valueincreases For undrained conditions with B = 137 m and V(cu) = 03 theratio MR = micro(Ru)micro(Fu) = 2757813587 = 203 whereas for V(cu) = 0when micro(Ru) = Rk then MR = Rkmicro(Fu) = 2349113587 = 173

The fact that MR increases as V(cu) increases demonstrates the problemwith using MR which is based on the mean R value as the safety factorand not taking account of the variability of cu because it is not logical thatthe safety factor should increase as V(cu) increases This is confirmed bythe graphs of β and PfULS plotted against V(cu) in Figures 86 and 87which show as expected that for a given foundation size the reliabilityindex β decreases and the ULS probability of failure of the foundationPfULS increases as V(cu) increases This problem with MR is overcome ifthe safety factor is calculated using a cautious estimate of the mean R value

330 Trevor L L Orr and Denys Breysse

1

15

2

25

3

35

000 010 020 030 040

B=171 m

B=137 m

V(cu)

OFS = 161 (B=137 m)

OFS = 244 (B=171m)

MR

Figure 85 Change in mean resistance mean load ratio MR with change in V(cu)

00

05

10

15

20

25

30

35

40

45

50

040302010

B=171 m

B=137m

V(cu)

b

Figure 86 Change in reliability index β with change in V(cu)

that takes account of the variability of cu for example using the OFS andthe Eurocode 7 characteristic value as defined in Eurocode 7 rather than themean R value For undrained conditions with B = 171 m OFS = 244 ascalculated above while for B=139 m OFS = 161 However these OFS val-ues are constant as V(cu) increases as shown in Figure 85 and thus the OFSvalue provides no information on how the reliability of a design changes asV(cu) changes The fact that the OFS value does not provide any informationon how the reliability of a design changes as V(cu) changes demonstrates theimportance of and hence the interest in reliability modeling

Eurocode 7 and RBD 331

10E-07

10E-06

10E-05

10E-04

10E-03

10E-02

10E-01

10E+0000 01 02 03 04

B=171 m

B=137 m

V(cu)

PfULS

Figure 87 Change in probability of failure Pf with change in V(cu)

The graphs in Figures 86 and 87 show that for a given foundation sizethe reliability index β decreases and the ULS probability of failure of thefoundation for undrained conditions PfUL increases as V(cu) increases Thetarget reliability index β = 38 and the corresponding target probability offailure PfULS = 72times10minus5 are only achieved if V(cu) does not exceed 017for B = 171 m the design width required for drained conditions whichexceeds that for undrained conditions Since this V(cu) value is less than thelowest V(cu) value of 020 typically encountered in practice as shown bythe range of V(cu) in Table 83 this demonstrates that undrained designs toEurocode 7 such as this spread foundation have a calculated ULS probabil-ity of failure greater than the target value Choosing the mean V(cu) value of030 from Table 83 for this example with B = 171 m gives β = 230 withPfULS = 106 times 10minus2

816 Monte Carlo analysis of the spreadfoundation example

Since a FORM analysis is an approximate analytical method for calculat-ing the reliability of a structure an alternative and better method is to usea Monte Carlo analysis which involves carrying out a large number ofsimulations using the full range of combinations of the variable param-eters to assess the reliability of a structure In this section the spreadfoundation example in Section 813 is analyzed using Monte Carlo simu-lations to estimate the reliability level of the design solution obtained bythe Eurocode 7 partial factor design method As noted in Section 812 and

332 Trevor L L Orr and Denys Breysse

shown in Table 86 the target reliability levels in EN 1990 correspond to β

values equal to 38 at the ULS and 15 at the SLS respectively The designfoundation width B to satisfy the ULS using DA1C2 was found to be171 m (see Table 87) and this B value also satisfies the SLS requirementof the foundation settlement not exceeding a limiting value Cd of 25 mm

8161 Simulations treating both loads and the three soilparameters as random variables

Considering the example in Section 813 five parameters can be consideredas being random variables two on the ldquoloading siderdquo G and Q and three onthe ldquoresistance siderdquo tanφprime cu and mv The foundation breadth B depthd and the soil weight density γ are considered to be constants For thesake of simplicity all of the random variables are assumed to have normaldistributions with coefficients of variation V within the normal rangesfor these parameters (see the V values for loads in Section 88 and for soilparameters in Table 83) Since the parameter values given in the exampleare the characteristic values the mean values for the reference case need tobe calculated as in the case of the FORM analyses in Section 814 usingthe appropriate V values and Equations (827)ndash(829) The mean values andstandard deviations were calculated in Section 814 using these equationsfor the five random parameters and are given in Table 88

When using Monte Carlo simulations a fixed number of simulations ischosen (typically N = 104 or 105 and then for each simulation a set ofrandom variables is generated and the ULS and SLS conditions are checkedIt can happen that during the generation process with normally distributedvariables when V is high some negative parameter values can be generatedHowever these values have no physical meaning When this occurs they aretaken to be zero and the process is continued It has been checked that thesevalues have no significant influence on the estimated failure probability Anexample of a distribution generated by this process is the cumulative distribu-tion of φprime values in Figure 88 for the random variable tanφprime plotted aroundthe mean value of φprime = 364 Since in Eurocode 7 the ULS criterion to besatisfied is Fd le Rd it is checked in the ULS simulations that the calculatedaction F[G Q] does not exceed the calculated resistance R[B tan φprime] Thenumber of occurrences for which F gtR is counted (NFULS) and the estimatedULS failure probability PfULS is the ratio of NFULS to N

PfULS = NFULSN (830)

Similarly since the SLS criterion to be satisfied in Eurocode 7 is Ed le Cdin the SLS simulations it is checked that the action effect E[B G Q cu mv]is less than the limiting value of the deformation of the structure at the SLSCd The number of occurrences for which EgtCd is counted (NFSLS) and the

Eurocode 7 and RBD 333

0010203040506070809

1

20 25 30 35 40 45 50

fprime (deg)

F (fprime)

364

Figure 88 Cumulative distribution of generated φprime values

Table 89 Estimated and target Pf and β values for spread foundation example

Probability of failure Pf Reliability Index β

Estimated Target Estimated Target

ULS 16 times 10minus3 72 times 10minus5 296 38SLS 49 times 10minus3 67 times 10minus2 258 15

estimated SLS failure probability PfSLS is the ratio of NFSLS to N

PfSLS = NfSLSN (831)

The estimated Pf values from the ULS and SLS simulations are 16 times 10minus3

and 49 times 10minus3 respectively corresponding to estimated β values of296 and 258 These values are presented in Table 89 together with thetarget values These values show that the target reliability is not satisfiedat the ultimate limit state but is satisfied at the serviceability limit statesince βULS = 296 is less than the ULS target value of 38 and βSLS = 258is greater than the SLS target value of 15 This is similar to the resultobtained in Section 814 when using the FORM analyses to calculate thePf value for undrained conditions where for B = 171 m and V(cu) = 03PfULS = 969times10minus3 corresponding to β = 234 The influence of variationsin the V values for the soil parameter on the Pf value obtained from theMonte Carlo analyses is examined in Section 816

A limitation of the Monte Carlo simulations is that the calculated fail-ure probabilities obtained at the end of the simulations depend not only onthe variability of the soil properties which are focused on here but also

334 Trevor L L Orr and Denys Breysse

on the random distribution of the loading This prevents too general con-clusions being drawn about the exact safety level or β value correspondingto a Eurocode design since these values change if different assumptions aremade regarding the loading It is more efficient and more realistic to ana-lyze the possible ranges of these values and to investigate the sensitivity ofthe design to changes in the assumptions about the loading For examplea value of V = 005 has been chosen for the permanent load when accordingto EN 1990 a constant value equal to the mean value could be chosen as analternative (see Section 88)

Another limitation concerning the Monte Carlo simulations arises fromthe assumption of the statistical independence of the three soil propertiesThis assumption is not normally valid in practice since soil with a highcompressibility is more likely to have a low modulus It is of course possibleto perform simulations assuming some dependency but such simulationsneed additional assumptions concerning the degree of correlation betweenthe variables hence more data and the generality of the conclusions willbe lost It should however be pointed out that assuming the variables areindependent usually underestimates the probabilities of unfavorable eventsand hence is not a conservative assumption

817 Effect of soil parameter variability on thereliability of a foundation in drainedconditions

To investigate how the variability of the soil parameters affects the relia-bility of a drained foundation design and a settlement analysis simulationsare carried out based on reference coefficients of variation Vref for the fiverandom variables equal to the V values given in Table 88 that were used forthe FORM analyses The sensitivity of the calculated probability of failureis investigated for variations in the V values of the three soil parametersaround Vref in the range Vmin to Vmax shown in Table 810 All the prob-ability estimates are obtained with at least N = 40000 simulations thisnumber being doubled in the case of small probabilities All computationsare carried out for the square foundation with B = 171 m resulting from thepartial factor design using DA1C2 For each random variable the mean andstandard deviations of the Gaussian distribution are chosen so that usingEquation 829 they are consistent with the given characteristic value as

Table 810 Range of variation of V values for the three soil parameters

Vmin Vref Vmax

tanφprime 005 010 021cu (kPa) 015 030 045mv (m2kN) 020 040 060

Eurocode 7 and RBD 335

explained in Section 814 The consequence of this is that as V increases themean and standard deviation have both to increase to keep the characteristicvalue constant and equal to the given value

The influence of the V value for each soil parameter on the reliability of thespread foundation is investigated by varying the V value for one parameterwhile keeping the V values for the other parameters constant The resultspresented are

bull the influence of V(tanφprime) on the estimated PfULS and the estimated βULSin Figures 89 and 810

bull the influence of V(mv) on the estimated PfSLS and the estimated βSLS inFigures 811 and 812

10E-05

10E-04

10E-03

10E-02

10E-01

10E+000 005 01 015 02 025

target probability at ULS

PfULS

V(tanfprime)Vref

Figure 89 Influence of V(tanφprime) on PfULS and comparison with ULS target value

(Pf = 72 times 10minus5)

0

05

1

15

2

25

3

35

4

45

0 02502015010005

βULS

target reliability index = 38

V(tanfprime)Vref

Figure 810 Influence of V(tanφprime) on βULS and comparison with ULS target value

336 Trevor L L Orr and Denys Breysse

10E-05

10E-04

10E-03

10E-02

10E-01

10E+0001 02 03 04 05 06 07

PfSLS

Vref V(mv)

target probability at SLS

Figure 811 Influence of V(mv) on PfSLS and comparison with SLS target value

(Pf = 67 times 10minus2)

0

05

1

15

2

25

3

35

4

01 02 03 04 05 06 07

bSLS

target reliability index = 15

Vref V(mv)

Figure 812 Influence of V(mv) on βSLS and comparison with SLS target value

When the estimated βULS values in Figure 810 are examined it is foundthat the target level is not satisfied since for Vref(tanφprime) = 01 βULS is 295which gives a probability of failure of about 10minus3 instead of the targetvalue of 72 times 10minus5 The graph of Pf in Figure 89 shows that the targetprobability level can only be achieved if the coefficient of variability of tanφprimeis less than 008

The influence of V(cu) on the estimated βSLS has not been investigateddirectly only in combination with the influence of V(mv) on βSLS The reasonis because the variation in cu has less effect on the calculated settlementsthan the variation in mv In fact the immediate settlement estimated from

Eurocode 7 and RBD 337

Eu = 750cu is only 49110 = 45 of the total deterministic settlementcalculated in Section 813 and cu is less variable than mv Hence thevariability of mv would normally be the main source of SLS uncertaintyHowever the situation is complicated by the fact that the immediatesettlement is proportional to 1cu which is very large if cu is near zero (or evennegative if a negative value is generated for cu) for simulations when V(cu) isvery large This results from the assumption of a normal distribution for cuand a practical solution has been obtained by rejecting any simulated valuelower than 10 kPa Another way to calculate the settlement would have beento consider a log-normal distribution of the parameters In all simulationsthe coefficients of variation of both cu and mv were varied simultaneouslywith V(cu) = 075V(mv) This should be borne in mind when interpretingthe graphs of the estimated PfSLS and βSLS values plotted against V(mv) inFigures 811 and 812 These show that the target PfSLS and βSLS values areachieved in all cases even with V(mv) = 06 and that when the variabili-ties are at their maximum ie when V(mv) = 06 and V(cu) = 045 thenPfSLS = 515 times 10minus2 and βSLS = 163

818 Influence of model errors on the reliabilitylevels

In this section other components of safety and the homogeneity of safety andcode calibration are considered The simulations considered in this sectioncorrespond to those using the reference set of coefficients of variation inTable 810 which give βSLS = 258 and βULS = 296 presented in Table 89and discussed in Section 816

As noted in Section 811 the partial factors in Eurocode 7 account foruncertainties in the models for the loads and resistances as well as foruncertainties in the loads material properties and resistances themselvesConsider for example the model (Equation (815)) used to calculate theconsolidation settlement of the foundation example in Section 813 Untilnow the way the settlement was calculated has not been examined and thecalculation model has been assumed to be correct In fact the consolidationsettlement has been calculated as the sum of the settlements of a limited num-ber of layers below the foundation the stress in each layer being deducedfrom the Fadum chart If there are i layers summing the coefficients of influ-ence αi for each layer gives a global coefficient α = 158 for the foundationsettlement Also the reduction coefficient micro has been taken equal to 055thus the product αmicro is equal to 158 times 055 = 087

Thus the consolidation settlement calculation model has some uncertain-ties for instance because of the influence of the layers at greater depths andbecause micro is an empirical coefficient whose value is chosen more on the basisof ldquoexperiencerdquo than by relying on a reliable theoretical model For instanceif the chosen micro value is changed from 055 to 075 in a first step and then to

338 Trevor L L Orr and Denys Breysse

10 in a second step the βSLS value obtained from the simulations reducesfrom 258 to 216 in the first step and to 127 in the second step this lastvalue being well below the target βSLS value

On the other hand if the maximum allowable settlement is only 20 mminstead of 25 mm and if α and micro are unchanged then βSLS decreases from258 to 206 These two examples illustrate the very high sensitivity of thereliability index to the values of the coefficients used in the model to calculatethe settlement and to the value given or chosen for the limiting settlementFor the foundation example considered above this sensitivity is larger thanthat due to a reasonable change in the coefficients of variation of the soilproperties

819 Uniformity and consistency of reliability

As noted in Section 84 the aim of Eurocode 7 is to provide geotechnicaldesigns with the appropriate degrees of reliability This means that the cho-sen target probability of failure should take account of the mode of failurethe consequences of failure and the public aversion to failure It thus canvary from one type of structure to another but it should also be as uniformas possible for all similar components within a given geotechnical categoryfor example for all spread foundations for all piles and for all slopes Itshould also be uniform (consistent) for different sizes of a given type ofstructure As pointed out in Section 812 this is achieved for ULS designsby choosing γ values such that the reliability levels for representative struc-tures are as close as possible to the specified target reliability index βULSvalue of 38

To achieve the required reliability level the partial factors need to becalibrated As part of this process the influence of the size of the founda-tion on the probability of failure is investigated The simplest way to carryout this is to change the magnitude of the loading while keeping all theother parameters constant Thus a scalar multiplying factor λ is intro-duced and the safety level of foundations designed to support the loadingλ(Gk + Qk) is analyzed for drained conditions as the load factor λ variesFor nine λ values in the range 04ndash20 the design foundation width B isdetermined for DA1C2 and constant (Gk +Qk) and soil parameter valuesThe resulting graphs of foundation size B and characteristic bearing pres-sure pk increase monotonically as functions of λ as shown by the curveson Figure 813

The characteristic bearing pressure pk increases with load factor λ sincethe characteristic load λ(Gk + Qk) increases at a rate greater than B2 Thisoccurs because B increases at a smaller rate than the load factor as can beseen from the graph of B against λ in Figure 813 Examination of the graphsin Figure 813 shows that variation of λ in the range 04ndash20 results in Bvarying in the range 113ndash233 m and pk varying in the range 472ndash553 kPa

Eurocode 7 and RBD 339

0

05

1

15

2

25

3

Load factor λ

Fou

ndat

ion

wid

th B

(m

)

0025201510050

1000

2000

3000

4000

5000

6000

Cha

ract

eris

tic b

earin

g pr

essu

re

pk(k

Pa)

B

pk

Figure 813 Variation in foundation width B (m) and characteristic bearing pressure pwith load factor λ

0

05

1

15

2

25

3

35

0 025 05 075 1 125 15 175 2 225 25

ULS

SLS

β

λ

Figure 814 Graphs showing variation in reliability index β with load factor λ for ULSand SLS

The variation in pk corresponds to a variation between minus8 and +8around the 514 kPa value obtained when λ = 1

Monte Carlo simulations performed for the same nine λ values as aboveand using the V values in Table 88 for the five load and soil parameterstreated as random variables result in estimates of the reliability indicesfor the ULS and SLS for each λ value Graphs of the estimated βULS andβSLS values obtained from the simulations are plotted in Figure 814 Thesegraphs show that βULS remains constant as λ varies and remains slightly

340 Trevor L L Orr and Denys Breysse

below 30 for all λ values Thus for this example and this loading sit-uation the Eurocode 7 semi-probabilistic approach provides designs thatare consistent with regard to the reliability level at the ULS (any smallvariations in the graph are only due to statistical noise) However exam-ining the graph of βSLS against λ in Figure 814 it can be seen that as Bincreases βSLS decreases and hence PfSLS increases which demonstratesthat the Eurocode 7 semi-probabilistic approach does not provide SLSdesigns with a consistent reliability level In this example the reliability leveldecreases as λ increases although for λ less than 25 the βSLS values obtainedremain above the target level of 15

The reliability level of geotechnical designs depends on the characteristicsoil property value chosen which as noted in Section 89 is a cautious esti-mate of the mean value governing the occurrence of the limit state It was alsonoted that the failure of a large foundation involving a large slip surface or alarge volume of ground will be governed by a higher characteristic strengthvalue than the value governing the failure of a small foundation involvinga small volume of ground Consequently depending on the uniformity ofthe ground with depth the ULS reliability level may actually increase as thefoundation size increases rather than remaining constant as indicated by theβULS graph in Figure 814 The correct modeling of the change in both βULSand βSLS as the load factor and hence foundation size is increased needs amore refined model of ground properties than that used in the present model

If the ground property is considered as a random variable it has also tobe described as having a spatial correlation ie the property at a given pointis only slightly different from that at the neighboring points The spatialcorrelation can be measured in the field (Bolle 1994 Jaksa 1995 Aleacuten1998 Moussouteguy et al 2002) and modeled via geostatistics and ran-dom field theory Using this theory the ground property is considered as aspatially distributed random variable the modeling of which in the simplercases requires the long-range variance which is identical to the V parameterdiscussed above and a correlation length (or fluctuation scale) lc whichdescribes how the spatial correlation decreases with distance

Thus the effects of ground variability on the response of structures andbuildings resting on the ground can be predicted However it is not possi-ble to summarize how ground variability or uncertainty in the geotechnicaldata affects the response such as the settlement of structures and build-ings since the spatial variation can induce soilndashstructure interaction patternsthat are specific to particular ground conditions and structures A theoreticalinvestigation by Breysse et al (2005) has shown that the response of a struc-ture is governed by a dimensional ratio obtained by dividing a characteristicdimension of the structure for instance the distance between the founda-tion supports or a pipe length by the soil correlation length Eurocode 7requires the designer to consider the effects of soil variability However soilndashstructure interaction analyses to take account of the effects of soil variability

Eurocode 7 and RBD 341

are not a part of routine geotechnical designs ie Geotechnical Category 2Situations where the soil variability needs to be included in the analysesare Geotechnical Category 3 and hence no specific guidance is provided inEurocode 7 for such situations since they often involve nonlinear constitu-tive laws for the materials An example of such a situation is where the effectsof soil variability on the reliability of a piled raft have been analyzed andconfirmed by Bauduin (2003) and Niandou and Breysse (2005) Howeverproviding details of these analyses is beyond the scope of this chapter

820 Conclusions

This chapter explains the reliability basis for Eurocode 7 the new Europeanstandard for geotechnical design Geotechnical designs to Eurocode 7 aimto achieve the required reliability for a particular geotechnical structure bytaking into account all the relevant factors at all stages of the design byconsidering all the relevant limit states and by the use of appropriate designcalculations For ultimate limit state design situations the required levelof reliability is achieved by applying appropriate partial factors to char-acteristic values of the loads and the soil parameters or resistances Forserviceability limit state design situations the required level of reliabilityis achieved by using characteristic loads and soil parameter values with allthe partial factor values equal to unity The ultimate limit state partial fac-tors used in design calculations to Eurocode 7 are selected on the basis oflong experience and may be calibrated using reliability analyses Whetherpartial factors are applied to the soil parameters or the resistances has givenrise to three Design Approaches with different sets of partial factors whichresult in different designs and hence different reliabilities depending on theDesign Approach adopted The reliability of geotechnical designs has beenshown to be significantly dependent on the variability of the soil parame-ters and on the assumptions in the calculation model Traditional factorsof safety do not take account of the variability of the soil and hence can-not provide a reliable assessment of the probability of failure or actualsafety

The advantage of reliability analyses is that they take account of thevariability of the parameters involved in a geotechnical design and sopresent a consistent analytical framework linking the variability to the tar-get probability of failure and hence providing a unifying framework betweengeotechnical and structural designs (Phoon et al 2003) It has been shownthat due to uncertainty in the calculation models and model errors relia-bility analyses tend to overpredict the probability of geotechnical designsReliability analyses are particularly appropriate for investigating and eval-uating the probability of failure in the case of complex design situations orhighly variable ground conditions and hence offer a more rational basis forgeotechnical design decisions in such situations

342 Trevor L L Orr and Denys Breysse

References

Aleacuten C (1998) On probability in geotechnics Random calculation models exempli-fied on slope stability analysis and groundndashsuperstructure interaction PhD thesisChalmers University Sweden

Bauduin C (2003) Assessment of model factors and reliability index for ULS designof pile foundations In Proceedings Conference BAP IV Deep Foundation onBored amp Auger Piles Ghent 2003 Ed W F Van Impe Millpress Rotterdampp 119ndash35

Bolle A (1994) How to manage the spatial variability of natural soils InProbabilities and Materials Ed D Breysse Kluwer Dordrecht pp 505ndash16

Breysse D Kouassi P and Poulain D (1999) Influence of the variability ofcompacted soils on slope stability of embankments In Proceedings InternationalConference on the Application of Statistics and Probability ICASP 8 Sydney EdsR E Melchers and M G Stewart Balkema Rotterdam 1 pp 367ndash73

Breysse D Niandou H Elachachi S M and Houy L (2005) Generic approachof soilndashstructure interaction considering the effects of soil heterogeneity Geotech-nique LV(2) pp 143ndash50

Brinch Hansen J (1956) Limit design and safety factors in soil mechanics (inDanish with an English summary) In Bulletin No 1 Danish Geotechnical InstituteCopenhagen

Cherubini C and Orr T L L (1999) Considerations on the applicability ofsemi-probabilistic Bayesian methods to geotechnical design In Proceedings XXConvegno Nazionale di Geotechnica Parma Associatione Geotecnica Italianapp 421ndash6

Craig R F (2004) Craigrsquos Soil Mechanics Taylor amp Francis LondonDGI (1978) Danish Code of Practise for Foundation Engineering Danish Geotech-

nical Institute CopenhagenDuncan J M (2000) Factors of safety and reliability in geotechnical engineer-

ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

EC (2002) Guidance Paper L ndash Application and Use of Eurocodes EuropeanCommission Brussels

Gulvanessian H Calgaro J-A and Holickyacute M (2002) Designersrsquo Guide to EN1990 ndash Eurocode 1990 Basis of Structural Design Thomas Telford London

Honjo Y Yoshida I Hyodo J and Paikowski S (2005) Reliability analyses forserviceability and the problem of code calibration In Proceedings InternationalWorkshop on the Evaluation of Eurocode 7 Trinity College Dublin pp 241ndash9

Jaksa M B (1995) The influence of spatial variability on the geotechnical designproperties of a stiff overconsolidated clay PhD thesis University of AdelaideAustralia

Moussouteguy N Breysse D and Chassagne P (2002) Decrease of geotechnicaluncertainties via a better knowledge of the soilrsquos heterogeneity Revue Franccedilaisede Geacutenie Civil 3 343ndash54

Niandou H and Breysse D (2005) Consequences of soil variability and soilndashstructure interaction on the reliability of a piled raft In Proceedings of Interna-tional Conference on Statistics Safety and Reliability ICOSSARrsquo05 Rome EdsG Augusti G I Schueumlller and M Ciampoli Millpress Rotterdam pp 917ndash24

Eurocode 7 and RBD 343

Orr T L L (1994) Probabilistic characterization of Irish till properties In Risk andReliability in Ground Engineering Thomas Telford London pp 126ndash33

Orr T L L (2005) Design examples for the Eurocode 7 Workshop In Proceedingsof the International Workshop on the Evaluation of Eurocode 7 Trinity CollegeDublin pp 67ndash74

Orr T L L and Farrell E R (1999) Geotechnical Design to Eurocode 7 SpringerLondon

Ovesen N K (1995) Eurocode 7 A European code of practise for geotechnicaldesign In Proceedings International Symposium on Limit State Design in Geotech-nical Engineering ISLSD 93 Copenhagen Danish Geotechnical Institute 3pp 691ndash710

Ovesen N K and Denver H (1994) Assessment of characteristic values of soilparameters for design In Proceedings XIII International Conference on SoilMechanics and Foundation Engineering New Delhi Balkema 1 437ndash60

Phoon K K and Kulhawy F H (2005) Characterization of model uncertainties fordrilled shafts under undrained axial loading In Contemporary Issues in Founda-tion Engineering (GSP 131) Eds J B Anderson K K Phoon E Smith J E LoehrASCE Reston

Phoon K K Becker D E Kulhawy F H Honjo Y Ovesen N K and Lo S R(2003) Why consider reliability analysis for geotechnical limit state design InProceedings International Workshop on Limit State Design in Geotechnical Engi-neering Practise Eds K K Phoon Y Honjo and D Gilbert World ScientificPublishing Company Singapore pp 1ndash17

Schneider H R (1997) Definition and determination of characteristic soil prop-erties In Proceedings XII International Conference on Soil Mechanics andGeotechnical Engineering Hamburg Balkema Rotterdam pp 4 2271ndash4

Student (1908) The probable error of a mean Biometrica 6 1ndash25

Chapter 9

Serviceability limit statereliability-based design

Kok-Kwang Phoon and Fred H Kulhawy

91 Introduction

In foundation design the serviceability limit state often is the governingcriterion particularly for large-diameter piles and shallow foundationsIn addition it is widely accepted that foundation movements are difficultto predict accurately Most analytical attempts have met with only limitedsuccess because they did not incorporate all of the important factors suchas the in-situ stress state soil behavior soilndashfoundation interface charac-teristics and construction effects (Kulhawy 1994) A survey of some ofthese analytical models showed that the uncertainties involved in applyingeach model and evaluating the input parameters were substantial (Callananand Kulhawy 1985) Ideally the ultimate limit state (ULS) and the service-ability limit state (SLS) should be checked using the same reliability-baseddesign (RBD) principle The magnitude of uncertainties and the target reli-ability level for SLS are different from those of ULS but these differenceswill be addressed consistently and rigorously using reliability-calibrateddeformation factors (analog of resistance factors)

One of the first systematic studies reporting the development of RBDcharts for settlement of single piles and pile groups was conducted byPhoon and co-workers (Phoon et al 1990 Quek et al 1991 1992)A more comprehensive implementation that considered both ULS andSLS within the same RBD framework was presented in EPRI ReportTR-105000 (Phoon et al 1995) SLS deformation factors (analog of resis-tance factors) were derived by considering the uncertainties underlyingnonlinear loadndashdisplacement relationships explicitly in reliability calibra-tions Nevertheless the ULS still received most of the attention in laterdevelopments although interest in applying RBD for the SLS appears tobe growing in the recent literature (eg Fenton et al 2005 Misra andRoberts 2005 Zhang and Ng 2005 Zhang and Xu 2005 Paikowsky and

Serviceability limit state reliability-based design 345

Lu 2006) A wider survey is given elsewhere (Zhang and Phoon 2006)A special session on ldquoServiceability Issues in Reliability-Based Geotechni-cal Designrdquo was organized under the Risk Assessment and ManagementTrack at Geo-Denver 2007 the ASCE Geo-Institute annual conferenceheld in February 2007 in Denver Colorado Four papers were presentedon excavation-induced ground movements (Boone 2007 Finno 2007Hsiao et al 2007 Schuster et al 2007) and two papers were pre-sented on foundation settlements (Roberts et al 2007 Zhang and Ng2007)

The reliability of a foundation at the ULS is given by the probability ofthe capacity being less than the applied load If a consistent load test inter-pretation procedure is used then each measured loadndashdisplacement curvewill produce a single ldquocapacityrdquo The ratio of this measured capacity to apredicted capacity is called a bias factor or a model factor A lognormaldistribution usually provides an adequate fit to the range of model factorsfound in a load test database (Phoon and Kulhawy 2005a b) It is natu-ral to follow the same approach for the SLS The capacity is replaced byan allowable capacity that depends on the allowable displacement (Phoonet al 1995 Paikowsky and Lu 2006) The distribution of the SLS bias ormodel factor can be established from a load test database in the same wayThe chief drawback is that the distribution of this SLS model factor has tobe re-evaluated when a different allowable displacement is prescribed It istempting to argue that the loadndashdisplacement behavior is linear at the SLSand subsequently the distribution of the model factor for a given allow-able displacement can be extrapolated to other allowable displacements bysimple scaling However for reliability analysis the applied load follows aprobability distribution and it is possible for the loadndashdisplacement behav-ior to be nonlinear at the upper tail of the distribution (corresponding tohigh loads) Finally it may be more realistic to model the allowable dis-placement as a probability distribution given that it is affected by manyinteracting factors such as the type and size of the structure the propertiesof the structural materials and the underlying soils and the rate and uni-formity of the movement The establishment of allowable displacements isgiven elsewhere (Skempton and MacDonald 1956 Bjerrum 1963 Grantet al 1974 Burland and Wroth 1975 Burland et al 1977 Wahls 1981Moulton 1985 Boone 2001)

This chapter employs a probabilistic hyperbolic model to performreliability-based design checks at the SLS The nonlinear features of theloadndashdisplacement curve are captured by a two-parameter hyperbolic curve-fitting Equation The uncertainty in the entire loadndashdisplacement curve isrepresented by a relatively simple bivariate random vector containing thehyperbolic parameters as its components It is not necessary to evaluatea new model factor and its accompanying distribution for each allowable

346 Kok-Kwang Phoon and Fred H Kulhawy

displacement It is straightforward to introduce the allowable displacementas a random variable for reliability analysis To accommodate this additionalsource of uncertainty using the standard model factor approach it is neces-sary to determine a conditional probability distribution of the model factor asa continuous function of the allowable displacement However the approachproposed in this chapter is significantly simpler Detailed calculation steps forsimulation and first-order reliability analysis are illustrated using EXCELtradeto demonstrate this simplicity It is shown that a random vector contain-ing two curve-fitting parameters is the simplest probabilistic model forcharacterizing the uncertainty in a nonlinear loadndashdisplacement curve andshould be the recommended first-order approach for SLS reliability-baseddesign

92 Probabilistic hyperbolic model

The basic idea behind the probabilistic hyperbolic model is to (a) reduce themeasured loadndashdisplacement curves into two parameters using a hyperbolicfit (b) normalize the resulting hyperbolic curves using an interpreted failureload to reduce the data scatter and (c) model the remaining scatter usingan appropriate random vector (possibly correlated andor non-normal) forthe curve-fitting hyperbolic parameters There are three advantages in thisempirical approach First this approach can be applied easily in practicebecause it is simple and does not require input parameters that are diffi-cult to obtain However the uncertainties in the stiffness parameters areknown to be much larger than the uncertainties in the strength parameters(Phoon and Kulhawy 1999) Second the predictions are realistic becausethe approach is based directly on load test results Third the statistics ofthe model can be obtained easily as described below The first criterion isdesirable but not necessary for RBD The next two criteria however arecrucial for RBD If the average model bias is not addressed different mod-els will produce different reliability for the same design scenario as a resultof varying built-in conservatism which makes reliability calculations fairlymeaningless The need for robust model statistics should be evident becauseno proper reliability analysis can be performed without these data

921 Example Augered Cast-in-Place (ACIP) pile underaxial compression

Phoon et al (2006) reported an application of this empirical approach toan ACIP pile load test database The ACIP pile is formed by drilling acontinuous flight auger into the ground and upon reaching the requireddepth pumping sandndashcement grout or concrete down the hollow stem

Serviceability limit state reliability-based design 347

as the auger is withdrawn steadily The side of the augered hole is sup-ported by the soil-filled auger and therefore no temporary casing or slurryis needed After reaching the ground surface a reinforcing steel cage ifrequired is inserted by vibrating it into the fresh grout The databasecompiled by Chen (1998) and Kulhawy and Chen (2005) included casehistories from 31 sites with 56 field load tests conducted mostly in sandycohesionless soils The diameter (B) depth (D) and DB of the pilesranged from 030 to 061 m 18 to 261 m and 51 to 682 respec-tively The associated geotechnical data typically are restricted to thesoil profile depth of ground water table and standard penetration test(SPT) results The range of effective stress friction angle is 32ndash47 degreesand the range of horizontal soil stress coefficient is 06ndash24 (estab-lished by correlations) The loadndashdisplacement curves are summarized inFigure 91a

The normalized hyperbolic curve considered in their study is expressed as

QQSTC

= ya + by

(91)

in which Q = applied load QSTC = failure load or capacity interpretedusing the slope tangent method a and b = curve-fitting parameters andy = pile butt displacement Note that the curve-fitting parameters are phys-ically meaningful with the reciprocals of a and b equal to the initial slopeand asymptotic value respectively (Figure 92) The slope tangent methoddefines the failure load at the intersection of the loadndashdisplacement curvewith the initial slope (not elastic slope) line offset by (015 + B120) inchesin which B = shaft diameter in inches Phoon et al (2006) observed that

0 0 5 10 15 20 25 3010 20 30 40 50 60 70 80Displacement (mm)

0

500

1000

1500

2000

Load

Q (

kN)

(a) Displacement (mm)

00

10

20

30

QQ

ST

C

DB gt 20

(b)

c30-1

Figure 91 (a) Loadndashdisplacement curves for ACIP piles in compression and (b) fitted hyper-bolic curves normalized by slope tangent failure load (QSTC) for DB gt 20 FromPhoon et al 2006 Reproduced with the permission of the American Society ofCivil Engineers

348 Kok-Kwang Phoon and Fred H Kulhawy

00

04

08

12

16

0 20 40 60 80y (mm)

QQ

ST

C

1b = 1613

slope = 1a = 0194 mmminus1

QQSTC = y(515 + 062y)

Figure 92 Definition of hyperbolic curve-fitting parameters

the normalized loadndashdisplacement curves for long piles (DB gt 20) clustertogether quite nicely (Figure 91b) Case 30-1 is a clear outlier within thisgroup and is deleted from subsequent analyses There are 40 load tests in thisgroup of ldquolongrdquo piles with diameter (B) depth (D) and DB ranging from030 to 061 m 75 to 261 m and 210 to 682 respectively The range ofeffective stress friction angle is 32ndash43 degrees and the range of horizontalsoil stress coefficient is 06ndash21

The curve-fitting Equation is empirical and other functional forms canbe considered as shown in Table 91 However the important criterionis to apply a curve-fitting Equation that produces the least scatter in thenormalized loadndashdisplacement curves

922 Model statistics

Each continuous loadndashdisplacement curve can be reduced to two curve-fitting parameters as illustrated in Table 92 for ACIP piles Using the datagiven in the last two columns of this table one can construct an appropriatebivariate random vector that can reproduce the scatter in the normalized loadover the full range of displacements The following steps are recommended

1 Validate that the variations in the hyperbolic parameters are indeedldquorandomrdquo

2 Fit the marginal distributions of a and b using a parametric form(eg lognormal distribution) or a non-parametric form (eg Hermitepolynomial)

Serviceability limit state reliability-based design 349

Table 91 Curve-fitting Equations for loadndashdisplacement curves from various foundations

Foundation type Loading mode Curve-fitting Equation Reference source

Spreadfoundation

Uplift QQm = y(a+by) Phoon et al 19952003 2007Phoon andKulhawy 2002b

Drilled shaft Uplift QQm = y(a + by) Phoon et al 1995Phoon andKulhawy 2002a

Compression(undrained)

QQm = (yB)[a + b(yB)] Phoon et al 1995

Compression(drained)

QQm = a(yB)b Phoon et al 1995

Lateral-moment

QQm = (yD)[a + (yD)] Phoon et al 1995Kulhawy andPhoon 2004

Augeredcast-in-place(ACIP) pile

Compression(drained)

QQm = y(a + by) Phoon et al 2006

Pressure-injectedfooting

Uplift(drained)

QQm = y(a + by) Phoon et al 2007

Interpreted failure capacity (Qm) tangent intersection method for spread foundations L1ndashL2 methodfor drilled shafts under uplift and compression hyperbolic capacity for drilled shafts under lateral-moment loading slope tangent method for ACIP piles and pressure-injected footings

3 Calculate the productndashmoment correlation between a and b using

ρab =

nsumi=1

(ai minus a

)(bi minus b

)radic

nsumi=1

(ai minus a

)2 nsumi=1

(bi minus b

)2(92)

in which (ai bi) denotes a pair of a and b values along one row inTable 92 n = sample size (eg n = 40 in Table 92) and a and b = thefollowing sample means

a =

nsumi=1

ai

n

b =

nsumi=1

bi

n(93)

Table 92 Hyperbolic curve-fitting parameters for ACIP piles under axial compression[modified from load test data reported by Chen (1998)]

Case no B (m) D (m) DB SPT-N QSTC (kN) a (mm) b

c1 041 192 473 184 19571 8382 0499c2 041 149 368 234 17570 10033 0382c3 041 101 248 391 17614 3571 0679c4 030 107 350 166 3670 4296 0615c5-1 041 177 435 214 20817 7311 0445c5-2 041 198 488 146 19037 5707 0591c6 041 137 338 146 7295 3687 0679c7 030 118 387 116 6850 2738 0701c8-1 036 120 339 237 10720 3428 0723c9 036 104 292 184 16547 7559 0461c10 041 91 225 290 14678 5029 0582c11 036 119 334 316 16547 2528 0744c12 041 131 323 153 11031 5260 0533c13 036 116 326 358 16057 4585 0632c14 041 118 290 43 8896 2413 0770c15 036 122 343 216 24909 15646 0328c16-1 041 134 330 270 10675 4572 0607c16-2 041 134 330 267 10764 7376 0613c17 041 90 221 142 5516 2047 0682c18-1 036 139 390 344 33805 12548 0380c19-1 036 192 540 271 13344 5829 0570c19-2 036 242 682 210 11832 6419 0559c19-3 041 261 641 200 12276 4907 0690c19-4 041 186 458 224 21528 4426 0692c20 052 166 319 304 37363 1312 0840c21 036 78 219 165 8896 8128 0510c22-1 061 200 328 114 17792 10297 0640c22-2 041 149 368 77 9519 4865 0487c22-3 041 199 490 100 11743 5599 0517c23-1 036 75 210 227 13166 4511 0622c24-1 041 95 234 117 11832 3142 0501c24-2 041 95 234 117 11743 3353 0513c24-3 041 95 234 215 10052 4104 0367c24-4 041 90 221 80 8629 4928 0430c25-1 061 175 287 188 10675 1006 0780c25-2 061 175 287 188 12632 1237 0923c25-3 061 160 263 182 10764 1352 0932c25-4 061 200 328 184 11120 4128 1050c25-5 061 175 287 188 15479 4862 0748c27-3 046 121 264 144 6405 3054 0792

Depth-averaged SPT-N value along shaft

Serviceability limit state reliability-based design 351

For the hyperbolic parameters in Table 92 the productndashmomentcorrelation is minus0673

4 Construct a bivariate probability distribution for a and b using thetranslation model

Note that the construction of a bivariate probability distribution for aand b is not an academic exercise in probability theory Each pair ofa and b values within one row of Table 92 is associated with a singleloadndashdisplacement curve [see Equation (91)] Therefore one should expecta and b to be correlated as a result of this curve-fitting exercise Thisis similar to the well-known negative correlation between cohesion andfriction angle A linear fit to a nonlinear MohrndashCoulomb failure enve-lope will create a small intercept (cohesion) when the slope (frictionangle) is large or vice versa If one adopts the expedient assumptionthat a and b are statistically independent random variables then thecolumn of a values can be shuffled independently of the column of bvalues In physical terms this implies that the initial slopes of the loadndashdisplacement curves can be varied independently of their asymptotic valuesThis observation is not supported by measured load test data as shownin Section 933 In addition there is no necessity to make this simplify-ing assumption from a calculation viewpoint Correlated a and b valuescan be calculated with relative ease as illustrated below using the datafrom Table 92

93 Statistical analyses

931 Randomness of the hyperbolic parameters

In the literature it is common to conclude that a parameter is a randomvariable by presenting a histogram However it is rarely emphasized thatparameters exhibiting variations are not necessarily ldquorandomrdquo A randomvariable is an appropriate probabilistic model only if the variations arenot explainable by deterministic variations in the database for examplefoundation geometry or soil property Figure 93 shows that the hyperbolicparameters do not exhibit obvious trends with DB or the average SPT-Nvalue

932 Marginal distributions of a and b

The lognormal distribution provides an adequate fit to both hyperbolicparameters as shown in Figure 94 (p-values from the AndersonndashDarlinggoodness-of-fit test pAD are larger than 005) Note that SD is the stan-dard deviation COV is the coefficient of variation and n is the samplesize Because of lognormality a and b can be expressed in terms of standard

0

4

8

12

0 10 20 30 40

SPT-N

a pa

ram

eter

(m

m)

0

4

8

12

10 20 30 40 50

DBa

para

met

er (

mm

)

0

04

08

12

0 10 20 30 40 50

SPT-N

b pa

ram

eter

0

04

08

12

10 20 30 40 50

DB

b pa

ram

eter

Figure 93 Hyperbolic parameters versus DB and SPT-N for ACIP piles

0 5 10 15 20a parameter (mm)

0

01

02

03

Rel

ativ

e fr

eque

ncy

Mean = 515 mmSD = 307 mmCOV = 060

n = 40pAD = 0183

00 05 10 15 20b parameter

0

01

02

03

Rel

ativ

e fr

eque

ncy

Mean = 062SD = 016COV = 026

n = 40pAD = 0887

Figure 94 Marginal distributions of hyperbolic parameters for ACIP piles From Phoonet al 2006 Reproduced with the permission of the American Society of CivilEngineers

Serviceability limit state reliability-based design 353

normal variables X1 and X2 as follows

a = exp(ξ1X1 +λ1)

b = exp(ξ2X2 +λ2) (94)

The parameters λ1 and ξ1 are the equivalent normal mean and equivalentnormal standard deviation of a respectively as given by

ξ1 =radic

ln(1 + s2

am2a

)= 0551

λ1 = ln(ma) minus 05ξ21 = 1488 (95)

in which the mean and standard deviation of a are ma = 515 mm andsa = 307 mm respectively The parameters λ2 and ξ2 can be calculated fromthe mean and standard deviation of b in the same way and are given byλ2 = minus0511 and ξ2 = 0257

There is no reason to assume that a lognormal probability distribution willalways provide a good fit for the hyperbolic parameters A general but simpleapproach based on Hermite polynomials can be used to fit any empiricaldistributions of a and b

a = a10H0(X1) + a11H1(X1) + a12H2(X1) + a13H3(X1)

+ a14H4(X1) + a15H5(X1) +middotmiddot middot (96)

b = a20H0(X2) + a21H1(X2) + a22H2(X2) + a23H3(X2)

+ a24H4(X2) + a25H5(X2) +middotmiddot middot

in which the Hermite polynomials Hj() are given by

H0(X) = 1

H1(X) = X (97)

Hk+1(X) = X Hk(X) minus k Hkminus1(X)

The advantages of using a Hermite expansion are (a) Equation (96) isdirectly applicable for reliability analysis (Phoon and Honjo 2005 Phoonet al 2005) (b) a short expansion typically six terms is sufficient and(c) Hermite polynomials can be calculated efficiently using the recursion for-mula shown in the last row of Equation (97) The main effort is to calculatethe Hermite coefficients in Equation (96) Figure 95 shows that the entirecalculation can be done with relative ease using EXCEL Figure 96 comparesa and b values simulated using the lognormal distribution and the Hermiteexpansion with the measured values in Table 92

354 Kok-Kwang Phoon and Fred H Kulhawy

Column

A Sort values of ldquoardquo using ldquoData gt Sort gt Ascendingrdquo Let f be a 40times1 vector containing values in column A B Rank the values in column A in ascending order eg B4 = RANK(A4$A$4$A$431) C Calculate empirical cumulative distribution F(a) using (rank)(sample size +1) eg C4 = B441 D Calculate standard normal variate using Φminus1[F(a)] eg D4 = NORMSINV(C4)FK Calculate Hermite polynomials using recursion eg F4 = 1 G4 = D4 H4 = G4lowast$D4-(H$3-1)lowastF4 M Let H be a 40times6 matrix containing values in column F to K Calculate the Hermite coefficients in column M (denoted by a 6times1 vector a1) by HTH a1 = HTf The EXCEL array formula is =MMULT(MINVERSE(MMULT(TRANSPOSE(F4K43)F4K43)) MMULT(TRANSPOSE(F4K43)A4A43))

EXCEL functions

Figure 95 Calculation of Hermite coefficients for a using EXCEL

Hyperbolic parameters for uplift loadndashdisplacement curves associatedwith spread foundations drilled shafts and pressure-injected footings areshown in Figure 97 Although lognormal distributions can be fitted tothe histograms (dashed line) they are not quite appropriate for the bparameters describing the asymptotic uplift loadndashdisplacement behavior ofspread foundations and drilled shafts (p-values from the AndersonndashDarlinggoodness-of-fit test pAD are smaller than 005)

933 Correlation between a and b

The random vector (a b) is described completely by a bivariate probabil-ity distribution The assumption of statistical independence commonly isadopted in the geotechnical engineering literature for expediency The prac-tical advantage is that the random components can be described completely

Serviceability limit state reliability-based design 355

a parameter (mm)

Per

cent

1614121086420

100

80

60

40

20

0

MeasuredHermiteLognormal

b parameter

Per

cent

150125100075050

100

80

60

40

20

0

MeasuredHermiteLognormal

Figure 96 Simulation of hyperbolic parameters using lognormal distribution and Hermiteexpansion

by marginal or univariate probability distributions such as those shown inFigures 94 and 97 Another advantage is that numerous distributions aregiven in standard texts and the data on hand usually can be fitted to oneof these classical distributions The disadvantage is that this assumption canproduce unrealistic scatter plots between a and b if the data are stronglycorrelated For hyperbolic parameters it appears that strong correlationsare the norm rather than the exception (Figure 98)

It is rarely emphasized that the multivariate probability distributions (evenbivariate ones) underlying random vectors are very difficult to constructtheoretically to estimate empirically and to simulate numerically A cur-sory review of basic probability texts reveals that non-normal distributionsusually are presented in the univariate form It is not possible to generalize

356 Kok-Kwang Phoon and Fred H Kulhawy

Hyperbolic parameters Foundation

ba (mm)

Spreadfoundation

0 4 8 12 16 20

a parameter (mm)

0

01

02

03

04R

elat

ive

freq

uenc

y

Mean = 713 mmSD = 466 mmCOV = 065

n = 85pAD = 0032

28

02 04 06 08 10 12 14

b parameter

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 075SD = 014COV = 018

n = 85pAD lt0005

Drilled shaft

a parameter (mm)

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 134 mmSD = 073 mmCOV = 054

n = 48pAD = 0887

b parameter

0

02

04

06

08

Rel

ativ

e fr

eque

ncy

Mean = 089SD = 0063COV = 007

n = 48pAD lt 0005

Pressure-injectedfooting

0 1 2 3 4 5

0 1 2 3 4 5

a parameter (mm)

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 138 mmSD = 095 mmCOV = 068

n = 25pAD =0772

02 04 06 08 10 12 14

02 04 06 08 10 12 14

b parameter

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

Mean = 077SD = 021COV = 027

n = 25pAD = 0662

Figure 97 Marginal distributions of hyperbolic parameters for uplift loadndashdisplacementcurves associated with spread foundations drilled shafts and pressure-injectedfootings From Phoon et al 2007 Reproduced with the permission of theAmerican Society of Civil Engineers

these univariate formulae to higher dimensions unless the random variablesare independent This assumption is overly restrictive in reliability analysiswhere input parameters can be correlated by physics (eg sliding resistanceincreases with normal load undrained shear strength increases with loadingrate etc) or by curve-fit (eg cohesion and friction angle in a linear MohrndashCoulomb failure envelope hyperbolic parameters from loadndashdisplacementcurves) A full discussion of these points is given elsewhere (Phoon 2004a2006)

Serviceability limit state reliability-based design 357

0

4

8

12

16

20

02 04 06 08 10 12 14

b parameter

a pa

ram

eter

(m

m)

Augered cast-in-place pile(compression)

0

4

8

12

16

20

b parametera

para

met

er (

mm

)

claysand

Spread foundation (uplift)

No tests = 40a Mean = 515 mm SD = 307 mm COV = 060b Mean = 062 SD = 016 COV = 026Correlation = -067

No tests = 85a Mean = 713 mm SD = 466 mm COV = 065b Mean = 075 SD = 014 COV = 018Correlation = -024

0

1

2

3

4

5

02 04 06 08 10 12 14b parameter

a pa

ram

eter

(m

m)

claysand

Drilled shaft (uplift)

0

1

2

3

4

5

02 04 06 08 10 12 14

02 04 06 08 10 12 14

b parameter

a pa

ram

eter

(m

m)

Pressure-injected footing(uplift)

No tests = 48a Mean = 134 mm SD = 073 mm COV = 054b Mean = 089 SD = 0063 COV = 007Correlation = -059

No tests = 25a Mean = 138 mm SD = 095 mm COV = 068b Mean = 077 SD = 021 COV = 027Correlation = -073

Figure 98 Correlation between hyperbolic parameters From Phoon et al 2006 2007Reproduced with the permission of the American Society of Civil Engineers

94 Bivariate probability distribution functions

941 Translation model

For bivariate or higher-dimensional probability distributions there are fewpractical choices available other than the multivariate normal distributionIn the bivariate normal case the entire dependency relationship between bothrandom components can be described completely by a productndashmoment cor-relation coefficient [Equation (92)] The practical importance of the normaldistribution is reinforced further because usually there are insufficient data tocompute reliable dependency information beyond the correlation coefficient

It is expected that a bivariate probability model for the hyperbolicparameters will be constructed using a suitably correlated normal model

358 Kok-Kwang Phoon and Fred H Kulhawy

The procedure for this is illustrated using an example of hyperbolicparameters following lognormal distributions

1 Simulate uncorrelated standard normal random variables Z1 and Z2(available in EXCEL under ldquoTools gt Data Analysis gt Random NumberGenerationrdquo)

2 Transform Z1 and Z2 into X1 (mean = 0 standard deviation = 1) andX2 (mean = 0 standard deviation = 1) with correlation ρX1X2

by

X1 = Z1

X2 = Z1ρX1X2+ Z2

radic1 minusρ2

X1X2(98)

3 Transform X1 and X2 to lognormal a and b using Equation (94)

Note that the correlation between a and b (ρab) as given below is not thesame as the correlation between X1 and X2 (ρX1X2

)

ρab = exp(ξ1ξ2ρX1X2) minus 1radic

[exp(ξ21 ) minus 1][exp(ξ2

2 ) minus 1](99)

Therefore one needs to back-calculate ρX1X2from ρab ( = minus067) using

Equation (99) before computing Equation (98) The main effort in the con-struction of a translation-based probability model is calculating the equiv-alent normal correlation For the lognormal case a closed-form solutionexists [Equation (99)] Unfortunately there are no convenient closed-formsolutions for the general case One advantage of using Hermite polynomialsto fit the empirical marginal distributions of a and b [Equation (96)] is thata simple power series solution can be used to address this problem

ρab =

infinsumk=1

ka1ka2kρkX1X2radicradicradicradic( infinsum

k=1ka2

1k

)(infinsum

k=1ka2

2k

) (910)

The bivariate lognormal model based on ρX1X2= minus08 [calculated from

Equation (99) with ρab = minus07] produces a scatter of simulated a andb values that is in reasonable agreement with that exhibited by the mea-sured values (Figure 99a) In contrast adopting the assumption of statisticalindependence between a and b corresponding to ρX1X2

= 0 produces theunrealistic scatter plot shown in Figure 99b in which there are too many

Serviceability limit state reliability-based design 359

0

4

8

12

16

20

02 04 06 08 10 12 02 04 06 08 10 12

b parameter

a pa

ram

eter

(m

m)

MeasuredLognormal

(a)0

4

8

12

16

20

b parameter

a pa

ram

eter

(m

m)

MeasuredLognormal

(b)

Figure 99 Simulated hyperbolic parameters for ACIP piles (a) ρX1X2= minus08 and

(b) ρX1X2= 0

simulated points above the observed scatter as highlighted by the dashedoval Note that the marginal distributions for a and b in Figures 99a andb are almost identical (not shown) The physical difference produced bycorrelated and uncorrelated hyperbolic parameters can be seen in the simu-lated loadndashdisplacement curves as well (Figures 910bndashd) These simulatedcurves are produced easily by substituting simulated pairs of a and b intoEquation (91)

942 Rank model

By definition the productndashmoment correlation ρab is bounded between ndash1and 1 One of the key limitations of a translation model is that it is notpossible to simulate the full range of possible productndashmoment correlationsbetween a and b (Phoon 2004a 2006) Based on the equivalent normalstandard deviations of a and b for ACIP piles (ξ1 = 0551 and ξ2 = 0257) itis easy to verify that Equation (99) with ρX1X2

=minus1 will result in a minimumvalue of ρab = minus085 The full relationship between translation non-normalcorrelation and equivalent normal correlation is shown in Figure 911 Moreexamples are given in Phoon (2006) as reproduced in Figure 912 (noteρab = ρY1Y2

)If a translation-based probability model is inadequate a simple alternate

approach is to match the rank correlation as follows

1 Convert a and b to their respective ranks Note that these ranks arecalculated based on ascending values of a and b ie the smallest valueis assigned a rank value equal to 1

2 Calculate the rank correlation using the same Equation as the productndashmoment correlation [Equation (92)] except the rank values are used in

360 Kok-Kwang Phoon and Fred H Kulhawy

0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80Displacement (mm)

00

10

20

30

QQ

ST

C

00

10

20

30

QQ

ST

C

00

10

20

30Q

QS

TC

(a)

Displacement (mm)

0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80Displacement (mm) Displacement (mm)

(b)

00

10

20

30

QQ

ST

C

(c) (d)

Figure 910 Normalized hyperbolic curves for ACIP piles (a) measured (b) ρX1X2= minus08

(c) ρX1X2= minus04 and (d) ρX1X2

= 0

place of the actual a and b values The rank correlation for Table 92is ndash071

3 Simulate correlated standard normal random variables usingEquation (98) with ρX1X2

equal to the rank correlation computed above4 Simulate independent non-normal random variables following any

desired marginal probability distributions5 Re-order the realizations of the non-normal random variables such that

they follow the rank structure underlying the correlated standard normalrandom variables

The simulation procedure for hyperbolic parameters of ACIP piles is illus-trated in Figure 913 Note that the simulated a and b values in columnsM and N will follow the rank structure (columns J and K) of the corre-lated standard normal random variables (columns H and I) because theexponential transform (converting normal to lognormal) is a monotonicincreasing function The theoretical basis underlying the rank model is dif-ferent from the translation model and is explained by Phoon et al (2004)

Serviceability limit state reliability-based design 361

minus10

minus05

00

05

10

minus10 minus05 00 05 10

Normal correlation rX1X2

Non

-nor

mal

cor

rela

tion

ra

b

-067

minus078

Figure 911 Relationship between translation non-normal correlation and equivalentnormal correlation

and Phoon (2004b) It suffices to note that there is no unique method of con-structing correlated non-normal random variables because the correlationcoefficient (productndashmoment or rank) does not provide a full description ofthe association between two non-normal variables

95 Serviceability limit state reliability-baseddesign

This section presents the practical application of the probabilistic hyper-bolic model for serviceability limit state (SLS) reliability-based design TheSLS occurs when the foundation displacement (y) is equal to the allowabledisplacement (ya) The foundation has exceeded serviceability if ygt ya Con-versely the foundation is satisfactory if y lt ya These three situations can bedescribed concisely by the following performance function

P = y minus ya = y(Q) minus ya (911)

An alternate performance function is

P = Qa minus Q = Qa(ya) minus Q (912)

Figure 914 illustrates the uncertainties associated with these performancefunctions In Figure 914a the applied load Q is assumed to be deterministic

362 Kok-Kwang Phoon and Fred H Kulhawy

minus1 minus05 0 05 1minus1

minus05

0

05

1

r Y1Y

2

minus1

minus05

0

05

1

r Y1Y

2

(a) x1 = 03 x2 = 03

minus1 minus05 0 05 1rX1X2

rX1X2

(b) x1= 1 x2 = 1

minus1 minus05 0 05 1minus1

minus05

0

05

1

rX1X2rX1X2

r Y1Y

2

(c) x1 = 1 x2 = 03

minus1 minus05 0 05 1minus1

minus05

0

05

1

r Y1Y

2

(d) x1 = 2 x2 = 03

Figure 912 Some relationships between observed correlation (ρY1Y2) and correlation of

underlying equivalent Gaussian random variables (ρX1X2) From Phoon 2006

Reproduced with the permission of the American Society of Civil Engineers

to simplify the visualization It is clear that the displacement follows a distri-bution even if Q is deterministic because the loadndashdisplacement curve y(Q)is uncertain The allowable displacement may follow a distribution as wellIn Figure 914b the allowable displacement is assumed to be determinis-tic In this alternate version of the performance function the allowable loadQa follows a distribution even if ya is deterministic because of the uncer-tainty in the loadndashdisplacement curve The effect of a random load Q andthe possibility of upper tail values falling on the nonlinear portion of theloadndashdisplacement curve are illustrated in this figure

Serviceability limit state reliability-based design 363

Column

A Measured ldquoardquo values from Table 92 B Measured ldquobrdquo values from Table 92 C Rank the values in column A in ascending order eg C3 = RANK(A3$A$3$A$421) D Rank the values in column B in ascending order eg D3 = RANK(B3$B$3$B$421)F G Simulate two columns of independent standard normal random variables using Tools gt Data Analysis gt Random Number Generation gt Number of Variables = 2 Number of Random Numbers = 40 Distribution = Normal Mean = 0 Standard deviation = 1 Output Range = $F$3 H Set X1 = Z1 eg H3 = F3

I Set X2 = Z1rX1X2 + Z2(1 ndash rX1X2

2)05 with rX1X2 = rank correlation between columns C

and D eg I3 = F3(ndash071)+G3SQRT(1ndash071^2) M Simulated ldquoardquo values eg M3 = EXP(H30551+1488) N Simulated ldquobrdquo values eg N3 = EXP(I30257ndash0511)

EXCEL functions

Figure 913 Simulation of rank correlated a and b values for ACIP piles using EXCEL

The basic objective of RBD is to ensure that the probability of failure of acomponent does not exceed an acceptable threshold level For ULS or SLSthis objective can be stated using the performance function as follows

pf = Prob(P lt 0) le pT (913)

in which Prob(middot) = probability of an event pf = probability of failure andpT = acceptable target probability of failure A more convenient alternativeto the probability of failure is the reliability index (β) which is defined as

β = minusΦminus1(pf ) (914)

in which Φminus1(middot) = inverse standard normal cumulative function

364 Kok-Kwang Phoon and Fred H Kulhawy

00

04

08

12

16

y (mm)

Random allowabledisplacement

Deterministicapplied load

Random displacement caused by uncertainty inloadndashdisplacement curve

(a)

00

04

08

12

16

0 20 40 60 80

0 20 40 60 80

y (mm)

QQ

ST

CQ

QS

TC

Deterministic allowabledisplacement

Random applied load

Random allowable loadcaused by uncertainty in loadndashdisplacement curve

(b)

Figure 914 Serviceability limit state reliability-based design (a) deterministic applied loadand (b) deterministic allowable displacement

951 First-order reliability method (FORM)

The probability of failure given by Equation (913) can be calculated eas-ily using the first-order reliability method (FORM) once the probabilistichyperbolic model is established

pf = Prob(Qa lt Q

)= Prob(

ya

a + byaQm lt Q

)(915)

The interpreted capacity Qm allowable displacement ya and applied load Qare assumed to be lognormally distributed with for example coefficients ofvariation (COV) = 05 06 and 02 respectively The COV of Qm is related

Serviceability limit state reliability-based design 365

to the capacity prediction method and the quality of the geotechnical dataPhoon et al (2006) reported a COV of 05 associated with the predictionof compression capacity of ACIP piles using the Meyerhof method TheCOV of ya is based on a value of 0583 reported by Zhang and Ng (2005)for buildings on deep foundations The COV of Q = 02 is appropriatefor live load (Paikowsky 2002) The hyperbolic parameters are negativelycorrelated with an equivalent normal correlation of ndash08 and are lognormallydistributed with statistics given in Figure 94 The mean of ya is assumed tobe 25 mm Equation (915) can be re-written to highlight the mean factor ofsafety (FS) as follows

pf = Prob

(ya

a + byalt

mQQlowast

mQmQlowast

m

)= Prob

(ya

a + byalt

1FS

QlowastQlowast

m

)(916)

in which mQmand mQ = mean of Qm and Q FS = mQm

mQ and Qlowastm and

Qlowast = unit-mean lognormal random variables with COV = 05 and 02 Notethat FS refers to the ultimate limit state It is equivalent to the global factorof safety adopted in current practice

Figure 915 illustrates that the entire FORM calculation can be performedwith relative ease using EXCEL The most tedious step in FORM is the searchfor the most probable failure point which requires a nonlinear optimizerLow and Tang (1997 2004) recommended using the SOLVER function inEXCEL to perform this tedious step However their proposal to performoptimization in the physical random variable space is not recommendedbecause the means of these random variables can differ by orders of magni-tudes For example the mean b = 062 for the ACIP pile database while themean of Qm can be on the order of 1000 kN In Figure 915 the SOLVERis invoked by changing cells $B$14$B$18 which are random variables instandard normal space (ie uncorrelated zero-mean unit variance compo-nents) An extensive discussion of reliability analysis using EXCEL is givenelsewhere (Phoon 2004a)

For a typical FS = 3 the SLS reliability index for this problem is 221corresponding to a probability of failure of 00134

952 Serviceability limit state model factor approach

The allowable load can be evaluated from the interpreted capacity using aSLS model factor (MS) as follows

Qa = ya

a + byaQm = MSQm (917)

366 Kok-Kwang Phoon and Fred H Kulhawy

Cell

E14E18 E14=B14 E15=B14$K$6+B15SQRT(1-$K$6^2) E16=B16 E17=B17 E18=B18B22B26 B22=EXP(E14H6+G6) B23=EXP(E15H7+G7) etcD22 B24(B24B23+B22)-B26B25D3D25 = SQRT(MMULT(TRANSPOSE(B14B18)B14B18))D28 = NORMSDIST(-D25) Solver Set Target Cell $D$25 Equal To Min By Changing Cells $B$14$B$18 Subject to the Constraints $D$22 lt= 0

EXCEL functions

Figure 915 EXCEL implementation of first-order reliability method for serviceability limitstate

If MS follows a lognormal distribution then a closed-form solution existsfor Equation (915)

β =

ln

⎡⎢⎣(

mMsmQm

mQ

)radicradicradicradicradic 1 + COV2Q(

1 + COV2Ms

)(1 + COV2

Qm

)⎤⎥⎦

radicln[(

1 + COV2Ms

)(1 + COV2

Qm

)(1 + COV2

Q

)] (918)

This procedure is similar to the bias or model factor approach used in ULSRBD However the distribution of MS has to be evaluated for each allowabledisplacement Figure 916 shows two distributions of MS determined by

Serviceability limit state reliability-based design 367

05 10 15 20 25

QQSTC

05 10 15 20 25

QQSTC

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

0

01

02

03

04

Rel

ativ

e fr

eque

ncy

yallow = 15 mm

Mean = 106SD = 014COV = 014

n = 1000pAD lt 0005

yallow = 25 mmMean = 123

SD = 016COV = 013

n = 1000pAD lt 0005

Figure 916 Simulated distributions of SLS model factors (QQSTC ) for ACIP piles (allow-able displacement ya assumed to be deterministic)

substituting simulated values of a and b into ya(a + bya) with ya = 15 mmand 25 mm In contrast the foundation capacity is a single number (not acurve) and only one model factor distribution is needed

Based on first-order second-moment analysis the mean (mMs) and COV

(COVMs) of MS can be estimated as

mMsasymp ya

ma + mbya(919)

COVMsasympradic

s2a + y2

as2b + 2yaρabsasb

ma + mbya(920)

For ya = 25 mm the mean and COV of MS are estimated to be 121and 0145 which compare favorably with the simulated statistics shownin Figure 916 (mean = 123 and COV = 013)

For the example discussed in Section 951 the reliability index calculatedusing Equation (918) is 229 and the corresponding probability of failureis 00112 The correct FORM solution is β = 238 (pf = 00086) For adeterministic ya = 15 mm Equation (918) gives β = 200 (pf = 00227)while FORM gives β = 213 (pf = 00166) These small differences areto be expected given that the histograms shown in Figure 916 are notlognormally distributed despite visual agreement The null hypothesis oflognormality is rejected because the p-values from the Anderson-Darlinggoodness-of-fit test (pAD) are significantly smaller than 005 In our opin-ion one practical disadvantage of the SLS model factor approach is thatMS can not be evaluated easily from a load test database if ya followsa distribution Given the simplicity of the FORM calculation shown inFigure 915 it is preferable to use the probabilistic hyperbolic model

368 Kok-Kwang Phoon and Fred H Kulhawy

for SLS reliability analysis The distributions of the hyperbolic param-eters do not depend on the allowable displacement and there are nopractical difficulties in incorporating a random ya for SLS reliabilityanalysis

953 Effect of nonlinearity

It is tempting to argue that the loadndashdisplacement behavior is linear at theSLS However for reliability analysis the applied load follows a probabilitydistribution and it is possible for the loadndashdisplacement behavior to be non-linear at the upper tail of the distribution This behavior is illustrated clearlyin Figure 914b The probabilistic hyperbolic model can be simplified to a lin-ear model by setting b = 0 (physically this is equivalent to an infinitely largeasymptotic limit) For the example discussed in Section 951 the FORMsolution under this linear assumption is β = 276 (pf = 000288) Note thatthe solution is very unconservative because the probability of failure is about47 times smaller than that calculated for the correct nonlinear hyperbolicmodel If the mean allowable displacement is reduced to 15 mm and themean factor of safety is increased to 10 then the effect of nonlinearity is lesspronounced For the linear versus nonlinear case β = 350 (pf = 0000231)versus β = 336 (pf = 0000385) respectively

954 Effect of correlation

The effect of correlated hyperbolic parameters on the loadndashdisplacementcurves is shown in Figure 910 For the example discussed in Section 951the FORM solution for uncorrelated hyperbolic parameters is β = 205(pf = 00201) The probability of failure is 15 times larger than that calcu-lated for the correlated case For a more extreme case of FS = 10 and meanya = 50 mm the FORM solution for uncorrelated hyperbolic parametersis β = 442 (pf = 499 times 10minus6) Now the probability of failure is signifi-cantly more conservative It is 33 times larger than that calculated for thecorrelated case given by β = 467 (pf = 149 times 10minus6) Note that assumingzero correlation between the hyperbolic parameters does not always producethe most conservative (or largest) probability of failure If the hyperbolicparameters were found to be positively correlated perhaps with a weakequivalent normal correlation of 05 the FORM solution would be β = 425(pf = 106times10minus5) The probability of failure for the uncorrelated case is now21 times smaller than that calculated for the correlated case

The effect of a negative correlation is to reduce the COV of the SLS modelfactor as shown by Equation (920) A smaller COVMs leads to a higher reli-ability index as shown by Equation (918) The reverse is true for positivelycorrelated hyperbolic parameters

Serviceability limit state reliability-based design 369

955 Uncertainty in allowable displacement

The allowable displacement is assumed to be deterministic in the major-ity of the previous studies For the example discussed in Section 951the FORM solution for a deterministic allowable displacement is β = 238(pf = 000864) The probability of failure is 16 times smaller than that cal-culated for a random ya with COV = 06 For a more extreme case of FS = 10and mean ya = 50 mm the FORM solutions for deterministic and randomya are β = 469(pf = 136 times 10minus6) and β = 467(pf = 149 times 10minus6) respec-tively It appears that the effect of uncertainty in the allowable displacementis comparable to or smaller than the effect of correlation in the hyperbolicparameters

956 Target reliability index for serviceability limit state

The Eurocode design basis BS EN19902002 (British Standards Institute2002) specifies the safety serviceability and durability requirements fordesign and describes the basis for design verification It is intended to beused in conjunction with nine other Eurocodes that govern both struc-tural and geotechnical design and it specifies a one-year target reliabilityindex of 29 for SLS Phoon et al (1995) recommended a target reliabil-ity index of 26 for transmission line (and similar) structure foundationsbased on extensive reliability calibrations with existing designs for differentfoundation types loading modes and drainage conditions Zhang and Xu(2005) analyzed the settlement of 149 driven steel H-piles in Hong Kongand calculated a reliability index of 246 based on a distribution of mea-sured settlements at the service load (mean = 199 mm and COV = 023)and a distribution of allowable settlement for buildings (mean = 96 mm andCOV = 0583)

Reliability indices corresponding to different mean factors of safety anddifferent allowable displacements can be calculated based on the EXCELmethod described in Figure 915 The results for ACIP piles are calculatedusing the statistics given in Figure 94 and are presented in Figure 917It can be seen that a target reliability index = 26 is not achievable at amean FS = 3 for a mean allowable displacement of 25 mm However it isachievable for a mean FS larger than about 4 Note that the 50-year returnperiod load Q50 asymp 15 mQ for a lognormal distribution with COV = 02Therefore a mean FS = 4 is equivalent to a nominal FS = mQm

Q50 = 27A nominal FS of 3 is probably closer to prevailing practice than a meanFS = 3

It is also unclear if the allowable displacement prescribed in practice reallyis a mean value or some lower bound value For a lognormal distributionwith a COV = 06 a mean allowable displacement of 50 mm produces amean less one standard deviation value of 25 mm Using this interpretation a

370 Kok-Kwang Phoon and Fred H Kulhawy

1

2

3

4

5

1 2 3 4 5Mean factor of safety = mQmmQ

Rel

iabi

lity

inde

x

5025

15

mya

(mm)

EN1990EPRI

ab

515 mm062

mQm

06026

05

mean COV

02mQ

mya

Hyperbolic parameters

Capacity Qm

Applied load Q

Allowable displacement ya 06

Figure 917 Relationship between reliability index and mean factor of safety for ACIP piles

target reliability index of 26 is achievable at a mean FS about 3 or a nominalFS about 2

Overall the EPRI target reliability index for SLS is consistent withfoundation designs resulting from a traditional global FS between 2 and 3

96 Simplified reliability-based design

Existing implementations of RBD are based on a simplified approach thatinvolves the use of multi-factor formats for checking designs Explicit reli-ability calculations such as those presented in Section 95 are not donecommonly in current routine design Simplified RBD checks can be calibratedusing the following general procedure (Phoon et al 1995)

1 Select realistic performance functions for the ULS andor the SLS2 Assign probability models for each basic design parameter that are

commensurate with available statistical support3 Conduct a parametric study on the variation of the reliability level with

respect to each parameter in the design problem using the FORM toidentify appropriate calibration domains

4 Determine the range of reliability levels implicit in existing designs5 Adjust the resistance factors (for ULS) or deformation factors (for SLS)

in the RBD Equations until a consistent target reliability level is achievedwithin each calibration domain

Serviceability limit state reliability-based design 371

The calibration of simplified RBD checks was discussed by Phoon andKulhawy (2002a) for drilled shafts subjected to drained uplift by Phoonet al (2003) for spread foundations subjected to uplift and by Kulhawy andPhoon (2004) for drilled shafts subjected to undrained lateral-moment load-ing This section presents an example of SLS design of drilled shafts subjectedto undrained uplift (Phoon et al 1995) The corresponding ULS design wasgiven by Phoon et al (1995 2000)

961 Performance function for serviceability limit state

The uplift capacity of drilled shafts is governed by the following verticalequilibrium Equation

Qu = Qsu + Qtu + W (921)

in which Qu = uplift capacity Qsu = side resistance Qtu = tip resistanceand W = weight of foundation For undrained loading the side resistancecan be calculated as follows

Qsu = πBα

int D

0su(z)dz (922)

in which B = shaft diameter D = shaft depth α = adhesion factorsu = undrained shear strength and z = depth The α factor is calibratedfor a specific su test type For su determined from CIUC (consolidated-isotropically undrained compression) tests the corresponding adhesionfactor was determined by the following regression Equation

α = 031 + 017(su

pa) + ε (923)

in which pa = atmospheric stress asymp 100 kNm2 and ε = normal randomvariable with mean = 0 and standard deviation = 01 resulting from linearregression analysis The undrained shear strength (su) was modeled as alognormal random variable with mean (msu) = 25 minus 200 kNm2 and COV(COVsu

) = 10minus70 It has been well-established for a long time (eg Lumb1966) that there is no such thing as a ldquotypicalrdquo COV for soil parametersbecause the degree of uncertainty depends on the site conditions degreeof equipment and procedural control quality of the correlation model andother factors If geotechnical RBD codes do not consider this issue explicitlythen practicing engineers can not adapt the code design to their specificset of local circumstances such as site conditions measurement techniquescorrelation models standards of practice etc If code provisions do not allowthese adaptations then local ldquoad-hocrdquo procedures are likely to develop that

372 Kok-Kwang Phoon and Fred H Kulhawy

basically defeat the purpose of the code in the first place This situation isnot desirable A good code with proper guidelines is preferable

The tip resistance under undrained loading conditions develops fromsuction forces and was estimated by

Qtu = (minusu minus ui)Atip (924)

in which ui = initial pore water stress at the foundation tip or base u =change in pore water stress caused by undrained loading and Atip = tipor base area Note that no suction force develops unless ndashu exceeds uiTip suction is difficult to predict accurately because it is sensitive to thedrainage conditions in the vicinity of the tip In the absence of an accuratepredictive model and statistical data it was assumed that the tip suction stressis uniformly distributed between zero and one atmosphere [Note that manydesigners conservatively choose to disregard the undrained tip resistance inroutine design]

For the loadndashdisplacement curve it was found that the simple hyperbolicmodel resulted in the least scatter in the normalized curves (Table 91) Thevalues of a and b corresponding to each loadndashdisplacement curve were deter-mined by using the displacement at 50 of the failure load (y50) and thedisplacement at failure (yf ) as follows

a = y50yf

yf minus y50(925a)

b = yf minus 2y50

yf minus y50(925b)

Forty-two loadndashdisplacement curves were analyzed using Equations 925(a and b) The database is given in Table 93 and the statistics of a and bare summarized in Table 94 It can be seen that the effect of soil type issmall and therefore the statistics calculated using all of the data were usedIn this earlier study a and b were assumed to be independent lognormalrandom variables for convenience This assumption is conservative as notedin Section 954 The mean and one standard deviation loadndashdisplacementcurves are plotted in Figure 918 along with the data envelopes

Following Equation (912) the performance function at SLS is

P = Qua minus F = ya

a + byaQu minus F (926)

in which F = weather-related load effect for transmission line structure foun-dations = kV2 (Task Committee on Structural Loadings of ASCE 1991)V = Gumbel random variable with COV = 30 and k = deterministicconstant The probability distributions assigned to each design parameterare summarized in Table 95

Table 93 Hyperbolic curve-fitting parameters for drilled shafts under uplift (Phoon et al1995)

Casea Soil typeb Qcu (kN) Dd (m) Be (m) yf

50 (mm) ygf (mm) ah (mm) bh

51 C 320 317 063 048 1270 050 09681 C 311 457 061 090 1270 096 092125 C 311 457 061 102 1270 110 091151 C 285 244 061 134 671 168 075161 C 338 244 091 061 732 066 09125NE C 80 198 052 091 1270 098 09238A2 C 222 305 061 081 1270 087 09338A3 C 498 610 061 091 1270 098 092401 C 285 366 061 084 1270 090 093402 C 338 366 061 074 1270 079 094561 C 934 1006 035 250 2500 278 089562 C 552 680 035 125 1270 139 0895617 C 667 750 030 062 1270 066 0955623 C 667 649 045 062 1270 066 095631 C 2225 1158 152 140 1270 158 087644 C 1050 366 152 080 1270 085 093691 C 1388 2033 091 173 635 238 062731 C 338 280 061 081 1158 087 092736 C 507 369 076 122 1016 138 086141 S 231 244 091 037 1270 038 097181 S 445 305 091 135 1615 148 091201 S 409 305 091 134 2438 142 094411 S 365 305 091 033 508 035 093441 S 534 853 046 122 1270 135 0894610 S 25 137 034 115 1270 127 0904711 S 71 244 037 077 1270 082 0934812 S 163 366 037 154 1270 175 08649E S 296 427 041 115 1270 127 090511 S 525 366 091 061 1270 064 095621 S 890 640 107 049 635 053 092622 S 890 640 107 098 1400 105 092703 S 1557 610 061 102 1270 110 091951 S 338 1219 053 200 2624 216 092952 S 347 1219 038 312 1999 370 081992 S 1086 305 122 066 419 078 081993 S 1255 366 122 056 762 061 092211 S + C 454 1524 041 091 1270 098 092361 S + C 712 1036 041 191 1270 224 082421 S + C 1629 2134 046 134 1270 149 088501 S + C 100 290 030 147 820 178 078502 S + C 400 381 030 254 2311 285 088961 S + C 667 1219 053 133 1270 149 088

aCase number in Kulhawy et al (1983)bS = sand C = clay S + C = sand and claycFailure load determined by L1ndashL2 method (Hirany and Kulhawy 1988)dFoundation deptheFoundation diameterf Displacement at 50 of failure loadgDisplacement at failure loadhCurve-fitted parameters for normalized hyperbolic model based on Equation (925)

374 Kok-Kwang Phoon and Fred H Kulhawy

Table 94 Statistics of a and b for uplift loadndashdisplacement curve From Phoon andKulhawy 2002a Reproduced with the permission of the American Society of CivilEngineers

Soil type No of tests a b

Mean (mm) COV Mean COV

Clay 19 116 052 089 009Sand 17 122 067 091 005Mixed 6 180 036 086 006All 42 127 056 089 007

20

15

10

Nor

mal

ized

load

FQ

u

05

00 10 20

Displacement y (mm)

Data envelopes +- 1 Standard deviation

30

Mean equationFQu = y(127 + 089y)

40 50

Figure 918 Normalized uplift loadndashdisplacement curve for drilled shafts From Phoon andKulhawy 2002a Reproduced with the permission of the American Society ofCivil Engineers

962 Parametric study

To conduct a realistic parametric study typical values of the design param-eters should be used Herein the emphasis was on those used in the electricutility industry which sponsored the basic research study The drilled shaftdiameter (B) was taken as 1ndash3 m and the depth to diameter ratio (DB) wastaken as 3ndash10 The typical range of the mean wind speed (mV ) was back-calculated from the 50-year return period wind speed (v50) given in Table 95

Serviceability limit state reliability-based design 375

Table 95 Summary of probability distributions for design parameters

Parameter Description Distribution Mean COV

V Wind speed Gumbel 30ndash50a ms 030a Curve-fitting parameter

[Equation (924a)]Lognormal 127 mm 056

b Curve-fitting parameter[Equation (924b)]

Lognormal 089 007

ε Regression error associatedwith adhesion factormodel [(Equation (923)]

Normal 0 01

su Undrained shear strength Lognormal 25ndash200 kNm2 01ndash07Qtu Undrained tip suction stress Uniform 0ndash1 atmosb ndash

a50-year return period wind speed data from Task Committee on Structural Loadings (1991)bRange of uniform distribution

and the COV of V (COVV = 03) as follows

v50 = mV (1 + 259 COVV) (927)

The constant of proportionality k that was used in calculating the founda-tion load was determined by the following serviceability limit state designcheck

F50 = kv250 = Quan (928)

in which Quan = nominal allowable uplift capacity calculated using

Quan = ya

ma + mbyaQun (929)

in which ma = mean of a = 127 mm mb = mean of b = 089 and ya =allowable displacement = 25 mm The nominal uplift capacity (Qun) isgiven by

Qun = Qsun + Qtun + W (930)

in which Qsun = nominal uplift side resistance and Qtun = nominal uplift tipresistance The nominal uplift side resistance was calculated as follows

Qsun = πBDαnmsu (931)

αn = 031 + 017(msu

pa

) (932)

376 Kok-Kwang Phoon and Fred H Kulhawy

in which αn = nominal adhesion factor The nominal tip resistance (Qtun)was evaluated using

Qtun = ( WAtip

minus ui)Atip (933)

The variations of the probability of failure (pf ) with respect to the 50-yearreturn period wind speed allowable displacement limit foundation geome-try and the statistics of the undrained shear strength were given by Phoonet al (1995) It was found that pf is affected significantly by the mean andCOV of the undrained shear strength (msu and COVsu) Therefore the fol-lowing domains were judged to be appropriate for the calibration of thesimplified RBD checks

1 msu = 25ndash50 kNm2 (medium clay) 50ndash100 kNm2 (stiff clay) and100ndash200 kNm2 (very stiff clay)

2 COVsu = 10ndash30 30ndash50 and 50ndash70

For the other less-influential design parameters calibration can be per-formed over the entire range of typical values Note that the COVs of mostmanufactured structural materials fall between 10 and 30 For exam-ple Reacutethaacuteti (1988) citing the 1965 specification of the American ConcreteInstitute noted that the quality of concrete can be evaluated as follows

COV lt 10 excellent

COV = 10minus15 good

COV = 15minus20 satisfactory

COV gt 20 bad

The COVs of natural geomaterials usually are much larger and do not fallwithin a narrow range (Phoon and Kulhawy 1999)

963 Load and Resistance Factor Design (LRFD)

The following LRFD format was chosen for reliability calibration

F50 = ΨuQuan (934)

in which Ψu = deformation factor The SLS deformation factors werecalibrated using the following general procedure (Phoon et al 1995)

1 Partition the parameter space into several smaller domains following theapproach discussed in Section 962 For example the combination of

Serviceability limit state reliability-based design 377

msu = 25ndash50 kNm2 and COVsu = 30ndash50 is one possible calibrationdomain The reason for partitioning is to achieve greater uniformity inreliability over the full range of design parameters

2 Select a set of representative points from each domain For examplemsu = 30 kNm2 and COVsu = 40 constitutes one calibration pointIdeally the set of representative points should capture the full range ofvariation in the reliability level over the whole domain

3 Determine an acceptable foundation design for each calibration pointand evaluate the reliability index for each design Foundation design isperformed using a simplified RBD check [eg Equation (934)] and a setof trial resistance or deformation factors

4 Adjust the resistance or deformation factors until the reliability indicesfor the representative designs are as close to the target level (eg 32 forULS and 26 for SLS) as possible in a statistical sense

The results of this reliability calibration exercise are shown in Table 96A comparison between Figure 919a [existing design check based onEquation (928)] and Figure 919b [LRFD design check based onEquation (934)] illustrates the improvement in the uniformity of the reliabil-ity levels It is interesting to note that deformation factors can be calibratedfor broad ranges of soil property variability (eg COV of su = 10ndash3030ndash50 50ndash70) without compromising on the uniformity of reliabilityachieved Experienced engineers should be able to choose the appropriatedata category even with limited statistical data supplemented with first-order guidelines (Phoon and Kulhawy 1999) and reasoned engineeringjudgment

Table 96 Undrained uplift deformation factors for drilled shaftsdesigned using F50 = ΨuQuan (Phoon et al 1995)

Clay Undrained shear strength u

Mean (kNm2) COV ()

Medium 25ndash50 10ndash30 06530ndash50 06350ndash70 062

Stiff 50ndash100 10ndash30 06430ndash50 06150ndash70 058

Very stiff 100ndash200 10ndash30 06130ndash50 05750ndash70 052

Note Target reliability index = 26

378 Kok-Kwang Phoon and Fred H Kulhawy

(a) 10-1

10-2

10-3

Failu

re p

roba

bilit

y p

f

10-4

(b) 10-1

10-2

10-3

Failu

re p

roba

bilit

y p

f

10-4

0 10 20 30

Diameter = 2m

Depthdiameter = 6

Diameter = 2m

Depthdiameter = 6

mean supa = 05

mean supa = 10

mean supa = 05

mean supa = 10

F50 = Quan

ya = 25 mm

F50 = Ψu Quan

ya = 25 mm

40

COV su ()

COV su ()

50 60 70 80 90

0 10 20 30 40 50 60 70 80 90

Figure 919 Performance of SLS design checks for drilled shafts subjected to undraineduplift (a) existing and (b) reliability-calibrated LRFD (Phoon et al 1995)

Two key conclusions based on wider calibration studies (Phoon et al1995) are noteworthy

1 One resistance or deformation factor is insufficient to achieve a uniformtarget reliability across the full range of geotechnical COVs

Serviceability limit state reliability-based design 379

Table 97 Ranges of soil property variability for reliabilitycalibration in geotechnical engineering

Geotechnical parameter Property variability COV ()

Undrained shear strength Lowa 10ndash30Mediumb 30ndash50Highc 50ndash70

Effective stress friction angle Lowa 5ndash10Mediumb 10ndash15Highc 15ndash20

Horizontal stress coefficient Lowa 30ndash50Mediumb 50ndash70Highc 70ndash90

atypical of good quality direct lab or field measurementsbtypical of indirect correlations with good field data except for the standard

penetration test (SPT)ctypical of indirect correlations with SPT field data and with strictly empirical

correlations

2 In terms of COV three ranges of soil property variability (low mediumand high) are sufficient to achieve reasonably uniform reliability levelsfor simplified RBD checks The appropriate ranges depend on thegeotechnical parameter as shown in Table 97 Note that the cate-gories for effective stress friction angle are similar to those specifiedfor concrete

97 Conclusions

This chapter discussed the application of a probabilistic hyperbolic modelfor performing reliability-based design checks at the serviceability limit stateThe uncertainty in the entire loadndashdisplacement curve is represented by a rel-atively simple bivariate random vector containing the hyperbolic parametersas its components The steps required to construct a realistic random vectorare illustrated using forty measured pairs of hyperbolic parameters for ACIPpiles It is important to highlight that these hyperbolic parameters typicallyexhibit a strong negative correlation A translation-based lognormal modelor a more general translation-based Hermite model is needed to capture thiscorrelation aspect correctly The common assumption of statistical indepen-dence can circumvent the additional complexity associated with a translationmodel because the bivariate probability distribution reduces to two signif-icantly simpler univariate distributions in this special case However thescatter in the measured loadndashdisplacement curves can not be reproducedproperly by simulation under this assumption

380 Kok-Kwang Phoon and Fred H Kulhawy

The nonlinear feature of the loadndashdisplacement curve is captured by thehyperbolic curve-fitting Equation This nonlinear feature is crucial in reli-ability analysis because the applied load follows a probability distributionand it is possible for the loadndashdisplacement behavior to be nonlinear at theupper tail of the distribution Reliability analysis for the more general caseof a random allowable displacement can be evaluated easily using an imple-mentation of the first-order reliability method in EXCEL The proposedapproach is more general and convenient to use than the bias or modelfactor approach because the distributions of the hyperbolic parameters donot depend on the allowable displacement The random vector contain-ing two curve-fitting parameters is the simplest probabilistic model forcharacterizing the uncertainty in a nonlinear loadndashdisplacement curve andshould be the recommended first-order approach for SLS reliability-baseddesign

Simplified RBD checks for SLS and ULS can be calibrated within a sin-gle consistent framework using the probabilistic hyperbolic model Becauseof the broad range of geotechnical COVs one resistance or deformationfactor is insufficient to achieve a uniform target reliability level (the keyobjective of RBD) In terms of COV three ranges of soil property variabil-ity (low medium and high) are more realistic for reliability calibration ingeotechnical engineering Numerical values of these ranges vary as a func-tion of the influential geotechnical parameter(s) Use of these ranges allowsfor achieving a more uniform target reliability level

References

Bjerrum L (1963) Allowable settlement of structures In Proceedings of the3rd European Conference on Soil Mechanics and Foundation Engineering (3)Wiesbaden pp 135ndash7

Boone S J (2001) Assessing construction and settlement-induced building damagea return to fundamental principles In Proceedings Underground ConstructionInstitution of Mining and Metallurgy London pp 559ndash70

Boone S J (2007) Assessing risks of construction-induced building damage forlarge underground projects In Probabilistic Applications in Geotechnical Engi-neering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

British Standards Institute (2002) Eurocode Basis of structural design BS EN19902002 London

Burland J B and Wroth C P (1975) Settlement of buildings and associ-ated damage Building Research Establishment Current Paper Building ResearchEstablishment Watford

Burland J B Broms B B and DeMello V F B (1977) Behavior of founda-tions and structures state of the art report In Proceedings of the 9th Interna-tional Conference on Soil Mechanics and Foundation Engineering (2) Tokyopp 495ndash546

Serviceability limit state reliability-based design 381

Callanan J F and Kulhawy F H (1985) Evaluation of procedures for predictingfoundation uplift movements Report ELndash4107 Electric Power Research InstitutePalo Alto

Chen J-R (1998) Case history evaluation of axial behavior of augered cast-in-placepiles and pressure-injected footings MS thesis Cornell University Ithaca NY

Fenton G A Griffiths D V and Cavers W (2005) Resistance factors forsettlement design Canadian Geotechnical Journal 42(5) 1422ndash36

Finno R J (2007) Predicting damage to buildings from excavation induced groundmovements In Probabilistic Applications in Geotechnical Engineering (GSP 170)Eds K K Phoon G A Fenton E F Glynn C H Juang D V Griffiths T FWolff and L M Zhang ASCE Reston (CDROM)

Grant R Christian J T and VanMarcke E H (1974) Differential settlement ofbuildings Journal of Geotechnical Engineering ASCE 100(9) 973ndash91

Hirany A and Kulhawy F H (1988) Conduct and interpretation of load tests ondrilled shaft foundations detailed guidelines Report EL-5915(1) Electric PowerResearch Institute Palo Alto

Hsiao E C L Juang C H Kung G T C and Schuster M (2007) Reliabil-ity analysis and updating of excavation-induced ground settlement for buildingserviceability evaluation In Probabilistic Applications in Geotechnical Engineer-ing (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Kulhawy F H (1994) Some observations on modeling in foundation engineer-ing In Proceeding of the 8th International Conference on Computer Methodsand Advances in Geomechanics (1) Eds H J Siriwardane and M M ZamanA A Balkema Rotterdam pp 209ndash14

Kulhawy F H and Chen J-R (2005) Axial compression behavior of augered cast-in-place (ACIP) piles in cohesionless soils In Advances in Designing and TestingDeep Foundations (GSP 129) Eds C Vipulanandan and F C Townsend ASCEReston pp 275ndash289 [Also in Advances in Deep Foundations (GSP 132) EdsC Vipulanandan and F C Townsend ASCE Reston 13 p]

Kulhawy F H and Phoon K K (2004) Reliability-based design of drilled shaftsunder undrained lateral-moment loading In Geotechnical Engineering for Trans-portation Projects (GSP 126) Eds M K Yegian and E Kavazanjian ASCEReston pp 665ndash76

Kulhawy F H OrsquoRourke T D Stewart J P and Beech J F (1983) Transmissionline structure foundations for uplift-compression loading load test summariesReport EL-3160 Electric Power Research Institute Palo Alto

Low B K and Tang W H (1997) Efficient reliability evaluation using spreadsheetJournal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K and Tang W H (2004) Reliability analysis using object-orientedconstrained optimization Structural Safety 26(1) 69ndash89

Lumb P (1966) Variability of natural soils Canadian Geotechnical Journal 3(2)74ndash97

Misra A and Roberts L A (2005) Probabilistic axial load displacement relation-ships for drilled shafts In Contemporary Issues in Foundation Engineering (GSP131) Eds J B Anderson K K Phoon E Smith and J E Loehr ASCE Reston(CDROM)

382 Kok-Kwang Phoon and Fred H Kulhawy

Moulton L K (1985) Tolerable movement criteria for highway bridges ReportFHWARD-85107 Federal Highway Administration Washington DC

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep founda-tions In Proceedings of the International Workshop on Foundation Design Codesand Soil Investigation in View of International Harmonization and PerformanceBased Design Tokyo A A Balkema Lisse pp 59ndash94

Paikowsky S G and Lu Y (2006) Establishing serviceability limit state in design ofbridge foundations In Foundation Analysis and Design Innovative Methods (GSP153) Eds R L Parsons L M Zhang W D Guo K K Phoon and M YangASCE Reston pp 49ndash58

Phoon K K (1995) Reliability-based design of foundations for transmission linestructures PhD thesis Cornell University Ithaca NY

Phoon K K (2004a) General non-Gaussian probability models for first-order reli-ability method (FORM) a state-of-the-art report ICG Report 2004-2-4 (NGIReport 20031091-4) International Centre for Geohazards Oslo

Phoon K K (2004b) Application of fractile correlations and copulas to non-Gaussian random vectors In Proceedings of the 2nd International ASRANet(Network for Integrating Structural Safety Risk and Reliability) ColloquiumBarcelona (CDROM)

Phoon K K (2006) Modeling and simulation of stochastic data In Geo-Congress 2006 Geotechnical Engineering in the Information Technology AgeEds D J DeGroot J T DeJong J D Frost and L G Braise ASCE Reston(CDROM)

Phoon K K and Honjo Y (2005) Geotechnical reliability analyses towards devel-opment of some user-friendly tools In Proceedings of the 16th InternationalConference on Soil Mechanics and Geotechnical Engineering Osaka (CDROM)

Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanadian Geotechnical Journal 36(4) 612ndash24

Phoon K K and Kulhawy F H (2002a) Drilled shaft design for transmission linestructures using LRFD and MRFD In Deep Foundations 2002 (GSP116) EdsM W OrsquoNeill and F C Townsend ASCE Reston pp 1006ndash17

Phoon K K and Kulhawy F H (2002b) EPRI study on LRFD and MRFDfor transmission line structure foundations In Proceedings International Work-shop on Foundation Design Codes and Soil Investigation in View of Interna-tional Harmonization and Performance Based Design Tokyo Balkema Lissepp 253ndash61

Phoon K K and Kulhawy F H (2005a) Characterization of model uncertaintiesfor laterally loaded rigid drilled shafts Geotechnique 55(1) 45ndash54

Phoon K K and Kulhawy F H (2005b) Characterization of model uncertaintiesfor drilled shafts under undrained axial loading In Contemporary Issues in Foun-dation Engineering (GSP 131) Eds J B Anderson K K Phoon E Smith andJ E Loehr ASCE Reston 13 p

Phoon K K Chen J-R and Kulhawy F H (2006) Characterization ofmodel uncertainties for augered cast-in-place (ACIP) piles under axial compres-sion In Foundation Analysis and Design Innovative Methods (GSP 153) EdsR L Parsons L M Zhang W D Guo K K Phoon and M Yang ASCE Restonpp 82ndash9

Serviceability limit state reliability-based design 383

Phoon K K Chen J-R and Kulhawy F H (2007) Probabilistic hyperbolic modelsfor foundation uplift movements In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Phoon K K Kulhawy F H and Grigoriu M D (1995) Reliability-based designof foundations for transmission line structures Report TR-105000 EPRI PaloAlto

Phoon K K Kulhawy F H and Grigoriu M D (2000) Reliability-based designfor transmission line structure foundations Computers and Geotechnics 26(3ndash4)169ndash85

Phoon K K Kulhawy F H and Grigoriu M D (2003) Multiple resistancefactor design (MRFD) for spread foundations Journal of Geotechnical andGeoenvironmental Engineering ASCE 129(9) 807ndash18

Phoon K K Nadim F and Lacasse S (2005) First-order reliability methodusing Hermite polynomials In Proceedings of the 9th International Conferenceon Structural Safety and Reliability Rome (CDROM)

Phoon K K Quek S T Chow Y K and Lee S L (1990) Reliability analysis ofpile settlement Journal of Geotechnical Engineering ASCE 116(11) 1717ndash35

Phoon K K Quek S T and Huang H W (2004) Simulation of non-Gaussianprocesses using fractile correlation Probabilistic Engineering Mechanics 19(4)287ndash92

Quek S T Chow Y K and Phoon K K (1992) Further contributions toreliability-based pile settlement analysis Journal of Geotechnical EngineeringASCE 118(5) 726ndash42

Quek S T Phoon K K and Chow Y K (1991) Pile group settlement a proba-bilistic approach International Journal of Numerical and Analytical Methods inGeomechanics 15(11) 817ndash32

Reacutethaacuteti L (1988) Probabilistic Solutions in Geotechnics Elsevier New YorkRoberts L A Misra A and Levorson S M (2007) Probabilistic design methodol-

ogy for differential settlement of deep foundations In Probabilistic Applications inGeotechnical Engineering (GSP 170) Eds K K Phoon G A Fenton E F GlynnC H Juang D V Griffiths T F Wolff amp L M Zhang ASCE Reston(CDROM)

Schuster M J Juang C H Roth M J S Hsiao E C L and Kung G T C (2007)Serviceability limit state for probabilistic characterization of excavation-inducedbuilding damage In Probabilistic Applications in Geotechnical Engineering (GSP170) Eds K K Phoon G A Fenton E F Glynn C H Juang D V GriffithsT F Wolff and L M Zhang ASCE Reston (CDROM)

Skempton A W and MacDonald D H (1956) The allowable settlement ofbuildings Proceedings of the Institution of Civil Engineers Part 3(5) 727ndash68

Task Committee on Structural Loadings (1991) Guidelines for Electrical Transmis-sion Line Structural Loading Manual and Report on Engineering Practice 74ASCE New York

Wahls H E (1981) Tolerable settlement of buildings Journal of GeotechnicalEngineering Division ASCE 107(GT11) 1489ndash504

Zhang L M and Ng A M Y (2005) Probabilistic limiting tolerable displacementsfor serviceability limit state design of foundations Geotechnique 55(2) 151ndash61

384 Kok-Kwang Phoon and Fred H Kulhawy

Zhang L M and Ng A M Y (2007) Limiting tolerable settlement and angulardistortion for building foundations In Probabilistic Applications in GeotechnicalEngineering (GSP 170) Eds K K Phoon G A Fenton E F Glynn C H JuangD V Griffiths T F Wolff and L M Zhang ASCE Reston (CDROM)

Zhang L M and Phoon K K (2006) Serviceability considerations in reliability-based foundation design In Foundation Analysis and Design Innovative Methods(GSP 153) Eds R L Parsons L M Zhang W D Guo K K Phoon and M YangASCE Reston pp 127ndash36

Zhang L M and Xu Y (2005) Settlement of building foundations based onfield pile load tests In Proceedings of the 16th International Conference on SoilMechanics and Geotechnical Engineering Osaka (CDROM)

Chapter 10

Reliability verification usingpile load tests

Limin Zhang

101 Introduction

Proof pile load tests are an important means to cope with uncertainties in thedesign and construction of pile foundations In this chapter the term loadtest refers to a static load test following specifications such as ASTM D1143BS8004 (BSI 1986) ISSMFE (1985) and PNAP 66 (Buildings Department2002) Load tests serve several purposes Some of their functions include ver-ification of design parameters (this is especially important if the geotechnicaldata are uncertain) establishment of the effects of construction methods onfoundation capacities and provision of data for the improvement of designmethodologies in use and for research purposes (Passe et al 1997 Zhangand Tang 2002) In addition to these functions savings may be derived fromload tests (Hannigan et al 1997 Passe et al 1997) With load tests lowerfactors of safety (FOS) or higher-strength parameters may be used Hencethere are cost savings even when no changes in the design are made after theload tests For example the US Army Corps of Engineers (USACE) (1993)the American Association of State Highway and Transportation Officials(AASHTO) (1997) and Geotechnical Engineering Office (2006) recommendthe use of different FOSs depending on whether load tests are carried out ornot to verify the design The FOSs recommended by the USACE are shownin Table 101 The FOS under the usual load combination may be reducedfrom 30 for designs based on theoretical or empirical predictions to 20for designs based on the same predictions that are verified by proof loadtests

Engineers have used proof pile tests in the allowable stress design (ASD) asfollows The piles are sized using a design method suitable for the soil condi-tions and the construction procedure for the project considered In designingthe piles a FOS of 20 is used when proof load tests are utilized Preliminarypiles or early test piles are then constructed necessary design modificationsmade based on construction control and proof tests on the test piles orsome working piles conducted If the test piles do not reach a prescribed fail-ure criterion at the maximum test load (ie twice the design load) then the

386 Limin Zhang

Table 101 Factor of safety for pile capacity (after USACE 1993)

Method of determining capacity Loading condition Minimum factor of safety

Compression Tension

Theoretical or empirical Usual 20 20prediction to be verified Unusual 15 15by pile load test Extreme 115 115

Theoretical or empirical Usual 25 30prediction to be verified Unusual 19 225by pile driving analyzer Extreme 14 17

Theoretical or empirical Usual 30 30prediction not verified Unusual 225 225by load test Extreme 17 17

design is validated otherwise the design load for the piles must be reducedor additional piles or pile lengths must be installed

In a reliability-based design (RBD) the value of proof load tests can be fur-ther maximized In a RBD incorporating the Bayesian approach (eg Ang andTang 2007) the same load test results reveal more information Predictedpile capacity using theoretical or empirical methods can be very uncertainResults from load tests not only suggest a more realistic pile capacity valuebut also greatly reduce the uncertainty of the pile capacity since the errorassociated with load-test measurements is much smaller than that associatedwith predictions In other words the reliability of a design for a test site canbe updated by synthesizing existing knowledge of pile design and site-specificinformation from load tests

For verification of the reliability of designs and calibration of new theoriesit is preferable that load tests be conducted to failure Load tests are oftenused as proof tests however the maximum test load is usually specified astwice the anticipated design load (ASTM D 1143 Buildings Department2002) unless failure occurs first In many cases the pile is not brought tofailure at the maximum test load Ng et al (2001a) analyzed more than 30cases of static load tests on large-diameter bored piles in Hong Kong Theyfound that the Davisson failure criterion was reached in less than one half ofthe cases and in no case was the 10-diameter settlement criterion suggestedby ISSMFE (1985) and BSI (1986) reached In the Hong Kong Driven PileDatabase developed recently (Zhang et al 2006b) only a small portion ofthe 312 static load tests on steel H-piles were conducted to failure as specifiedby the Davisson criterion

Kay (1976) Baecher and Rackwitz (1982) Lacasse and Goulois (1989)and Zhang and Tang (2002) investigated updating the reliability of pilesusing load tests conducted to failure Zhang (2004) studied how the majority

Reliability verification 387

of proof tests not conducted to failure increases the reliability of pilesand how the conventional proof test method can be modified to allow for agreater value in the RBD framework

This chapter consists of three parts A systematic method to incorpo-rate the results of proof load tests into foundation design is first describedSecond illustrative acceptance criteria for piles based on proof load testsare proposed for use in the RBD Finally modifications to the conventionalproof test procedures are suggested so that the value derived from the prooftests can be maximized Reliability and factor of safety are used together inthis chapter since they provide complementary measures of an acceptabledesign (Duncan 2000) as well as better understanding of the effect of prooftests on the costs and reliability of pile foundations This chapter will focuson ultimate limits design Discussion on serviceability limit states is beyondthe scope of the chapter

102 Within-site variability of pile capacity

In addition to uncertainties with site investigation laboratory testing andprediction models the values of capacity of supposedly identical piles withinone site also vary Suppose several ldquoidenticalrdquo test piles are constructedat a seemingly uniform site and are load-tested following an ldquoidenticalrdquoprocedure The measured values of the ultimate capacity of the piles wouldusually be different due to the so-called ldquowithin-siterdquo variability follow-ing Baecher and Rackwitz (1982) For example Evangelista et al (1977)tested 22 ldquoidenticalrdquo bored piles in a sandndashgravel site The piles were ldquoallrdquo08 m in diameter and 20 m in length and construction of the piles wasassisted with bentonite slurry The load tests revealed that the coefficient ofvariation (COV) of the settlement of these ldquoidenticalrdquo piles at the intendedworking load was 021 and the COV of the applied loads at the meansettlement corresponding to the intended load was 013

Evangelista et al (1977) and Zhang and Tang (2002) described severalsources of the within-site variability of the pile capacity inherent variabil-ity of properties of the soil in the influence zone of each pile constructioneffects variability of pile geometry (length and diameter) variability of prop-erties of the pile concrete and soil disturbance caused by pile driving andafterward set-up The effects of both construction and set-up are worth aparticular mention because they can introduce many mechanisms that causelarge scatter of data One example of construction effects is the influence ofdrilling fluids on the capacity of drilled shafts According to OrsquoNeill andReese (1999) improper handling of bentonite slurry alone could reduce thebeta factor for pile shaft resistance from a common range of 04ndash12 to below01 Set-up effects refer to the phenomenon that the pile capacity increaseswith time following pile installation Chow et al (1998) Shek et al (2006)and many others revealed that the capacity of driven piles in sand can increase

388 Limin Zhang

from a few percent to over 200 after the end of initial driving Such effectsmake the pile capacity from a load test a ldquonominalrdquo value

The variability of properties of the soil and the pile concrete is affected bythe space over which the properties are estimated (Fenton 1999) The issueof spatial variability of soils is the subject of interest in several chapters of thisbook Note that after including various effects the variability of the soils at asite may not be the same as the variability of the pile capacity at the same siteDasaka and Zhang (2006) investigated the spatial variability of a weatheredground using random field theory and geostatistics The scale of fluctuationof the depth of competent rock (moderately decomposed rock) beneath abuilding block is approximately 30 m whereas the scale of fluctuation ofthe as-build depth of the piles that provide the same nominal capacity isonly approximately 10 m

Table 102 lists the values of the coefficient of variation (COV) of thecapacity of driven piles from load tests in eight sites These values rangefrom 012 to 028 Note that the variability reported in the table is among testpiles in one site the variability among production piles and among differentsites may be larger (Baecher and Rackwitz 1982) In this chapter a meanvalue of COV = 020 is adopted for analysis This assumes that the standarddeviation value is proportional to the mean pile capacity

Kay (1976) noted that the possible values of the pile capacity within asite favor a log-normal distribution This may be tested with the data from16 load tests in a site in southern Italy reported by Evangelista et al (1977)

Table 102 Within-site viability of the capacity of driven piles in eight sites

Site Numberof piles

Diameter(m)

Length(m)

Soil COV References

AshdodIsrael

12 ndash ndash Sand 022 Kay 1976

BremerhavenGermany

9 ndash ndash Sand 028 Kay 1976

San FranciscoUSA

5 ndash ndash Sand andclay

027 Kay 1976

Southern Italy 12 040 80 Sand andgravel

025 Evangelistaet al 1977

Southern Italy 4 040 120 Sand andgravel

012 Evangelistaet al 1977

Southern Italy 17 052 180 Sand andgravel

019 Evangelistaet al 1977

Southern Italy 3 036 73 Sand andgravel

012 Evangelistaet al 1977

Southern Italy 4 046 70 Sand andgravel

014 Evangelistaet al 1977

Southern Italy 16 050 150 Clay sandand gravel

020 Evangelistaet al 1977

Reliability verification 389

00

02

04

06

08

10

0 500 1000 1500 2000Pile capacity (kN)

Cu

mu

lati

ve d

istr

ibu

tio

n

Measurements

Theoretical normal

Theoretical log-normal

Maximumdeviation

5 criticalvalue

Normal 026 032Log-normal 022 032

Figure 101 Observed and theoretical distributions of within-site pile capacity

Figure 101 shows the measured and assumed normal and log-normal distri-butions of the pile capacity in the site Using the KolmogorovndashSmirnov testneither the normal nor the log-normal theoretical curves appear to fit theobserved cumulative curve very well However it is acceptable to adopt alog-normal distribution for mathematical convenience since the maximumdeviation in Figure 101 is still smaller than the 5 critical value The tailpart of the cumulative distribution of the pile capacity is of more interestto designers since the pile capacity in that region is smaller In Figure 101it can be seen that the assumed log-normal distribution underestimates thepile capacity at a particular cumulative percentage near the tail Thereforethe assumed distribution will lead to results on the conservative side

From the definition the within-site variability is inherent in a particulargeological setting and a geotechnical construction procedure at a specificsite The within-site variability represents the minimum variability for aconstruction procedure at a site which cannot be reduced using load tests

103 Updating pile capacity with proof load tests

1031 Proof load tests that pass

For proof load tests that are not carried out to failure the pile capacityvalues are not known although they are greater than the maximum testloads Define the variate x as the ratio of the measured pile capacity tothe predicted pile capacity (called the ldquobearing capacity ratiordquo hereafter)At a particular site x can be assumed to follow a log-normal distribution(Whitman 1984 Barker et al 1991) with the mean and standard deviation

390 Limin Zhang

of x being micro and σ and those of ln(x) being η and ξ where σ or ξ describes thewithin-site variability of the pile capacity Suppose the specified maximumtest load corresponds to a value of x = xT For example if the maximumtest load is twice the design load and a FOS of 20 is used then the value ofxT is 10 From the log-normal probability density function the probabilitythat the test pile does not fail at the maximum test load (ie x gt xT) is

p(x ge xT) =infinint

xT

1radic2πξx

exp

minus1

2

(ln(x) minusη

ξ

)2

dx (101)

Noting that y = (ln(x) minusη)ξ Equation (101) becomes

p(x ge xT) =infinint

ln(xT)minusη

ξ

1radic2π

exp(

minus12

y2)

dy = 1 minus

(ln(xT) minusη

ξ

)

=

(minus ln(xT) minusη

ξ

)(102)

where is the cumulative distribution function of the standard normaldistribution Suppose that n proof tests are conducted and none of the testpiles fails at xT If the standard deviation ξ of ln(x) is known but its meanmicro or η is a variable then the probability that all of the n test piles do notfail at xT is

L(micro) =nprod

i=1

pX(x ge xT)∣∣∣micro = n

(minus ln(xT) minusη

ξ

)(103)

L(micro) is also called the ldquolikelihood functionrdquo of micro Given L(micro) the updateddistribution of the mean of the bearing capacity ratio is (eg Ang andTang 2007)

f primeprime(micro) = kn(

minus ln(xT) minusη

ξ

)f prime(micro) (104)

where f prime(micro) is the prior distribution of micro which can be constructed basedon the empirical log-normal distribution of x and the within-site variabilityinformation (Zhang and Tang 2002) and k is a normalizing constant

k =⎡⎣ infinintminusinfin

n(

minus ln(xT) minusη

ξ

)f prime(micro)dmicro

⎤⎦

minus1

(105)

Reliability verification 391

Given an empirical distribution N(microXσX) the prior distribution can alsobe assumed as a normal distribution with the following parameters

microprime = microX (106)

σ prime =radicσ 2

X minusσ 2 (107)

The updated distribution of the bearing capacity ratio x is thus (eg Angand Tang 2007)

fX(x) =infinint

minusinfinfX(x |micro )f primeprime(micro)dmicro (108)

where fX(x |micro ) is the distribution of x given the distribution of its mean Thisdistribution is assumed to be log-normal as mentioned earlier

1032 Proof load tests that do not pass

More generally suppose only r out of n test piles do not fail at x = xT Theprobability that this event occurs now becomes

L(micro) =(

nr

)[p(x ge xT)

∣∣∣micro ]r [1 minus p(x ge xT)∣∣∣micro ](nminusr)

(109)

and the normalizing constant k is

k=⎡⎣ infinintminusinfin

(nr

)[p(xgexT)

∣∣∣micro ]r[1minusp(xgexT)∣∣∣micro ](nminusr)

f prime(micro)dmicro

⎤⎦

minus1

(1010)

Accordingly the updated distribution of the mean of the bearing capacityratio is

f primeprime(micro) = k(

nr

)[p(x ge xT)

∣∣∣micro ]r [1 minus p(x ge xT)∣∣∣micro ](nminusr)

f prime(micro) (1011)

Equation (1011) has two special cases When all tests pass (ie r = n)Equation (1011) reduces to Equation (104) If none of the tests passes(ie r = 0) a dispersive posterior distribution will result The updated distri-bution of the bearing capacity ratio can be obtained using Equation (108)

1033 Proof load tests conducted to failure

Let us now consider the proof load tests carried out to failure ie caseswhere the pile capacity values are known To start with the log-normally

392 Limin Zhang

distributed pile capacity assumed previously is now transformed into anormal variate If the test outcome is a set of n observed values representinga random sample following a normal distribution with a known standarddeviation σ and a mean of x then the distribution of the pile capacity canbe updated directly using Bayesian sampling theory Given a known priornormal distribution Nmicro(microprimeσ prime) the posterior density function of the bearingcapacity ratio f primeprime

X(x) is also normal (Kay 1976 Zhang and Tang 2002 Angand Tang 2007)

fXprimeprime (x) = NX(microXprimeprime σXprimeprime ) (1012)

where

microXprimeprime =

x(σ prime)2 +microprime(σ2

n

)(σ prime)2 +

(σ2

n

) (1013)

σXprimeprime = σ

radicradicradicradicradic1 +(σ prime2n

)(σ prime)2 +

(σ2

n

) (1014)

1034 Multiple types of tests

In many cases multiple test methods are used for construction quality assur-ance at a single site The construction control of driven H-piles in Hong Kong(Chu et al 2001) and the seven-step construction control process of Bell et al(2002) are two examples In the Hong Kong practice both dynamic formulaand static analysis are adopted for determining the pile length Early testpiles or preliminary test piles are required at the early stage of constructionto verify the construction workmanship and design parameters In additionup to 10 of working piles may be tested using high-strain dynamic testseg using a Pile Driving Analyzer (PDA) and one-half of these PDA testsshould be analyzed by a wave equation analysis such as the CAse Pile WaveAnalysis Program (CAPWAP) Finally at the end of construction 1 ofworking piles should be proof-tested using static loading tests With theseconstruction control measures in mind a low factor of safety is commonlyused Zhang et al (2006a) described the piling practice in the language ofBayesian updating A preliminary pile length is first set based primarily on adynamic formula The pile performance is then verified by PDA tests duringthe final setting tests The combination of the dynamic formula (prior) andthe PDA tests will result in a posterior distribution Taking the posteriordistribution after the PDA tests as a prior the pile performance is furtherupdated based on the outcome of the CAPWAP analyses and finally updatedbased on the outcome of the proof load tests This exercise can be repeatedif more indirect or direct verification tests are involved

Reliability verification 393

104 Reliability of piles verified by proof tests

1041 Calculation of reliability index

In a reliability-based design (RBD) the ldquosafetyrdquo of a pile can be describedby a reliability index β To be consistent with current efforts in code devel-opment such as AASHTOrsquos LRFD Bridge Design Specifications (Barkeret al 1991 AASHTO 1997 Withiam et al 2001 Paikowsky 2002) or theNational Building Code of Canada (NRC 1995 Becker 1996a b) the first-order reliability method (FORM) is used for calculating the reliability indexIf both resistance and load effects are log-normal variates then the reliabilityindex for a linear performance function can be written as (Whitman 1984)

β =ln

⎛⎝R

Q

radicradicradicradic1 + COV2Q

1 + COV2R

⎞⎠

radicln[(1 + COV2

R)(1 + COV2Q)]

(1015)

where Q and R = the mean values of load effect and resistance respectivelyCOVQ and COVR = the coefficients of variation (COV) for the load effectand resistance respectively If the only load effects to be considered are deadand live loads Barker et al (1991) Becker (1996b) and Withiam et al(2001) have shown that

R

Q=

λRFOS(

QD

QL+ 1)

λQDQD

QL+λQL

(1016)

and

COV2Q = COV2

QD + COV2QL (1017)

where QD and QL = the nominal values of dead and live loads respectivelyλRλQD and λQL = the bias factors for the resistance dead load and liveload respectively with the bias factor referring to the ratio of the mean valueto the nominal value COVQD and COVQL = the coefficients of variationfor the dead load and live load respectively and FOS = the factor of safetyin the traditional allowable stress design

If the load statistics are prescribed Equations (1015) and (1016) indicatethat the reliability of a pile foundation designed with a FOS is a function ofλR and COVR As will be shown later if a few load tests are conducted ata site the values of λR and COVR associated with the design analysis can

394 Limin Zhang

be updated using the load test results If the results are favorable then theupdated COVR will decrease but the updated λR will increase The reliabilitylevel or the β value will therefore increase Conversely if a target reliabilityβT is specified the FOS required to achieve the βT can be calculated usingEquations (1015)ndash(1017) and the costs of construction can be reduced ifthe load test results are favorable

1042 Example design based on an SPT method andverified by proof tests

For illustration purposes a standard penetration test (SPT) method proposedby Meyerhof (1976) for pile design is considered This method uses the aver-age corrected SPT blow count near the pile toe to estimate the toe resistanceand the average uncorrected blow count along the pile shaft to estimate theshaft resistance According to statistical studies conducted by Orchant et al(1988) the bias factor and COV of the pile capacity from the SPT methodare λR = 130 and COVR = 050 respectively The within-site variability ofthe pile capacity is assumed to be COV = 020 (Zhang and Tang 2002)which is smaller than the COVR of the design analysis This is because theCOVR of the prediction includes more sources of errors such as model errorsand differences in construction effects between the site where the model isapplied and the sites from which the information was extracted to formulatethe model (Zhang et al 2004)

Calculations for updating the probability distribution can be conductedusing an Excel spreadsheet Figure 102 shows the empirical distribution ofthe bearing capacity ratio x based on the given λR and COVR values andthe updated distributions after verification by proof tests The translatedmean η and standard deviation ξ of ln(x) calculated based on the givenλR and COVR are used to define the empirical log-normal distributionIn Figure 102(a) all proof tests are positive (ie the test piles do not failat twice the design load) the mean value of the updated pile capacity afterverification by the proof tests increases with the number of tests while theCOV value decreases Specifically the updated mean increases from 130for the empirical distribution to 174 after the design has been verified bythree positive tests In Figure 102(b) the cases in which no test one test twotests and all three tests are positive are considered As expected the updatedmean decreases significantly when the number of positive tests decreases Theupdated mean and COV values with different outcomes from the proof testsare summarized in Table 103 The updated distributions in Figure 102may be approximated by the log-normal distribution for the convenience ofreliability calculations using Equation (1015) The log-normal distributionfits the cases with some non-positive tests very well For the cases in whichall tests are either positive or negative the log-normal distribution slightlyexaggerates the scatter of the distributions as shown in Figure 102(b)

Reliability verification 395

00

05

10

15

20

00 05 10 15 20 25 30 35 40Bearing capacity ratio x

Pro

bab

ility

den

sity

1 positive test

2 positive tests

3 positive tests

5 positive tests

Empirical distribution

00

05

10

15

20

00 05 10 15 20 25 30 35 40

Bearing capacity ratio x

Pro

bab

ility

den

sity

3 positive out of 3 tests

3 out of 3 approx log-normal

2 positive out of 3 tests

1 positive out of 3 tests

none positive out of 3 tests

Empirical distribution

(a)

(b)

Figure 102 Distributions of the pile capacity ratio after verification by (a) proof tests thatpass at twice the design load and (b) proof tests in which some piles fail

The use of the log-normal distribution is therefore slightly on the con-servative side Because of the differences in the distributions updated byproof tests of different outcomes the COV values in Table 103 do notchange in a descending or ascending manner as the number of positive testsdecreases

The following typical load statistics in the LRFD Bridge Design Specifi-cations (AASHTO 1997) are adopted for illustrative reliability analysesλQD = 108 λQL = 115 COVQD = 013 and COVQL = 018 Thedead-to-live load ratio QDQL is structure-specific Investigations by

396 Limin Zhang

Table 103 Updated mean and COV of the bearing capacity ratio based on SPT(Meyerhof 1976) and verified by proof tests

Total numberof tests

Number ofpositive tests

Mean COV at FOS = 20

4 4 177 038 2673 119 024 2382 104 024 2001 091 024 1570 069 030 060

3 3 174 038 2582 114 025 2211 096 025 1700 072 031 067

2 2 168 039 2451 106 026 1910 075 032 077

1 1 159 041 2230 082 034 095

Barker et al (1991) and McVay et al (2000) show that β is relatively insen-sitive to this ratio For the SPT method considered the calculated β valuesare 192 and 190 for distinct QDQL values of 369 (75 m span length) and158 (27 m span length) respectively if a FOS of 25 is used The differ-ence between the two β values is indeed small In the following analyses thelarger value of QDQL = 369 which corresponds to a structure on whichthe dead loads dominate is adopted This QDQL value was used by Barkeret al (1991) and Zhang and Tang (2002) and would lead to factors of safetyon the conservative side

Figure 103 shows the reliability index β values for single piles designedwith a FOS of 20 and verified by several proof tests Each of the curves inthis figure represents the reliability of the piles when they have been verifiedby n proof tests out of which r tests are positive [see the likelihood functionEquation (109)] The β value corresponding to the empirical distribution(see Figure 102) and a FOS of 20 is 149 If one conventional proof test isconducted to verify the design and the test is positive then the β value willbe updated to 223 The updated β value will continue to increase if moreproof tests are conducted and if all the tests are positive In the cases whennot all tests are positive the reliability of the piles will decrease with thenumber of tests that are not positive For instance the β value of the pilesverified by three positive tests is 258 If one two or all of the three test pilesfail the β values decrease to 221 170 and 067 respectively If multipleproof tests are conducted the target reliability marked by the shaded zone

Reliability verification 397

00

05

10

15

20

25

30

0 1 2 3 4 5

Number of positive tests

Rel

iab

ility

ind

ex b

All tests positiveNumber of positive tests out of 4 testsNumber of positive tests out of 3 testsNumber of positive tests out of 2 testsNumber of positive tests out of 1 test

Target reliability zone βTS =17ndash25

Figure 103 Reliability of single driven piles designed with a FOS of 20 and verified byconventional proof tests of different outcomes

can still be satisfied even if some of the tests are not positive Issues on targetreliability will be discussed later in this chapter

The FOS that will result in a sufficient level of reliability is of interest toengineers Figure 104 shows the calculated FOS values required to achievea β value of 20 for the pile foundation verified by proof tests with differentoutcomes It can be seen that a FOS of 20 is sufficient for piles that areverified by one or more consecutive positive tests or by three or four testsin which no more than one test is not positive However larger FOS valuesshould be used if the only proof test or one out of two tests is not positive

The updating effects of the majority of the proof tests conducted to twicethe design load and the tests in which the piles have a mean measured capac-ity of twice the design load are different Table 104 compares the updatedstatistics of the two types of tests For the proof tests that are not con-ducted to failure the updated mean bearing capacity ratio and β valuessignificantly increase with the number of tests For the tests that are con-ducted to failure at x = 10 the updated mean value will approach 10 andthe updated COV value will approach the assumed within-site COV valueof 020 as expected when the number of tests is sufficiently large This isbecause test outcomes carry a larger weight than that of the prior informationas described in Equations (1013) and (1014)

398 Limin Zhang

00

05

10

15

20

25

30

35

0 1 2 3 4 5

Number of positive tests

Req

uir

ed f

acto

r o

f sa

fety

All tests positive

Number of positive tests out of 4 tests

Number of positive tests out of 3 tests

Number of positive tests out of 2 tests

Number of positive tests out of 1 test

Factor of safety commonlyused for design verified byconventional load tests

Figure 104 Factor of safety for single driven piles that are required to achieve a targetreliability index of 20 after verification by proof load tests

Table 104 Comparison of the updating effects of proof tests that pass at xT = 10 andtests in which the test piles fail at a mean bearing capacity ratio of xT = 10

Numberof tests

Tests that pass at xT = 10 Tests that fail at xT = 10

Mean COV Mean COV

0 130 050 149 130 050 1491 159 041 223 106 027 1892 168 039 245 104 024 1983 174 038 258 104 023 2024 177 038 267 103 022 2045 180 037 274 103 022 205

10 189 036 294 102 021 209

NoteMean and COV of the bearing capacity ratio x

1043 Effect of accuracy of design methods

The example in the previous section focuses on a single design methodOne may ask if the observations from the example would apply to otherdesign methods Table 105 presents statistics of a number of commonlyused methods for design and construction of driven piles (Zhang et al 2001)

Tabl

e10

5R

elia

bilit

yst

atis

tics

ofsi

ngle

driv

enpi

les

(aft

erZ

hang

etal

20

01)

Met

hod

cate

gory

Pred

ictio

nm

etho

dN

umbe

rof

case

sBi

asfa

ctor

λR

Coef

ficie

ntof

varia

tion

COV R

ASD

fact

orof

safe

tyFO

S

Relia

bilit

yin

dex

Re

fere

nces

Dyn

amic

met

hods

CA

PWA

P(E

OD

Flo

rida

data

base

)44

160

035

25

311

McV

ayet

al(

2000

)

with

mea

sure

men

tsC

APW

AP

(BO

RF

lori

dada

taba

se)

791

260

352

52

55M

cVay

etal

(20

00)

(Con

stru

ctio

nst

age)

CA

PWA

P(E

OD

nat

iona

lda

taba

se)

125

163

049

25

239

Paik

owsk

yan

dSt

ener

sen

(200

0)

CA

PWA

P(B

OR

nat

iona

lda

taba

se)

162

116

034

25

238

Paik

owsk

yan

dSt

ener

sen

(200

0)

Ener

gyap

proa

ch(E

OD

Fl

orid

ada

taba

se)

271

110

342

52

29M

cVay

etal

(20

00)

Ener

gyap

proa

ch(B

OR

Fl

orid

ada

taba

se)

720

840

363

252

11M

cVay

etal

(20

00)

Ener

gyap

proa

ch(E

OD

na

tiona

ldat

abas

e)12

81

080

402

51

94Pa

ikow

sky

and

Sten

erse

n(2

000)

Ener

gyap

proa

ch(B

OR

na

tiona

ldat

abas

e)15

30

790

373

251

92Pa

ikow

sky

and

Sten

erse

n(2

000)

Stat

icm

etho

dsA

lpha

met

hod

clay

type

Indash

110

021

25

308

Sidi

(198

5)B

arke

ret

al(

1991

)(D

esig

nst

age)

Alp

ham

etho

dcl

ayty

peII

ndash2

340

572

52

72Si

di(1

985)

Bar

ker

etal

(19

91)

Beta

met

hod

ndash1

030

212

52

82Si

di(1

985)

Bar

ker

etal

(19

91)

CPT

met

hod

ndash1

030

362

51

98O

rcha

ntet

al(

1988

)Ba

rker

etal

(19

91)

Schm

ertm

ann

(197

8)La

mbd

am

etho

dcl

ayty

peI

ndash1

020

412

51

74Si

di(1

985)

Bar

ker

etal

(19

91)

Mey

erho

frsquosSP

Tm

etho

dndash

130

050

25

192

Orc

hant

etal

(19

88)

Bark

eret

al

(199

1)

Not

eT

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a

400 Limin Zhang

In this table failure of piles is defined by the Davisson criterion For eachdesign method in the table the driven pile cases are put together regard-less of ground conditions or types of pile response (ie end-bearing orfloating) Ideally the cases should be organized into several subsets accord-ing to their ground conditions and types of pile response The statisticsin the table are therefore only intended to be used to illustrate the pro-posed methodology The bias factors of the methods in the table varyfrom 079 to 234 and the COV values vary from 021 to 057 Indeedthe ASD approach results in designs with levels of safety that are ratheruneven from one method to another (ie β = 174ndash311) If a FOS of 20is used for all these methods and each design analysis is verified by twopositive proof tests conducted to twice the design load (ie xT gt 10)the statistics of these methods can be updated as shown in Table 106The updated bias factors are greater but the updated COVR values aresmaller than those in Table 105 The updated β values of all thesemethods fall into a narrow range (ie β = 222ndash289) with a mean ofapproximately 25

Now consider the case when a FOS of 20 is applied to all these methods inTable 105 and the design is verified by two proof tests conducted to failureat an average load of twice the design load (ie xT = 10) The correspondingupdated statistics using the Bayesian sampling theory [Equations (1012)ndash(1014)] are also shown in Table 106 Both the updated bias factors (λR =097ndash112) and the updated COVR values (COVR = 021ndash024) fall intonarrow bands Accordingly the updated β values also fall into a narrowband (ie β = 178ndash231) with a mean of approximately 20

The results in Table 106 indicate that the safety level of a design veri-fied by proof tests is less influenced by the accuracy of the design methodThis is logical in the context of Bayesian statistical theory in which theinformation of the empirical distribution will play a smaller role when moremeasured data at the site become available This is consistent with foun-dation engineering practice In the past reliable designs had been achievedby subjecting design analyses of varying accuracies to proof tests and otherquality control measures (Hannigan et al 1997 OrsquoNeill and Reese 1999)This also shows the effectiveness of the observational method (Peck 1969)with which uncertainties can be managed and acceptable safety levels canbe maintained by acquiring additional information during constructionHowever the importance of the accuracy of a design method in sizing the pileshould be emphasized A more accurate design method has a smaller COVRand utilizes a larger percentage of the actual pile capacity (McVay et al2000) Hence the required safety level can be achieved more economicallyIt should also be noted that the within-site COV is an important parameterIf the within-site COV at the site is larger than 02 as assumed in thischapter the updated reliability will be lower than the results presented inTable 106

Tabl

e10

6R

elia

bilit

yst

atis

tics

ofsi

ngle

driv

enpi

les

desi

gned

usin

ga

FOS

of2

0an

dve

rifie

dby

two

load

test

s

Pred

ictio

nm

etho

dVe

rified

bytw

opo

sitive

test

sx T

gt1

0Ve

rified

bytw

ote

sts

with

am

ean

x T=

10

Bias

fact

orλ

R

Coef

ficie

ntof

varia

tion

COV R

Relia

bilit

yin

dex

Bi

asfa

ctor

λR

Coef

ficie

ntof

varia

tion

COV R

Relia

bilit

yin

dex

CA

PWA

P(E

OD

Flo

rida

data

base

)1

710

322

891

120

242

21C

APW

AP

(BO

RF

lori

dada

taba

se)

146

030

262

107

024

207

CA

PWA

P(E

OD

nat

iona

ldat

abas

e)1

890

422

581

070

242

05C

APW

AP

(BO

Rn

atio

nald

atab

ase)

138

029

254

105

024

202

Ener

gyap

proa

ch(E

OD

Flo

rida

data

base

)1

340

282

501

040

241

99En

ergy

appr

oach

(BO

RF

lori

dada

taba

se)

122

028

226

098

024

181

Ener

gyap

proa

ch(E

OD

nat

iona

ldat

abas

e)1

420

312

461

030

241

95En

ergy

appr

oach

(BO

Rn

atio

nald

atab

ase)

120

028

222

097

024

178

Alp

ham

etho

dcl

ayty

peI

112

021

240

109

021

231

Alp

ham

etho

dcl

ayty

peII

254

052

269

109

024

209

Beta

met

hod

107

021

222

103

021

210

CPT

met

hod

133

029

243

102

024

193

Lam

bda

met

hod

clay

type

I1

400

322

421

020

241

92M

eyer

hofrsquos

SPT

met

hod

168

039

245

104

024

198

Not

e(1

)T

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a(2

)x T

=Rat

ioof

the

max

imum

test

load

toth

epr

edic

ted

pile

capa

city

402 Limin Zhang

105 Acceptance criterion based on proof loadtests

1051 General principle

In the conventional allowable stress design approach acceptance of a designusing proof tests is based on whether the test piles fail before they are loadedto twice the design load Presumably the level of safety of the designs thatare verified by ldquosatisfactoryrdquo proof tests is not uniform In a reliability-baseddesign a general criterion for accepting piles should be that the pile foun-dation as a whole after verification by the proof tests meets the prescribedtarget reliability levels for both ultimate limit states and serviceability limitstates Discussion on serviceability limit states is beyond the scope of thischapter

In order to define acceptance conditions ldquofailurerdquo of piles has to be definedfirst Many failure criteria exist in the literature Hirany and Kulhawy (1989)Ng et al (2001b) and Zhang and Tang (2001) conducted extensive reviewof failure criteria for piles and drilled shafts and discussed the bias in theaxial capacity arising from failure criteria A particular failure criterion maybe applicable only to certain deep foundation types and ground conditionsIn discussing a particular design method a failure criterion should alwaysbe specified explicitly In the examples presented in this chapter only drivenpiles are involved and only the Davisson failure criterion is used These exam-ples are for illustrative purposes Suitable failure criteria should be selectedin establishing acceptance conditions for a particular job

The next step in defining acceptance conditions is to select the target reli-ability of the pile foundation The target reliability of the pile foundationshould match that of the superstructure and of other types of foundationsMeyerhof (1970) Barker et al (1991) Becker (1996a) OrsquoNeill and Reese(1999) Phoon et al (2000) Zhang et al (2001) Paikowsky (2002) and oth-ers have studied the target reliability of foundations based on calibration ofthe ASD practice A target reliability index βT between 30 and 35 appearsto be suitable for pile foundations

The reliability of a pile group can be significantly higher than that of singlepiles (Tang and Gilbert 1993 Bea et al 1999 Zhang et al 2001) Sinceproof tests are mostly conducted on single piles it is necessary to find a targetreliability for single piles that matches the target reliability of the founda-tion system Based on calibration of the ASD practice Wu et al (1989)Tang (1989) and Barker et al (1991) found a range of reliability indexvalues between 14 and 31 and Barker et al (1991) ASSHTO (1997) andWithiam et al (2001) recommended target reliability index values of βTS =20ndash25 for single driven piles and βTS = 25ndash30 for single drilled shaftsZhang et al (2001) attributed the increased reliability of pile foundationsystems to group effects and system effects They calculated βTS values for

Reliability verification 403

single driven piles to achieve a βT value of 30 For pile groups larger thanfour piles the calibrated βTS values are mostly in the range of 20ndash28 ifno system effects are considered The βTS values decrease to 17ndash25 whena system factor of 125 is considered and further to 15ndash20 when a largersystem factor of 15 is considered For a structure supported by four or fewerfar-apart piles the pile group effect and system effect may not be depend-able (Zhang et al 2001 Paikowsky 2002) The target reliability of singlepiles should therefore be the same as that of the foundation system sayβTS = 30

1052 Reliability-based acceptance criteria

In the ASD approach it is logical to use a maximum test load of twicethe design load since a FOS of 20 is commonly used for designs verifiedby proof load tests Table 106 shows that the ASD approach using twoproof tests conducted to twice the design load can indeed lead to a uniformtarget reliability level around β = 25 regardless of the accuracy of the designmethod Even if the test piles fail at an average load of twice the design loada uniform reliability level around β = 20 can still be obtained Comparedwith recommended target reliability indices the reliability of the currentASD practice for large pile groups may be considered sufficient In the RBDapproach the target reliability is a more direct acceptance indicator FromEquations (1015) and (1016) a target reliability can be achieved by manycombinations of the proof test parameters such as the number of tests thetest load the FOS and the test outcomes Thus the use of other FOS valuesand test loads is equally feasible

Analyses with the SPT method (Meyerhof 1976) are again conductedto illustrate the effects of the load test parameters Figure 105 shows theupdated reliability index of piles designed using a FOS of 20 and verified byvarious numbers of tests conducted to 10ndash30 times the design load If theproof tests were carried out to the design load (k = 10) the tests would havea negligible effect on the reliability of the piles hence the effectiveness ofthe tests is minimal This is because the probability that the piles do not failat the design load is 96 based on the empirical distribution and the prooftests only prove an event of little uncertainty If the test load were set at15 times (k = 15) the design load the effectiveness of the proof tests is stilllimited since a βT value of 25 cannot be verified with a reasonable numberof proof tests The test load of twice (k = 20) the design load provides abetter solution when the design is verified by one or two positive prooftests the updated β values will be in the range of 17ndash25 which may beconsidered acceptable for large pile systems If the test load is set at threetimes (k = 30) the design load then a high target reliability of βT = 30can be verified by one or two successful proof tests Figure 106 furtherdefines suitable maximum test loads that are needed to achieve specific target

00

05

10

15

20

25

30

35

40

0 1 2 3 4 5

Number of positive tests

Rel

iab

ility

ind

ex β

k= 10k = 15k = 20k = 25k = 30

Target reliability zone βTS = 17-25 for large pile groups

Target reliability βTS = 30for non-redundant piles

Figure 105 Reliability index of single driven piles designed with a FOS of 20 and verifiedby proof tests conducted to various maximum test loads (in k times the designload)

00

10

20

30

40

15 20 25 30 35 40

Rat

io o

f m

axim

um

tes

t lo

ad t

o d

esig

n lo

ad

1 positive proof test2 positive proof tests

3 positive proof tests

5 positive proof tests

Target reliabilityzone βTS = 17ndash25 forlarge pile groups

Target reliability βTS = 30for non-redundant piles

Target reliability index b

Figure 106 Maximum test load that is required to verify the SPT design method using aFOS of 20

Reliability verification 405

reliability values using a limited number of proof tests A maximum test loadof 25 times or three times the design load is necessary for non-redundantpile groups where group effects and system effects are not presentotherwise the required target reliability of βT = 30 cannot be verified by areasonable number of conventional proof tests conducted only to twice thedesign load

Figure 107 shows the FOS values needed to achieve a β value of 25 Asthe verification test load increases the required FOS decreases In particularif two proof tests are conducted to 15 times (xT = 15) the predicted pilecapacity a βT of 25 can be achieved with a FOS of approximately 15compared with a FOS of 20 if a test load equal to the predicted pile capacity(xT = 10) is adopted

For codification purposes the effects of proof test loads on the reliability ofvarious design methods should be studied and carefully collected databasesare needed to characterize the variability of the design methods The analysisof the design methods in Table 105 is for illustrating the methodology onlyLimitations with the reliability statistics in the table have been pointed outearlier Table 107 presents the FOS values required to achieve βT values of20 25 and 30 when the design is verified by two proof tests conductedto different test loads Given a proof test load the FOS values required for

00

05

10

15

20

25

30

35

40

0 2 3 4 5

Number of positive tests

Req

uir

ed f

acto

r o

f sa

fety

xT = 075

xT = 050

xT = 125

xT = 150

xT = 10

Figure 107 Factor of safety that is required to achieve a target reliability level of βT = 25after verification by proof tests conducted to various maximum test loads

Tabl

e10

7Fa

ctor

sof

safe

tyre

quir

edto

achi

eve

targ

etre

liabi

lity

inde

xva

lues

ofβ

T=

20

βT

=2

5an

T=

30

Pred

ictio

nm

etho

dVe

rified

bytw

ote

sts

with

x Tgt

10

Verifi

edby

two

test

sw

ithx T

gt1

25Ve

rified

bytw

ote

sts

with

x Tgt

15

T

=2

0

T=

25

T

=3

0

T=

20

T

=2

5

T=

30

T

=2

0

T=

25

T

=3

0

CA

PWA

P(E

OD

Flo

rida

data

base

)1

421

722

081

271

521

831

131

351

62

CA

PWA

P(B

OR

Flo

rida

data

base

)1

591

912

291

391

651

971

231

461

73

CA

PWA

P(E

OD

nat

iona

lda

taba

se)

153

193

242

131

162

201

113

139

171

CA

PWA

P(B

OR

nat

iona

lda

taba

se)

165

197

236

143

170

202

126

150

178

Ener

gyap

proa

ch(E

OD

Fl

orid

ada

taba

se)

168

200

239

145

173

205

129

152

180

Ener

gyap

proa

ch(B

OR

Fl

orid

ada

taba

se)

183

218

259

155

184

219

136

161

190

Ener

gyap

proa

ch(E

OD

na

tiona

ldat

abas

e)1

682

032

451

431

712

051

241

481

77

Ener

gyap

proa

ch(B

OR

na

tiona

ldat

abas

e)1

852

202

621

571

862

211

371

621

91

Alp

ham

etho

dcl

ayty

peI

177

206

239

174

202

234

170

197

229

Alp

ham

etho

dcl

ayty

peII

138

181

236

122

157

203

107

136

174

Beta

met

hod

187

218

254

181

210

245

175

203

237

CPT

met

hod

171

205

245

147

175

208

129

153

181

Lam

bda

met

hod

clay

type

I1

712

062

491

441

732

071

251

491

78M

eyer

hofrsquos

SPT

met

hod

164

204

254

137

169

207

118

143

175

Mea

nva

lue

167

201

243

146

175

210

130

155

185

Not

e(1

)T

ype

Iref

ers

toso

ilsw

ithun

drai

ned

shea

rst

reng

thS u

lt50

kPa

Typ

eII

refe

rsto

soils

with

S ugt

50kP

a(2

)x T

=R

atio

ofth

em

axim

umte

stlo

adto

the

pred

icte

dpi

leca

paci

ty

Reliability verification 407

Table 108 Illustrative values of recommended factor of safety and maximum test load

T = 20 T = 25 T = 30

xT FOS k xT FOS k xT FOS k

100 170 17 100 200 20 100 245 25125 150 19 125 175 22 125 210 27150 130 20 150 155 24 150 185 28

Note(1)Based on the reliability statistics in Table 105 for driven piles(2) Pile failure is defined by the Davisson criterion(3) Two proof tests are assumed and the outcomes are assumed to be positive(4) xT = ratio of the maximum test load to the predicted pile capacity(5) FOS = rounded factor of safety(6) k = recommended ratio of the maximum test load to the design load (rounded values)

the design methods to achieve a prescribed target reliability level are ratheruniform Thus the mean of the FOS values for all the methods offers asuitable FOS for design Table 108 summarizes the rounded average FOSvalues that could be used with proof tests based on the limited reliabilitystatistics in Table 105 Rounded values of the ratio of the maximum testload to the design load k which is the multiplication of FOS by xT are alsoshown in Table 108

Table 108 provides illustrative criteria for the design and acceptance ofdriven pile foundations using proof tests The criteria may be implementedin the following steps

1 calculate the design load and choose a target reliability say βT = 252 size the piles using a FOS (eg 175) bearing in mind that proof load

tests will be conducted to verify the design3 construct a few trial piles4 proof test the trial piles to the required test load (eg 22 times the design

load)5 accept the design if the proof tests are positive

It can be argued that proof load tests should be conducted to a large load(eg xT = 150 or 28 times the design load in the case of FOS = 185and βT = 30) First this is required for verifying the reliability of non-redundant pile foundations in which the group effects and system effectsare not present Second the large test load warrants the use of a lowerFOS (eg smaller than 20) and considerable savings since the extra costsfor testing a pile to a larger load are relatively small Third more test pileswill fail at a larger test load (ie 71 of test piles may fail at xT = 15based on the empirical distribution in Figure 102) and hence the tests will

408 Limin Zhang

have greater research value for improvement of practice Malone (1992) hasmade a similar suggestion in studying the performance of piles in weatheredgranite

Because of the differences in the acceptance criteria adopted in the ASDand RBD approaches some ldquoacceptable proof testsrdquo in the ASD may becomeunacceptable in the RBD For example the reliability of a column sup-ported by a single large diameter bored pile may not be sufficient becausethe β value of the pile can still be smaller than βT = 30 even if the pilehas been proof-tested to twice the design load On the other hand someldquounsatisfactoryrdquo piles in the ASD may turn out to be satisfactory in theRBD provided that the design analysis and the outcome of the proof teststogether result in sufficient system reliability For example the requirementof βT = 20 can be satisfied when the test load is somewhat smaller thantwice the design load (see Table 108) or when one out of four tests fails (seeFigure 103)

106 Summary

It has been shown that pile load tests are an integral part of the design andconstruction process of pile foundations Pile load tests can be used as ameans for verifying reliability and reducing costs Whether a load test iscarried out to failure or not the test outcome can be used to ensure thatthe acceptance reliability is met using the methods described in this chapterThus contributions of load tests can be included in a design in a systematicmanner Although various analysis methods could arrive at considerablydifferent designs the reliability of the designs associated with these analysismethods is rather uniform if the designs adopt the same FOS of 20 and areverified by consecutive positive proof tests

In the reliability-based design the acceptance criterion is proposed to bethat the pile foundation as a whole after verification by the proof testsmeets the specified target reliability levels for both ultimate limit states andserviceability limit states This is different from the conventional allowablestress design in which a test pile will not be accepted if it fails before it hasbeen loaded to twice the design load Since the reliability of a single pile is notthe same as the reliability of a pile foundation system the target reliabilityfor acceptance purposes should be structure-specific

If the maximum test load were only around the design load little infor-mation from the test could be derived The use of a maximum test load ofone times the predicted pile capacity is generally acceptable for large pilegroup foundations However a larger test load of 15 times the predictedpile capacity is recommended First it is needed for verifying the reliability ofthe pile foundation in which group effects and system effects are not presentSecond it warrants the use of a smaller FOS and hence savings in pile foun-dation costs Third the percentage of test piles that may fail at 15 times

Reliability verification 409

the predicted pile capacity is larger The tests will thus have greater researchvalue for further improvement of practice

Acknowledgment

The research described in this chapter was substantially supported by theResearch Grants Council of the Hong Kong Special Administrative Region(Project Nos HKUST603502E and HKUST612603E)

References

AASHTO (1997) LRFD Bridge Design Specifications American Association of StateHighway and Transportation Officials Washington DC

Ang A H-S and Tang W H (2007) Probability Concepts in Engineering-Emphasis on Applications to Civil and Environmental Engineering 2nd editionJohn Wiley amp Sons New York

Baecher G R and Rackwitz R (1982) Factors of safety and pile load testsInternational Journal for Numerical and Analytical Methods in Geomechanics6 409ndash24

Barker R M Duncan J M Rojiani K B Ooi P S K Tan C K and Kim S G(1991) Manuals for the Design of Bridge Foundations NCHRP Report 343Transportation Research Board National Research Council Washington DC

Bea R G Jin Z Valle C and Ramos R (1999) Evaluation of reliabilityof platform pile foundations Journal of Geotechnical and GeoenvironmentalEngineering ASCE 125 696ndash704

Becker D E (1996a) Eighteenth Canadian geotechnical colloquium limit statesdesign for foundations Part I An overview of the foundation design processCanadian Geotechnical Journal 33 956ndash83

Becker D E (1996b) Eighteenth Canadian geotechnical colloquium limit statesdesign for foundations Part II Development for the national building code ofCanada Canadian Geotechnical Journal 33 984ndash1007

Bell K R Davie J R Clemente J L and Likins G (2002) Proven success fordriven pile foundations In Proceedings of the International Deep FoundationsCongress Geotechnical Special Publication No 116 Eds M W OrsquoNeill andF C Townsend ASCE Reston VA pp 1029ndash37

BSI (1986) BS 8004 British Standard Code of Practice for Foundations BritishStandards Institution London

Buildings Department (2002) Pile foundations-practice notes for authorizedpersons and registered structural engineers PNAP66 Buildings DepartmentHong Kong

Chow F C Jardine R J Brucy F and Nauroy J F (1998) Effect of time onthe capacity of pipe piles in dense marine sand Journal of Geotechnical andGeoenvironmental Engineering ASCE 124(3) 254ndash64

Chu R P K Hing W W Tang A and Woo A (2001) Review and way for-ward of foundation construction requirements from major client organizationsIn New Development in Foundation Construction The Hong Kong Institution ofEngineers Hong Kong

410 Limin Zhang

Dasaka S M and Zhang L M (2006) Evaluation of spatial variability of weath-ered rock for pile design In Proceedings of International Symposium on NewGeneration Design Codes for Geotechnical Engineering Practice 2ndash3 Nov 2006Taipei World Scientific Singapore (CDROM)

Duncan J M (2000) Factors of safety and reliability in geotechnical engineer-ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126307ndash16

Evangelista A Pellegrino A and Viggiani C (1977) Variability among piles ofthe same foundation In Proceedings of the 9th International Conference on SoilMechanics and Foundation Engineering Tokyo pp 493ndash500

Fenton G A (1999) Estimation for stochastic soil models Journal of GeotechnicalEngineering ASCE 125 470ndash85

Geotechnical Engineering Office (2006) Foundation Design and Construction GEOPublication 12006 Geotechnical Engineering Office Hong Kong

Hannigan P J Goble G G Thendean G Likins G E and Rausche F(1997) Design and Construction of Driven Pile Foundations Workshop ManualVol 1 Publication No FHWA-HI-97-014 Federal Highway AdministrationWashington DC

Hirany A and Kulhawy F H (1989) Interpretation of load tests on drilled shafts ndashPart 1 Axial compression In Foundation Engineering Current Principles andPractices Vol 2 Ed F H Kulhawy ASCE New York pp 1132ndash49

ISSMFE Subcommittee on Field and Laboratory Testing (1985) Axial pile loadingtest ndash Part 1 Static loading Geotechnical Testing Journal 8 79ndash89

Kay J N (1976) Safety factor evaluation for single piles in sand Journal ofGeotechnical Engineering ASCE 102 1093ndash108

Lacasse S and Goulois A (1989) Uncertainty in API parameters for predictionsof axial capacity of driven piles in sand In Proceedings of the 21st OffshoreTechnology Conference Houston TX pp 353ndash8

Malone A W (1992) Piling in tropic weathered granite In Proceedings ofInternational Conference on Geotechnical Engineering Malaysia TechnologicalUniversity Johor Bahru pp 411ndash57

McVay M C Birgisson B Zhang L M Perez A and Putcha S (2000) Loadand resistance factor design (LRFD) for driven piles using dynamic methods ndash aFlorida perspective Geotechnical Testing Journal ASTM 23 55ndash66

Meyerhof G G (1970) Safety factors in soil mechanics Canadian GeotechnicalJournal 7 349ndash55

Meyerhof G G (1976) Bearing capacity and settlement of pile foundations Journalof Geotechnical Engineering ASCE 102 195ndash228

Ng C W W Li J H M and Yau T L Y (2001a) Behaviour of large diameterfloating bored piles in saprolitic soils Soils and Foundations 41 37ndash52

Ng C W W Yau T L Y Li J H M and Tang W H (2001b) New failureload criterion for large diameter bored piles in weathered geomaterials Journal ofGeotechnical and Geoenvironmental Engineering ASCE 127 488ndash98

NRC (1995) National Building Code of Canada National Research CouncilCanada Ottawa

OrsquoNeill M W and Reese L C (1999) Drilled Shafts Construction Proce-dures and Design Methods Publication No FHWA-IF-99-025 Federal HighwayAdministration Washington DC

Reliability verification 411

Orchant C J Kulhawy F H and Trautmann C H (1988) Reliability-basedfoundation design for transmission line structures Vol 2 Critical evaluation ofin-situ test methods EL-5507 Final Report Electrical Power Institute Palo Alto

Paikowsky S G (2002) Load and resistance factor design (LRFD) for deep foun-dations In Foundation Design Codes ndash Proceedings IWS Kamakura 2002 EdsY Honjo O Kusakabe K Matsui M Kouda and G Pokharel A A BalkemaRotterdam pp 59ndash94

Paikowsky S G and Stenersen K L (2000) The performance of the dynamicmethods their controlling parameters and deep foundation specifications InProceedings of the Sixth International Conference on the Application of Stress-Wave Theory to Piles Eds S Niyama and J Beim A A Balkema Rotterdampp 281-304

Passe P Knight B and Lai P (1997) Load tests In FDOT Geotechnical NewsVol 7 Ed P Passe Florida Department of Transportation Tallahassee

Peck R B (1969) Advantages and limitations of the observational method in appliedsoil mechanics Geotechnique 19 171ndash87

Phoon K K Kulhawy F H and Grigoriu M D (2000) Reliability-based design fortransmission line structure foundations Computers and Geotechnics 26 169ndash85

Schmertmann J H (1978) Guidelines for cone penetration test performanceand design Report No FHWA-TS-78-209 Federal Highway AdministrationWashington DC

Shek M P Zhang L M and Pang H W (2006) Setup effect in long piles inweathered soils Geotechnical Engineering Proceedings of the Institution of CivilEngineers 159(GE3) 145ndash52

Sidi I D (1985) Probabilistic prediction of friction pile capacities PhD thesisUniversity of Illinois Urbana-Champaign

Tang W H (1989) Uncertainties in offshore axial pile capacity In Geotechni-cal Special Publication No 27 Vol 2 Ed F H Kulhawy ASCE New Yorkpp 833ndash47

Tang W H and Gilbert R B (1993) Case study of offshore pile system reliabilityIn Proceedings 25th Offshore Technology Conference Houston TX pp 677ndash86

US Army Corps of Engineers (1993) Design of Pile Foundations ASCE PressNew York

Whitman R V (1984) Evaluating calculated risk in geotechnical engineeringJournal of Geotechnical Engineering ASCE 110 145ndash88

Withiam J L Voytko E P Barker R M Duncan J M Kelly B C Musser S Cand Elias V (2001) Load and resistance factor design (LRFD) for highway bridgesubstructures Report No FHWA HI-98-032 Federal Highway AdministrationWashington DC

Wu T H Tang W H Sangrey D A and Baecher G B (1989) Reliability ofoffshore foundations ndash state of the art Journal of Geotechnical Engineering ASCE115 157ndash78

Zhang L M (2004) Reliability verification using proof pile load tests Journal ofGeotechnical and Geoenvironmental Engineering ASCE 130 1203ndash13

Zhang L M and Tang W H (2001) Bias in axial capacity of single bored piles aris-ing from failure criteria In Proceedings ICOSSARrsquo2001 International Associationfor Structural Safety and Reliability Eds R Crotis G Schuller and M ShinozukaA A Balkema Rotterdam (CDROM)

412 Limin Zhang

Zhang L M and Tang W H (2002) Use of load tests for reducing pilelength In Geotechnical Special Publication No 116 Eds M W OrsquoNeill andF C Townsend ASCE Reston pp 993ndash1005

Zhang L M Li D Q and Tang W H (2006a) Level of construction control andsafety of driven piles Soils and Foundations 46(4) 415ndash25

Zhang L M Shek M P Pang W H and Pang C F (2006b) Knowledge-based pile design using a comprehensive database Geotechnical EngineeringProceedings of the Institution of Civil Engineers 159(GE3) 177ndash85

Zhang L M Tang W H and Ng C W W (2001) Reliability of axially loadeddriven pile groups Journal of Geotechnical and Geoenvironmental EngineeringASCE 127 1051ndash60

Zhang L M Tang W H Zhang L L and Zheng J G (2004) Reducing uncer-tainty of prediction from empirical correlations Journal of Geotechnical andGeoenvironmental Engineering ASCE 130 526ndash34

Chapter 11

Reliability analysis of slopes

Tien H Wu

111 Introduction

Slope stability is an old and familiar problem in geotechnical engineeringSince the early beginnings impressive advances have been made and a wealthof experience has been collected by the profession The overall performanceof the general methodology has been unquestionably successful Neverthe-less the design of slopes remains a challenge particularly with new andemerging problems As with all geotechnical projects a design is based oninputs primarily loads and site characteristics The latter includes the subsoilprofile soil moisture conditions and soil properties Since all of the above areestimated from incomplete data and cannot be determined precisely for thelifetime of the structure an uncertainty is associated with each componentTo account for this the traditional approach is to apply a safety factorderived largely from experience This approach has worked well but diffi-culty is encountered where experience is inadequate or nonexistent Thenreliability-based design (RBD) offers the option of evaluating the uncertain-ties and estimating their effects on safety The origin of RBD can be tracedto partial safety factors Taylor (1948) explained the need to account fordifferent uncertainties about the cohesional and frictional components ofsoil strength and Lumb (1970) used the standard deviation to represent theuncertainty and expressed the partial safety factors in terms of the standarddeviations Partial safety factors were incorporated into design practice byDanish engineers in the 1960s (Hansen 1965)

This chapter reviews reliability methods as applied to slope stability anduncertainties in loads pore pressure strength and analysis Emphasis isdirected toward the simple methods the relevant input data and their appli-cations The use of the first-order second-moment method is illustratedby several detailed examples since the relations between the variables areexplicit and readily understood Advance methods are reviewed and thereader is referred to the relevant articles for details

414 Tien H Wu

112 Methods of stability and reliability analysis

Three types of analysis may be used for deterministic prediction of failurelimit equilibrium analysis displacement analysis for seismic loads and finiteelement analysis Reliability analysis can be applied to all three types

1121 Probability of failure

Reliability is commonly expressed as a probability of failure Pf or areliability index β defined as

Pf = 1 minusΦ[β] (111a)

If Fs has normal distribution

Pf = 1 minusΦ

[Fs minus 1σ (Fs)

] β = Fs minus 1

σ (Fs)(111b)

and if Fs has log-normal distribution

Pf = 1 minusΦ

[lnFs minus 05∆(Fs)

2

∆(Fs)

] β = lnFs minus 05∆(Fs)

2

∆(Fs)(111c)

Φ(β) = 1radic2π

int β

minusinfine(minus 1

2 z2)dz (111d)

Fs = safety factor and the overbar denotes the mean value σ () = standarddeviation ∆ = () coefficient of variation (COV) and ∆(Fs) = σ (Fs)FsEquation (111c) is approximate and should be limited to ∆lt 05

The term Fs minus1 Equation (111b) or ln Fs minus05∆(Fs)2 Equation (111c)

denotes the distance between the mean safety factor and Fs = 1 or failureThe term σ (Fs) or ∆(Fs) which represents the uncertainty about Fs isreflected by the spread of the probability density function (pdf) f (Fs)Figure 111 shows the pdf for normal distribution Curves 1 and 2 repre-sent two designs with the same mean safety factor Fs but with σ (Fs) = 04and 12 respectively The shaded area under each curve is Pf It can be seenthat Pf for design 2 is several times greater than for design 1 because of thegreater uncertainty The reliability index β in Equations (111b) and (111c)is a convenient expression that normalizes the safety factor with respect toits uncertainty Two designs with the same β will have the same Pf Bothnormal and ln-normal distributions have been used to represent soil proper-ties and safety factors The ln-normal has the advantage that it does not havenegative values However except for very large values of β the differencebetween the two distributions is not large

Reliability analysis of slopes 415

045

040

035

030

025

f(F

s)

Fs

020

015

010

005

0minus2 minus1 0

(1) σ = 04

(2) σ = 12

1 2 3 4 5 6 7

Figure 111 Probability distribution of Fs

While the mathematical rules for computation of probability are wellestablished the meaning of probability is not well defined It can be inter-preted as the relative frequency of occurrence of an event or a degree ofbelief The general notion is that it denotes the ldquochancerdquo that an event willoccur The reader should refer to Baecher and Christian (2003) for a thor-ough treatment of this issue What constitutes an acceptable Pf dependson the consequence or damage that would result from a failure This isa decision-making problem and is described by Gilbert in Chapter 5 Thecurrent approach in Load Resistance Factor Design (LRFD) is to choose aldquotargetrdquo β by calibration against current practice (Rojiani et al 1991)

1122 Limit equilibrium method

The method of slices (Fellenius 1936) with various modifications is widelyused for stability analysis Figure 112 shows the forces on a slice for a verylong slope failure or plane strain The general equations for the safety factorare (Fredlund and Krahn 1977)

Fsm = Mr

Mo=sum cprimelr + (P minus ul)r tanφprimesum

Wx minussumPf +sumkWe (112a)

Fsf = Fr

Fo=sum

cprimel cosα+sum (P minus ul) tanφprime cosαsumP sinα+sumkW

(112b)

416 Tien H Wu

x

e

W

kW

Xi+1

Ei

XiEi+1

Sm

P

I

f

r

α

Figure 112 Forces on failure surface

where subscripts m and f denote equilibrium with respect to moment andforce Mo Mr = driving and resisting moments Fo Fr = horizontal driv-ing force and resistance W = weight of slice P = normal force on baseof slice whose length is l r f = moment arms of the shear and normalforces on base of slice k = seismic coefficient e = moment arm of inertiaforce α = angle of inclination of base The expression for P depends on theassumption used to render the problem statically determinate The summa-tion in Equations (112) is taken over all the slices For unsaturated soilsthe pore pressure consists of the poreair pressure ua and the porewater pres-sure uw The equations for Fsm and Fsf are given in Fredlund and Rahardjo(1993)

The first-order second moment method (FOSM) is the simplest methodto calculate the mean and variance of Fs For a function Y = g(X1 Xn)in which X1 Xn = random variables the mean and variance are

Y = g[X1 Xn)] (113a)

V(Y) =sum

i

(partgpartXi

)2

V(Xi) + sum

i

sumj

(partgpartXi

)(partgpartXj

)Cov(XiXj)

= ρijσ (Xi)σ (Xj) (113b)

where partgpartXi = sensitivity of g to Xi Cov (Xi Xj) = covariance of Xi Xj ρij =correlation coefficient Given the mean and variance of the input variables Xi

Reliability analysis of slopes 417

to Equations (112) Equations (113) are used to calculate Fs and V(Fs)The distribution of Y is usually unknown except for very simple forms ofthe function g If the normal or ln-normal distribution is assumed β and Pfcan be calculated via Equations (111)

In many geotechnical engineering problems the driving moment is welldefined so that its uncertainty is small and may be ignored Then only V(Mr)and V(Fr) need to be evaluated Let the random variables be cprime tanφprime and uApplication of Equation (113b) to the numerator in Equation (112a) yields

V(Mr) = r2l2V(cprime) +[r2(P minus ul)2]V(tanϕprime)

+r2l(P minus ul)ρcφ σ (cprime)σ (tanϕprime) +[minusrl tanϕprime]2V(u) (114a)

If a total stress analysis is performed for undrained loading or φ = 0

V[Mr] = r2l2V(c) (114b)

Tang et al (1976) and Yucemen and Tang (1975) provide examples ofapplication of FOSM to total stress analysis and effective stress analysis ofslopes Some of the simplifications that have been used include the assump-tion of independence between Xi and Xj and use of the Fs for the criticalcircle determined by limit equilibrium analysis to calculate Pf

Example 1

The slope failure in Chicago described by Tang et al (1976) and Ireland(1954) Figure 113 is used to illustrate the relationship between the meansafety factor Fs the uncertainty about Fs and the failure probability Pf

SandUpper ClayMiddle ClayLower Clay

5

0

-5

Ele

v m

-10Critical slip surfacefor new slope (3)

Original Slope

3 deg

2 deg

1 deg

Critical slip surfacefor original slope (2) Observed slip surface (1)

New slope21

31

0 200su kPa

Figure 113 Slope failure in Chicago (adapted from Ireland (1954) and Tang et al (1976))su = undrained shear strength

418 Tien H Wu

Table 111 Reliability index and failure probabilities Chicago slope

Slope Slip surface Fs ∆(Mr) Ln-normal Normal

β Pf β Pf

Existing Observed 118 018 082 020 084 021Critical 086 018 minus092 082 minus010 082

Redesigned Critical 129 016 162 005 149 007

The mean safety factor calculated with the means of the undrained shearstrengths is 118 for the observed slip surface The uncertainty about Fscomes from the uncertainty about the resisting moment expressed as∆(MR)which in turn comes from the uncertainty about the shear strength ofthe soil and inaccuracies in the stability analysis The total uncertainty is∆(MR) = ∆(Fs) = 018 Equation (112c) which assumes the ln-normal dis-tribution for Fs gives β = 082 Pf = 02 (Table 111) For the critical slipsurface determined by limit equilibrium analysis similar calculations yieldβ = minus092 and Pf = 082 A redesigned slope with Fs = 129 has β = 162and Pf = 005 For comparison the values of β and Pf calculated with thenormal distribution are also shown in Table 111 The evaluation of theuncertainties and ∆(MR) is described in Example 3

With respect to the difference between the critical failure surface (2) andthe observed failure surface (1) it should be noted that all of the soil sampleswere taken outside the slide area (Ireland 1954) Hence the strengths alongthe failure surfaces remain questionable In probabilistic terms the perti-nent question now becomes given that failure has or has not occurredwhat is the shear strength This issue is addressed in Section 1137 (see alsoChristian 2004)

Example 2

During wet seasons slope failures on hillsides with a shallow soil coverand landfill covers may occur with seepage parallel to the ground surface(Soong and Koerner 1996 Wu and Abdel-Latif 2000) The failure surfaceapproaches a plane Figure 114 and Equations (112) reduce to

Fsf = Fr

Fo= Fsm = (P minus ul) tanϕprime cosα

P sinα= (γh minus γwhw)tanϕprime cosα

γhsinα

(115)

with P = γhcosα ul = γwhw cosα γ = unit weight of soil γw = unit weightof water and h and hw are as shown in Figure 114 It is assumed that γ is

Reliability analysis of slopes 419

h

α

hw

I

Figure 114 Infinite slope

the same above and below the water table and k = 0 The major hazardcomes from the rise in the water level hw Procedures for estimating hw aredescribed in Section 1124

For the present example consider a slope with h = 06 m α = 22 cprime = 0ϕprime = 30 Assume that the annual maximum of hw has a mean and standarddeviation equal to hw = 012 m and σ (hw) = 0075 m Substituting the meanvalues into Equation (115) gives FR = 05 kN and Fs = 128 FollowingEquation (113b) the variance of FR is

V(FR) = [minusγw cosα tanϕprime]2V(hw) +[(γh minus γwhw)cosα]2V(tanϕprime)(116a)

If V(tanϕ) = 0

V(FR) = [γw cos22 tan30]2[0075]2 = 00014kN2 (116b)

∆(FR) = radic0001405 = 0075 (116c)

Then β = 325 Pf = 0001 according to Equations (111c) and (111d) Thisis the annual failure probability

For a design life of 20 years the failure probability may be found withthe binomial distribution The probability of x occurrences in n independenttrials with a probability of occurrence p per trial is

P(X = x) = nx(n minus x)p

x(1 minus p)nminusx (117)

With p = 0001 and n = 20 x = 0 P(X = 0) = 099 which is the probabilityof no failure The probability of failure is 1 minus 099 = 001 To illustrate the

420 Tien H Wu

importance of hw let σ (hw) = 003 m Similar calculations lead to β = 85and Pf asymp 0

Consider next the additional uncertainty due to uncertainty about tan ϕprimeUsing σ (tanϕprime) = 002 in Equation (116a) with σ (hw) = 0075 m gives

V(FR) = [(06γ minus 012γw)cos22]2(002)2

+[γw cos22 tan30]2[0075]2

= 00017kN2 (118)

The annual failure probability becomes 00013 Note that this cannot be usedin Equation (117) to compute the failure probability in 20 years becausetanϕprime in successive years are not independent trials

While FOSM is simple to use it has shortcomings which include theassumption of linearity in Equations (113) It is also not invariant withrespect to the format used for safety Baecher and Christian (2003) givesome examples The HasoferndashLind method also called the first-order relia-bility method (FORM) is an improvement over FOSM It uses dimensionlessvariables

Xlowasti = Xi minus Xiσ [Xi] (119a)

and reliability is measured by the distance of the design point from the failurestate

g(Xlowast1 X

lowasti ) = 0 (119b)

which gives the combination of X1 Xn that will result in failure For limitequilibrium analysis g = Fs minus 1 = 0 Figure 115 shows g as a function ofXlowast

1 and Xlowast2 The design point is shown as a and ab is the minimum distance

to g = 0 Except for very simple problems the search for the minimum ab isdone via numerical solution Low et al (1997) show the use of spread sheet

X2

g(X2 X2)=0

X1

a

b

Figure 115 First-order reliability method

Reliability analysis of slopes 421

to search for the critical circle A detailed treatment on the use of spreadsheet and an example are given by Low in Chapter 3

The distribution of Fs and Pf can be obtained by simulation Values of theinput parameters (eg cprime ϕprime etc) that represent the mean values along thepotential slip surface are random variables generated from the distributionfunctions and are used to calculate the distribution of Fs From this the fail-ure probability P(Fs le 1) can be determined Examples include Chong et al(2000) and El-Ramly et al (2003) Several commercial software packagesalso have an option to calculate β and Pf by simulation Most do simulationson the critical slip surface determined from limit equilibrium analysis

1123 Displacement analysis

The Newmark (1965) method for computing displacement of a slope duringan earthquake considers a block sliding on a plane slip surface (Figure 116a)Figure 116b shows the acceleration calculated for a given ground accelera-tion record Slip starts when ai exceeds the yield acceleration ay at Fs le 1The displacement during each slip is

yi = (ai minus ay)t2i 2 (1110a)

where ai = acceleration during time interval ti For n cycles with Fs le 1 thedisplacement is

Y =nsumi

yi (1110b)

a

ai

ti

ay

t

(a)

(b)

Figure 116 Displacement by Newmarkrsquos method

422 Tien H Wu

The displacement analysis assumes that the soil strength is constantLiquefaction is not considered Displacement analysis has been used to eval-uate the stability of dams and landfills Statistics of ground motion recordswere used to compute distribution of displacements by Lin and Whitman(1986) and Kim and Sitar (2003) Kramer (1996) gives a comprehensivereview of slopes subjected to seismic hazards

1124 Finite element method

The finite element method (FEM) is widely used to calculate stress strainand displacement in geotechnical structures In probabilistic FEM materialproperties are represented as random variables or random fields

A simplified case is where only the uncertainties about the average materialproperties need be considered Auvinet et al (1996) show a procedure whichtreats the properties xi as random variables Consider the finite elementequations for an elastic material

P = [K]Y (1111a)

where P = applied nodal forces [K] = stiffness matrix Y = nodaldisplacements Differentiating and transposing terms yields

[K]partYpartxi

= partPpartxi

minus partKpartxi

Y (1111b)

where xi = material properties This is analogous to Equation (1111a) ifwe let

partYpartxi

= Ylowast (1111c)

Plowast = partPpartxi

minus partKpartxi

Y (1111d)

Then the same FEM program can be used to solve for Ylowast which is equal tothe sensitivity of displacement to the properties The means and variancesof material properties are used to calculate the first two moments of thedisplacement via FOSM Equations (113) Moments of strain or stress canbe calculated in the same manner

The perturbation method considers the material as a random field Equa-tions (1111) are extended to second-order and used to compute the first twomoments of displacement stress and strain (eg Liu et al 1986) Sudretand Berveiller describe an efficient spectral-based stochastic finite elementmethod in Chapter 7

Another approach is simulation A FEM analysis is done with materialproperties generated from the probability distribution of the properties

Reliability analysis of slopes 423

Griffith and Fenton (2004) studied the influence of spatial variation insoil strength on failure probability for a saturated soil slope subjected toundrained loading The strength is represented as a random field and thegenerated element properties account for spatial variation and correlation

For dynamic loads deterministic analyses with FEM and various materialmodels have been made (eg Seed et al 1975 Lacy and Prevost 1987 Zerfaand Loret 2003) to yield pore pressure generation and embankment defor-mation for given earthquake motion records See Popescu et al (Chapter 6)for stochastic FEM analysis of liquefaction

1125 Pore pressure seepage and infiltration

For deep-seated failures where the pore pressure is controlled primarily bygroundwater it can be estimated from measurements or from seepage calcu-lations It is often assumed that the pore pressure in the zone above the watertable the vadose zone is zero This may be satisfactory where the vadosezone is thin Estimation of pore pressure or suction (ψ) in the vadose zoneis complex Infiltration from the surface due to rainfall and evaporation isneeded to calculate the suction Where the soil layer is thin one-dimensionalsolution may be used to calculate infiltration A commonly used solution isRichards (1931) equation which in one-dimension is

partθ

partψ

partψ

partt= part

partz

[Kpartψ

partz

]+ Q (1112)

where θ = volumetric moisture content ψ = soil suction K = permeabilityQ = source or sink term partθpartψ = soil moisture versus suction relationshipNumerical solutions are usually used This equation has been used formoisture flow in landfill covers (Khire et al 1999) and slopes (Andersonet al 1988) Two-dimensional flow may be solved via FEM Fredlund andRahardjo (1993) provide the details on the numerical solutions

The soil moisture versus suction relation in Equation (1112) also calledthe soil water characteristic curve is a material property A widely usedrelationship is the van Genuchten (1980) equation

θ = θr + θs minus θr

[1 + (aψ)n]m (1113)

where θr θs = residual and saturated volumetric water contents a m andn = shape parameters The Fredlund and Xing (1994) Equation which hasa rational basis is

θ = θs

[1

ln[e + (ψa)n

]m

(1114)

where a and m = shape parameters

424 Tien H Wu

The calculated pore pressures are used in a limit equilibrium or finiteelement analysis Examples of application to landslides on unsaturated slopessubjected to rainfall infiltration include Fourie et al (1999) and Wilkinsonet al (2000)

Perturbation methods have been applied to continuity equations such asEquation (1112) to study flow through soils with spatial variations in per-meability (eg Gelhar 1993) Simulation was used by Griffith and Fenton(1997) for saturated flow and by Chong et al (2000) and Zhang et al(2005) to study the effect of uncertainties about the hydraulic properties ofunsaturated soils on soil moisture and slope stability A more complicatedissue is the presence of macro-pores and fractures (eg Beven and Germann1982) This is difficult to incorporate into seepage analysis largely becausetheir geometry is not well known Field measurements (eg Pierson 1980Johnson and Sitar 1990) have shown seepage patterns that differ substan-tially from those predicted by simple models Although the causes were notdetermined flow through macropores is one likely cause

113 Uncertainties in stability analysis

Uncertainties encountered in predictions of stability are considered underthree general categories material properties boundary conditions andanalytical methods The uncertainties involved in estimating the relevantsoil properties are familiar to geotechnical engineers Boundary conditionsinclude geometry loads and groundwater levels infiltration and evapora-tion at the boundaries and ground motions during earthquakes Errors andassociated uncertainties may be random or systematic Uncertainties due todata scatter are random while model errors are systematic Random errorsdecrease when averaged over a number of samples but systematic errorsapply to the average value

1131 Soil properties

The uncertainties about a soil property s can be expressed as

Nssm = s + ζ (1115)

where sm = value measured in a test Ns = bias or inaccuracy of the testmethod ζ = testing error which is the precision of the test procedure Therandom variable sm has mean = sm variance = V(sm) and COV =∆(sm) =σ (sm) sm It includes the natural variability of the material with COV ∆(s)and variance V(s) and testing error with zero mean and variance V(ζ ) Themodel error Ns is the combination of Nirsquos (i = 12 ) that represent biasfrom the ith source with mean Ni and COV ∆i The true bias is of courseunknown since the perfect test is not available

Reliability analysis of slopes 425

For n independent samples tested the uncertainty about sm due toinsufficient samples has a COV

∆0 = ∆(sm)radic

n (1116)

For high-quality laboratory and in-situ tests the COV of ζ is around 01(Phoon and Kulhawy 1999) Hence unless n is very small the testing erroris not likely to an important source of uncertainty

Input into analytical models requires the average s over a given distance(eg limiting equilibrium analysis) or a given area or volume (eg FEM)Consider the one-dimensional case The average of sm over a distance Lsm(L) has a mean and variance sm and V [sm(L)] To evaluate V [sm(L)]the common procedure is to fit a trend to the data to obtain the mean asa function of distance The departures from the trend are considered to berandom with variance V(sm) To evaluate the uncertainty about sm(L) dueto the random component it is necessary to consider its spatial nature Thedepartures from the mean at points i and j separated by distance r arelikely to be correlated To account for this correlation VanMarcke (1977)introduced the variance reduction factor

Γ 2 = V[ sm(L)]V[sm] (1117a)

A simple expression is

Γ 2 = 1 if L le δ (1117b)

Γ 2 = δL if L gt δ (1117c)

where δ = correlation distance which measures the decay in the correlationcoefficient

ρ = eminus2rδ (1118)

Lδ can also be considered as the equivalent number of independent samplesne contained within L Then the variance and COV are

V[ sm(L)] = Γ 2V[sm] = V[sm]ne (1119a)

∆e = Γ∆(sm) = ∆(sm)radic

ne (1119b)

Extensions to two and three dimensions are given in VanMarcke (1977)It should be noted that ∆0 applies to the average strength and is system-atic while ∆e changes with L and ne A more detailed treatment of thissubject is given by Baecher and Christian in Chapter 2 Data on V(sm)∆(sm) and δ for various soils have been summarized by Lumb (1974)

426 Tien H Wu

DeGroot and Baecher (1993) Phoon and Kulhawy (1999) and Baecher andChristian (2003) Data on some unsaturated soils have been reviewed byChong et al (2000) and Zhang et al (2005)

In limit equilibrium analysis the average in-situ shear strength to be usedin Equations (112) contains all of the uncertainties described above It canbe expressed as (Tang et al 1976)

s = NsS (1120a)

where s = estimated shear strength and is usually taken as the average of themeasured values sm The combined uncertainty is represented by N

N = N0NeNs = N0Nei

Ni (1120b)

where N0 = uncertainty about the population mean Ne = uncertainty aboutthe mean value along the slip surface and Ni = uncertainty due to ith sourceof model error Equations (113) are used to obtain the mean and COV

s = i

Nism = Nssm (1121a)

∆(s) = [∆20 +∆2

e +∆2i ]12 (1121b)

Note that N0 = Ne = 1 It is assumed that the model errors are independentTwo examples of uncertainties about soil properties due to data scatter

∆(sm) are shown in Table 112 All of the data scatter is attributed to spatialvariation The values of ∆0 and ∆e representing the systematic and ran-dom components are also given For the Chicago clay the value of δ wasobtained from borehole samples and represents δy For the James Bay siteboth δx and δy were obtained Because a large segment of the slip surfaceis nearly horizontal δ is large Consequently Γ is small This illustrates thesignificance of δ

Table 112 Uncertainties due to data scatter

Site Soil sm (kPa) ∆(sm) ∆0 δ(m) ∆e Ref

Chicago upper clay 510 051 0083 02 0096 Tang et almiddle clay 300 026 0035 02 0035 (1976)lower clay 370 032 0056 02 0029

James Bay lacustrineclay

312 027 0045 40+ deGroot andBaecher (1993)

slipsurfacelowast 014 24 0063 Christian et al

(1994)

+δx lowastfor circular arc slip surface and H = 12 m

Reliability analysis of slopes 427

Examples of estimated bias in the strength model made between 1970and 1993 are shown in Table 113 Since the true model error is unknownthe estimates are based on data from laboratory or field studies and aresubjective in nature because judgment is involved in the interpretation ofpublished data Note that different individuals considered different possiblesources of error For the James Bay site the model error of the field vane sheartests consists of the data scatter in Bjerrumrsquos (1972) correction factor Thecorrection factors Ni for the various sources of errors are mostly between09 and 11 with an average close to 10 The ∆irsquos for the uncertainties fromthe different sources range between 003 and 014 and the total uncertainties(∆2

i )12 are about the same order of magnitude as those from data scatter(Table 112)

When two or more parameters describing a soil property are derived byfitting a curve to test data the correlation between the parameters may beimportant Data on correlations are limited Examples are the correlationbetween cprime and φprime (Lumb 1970) and between parameters a n and ks in theFredlund and Xing (1994) equation (Zhang et al 2003)

To illustrate the influence of correlation between properties consider theslope in a cprime ϕprime material (Figure 117) analyzed by Fredlund and Krahn(1977) by limit equilibrium and by Low et al (1997) by FORM The randomvariables X1 X2 in Equation (119) are cprime ϕprime The correlation coefficientρcϕ is ndash05 The critical circles from the limit equilibrium analysis (LEA)and that from FORM are also shown Note that FORM searches for themost unfavorable combination of cprime and ϕprime or point b in Figure 115 whichare cprime = 013 kPa and ϕprime = 235 The shallow circle is the result of thelow cprime and high ϕprime (Taylor 1948) The difference reflects the uncertaintyabout the material model and may be expected to decrease with decreasingvalues of ρcϕ and the COVs of cprime and ϕprime Lumbrsquos (1970) study on threeresidual soils show that ρcϕ le minus02 ∆(c) asymp 015 and ∆(ϕprime) asymp 005 whichare considerably less than the values used by Low et al It should be addedthat software packages that use simulation on the critical slip surface fromlimit equilibrium analysis cannot locate point b in Figure 115 and will notproperly account for correlation

1132 Loads and geometry

The geometry and live and dead loads for most geotechnical structures areusually well defined except for special cases When there is more than oneload on the slope a simple procedure is to use the sum of the loads Howeverthis may be overly conservative if the live loads have limited durations Thenfailure can be modeled as a zero-crossing process (Wen 1990) with the loadsrepresented as Poisson processes An application to landslide hazard is givenby Wu (2003)

Table 113 Uncertainties due to errors in strength model

Material Test Ni ∆i Ns (∆2i )12 Ref

Sampledisturbance

Stress state Anisotropy Samplesize

Strain rate Progressfailure

Total

Detroit clay unconfcomp

115008 100003 086009 X X X 099 012 Wu and Kraft(1970)

ChicagoUpper clay unconf

comp(a)1380024(b)105002

105003 10 003 075 009 080014 093003 Tang et al(1976)

Middle clay (a)1380024(b)105002

105003 10 003 093 005 080014 097003

Lower clay (a)1380024(b)105002

105003 10 003 093 005 080014 097003 105 017

Labrador clay fieldvane

X X X X X X 10 015 Christian et al(1994)

Reliability analysis of slopes 429

100

10

20

20 30 40 50 (m)

LEAcprime = 285 kPaφprime = 20degβ = 357FORMcprime = 130 kPaφprime = 23degβ = 290

Figure 117 FORM analysis of a slope (adapted from Low et al (1997))

1133 Soil boundaries

Subsoil profiles are usually constructed from borehole logs It is common tofind the thickness of individual soil layers to vary spatially over a site Wherethere is no sharp distinction between different soil types judgment is requiredto choose the boundaries Uncertainties about boundary and thickness resultin uncertainty about the mean soil property Both systematic and randomerrors may be present Their representation depends on the problem at handAn example is given in Christian et al (1994) Errors in the subsoil modelwhich result from failure to detect important geologic details could haveimportant consequences (eg Terzaghi 1929) but are not included herebecause available methods cannot adequately account for errors in judgment

1134 Flow at boundaries

Calculation of seepage requires water levels or flow rates at the bound-aries For levees and dams flood levels and durations at the upstream slopeare derived from hydrological studies (eg Maidman 1992) Impermeableboundaries often used to represent bedrock or clay layers may be perviousbecause of fractures or artesian pressures Modeling these uncertainties isdifficult

For calculation of infiltration rainfall depth and duration for returnperiod Tr are available (Hershfield 1961) However rainfall and dura-tion are correlated and simulation using rainfall statistics is necessary(Richardson 1981) Under some conditions the precipitation per storm con-trols (Wu and Abdel-Latif 2000) Then this can be used in a seepage modelto derive the mean and variance of pore pressure In unsaturated soils failurefrequently occurs due to reduction in suction from infiltration (eg Krahnet al 1989 Fourie et al 1999 Aubeny and Lytton 2004 and others)Usually both rainfall and duration are needed to estimate the suction or crit-ical depth (eg Fourie et al 1999) Then a probabilistic analysis requiressimulation

430 Tien H Wu

Evapotranspiration is a term widely used for water evaporation from avegetated surface It may be unimportant during rainfall events in humidand temperate regions but is often significant in arid areas (Blight 2003)The Penman equation with subsequent modifications (Penman 1948Monteith 1973) is commonly used to calculate the potential evapotran-spiration The primary input data are atmospheric conditions saturationvapor pressure vapor pressure of the air wind speed and net solar radiationMethods to estimate actual evapotranspiration from potential evapotranspi-ration are described by Shuttleworth (1993) and Dingman (2002) Genera-tion of evapotranspiration from climate data (Ritchie 1972) has been usedto estimate moisture in landfill covers (Schroeder et al 1994) Model errorand uncertainty about input parameters are not well known

1135 Earthquake motion

For pseudostatic analysis empirical rules for seismic coefficients are availablebut the accuracy is poor Seismic hazard curves that give the probabilitythat an earthquake of a given magnitude or given maximum accelerationwill be exceeded during given time exposures (T) are available for the US(Frankel et al 2002) Frankel and Safak (1998) reviewed the uncertaintiesin the hazard estimates The generation of ground motion parameters andrecords for design is a complicated process and subject of active currentresearch It is beyond the scope of this chapter A comprehensive reviewof this topic is given in Sommerville (1998) The errors associated with thevarious procedures are largely unknown

It should be noted that in some problems the magnitude and accelerationof future earthquakes may be the predominant uncertainty and estimates ofthis uncertainty with current methods are often sufficient for making designdecisions An example is the investigation of the seismic stability of theMormon Island Auxiliary Dam in California (Sykora et al 1991) Limitequilibrium analysis indicated that failure is likely if liquefaction occurs in agravel layer Dynamic FEM analysis indicated that an acceleration k = 037from a magnitude 525 earthquake or k = 025 from a magnitude 65 earth-quake would cause liquefaction However using the method of Algermissanet al (1976) the annual probability of occurrence of an earthquake with amagnitude of 525 or larger is less than 00009 which corresponds to a returnperiod of 1100 years Therefore the decision was made not to strengthenthe dam

1136 Analytical models

In limit equilibrium analysis different methods usually give different safetyfactors For plane strain the safety factors obtained by different methodswith the exception of the Fellenius (1936) method are generally within plusmn7

Reliability analysis of slopes 431

Table 114 Errors in analytical model

Analysis Ni∆i Na∆a Ref

3D Slip surf Numerical Total

Cut ϕ = 0 105003 095006 X 10 0067 Wu and Kraft(1970)

Cut ϕ = 0 X X X 098 0087 Tang et al(1976)

Embankment ϕ = 0 11005 10005 10002 10007 Christianet al (1994)

Landfill cprime ϕprime 11016 X X 11016 Gilbert et al(1998)

(Whitman and Bailey 1967 Fredlund and Krahn 1977) A more importanterror is the use of the plane-strain analysis for failure surfaces which areactually three-dimensional Azzouz et al (1983) Leshchinsky et al (1985)and Michalowski (1989) show that the error introduced by using a two-dimensional analysis has a bias factor of Na = 11 minus 13 for ϕ = 0 minus 25Table 114 shows estimates of various errors in the limit equilibrium modelThe largest model error ∆a = 016 is for three-dimensional (3D) effect in alandfill (Gilbert et al 1998)

Ideally a 3D FEM analysis using the correct material model should givethe correct result However even fairly advanced material models are notperfect The model error is unknown and has been estimated by comparisonwith observed performance The review by Duncan (1996) shows variousdegrees of success of FEM predictions of deformations under static loadingIn general Type A predictions (Lambe 1973) for static loading cannot beexpected to come within plusmn10 of observed performances This is wherethe profession has plenty of experience The model error and the associateduncertainty may be expected to be much larger in problems where expe-rience is limited One example is the prediction of seepage and suction inunsaturated soils

1137 Bayesian updating

Bayesian updating allows one to combine different sources of informationto obtain an ldquoupdatedrdquo estimate Examples include combining direct andindirect measurements of soil strength (Tang 1971) and properties of land-fill materials measured in different tests (Gilbert et al 1998) It can alsobe used to calibrate predictions with observation In geotechnical engineer-ing back-analysis or solving the inverse problem is widely used to verifydesign assumptions when measured performance is available An example is

432 Tien H Wu

a slope failure This observation (Fs = 1) may be used to provide an updatedestimate of the soil properties or other input parameters

In Bayes theorem the posterior probability is

P = P[Ai|B] = P[B|Ai]P[Ai]P[B] (1122a)

where

P[B] =nsum

i=1

P[B|Ai]P[Ai] (1122b)

P[Ai|B] = probability of Ai (shear strength is si) given B (failure) has beenobserved P[B|Ai] = probability that event B occurs (slope fails) given eventAi occurs (the correct strength is used) P[Ai] = prior probability that Aioccurs In a simple case P[Ai] represents the uncertainty about strength dueto data scatter etc P[B|Ai] represents the uncertainty about the safety factordue to uncertainties about the strength model stability analysis boundaryconditions etc Example 5 illustrates a simple application Gilbert et al(1998) describe an application of this method to the Kettleman Hills landfillwhere there are several sources of uncertainty

Regional records of slope performance when related to some significantparameter such as rainfall intensity are valuable as an initial estimate offailure probability Cheung and Tang (2005) collected data on failure prob-ability of slopes in Hong Kong as a function of age This was used as priorprobability in Bayesian updating to estimate the failure probability for aspecific slope The failure probability based on age is P[Ai] and P[B|Ai]is the pdf of β as determined from investigations for failed slopes and β

is the reliability index for the specific slope under investigation To applyEquation (1122a)

P[B] = P[B|Ai]P[Ai]+ P[B|Aprimei]P[Arsquo

i] (1122c)

P[Arsquoi] = 1 minus P[Ai]

where P[B|Aprimei] = pdf of β for stable slopes (1122d)

Example 3

The analysis of the slope failure in Chicago by Tang et al (1976) is reviewedhere to illustrate the combination of uncertainties using Equations (1120)and (1121) The undrained shear strength s was measured by unconfinedcompression tests on 508 cm Shelby tube samples The subsoil consistsmainly of ldquogritty blue clayrdquo whose average strength varies with depthFigure 113 The clay was divided into three layers with values of sm and

Reliability analysis of slopes 433

∆(sm) as given in Table 112 The value of δ is the same for the three claylayers The values of ∆0 calculated according to Equation (1116) are givenin Table 112 The correlation coefficients ρij between the strength of theupper and middle middle and lower and upper and lower layers are 066059 and 040 respectively

The sources of uncertainties about the shear strength model are listed inTable 113 The errors were evaluated by review of published data mostlylaboratory studies on the influence of the various factors on strength Thesewere used to establish a range of upper and lower limits for Ni To calculateNi and ∆i a distribution of Ni is necessary The rectangular and triangulardistributions between the upper and lower limits were used and the meanand variance of Ni were calculated with the relations given in Ang and Tang(1975) Clearly judgment was exercised in choosing the sources of errorand the ranges and distributions of Ni The estimated values of Ni and ∆iare shown in Table 113 Two sets of values are given for sample distur-bance (a) denotes the strength change due to mechanical disturbance and(b) denotes that due to stress changes from sampling

The resisting moment in Equations (112) can be written as

MR = ri

sili (1123a)

where i denotes the soil layer According to Equation (113a) the mean is

MR = Nri

sili (1123b)

According to Equation (1121a)

N = 138 times 105 times 105 times 10 times 075 times 080 times 093 = 085 (1123c)

for the upper clay and the values of Ni are as given in Table 113The calculated MR for the different segments are given in Table 115 andthe sum is MR = 2717MN minus mm

To evaluate the variance or COV of Mr the first step is to evaluate ∆(s) forthe segments of the slip surface passing through the different soil layers Forthe upper clay layer substituting ∆0 in Table 112 and the ∆irsquos in Table 113into Equation (1121b) gives

∆(s1) = [(0083)2 + (0024)2 + (002)2 + (003)2 + (003)2 + (009)2

+ (014)2 + (003)2]12 = 020 (1124a)

for failure surface 1 Since ne is large the contribution of ∆e is ignoredThe values of ∆(s) = ∆(MR) and σ (MR) for the clay layers are given in

434 Tien H Wu

Table 115 Mean and standard deviation of resisting moment Chicago slope

Layer MRMN minus mm N ∆(MR) σ (MR)MN minus mm

Upper clay 401 085 020 080Middle clay 570 110 016 091Lower clay 1690 10 017 288Sand 056 2717 015 412

018lowast

lowastWith model error ∆a

Table 115 Substitution into Equation (113b) gives

V(MR) = (080)2 + (091)2 + (288)2 + 2[(080 times 091 times 066)

+ (080 times 288 times 059) + (091 times 288 times 04)]= 1706(MN minus mm)2 (1124b)

∆(MR) = [1706]122717 = 0152 (1124c)

To include the error Na in the stability analysis model Na and ∆a givenin Table 113 are applied to MR The combined uncertainty is

∆(MR) = [∆2a +∆2

s ]12 = [(0087)2 + (0152)2]12 = 018 (1124d)

The calculation of β and Pf are described in Example 1

Example 4

The failure of a slope in Cleveland (Wu et al 1975) Figure 118 is used toillustrate the choice of soil properties and its influence on predicted failureprobability The subsoil consists of layers of silty clay and varved clay Failureoccurred shortly after construction and the shape of the failure surface (1)shown in Figure 118 suggested the presence of a weak layer near elev184 m An extensive investigation following failure revealed the presenceof one or more thin layers of gray clay Most of these were encounterednear elev 184 m Many samples of this material contained slickensides andhad very low strengths It was assumed that the horizontal portion of theslip surface passed through this weak material Because no samples of theweak material were recovered during the initial site exploration its presencewas not recognized A simplified analysis of the problem is used for thisexample The layers of silty clay and varved clay are considered as one unitand the weak gray clay is treated as thin layer at elev 184 m (Figure 118)

Reliability analysis of slopes 435

210

(2)

200

Failure surface (2)

Failure surface (1)

Silty Clay

0 200 kPasu

Varved Clay Till

190

Ele

v (

m)

180

Figure 118 Slope failure in Cleveland (adapted from Wu et al 1975) su = undrained shearstrength

Table 116 Strength properties Cleveland slope

Analysis One material Two materials

Combineddata

Varved clayand silty clay

Weak gray claylayer

sm kPa 671 831 395σm (sm) kPa 383 384 168∆(sm) 057 046 042n 19 12 7δy m 33 33 ndashδx m ndash ndash 33∆0 013 013 016ne 56 41 24∆e 0076 0072 027[(∆0)2 + (∆e)2]12 015 015 031

The undrained shear strengths sm of the two materials as measured by theunconfined compression test are summarized in Table 116 The correlationdistance of the silty clay and varved clay δy is determined by VanMarckersquos(1977) method The number of samples of the weak gray clay is too smallto allow calculation of δx Hence it is arbitrarily taken as 10δy

436 Tien H Wu

Consider first the case where the silty clay and varved clay and the weakmaterial are treated as one material All the measured strengths are treatedas samples from one population (ldquocombined datardquo in Table 116) For sim-plification the model errors Ni and ∆i are ignored Then only ∆0 and ∆econtribute to ∆(s) in Equation (1121b) The values of ∆0 and ∆e are eval-uated as described in Example 2 and given in Table 116 Limit equilibriumanalysis gives Fs = 136 for the circular arc shown as (2) in Figure 118The values of β and Pf calculated by FOSM are given in Table 117Values of β and Pf calculated by simulation with SLOPEW (GEO-SLOPEInternational) where s is modeled as a normal variate are also given inTable 117

Next consider the subsoil to be composed of two materials with sm and∆(sm) as given in Table 116 The values of ∆0 and ∆e are evaluated as beforeand given in Table 116 The larger values of∆e and [(∆0)2+(∆e)

2]12 are theresult of the large δx of the weak material Values of Fs β and Pf for the com-posite failure surface (1) calculated by FOSM and by simulation are givenin Table 117 The Pf is much larger than that for the circular arc (2) Thisillustrates the importance of choosing the correct subsoil model Figure 119shows the distribution of Fs from simulation Note that SLOPEW uses thenormal distribution to represent the simulated Fs Hence the mean of thenormal distribution may differ from the Fs obtained by limit equilibriumanalysis for the critical slip surface

In the preceding analysis of the two-material model it is assumed that thestrengths of the two materials are uncorrelated The available data are insuf-ficient to evaluate the correlation coefficient and geologic deductions couldbe made in favor of either correlation or no correlation To illustrate theinfluence of weak correlation it is assumed that ρij = 02 The β calculatedby FOSM is reduced from 164 to 149

Table 117 Calculated reliabilities Cleveland slope

Analysis One material Two materials

Limit equil Fs 136 126FOSM ∆(Fs) 015 014no model β 197 164error Pf 0025 008Simulation Fs 136 131no model β 248 164error Pf 0006 005FOSM [∆2

j ]12 014 014with model ∆a 0035 0035error ∆(Fs) 021 020

β 137 105Pf 009 015

Reliability analysis of slopes 437

025

020

015

Pf

010

005

006 08 10 12 14 16 18 20

Fs

Figure 119 Distribution of Fs Cleveland slope

Finally consider model errors The total uncertainty about strength modelbased on the unconfined compression test is estimated to be Ns = 10 and(∆2

i )12 = 014 Assume that the analytical model error has Na = 10∆a = 0035 Calculation with FOSM gives β = 137 and 105 for the one-and two-material models respectively (Table 117)

Example 5

If the slope in Example 4 is to be redesigned it is helpful to revise the shearstrength with the observation that failure has occurred Bayesrsquo TheoremEquations ( 1122) is used to update the shear strength of the weak layerin Example 4 Let P[B|Ai] = probability of failure given the shear strengthsi P[Ai] = prior probability that the shear strength is si In this simplifiedexample the distribution of si is discretized as shown in Figure 1110a whereP[Ai]= probabilities that the shear strength falls within the respective rangesThen P[B|Ai] is found from the distribution of Pf calculated with SLOPEWBecause of errors in the stability analysis it is assumed that failure will occurwhen the calculated Fs falls between 100plusmn005 Figure 119 shows that forsi = 395 kPa P[B|Ai] = P[095lt Fs lt 105] asymp 005 Other values of P[B|Ai]are found in the same way and plotted in Figure 1110b Substitution ofthe values in Figures 1110a and 1110b into Equations (1122) gives theposterior distribution Pprimeprime which is shown in Figure 1110c The mean of theupdated s is 303 kPa with a standard deviation of 83 kPa and COV = 027

438 Tien H Wu

04

P[Ai]

02

02

04

02

0 20 40Su kPa

60 80

P[B|Ai]

PPrime

01

0

0

Figure 1110 Updating of shear strength Cleveland slope

Note that ∆(s) after updating is still large This reflects the large COV ofP[B|Ai] in Figure 1110b due to the large ∆(sm) of the gray silty clay andvarved clay and expresses the dependence of the back-calculated strengthon the accuracy of the stability analysis and other input parameters (Leroueiland Tavanas 1981 Leonards 1982) Inclusion of model errors wouldfurther increase the uncertainty Gilbert et al (1998) provide a detailed

Reliability analysis of slopes 439

example of the evaluation of P[B|Ai] for the strength of a geomembranendashclayinterface

114 Case histories

Results from earlier studies are used to illustrate the relative importance ofuncertainties from different sources and their influence on the failure proba-bility Consider the uncertainties in a limit equilibrium analysis for undrainedloading of slopes in intact clays This is a common problem for which theprofession has plenty of experience Where the uncertainties about live anddead loads are negligible then there remain only the uncertainties aboutsoil properties due to data scatter and the strength model and the analyti-cal model Tables 112ndash114 show estimated uncertainties for several sitesAll three sources data scatter model errors for shear strength and stabilityanalysis contribute to Pf

Comparison of estimated failure probability with actual performance pro-vides a calibration of the reliability method Table 118 shows some relativelysimple cases with estimated uncertainties about input parameters The fail-ure probabilities calculated for mean safety factors larger than 13 withvalues of N asymp 1 and ∆(Fs) asymp 01 are well below 10minus2 The FEM analysisof Griffith and Fenton (2004) in which the undrained shear strength is arandom field with ∆(s) = 025 and ne = 15 shows that for Fs = 147 thefailure probability is less than 10minus2 This serves to confirm the results of thesimple analyses cited above In addition their analysis searches for the fail-ure surface in a spatially variable field which is not necessarily a circle Thedeparture from the critical circle in a limit equilibrium analysis depends onthe correlation distance (0 lt δ lt L) An important implication is that whenδx δy and δx is a very large the problem approaches that of a weak zone(Leonards 1982) as illustrated in Example 4

Table 118 Estimated failure probability and slope performance

Site Material Analysis Failuresurface

Fs ∆(Fs) Pf Stability Ref

Cleveland Clay Totalstress

Composite 118 026 03 Failure Wu et al1975

Chicago Clay Totalstress

Circular 118 018 02lowast Failure Tang et al1976

James Bay Clay Totalstress

Circular 153 013 0003 Stable Christianet al 1994

Alberta Clayshale

Effectivestress

Composite 131 012 00016 Stable El-Ramlyet al 2003

lowastObserved failure surface

440 Tien H Wu

The success of current practice using a safety factor of 15 or larger isenough to confirm that for simple cuts and embankments on intact clay theuncertainties (Tables 112ndash114) are not a serious problem Hence reliabilityanalysis and evaluation of uncertainties should be reserved for analysis ofmore complex site conditions or special design problems An example ofthe latter is the design of the dams at James Bay (Christian et al 1994)A reliability analysis was made to provide the basis for a rational decisionon the safety factors for the low dams (6 or 12 m) built in one stage andthe high dam (23 m) built by staged construction

The analysis using FOSM showed that for Fs asymp 15 β for the high damis much larger than that for the low dams However the uncertainty aboutFs for the low dams comes largely from spatial variation which is randomwhile that for the high dam comes largely from systematic errors Failures dueto spatial variation (eg low strength) are likely to be limited to a small zonebut failures due to systematic error would involve the entire structure andmay extend over a long segment Clearly the consequences of failure of alarge section in a high dam are much more serious than those of failureof small sections in a low dam Consideration of this difference led to thechoice of target βrsquos of 001 0001 and 00001 for the 6 m 12 m and 23 mdams respectively The corresponding Fsrsquos are 163 153 and 143 whenmodel error is not included These numbers are close enough to 150 thatFs = 150 was recommended for all three heights in the feasibility study andpreliminary cost estimates

Where experience is limited the estimation of uncertainties becomesdifficult because model errors are not well known The significance ofuncertainties about properties of unsaturated soils has only recently beeninvestigated Chong et al (2000) studied the influence of uncertainties aboutparameters in the van Genuchten equation Equation (1113) The parame-ters α and n have the largest influence on Fs and ∆(Fs) α alone can lead to a∆(Fs) asymp 01 and a reduction of Fs from 14 to 12 Zhang et al (2005) useddata on decomposed granite to evaluate the uncertainties about parametersa and n in the FredlundndashXing equation Equation (1114) Ks = saturatedpermeability θs = saturated moisture content and four parameters of thecritical-state model The correlations between a n Ks and θs were deter-mined from test data The values of ∆(ln a) ∆(ln n) and ∆(ln Ks) are largerthan 10 Under constant precipitation the calculated ∆(Fs) is 022 forFs = 142 These are model errors and are systematic It can be appreciatedthat ∆(Fs) and the corresponding failure probability are significantly largerthan those for the slopes in saturated soils given in Table 118 Uncertaintiesabout precipitation and evapotranspiration and spatial variation would fur-ther increase the overall uncertainty about pore pressure and stability Thesetwo examples should serve notice that for slopes on unsaturated soils thepractice of Fs = 15 should be viewed with caution and reliability analysisdeserves serious attention

Reliability analysis of slopes 441

115 Summary and conclusions

The limitations of reliability analysis are

1 Published values on ∆(sm) and δ of soils (eg Barker et al 1991 Phoonand Kulhawy 1999) are based on data from a limited number of sites(eg Appendix A Barker et al 1991) and show a wide range even forsoils with similar classification Therefore the choice of design parame-ters involves unknown errors Even estimation of these values from siteexploration data involves considerable errors unless careful samplingschemes are used (DeGroot and Baecher 1993 Baecher and Christian2003) The calculated failure probabilities may be expected to dependstrongly on these properties

2 Errors in strength and analysis models which result from simplificationsare often unknown Estimates are usually made from case historieswhich are limited in number particularly where there is little experi-ence Errors in climate and earthquake models are difficult to evaluateHence estimation of uncertainties about input variables contain a largesubjective element

3 The reliability analyses reviewed in this chapter consider only a lim-ited number of failure modes For a complex system where there aremany sources of uncertainties the system reliability can be evaluatedby expanding the scope via event trees This is beyond the scope of thischapter See Baecher and Christian (2003) for a detailed treatment

4 Available methods of reliability analysis cannot account for omissionsor errors of judgment A particularly serious problem in geotechnicalengineering is misjudgment of the site conditions

The limitations cited above should not discourage the use of reliabilityanalysis Difficulties due to inadequate information about site conditionsand model errors are familiar to geotechnical engineers and apply to bothdeterministic and probabilistic design Some examples of use of reliabilityanalysis in design are given below

1 Reliability can be used to compare different design options and helpdecision-making (eg Christian et al 1994)

2 Even when a comprehensive reliability analysis cannot be made sensitiv-ity analysis can be used to identify the random variables that contributemost to failure probability The design process can then concentrate onthese issues (eg Sykora et al 1991)

3 Bayesrsquos theorem provides a logical basis for combining different sourcesof information (eg Cheung and Tang 2005)

4 The methodology of reliability is well established and it should bevery useful in evaluating the safety of new and emerging geotechnical

442 Tien H Wu

problems as has been illustrated in the case of unsaturated soils (Chonget al 2000 Zhang et al 2005)

Acknowledgments

The writer thanks K K Phoon for valuable help and advice during thewriting of this chapter and W H Tang for sharing his experience and insightthrough many discussions and comments

References

Algermissen S T Perkins D M Thenhaus P C Hanson S L and Bender B L(1982) Probabilistic estimates of maximum acceleration and velocity in rock inthe contiguous United States Openfile Report 82-10033 Geological Survey USDepartment of the Interior Denver

Anderson M G Kemp M J and Lloyd D M (1988) Application of soil waterdifference models to slope stability problems In 5th International Symposium onLandslides A A Balkema Rotterdam Lausanne 1 pp 525ndash30

Ang A H-S and Tang W H (1975) Probability Concepts in Engineering Planningand Design Vol 1 John Wiley and Sons New York

Aubeny C P and Lytton R L (2004) Shallow slides in compacted high plastic-ity clay slopes Journal of Geotechnical and Geoenvironmental Engineering 130717ndash27

Auvinet G Bouyad A Orlandi S and Lopez A (1996) Stochastic finite ele-ment method in geomechanics In Uncertainty in the Geologic EnvironmentEds C D Shackelford P P Nelson and M J S Roth Geotechnical SpecialPublications 58 ASCE Reston VA 2 pp 1239ndash53

Azzouz A S Baligh MM and Ladd C C (1983) Corrected field vane strengthfor embankment design Journal of Geotechnical Engineering 120 730ndash4

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley and Sons Chichester UK

Barker R M Duncan J M Rojiani K B Ooi P S K Tan C K and KimS G (1991) Manuals for the design of bridge foundations NCHRP Report 343Transportation Research Board Washington DC

Beven K and Germann P (1982) Macropores and water flow through soils WaterResources Research 18 1311ndash25

Bjerrum L (1972) Embankments on soft ground state of the art report In Pro-ceedings Specialty Conference on Performance of Earth and Earth-supportedStructures ASCE New York 2 pp 1ndash54

Blight G B (2003) The vadose zone soil-water balance and transpiration rates ofvegetation Geotechnique 53 55ndash64

Cheung R W M and Tang W H (2005) Realistic assessment of slope stabilityfor effective landslide hazard management Geotechnique 55 85ndash94

Chong P C Phoon K K and Tan T S (2000) Probabilistic analysis of unsaturatedresidual soil slopes In Applications of Statistics and Probability Eds R E Melchersand M G Stewart Balkema Rotterdam pp 375ndash82

Reliability analysis of slopes 443

Christian J T (2004) Geotechnical engineering reliability how well do we knowwhat we are doing Journal of Geotechnical and Geoenvironmental Engineering130 985ndash1003

Christian J T Ladd C C and Baecher G B (1994) Reliability applied to slopestability analysis Journal of Geotechnical Engineering 120 2180ndash207

DeGroot D J and Baecher G B (1993) Estimating autocovariance of in-situ soilproperties Journal of Geotechnical Engineering 119 147ndash67

Dingman S L (2002) Physical Hydrology Prentice Hall Upper Saddle River NJDuncan J M (1996) State of the art limit equilibrium and finite element analysis of

slopes Journal Geotechnical and Geoenvironmental Engineering 122 577ndash97El-Ramly H Morgenstern N R and Cruden D M (2003) Probabilistic stabil-

ity analysis of a tailings dyke on pre-sheared clay-shale Canadian GeotechnicalJournal 40 192ndash208

Fellenius W (1936) Calculation of the stability of earth dams Proceedings SecondCongress on Large Dams 4 445ndash63

Fourie A B Rowe D and Blight G E (1999) The effect of infiltration on thestability of the slopes of a dry ash dump Geotechnique 49 1ndash13

Frankel A D and Safak E (1998) Recent trends and future prospects in seismichazard analysis In Geotechnical Earthquake Engineering and Soil Dynamics IIIGeotechnical Special Pub 75 Eds P Dakoulas M Yegian and R D Holtz ASCEReston VA

Frankel A D Petersen M D Mueller C S Haller K M WheelerR L Leyendecker E V Wesson R L Harmsen S C Cramer C H PerkinsD M and Rukstales K S (2002) Documentation for the 2002 update of thenational seismic hazard maps U S Geological Survey Open-file report 02-420

Fredlund D G and Krahn J (1977) Comparison of slope stability methods ofanalysis Canadian Geotechnical Journal 14 429ndash39

Fredlund D G and Rahardjo H (1993) Soil Mechanics for Unsaturated Soils JohnWiley and Sons New York

Fredlund D G and Xing A (1994) Equations for the soil-water suction curveCanadian Geotechnical Journal 31 521ndash32

Gelhar L W (1993) Stochastic Subsurface Hydrology Prentice Hall EnglewoodCliffs NJ

Gilbert R B Wright S G and Liedke E (1998) Uncertainty in back analysis ofslopes Kettleman Hills case history Journal of Geotechnical and Geoenvironmen-tal Engineering 124 1167ndash76

Griffith V D and Fenton G A (1997) Three-dimensional seepage through spatiallyrandom soil Journal of Geotechnical and Geoenvironmental Engineering 123153ndash60

Griffith V D and Fenton G A (2004) Probabilistic slope stability analysis byfinite elements Journal of Geotechnical and Geoenvironmental Engineering 130507ndash18

Hansen J Brinch (1965) The philosophy of foundation design design criteria safetyfactors and settlement limits In Bearing Capacity and Settlement of FoundationsEd A S Vesic Duke University Durham pp 9ndash14

Hershfield D M (1961) Rainfall frequency atlas of the United States TechPaper 40 US Department of Commerce Washington DC

444 Tien H Wu

Ireland H O (1954) Stability analysis of the Congress Street open cut in ChicagoGeotechnique 40 161ndash80

Johnson K A and Sitar N (1990) Hydrologic conditions leading to debris-flowinitiation Canadian Geotechnical Journal 27 789ndash801

Khire M V Benson C H and Bosscher P J (1999) Field data from a capil-lary barrier and model predictions with UNSAT-H Journal of Geotechnical andGeoenvironmental Engineering 125 518ndash27

Kim J and Sitar N (2003) Importance of spatial and temporal variability in theanalysis of seismically induced slope deformation In Applications of Statisticsand Probability in Civil Engineering Eds A Der Kiureghian S Madanat andJ M Pestana Balkema Rotterdam pp 1315ndash22

Krahn J Fredlund D G and Klassen M J (1989) Effect of soil suction on slopestability at Notch Hill Canadian Geotechnical Journal 26 2690ndash78

Kramer S L (1996) Geotechnical Earthquake Engineering Prentice Hall UpperSaddle NJ

Lacy S J and Prevost J H (1987) Nonlinear seismic analysis of earth dams SoilDynamics and Earthquake Engineering 6 48ndash63

Lambe T W (1973) Predictions in soil engineering Geotechnique 23 149ndash202Leonards G A (1982) Investigation of failures Journal of Geotechnical

Engineering 108 185ndash246Leroueil S and Tavanas F (1981) Pitfalls of back-analysis In 10th Interna-

tional Conference on Soil Mechanics and Foundation Engineering A A BalkemaRotterdam 1 pp 185ndash90

Leshchinsky D Baker R and Silver ML (1985) Three dimensional analysisof slope stability International Journal Numerical and Analytical Methods inGeomechanics 9 199ndash223

Lin J-S and Whitman R V (1986) Earthquake induced displacements of slidingblocks Journal of Geotechnical Engineering 112 44ndash59

Liu W K Belytschko T and Mani A (1986) Random field finite elementsInternational Journal of Numerical Methods in Engineering 23 1831ndash45

Low B F Gilbert R B and Wright S G (1998) Slope reliability analysis usinggeneralized method of slices Journal of Geotechnical and GeoenvironmentalEngineering 124 350ndash62

Lumb P (1970) Safety factors and the probability distribution of strength CanadianGeotechnical Journal 7 225ndash42

Lumb P (1974) Application of statistics in soil mechanics In Soil Mechanics ndash NewHorizons Ed IK Lee Elsevier New York pp 44ndash111

Maidman D R (1992) Handbook of Hydrology McGraw-Hill New YorkMichalowski R L (1989) Three-dimensional analysis of locally loaded slopes

Geotechnique 39(1) 27ndash38Monteith J L (1973) Principles of Environmental Physics Elsevier New YorkNewmark N M (1965) Effects of earthquakes on dams and embankments

Geotechnique 15 139ndash60Penman H L (1948) Natural evapotranspiration from open water bare soil and

grass Proceedings of the Royal Society A 193 120ndash46Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variability

Canadian Geotechnical Journal 36 612ndash24

Reliability analysis of slopes 445

Pierson T C (1980) Piezometric response to rainstorms in forested hillslopedrainage depression Journal of Hydrology (New Zealand) 19 1ndash10

Richards L A (1931) Capillary conduction of liquids through porous mediumJournal of Physics 72 318ndash33

Richardson C W (1981) Stochastic simulation of daily precipitation temperatureand solar radiation Water Resources Research 17 182ndash90

Ritchie J T (1972) A model for predicting evaporation from a row crop withincomplete plant cover Water Resources Research 8 1204ndash13

Rojiani K B Ooi P S K and Tan C K (1991) Calibration of load factor designcode for highway bridge foundations In Geotechnical Engineering CongressGeotechnical Special Publication No 217 ASCE Reston VA 2 pp 1353ndash64

Schroeder P R Morgan J M Walski T M and Gibson A C (1994) HydraulicEvaluation of Landfill Performance ndash HELP EPA6009-94xxx US EPA RiskReduction Engineering Laboratory Cincinnati OH

Seed H B Lee K L Idriss I M and Makdisi F I (1975) The slides in theSan Fernando dams during the earthquake of Feb 9 1971 Journal GeotechnicalEngineering Division ASCE 10 651ndash88

Shuttleworth W J (1993) Evaporation In Handbook of HydrologyEd D R Maidman McGraw-Hill New York

Sommerville P (1998) Emerging art earthquake ground motion In GeotechnicalEarthquake Engineering and Soil Dynamics III Eds P Dakoulas M Yegian andR D Holtz Geotechnical Special Pub 75 ASCE Reston VA pp 1ndash38

Soong T-Y and Koerner R M (1996) Seepage induced slope instability Journalof Geotextiles and Geomembranes 14 425ndash45

Sykora D W Koester J P and Hynes M E (1991) Seismic hazard assess-ment of liquefaction potential at Mormon Island Auxiliary Dam California USAIn Proceedings 23rd US-Japan Joint panel Meeting on Wind and Seismic EffectsNIST SP 820 Gaithersburg National Institute of Standards and Technologypp 247ndash67

Tang W H (1971) A Bayesian evaluation of information for foundation engineeringdesign In Statistics and Probability in Civil Engineering Ed P Lumb Hong KongUniversity Press Hong Kong pp 173ndash85

Tang W H Yucemen M S and Ang A H-S (1976) Probability-based short termdesign of soil slopes Canadian Geotechnical Journal 13 201ndash15

Taylor D W (1948) Fundamentals of Soil Mechanics John Wiley and SonsNew York

Terzaghi K (1929) Effect of minor geologic details on the safety of dams TechnicalPub 215 American Institute of Mining and Metallurgical Engineering New YorkNY pp 31ndash44

van Genuchten M Th (1980) A closed-form equation for predicting hydraulicconductivity of unsaturated soils Soil Science Society of America Journal 44892ndash8

VanMarcke E H (1977) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103 1227ndash46

Wilkinson P L Brooks S M and Anderson M G (2000) Design and applicationof an automated non-circular slip surface search within a combined hydrology andstability model (CHASM) Hydrological Processes 14 2003ndash17

446 Tien H Wu

Wen Y K (1990) Structural Load Modeling and Combinations for Performanceand Safety Evaluation Elsevier Amsterdam

Whitman R V and Bailey W A (1967) Use of computers for slope stabilityanalysis Journal of Soil Mechanics and Foundations Division ASCE 93 475ndash98

Wu T H (2003) Assessment of landslide hazard under combined loading CanadianGeotechnical Journal 40 821ndash9

Wu T H and Abdel-Latif M A (2000) Prediction and mapping of landslidehazard Canadian Geotechnical Journal 37 579ndash90

Wu T H and Kraft L M (1970) Safety analysis of slopes Journal of Soil Mechanicsand Foundations Division ASCE 96 609ndash30

Wu T H Thayer W B and Lin S S (1975) Stability of embankment on clayJournal of Geotechnical Engineering Division ASCE 101 913ndash32

Yucemen M S and Tang W H (1975) Long-term stability of soil slopes ndasha reliability approach In Applications of Statistics and Probability in Soil andStructural Engineering Deutsche Gesellschaft fur Erd-und Grundbau eV Essen2 pp 215ndash30

Zerfa F Z and Loret B (2003) Coupled dynamic elasticndashplastic analysis of earthstructures Soil Dynamics and Earthquake Engineering 23 4358ndash454

Zhang L L Zhang L M and Tang W H (2005) Rainfall-induced slope failureconsidering variability of soil properties Geotechnique 55 183ndash8

Notations

c = cohesionF = forceFs = safety factorh = depth of soil layerk = seismic coefficientK = permeability coefficientl = lengthM = momentN = model biasP = normal forcePf = failure probabilityQ = source or sink termr = distances = soil propertysm = measured soil propertyu = pore pressureT = return periodV() = varianceW = weightXY = random variableα = angle of inclination of failure surfaceβ = reliability index

Reliability analysis of slopes 447

γ = unit weightδ = correlation distanceζ = testing errorθ = volumetric water contentρ = corelation coefficientσ () = standard deviationϕ = angle of internal frictionψ = suction∆() = coefficient of variationamp = variance reduction function

Chapter 12

Reliability of levee systems

Thomas F Wolff

121 About levees

1211 Dams levees dikes and floodwalls

Dams and levees are two types of flood protection structures Both consistprimarily of earth or concrete masses intended to retain water Howeverthere are a number of important differences between the two and these dif-ferences drive considerations about their reliability As shown in Figure 121dams are constructed perpendicular to a river and are seldom more than 1 or2 km long They reduce flooding of downstream floodplains by retainingrunoff from storms or snowmelt behind the dam and releasing it in a con-trolled manner over a period of time The peak stage of the flood is reducedbut downstream stages will be higher than natural for some time afterwardBecause dam sites are fairly localized they can be chosen very deliberatelyand a high level of geotechnical exploration testing and analysis is usuallydone during design Many dams are multiple-use structures with one ormore of the following beneficial uses flood control water supply powersupply and recreation These often provide substantial economic benefitspermitting substantial costs to be expended in design and constructionFinally they are generally used on a daily basis and have some operatingstaff that can provide some level of regular observation

In contrast levees are constructed parallel to rivers and systems of leveesare typically tens to thousands of kilometers long (see Figure 121) Theyprevent flooding of the adjacent landward floodplain by keeping the riverconfined to one side of the levee or where levees are present on both sides of ariver between the levees As levees restrict the overbank flow area they mayactually increase river stages making a flood peak even higher than it wouldnaturally be However they can be designed high enough to account for thiseffect and to protect landside areas against some design flood event eventhough that event will occur at a higher stage with the levee in place Becauselevees are long and parallel to riverbanks designers often have a much

Reliability of levee systems 449

Dam

Levee

Figure 121 Dams and levees

more limited choice of locations than for dams Furthermore riverbank soildeposits may be complex and highly variable alternating between weak andstrong and pervious and impervious Levees tend to be single-use (floodcontrol) projects that may go unneeded and unused for long periods of timeCompared to dam design an engineer designing a levee may face greater lev-els of uncertainties yet proportionately smaller budgets for exploration andtesting Given all of these uncertainties it would seem that levees are inher-ently more fraught with uncertainty than dams If there are any ldquobalancingfactorsrdquo to this dilemma it may be that most dams tend to be much higherthan most levees and that levees designed for very rare events have a verylow probability of loading

There are some structures that lie in a ldquogray areardquo between dams andlevees Like levees they tend to be very long and parallel to watercoursesor water bodies But like dams they may permanently retain water to somepart of their height and provide further protection against temporary highwaters Notable examples are the levees around New Orleans Louisianathe dikes protecting much of the Netherlands and the Herbert Hoover Dikearound Lake Okeechobee Florida If more precise terminology is desiredthe author suggests using dike to refer to long structures that run parallelto a water body provide permanent retention of water to some level andperiodic protection against higher water levels

Finally another related structure is a floodwall (see Figure 122)A floodwall performs the same function as a levee but is constructed ofconcrete steel sheetpile or a combination As these materials provide much

450 Thomas F Wolff

RiverLandside

Figure 122 Inverted-T floodwall with sheetpile

greater structural strength floodwalls are much more slender and requiremuch less width than levees They are used primarily in urban areas wherethe cost of the floodwall would be less than the cost of real estate requiredfor a levee

1212 Levee terminology

Figure 123 illustrates the terminology commonly used to describe thevarious parts of the levee Directions perpendicular to the levee are statedas landside and riverside The crown refers to the flat area at the top ofthe levee or sometimes to the edges of that area The toe of a levee refersto the point where the slope meets the natural ground surface The fore-shore is the area between the levee and the river Construction material forthe levee may be dredged from the river but often is taken from borrowpits which are broad shallow excavations These are commonly located

River

Berm

Crown

Toe

Levee

Foreshore

Borrow pit

Riverside Landside

Figure 123 Levee terminology

Reliability of levee systems 451

riverside of the levee within the foreshore but occasionally may be foundon the landside

As one proceeds along the length of a levee the embankment geometry ofthe levee may vary as may the subsurface materials and their configurationA levee reach is a length of levee (often several hundred meters) for whichthe geometry and subsurface conditions are sufficiently similar that theycan be represented for analysis and design by a single two-dimensionalcross-section and foundation profile Using the cross-section representing thereach analyses are performed to develop a design template for use through-out that reach A design template specifies slope inclination crown and bermwidths and crown and berm elevations

The line of protection refers to the continuous locus of levees or otherfeatures (floodwalls gates or temporary works such as sandbags) thatseparate the flooded area from the protected area The line of protectionmust either tie to high ground or connect back to itself The protected areabehind a single continuous line of protection is referred to as a polderespecially in the Netherlands

An unintended opening in the line of protection is referred to as abreach When destructive flow passes through a breach creating significanterosive damage to the levee and significant landward flooding the breach issometimes called a crevasse

The following appurtenances may be used to provide increased protectionagainst failure

bull Stability berms are extensions of the primary embankment sectionMoving downslope from the crown the slope is interrupted by a nearlyflat area of some width (the berm) and then the slope continuesThe function of a stability berm is to provide additional weight andshear resistance to prevent sliding failures

bull Seepage berms may be similar in shape to stability berms but theirfunction is to provide weight over the landside toe area to counteractupward seepage forces due to underseepage

bull Cutoffs are impervious vertical features provided beneath a leveeThey may consist of driven sheet piles slurry walls or compacted claytrenches They reduce underseepage problems by physically reducingseepage under the levee and lengthening the seepage path

bull Relief wells are vertical cylindrical drains placed into the groundnear the landside toe If piezometric levels in the landside foundationmaterials exceed the ground surface water will flow from the relief wellskeeping uplift pressure buildup to a safe level as flood levels continueto rise

bull Riverside slope protection such as stone riprap or paving may beprovided to prevent wave erosion of the levee where there is potentialfor wave attack

452 Thomas F Wolff

122 Failure Modes

Levees are subject to failure by a variety of modes The following paragraphssummarize the five failure modes most commonly considered and which willbe addressed further

1221 Overtopping

The simplest and most obvious failure mode is overtopping The levee is builtto some height and the flood water level exceeds that height flooding theprotected area While the probability of flooding for water above the top ofa levee will obviously be near unity it may be somewhat lower as the leveemight be raised with sandbags during a floodfight operation

1222 Slope instability

Embankments or berms may slide due to the soil strength being insuffi-cient to resist the driving forces from the embankment weight and waterloading Of most concern is an earth slide during flood loading that wouldlower the levee crown below the water elevation resulting in breaching andsubsequent failure Usually such a slide would occur on the landside of thelevee Figure 124 shows the slide at the 17th St Canal levee in New Orleansfollowing the storm surge from Hurricane Katrina in 2005 At that sitea portion of the levee slid landward about 15ndash20 m landward as a rela-tively intact block ldquoDrawdownrdquo slope failures may occur on the riversideslope of a levee when flood waters recede more quickly than pore pressuresin the foundation soils dissipate Slope failures may occur on either slope

Figure 124 Slide at 17th St Canal levee New Orleans 2005 (photo by T Wolff)

Reliability of levee systems 453

during non-flood conditions one scenario involves levees built of highlyplastic clays cracking during dry periods and the cracks becoming filledwith water during rainy periods leading to slides Slope slides during non-flood periods can leave a deficient levee height if not repaired before the nextflood event

1223 Underseepage

During floods the higher water on the riverside creates a gradient and subse-quent seepage water under the levee as shown in Figure 125 Seepage mayemerge near-vertically from the ground on the landside of the levee Wherefoundation conditions are uniform the greatest pressure upward gradientand flow will be concentrated at the landside levee toe In some instancesthe water may seep out gently leaving the landside ground surface soft andspongy but not endangering the levee Where critical combinations of waterlevels soil types and foundation stratigraphy (layering) are present waterpressures below the surface or the velocity of the water at the surface maybe sufficient to move the soil near the levee toe Sand boils (Figure 126)may appear these are small volcano-like cones of sand built from founda-tion sands carried to the surface by the upward-flowing water Continuederosion of the subsoil may lead to settlement of the levee permitting overtop-ping or to catastrophic uncontrolled subsurface erosion sometimes termeda blowout

1224 Throughseepage

Throughseepage refers to detrimental seepage coming through the leveeabove the ground surface When seepage velocity is sufficient to move materi-als the resulting internal erosion is called piping Piping can occur if there arecracks or voids in the levee due to hydraulic fracturing tensile stresses decayof vegetation animal activity low density adjacent to conduits or any simi-lar location where there may be a preferential path of seepage For piping tooccur the tractive shear stress exerted by the flowing water must exceed thecritical tractive shear stress of the soil In addition to internal discontinuities

Figure 125 Underseepage

454 Thomas F Wolff

Figure 126 Sand boil (photo by T Wolff)

leading to piping high exit gradients on the downstream face of the leveemay cause piping and progressive backward erosion

1225 Surface erosion

Erosion of the levee section during flood events can lead to failure byreduction of the levee section especially near the crown As flood stagesincrease the potential increases for surface erosion from two sources(1) excessive current velocities parallel to the levee slope and (2) erosiondue to wave attack directly against the levee slope Protection is typicallyprovided by a thick grass cover occasionally with stone or concrete revet-ment at locations expected to be susceptible to wave attack During floodsadditional protection may be provided where necessary using dumped rocksnow fence or plastic sheeting

123 Research on geotechnical reliability of levees

The research literature is replete with publications discussing the hydrologicand economic aspects of levees in a probabilistic framework These focusprimarily on setting levee heights in a manner that optimizes the level ofprotection against parameters such as costs benefits damage and possible

Reliability of levee systems 455

loss of life In many of these studies the geotechnical reliability is eitherneglected suggesting that the relative probability of geotechnical failure isnegligible or left for others to provide suitable probabilities of failure forgeotechnical failure modes

Duncan and Houston (1983) estimated failure probabilities for Californialevees constructed from a heterogeneous mixture of sand silt and peat andfounded on peat of uncertain strength Stability failure due to riverside waterload was analyzed using a horizontal sliding block model The factor ofsafety was expressed as a function of the shear strength which is a randomvariable due to its uncertainty and the water level for which there is adefined annual exceedance probability Values for the annual probabilityof failure for 18 islands in the levee system were calculated by integratingnumerically over the joint events of high-water levels and insufficient shearstrength The obtained probability of failure values were adjusted based onseveral practical considerations first they were normalized with respect tolength of levee reach modeled (longer reaches should be more likely to failthan shorter one) and second they were adjusted from relative probabilityvalues to more absolute values by adjusting them with respect to the observednumber of failures

Peter (1982) in Canal and River Levees provides the most completereference-book treatise on levee design based on work in Slovakia Notableamong Peterrsquos work is a more up-to-date and extended treatment ofmathematical and numerical modeling than in most other references Under-seepage safety is cast as a function of particle size and size distribution andnot just gradient alone While Peterrsquos work does not address probabilisticmethods it is of note because it provides a very comprehensive collection ofanalytical models for a large number of potential failure modes which couldbe incorporated into probabilistic analyses

Vrouwenvelder (1987) provides a very thorough treatise on a proposedprobabilistic approach to the design of dikes and levees in the NetherlandsNotable aspects of this work include the following

bull It is recognized that exceedance frequency of the crest elevation is nottaken as the frequency of failure there is some probability of failurefor lower elevations and there is some probability of no failure orinundation above this level as an effort might be made to raise theprotection

bull Considered failure modes are overflowing and overtopping macro-instability (deep sliding) micro-instability (shallow sliding or erosionof the landside slope due to seepage) and piping (as used equivalent tounderseepage as used herein) In an example 11 parameters are taken asrandom variables which are used in conjunction with relatively simplemathematical physical models Aside from overtopping piping (under-seepage) is found to be the governing mode for the section studied slope

456 Thomas F Wolff

stability is of little significance to probability of failure Surface erosiondue to wave attack or parallel currents is not considered

bull The probabilistic procedure is aimed at optimizing the height and slopeangle of new dikes with respect to total costs including constructionand expected losses including property and life In the examplemacro-instability (slope failure) of the inner slope was found to havea low risk much less than 8 times 10minus8 per year Piping was found to besensitive to seepage path length probabilities of failure for piping var-ied but were several orders of magnitude higher (10minus2 to 10minus3per year)Micro-instability (landside sloughing due to seepage) was found to havevery low probabilities of failure Based on these results it was consid-ered that only overtopping and piping need be considered in a combinedreliability evaluation

bull The ldquolength problemrdquo (longer dikes are less reliable than equivalentshort ones) is discussed

The US Army Corps of Engineers first introduced probabilistic concepts tolevee evaluation in the United States in their Policy Guidance Letter No 26(USACE 1991) Prior to that time planning studies for federally funded leveeimprovements to existing non-federal levees were based on the assumptionthat the existing levee was essentially absent and provided no protectionFollowing 1991 it was assumed that the levee was present with some prob-ability which was a function of water elevation The function was definedas a straight line between two points The probable failure point (PFP) wastaken as the water elevation for which the probability of levee failure wasestimated to be 085 and the probable non-failure point (PNP) was definedas the water elevation for which the probability of failure was estimated at015 The only guidance for setting the coordinates of these points was aldquotemplate methodrdquo based only on the geometry of the levee section with-out regard for geotechnical conditions or the engineerrsquos judgment based onother studies

To better define the shape of the function relating probability of failureto water level research by Wolff (1994) led to a report for the US ArmyCorps of Engineers entitled Evaluating the Reliability of Existing LeveesIt reviewed past and current practice for deterministic analysis and designof levees and illustrated how they could be incorporated into a probabilisticmodel Similar to Vrouwenvelder (1987) the report considers slope stabilityunderseepage and through seepage it also considers surface erosion and aprovision for incorporating other information through judgmental proba-bilities The overall probability of failure considering multiple failure modesis treated by assuming the failure modes form a series system The reportwas later incorporated as Appendix B in the Corpsrsquo Engineer TechnicalLetter ETL 1110-2-556 ldquoRisk-Based Analysis in Geotechnical Engineeringfor Support of Planning Studiesrdquo (USACE 1999) It should be noted that the

Reliability of levee systems 457

guidance was intended to apply only to the problem of characterizing exist-ing levees in probabilistic-based economic and planning studies to evaluatethe feasibility of levee improvements as of this writing the Corps has notissued any guidance recommending that probabilistic procedures be usedfor the geotechnical aspects of levee design Some of the report was alsobriefly summarized by Wolff et al (1996) This chapter draws heavily onWolff (1994) and USACE (1999) with some additional consideration ofother failure mode models and subsequent work by others

In 1999 the Corpsrsquo Policy Guidance Letter No 26 (USACE 1999) wasupdated and that version is posted on the Internet at the time of this writingThe current version recommends consideration of multiple failure modes andpermits the PFP to be assigned probability values between 085 and 099and the PNP values between 001 and 085

In 2000 the National Research Council published Risk and Uncertaintyin Flood Damage Reduction Studies (National Research Council 2000)a critical review of the Corps of Engineersrsquo approach to the application ofprobabilistic methods in flood control planning The recommendations ofthe report included the following

the Corpsrsquo risk analysis method (should) evaluate the performance ofa levee as a spatially distributed system Geotechnical evaluation of alevee which may be many miles long should account for the potentialof failure at any point along the levee during a flood Such an analy-sis should consider multiple modes of levee failure (eg overtoppingembankment instability) correlation of embankment and foundationproperties hazards associated with flood stage (eg debris waves floodduration) and the potential for multiple levee section failures during aflood The current procedure treats a levee within each damage reach asindependent and distinct from one reach to the next Further within areach the analysis focuses on the portion of each levee that is most likelyto fail This does not provide a sufficient analysis of the performance ofthe entire levee

Further it notes

The Corpsrsquo new geotechnical reliability model would benefit greatlyfrom field validation hellip The committee recommends that the Corpsundertake statistical ex post studies to compare predictions of geotech-nical levee failure probabilities made by the reliability model againstfrequencies of actual levee failures during floods

The report noted that the simplified concepts of the Corpsrsquo Policy GuidanceLetter No 26 had been updated via the procedures in the 1999 guidanceIt noted that ldquothe numerical difference in risk analysis results compared

458 Thomas F Wolff

to the initial model (PFP and PNP) may not be large The updated modelhowever supports a more complete geotechnical analysis and should replacethe initial modelrdquo It further notes that the Corpsrsquo geotechnical model doesnot separate natural variability and model uncertainty (the former could beuncorrelated from reach to reach with the latter perfectly correlated) doesnot include duration of flooding and remains to be calibrated against thefrequency of actual levee failures Nevertheless no further changes have beenmade to the Corpsrsquo methodology as of this writing in 2006

Apel et al (2004) describe a comprehensive probabilistic approach to floodrisk assessment across the entire spectrum from rainfall way to economicdamage They incorporate the probability of failure vs water elevation func-tions introduced by Wolff (1994) in USACE (1999) but extend them from2D curves to 3D surfaces to include duration of flooding

Buijs et al (2003) describe the application of the reliability-based designtools (PC-Ring) developed in the Netherlands to levees in the UK andprovides some comparison of the issues around flood defenses in the twocountries In this study overtopping was found to be the dominatingfailure mode

A doctoral thesis by Voortman (2003) summarizes the history of dikedesign in the Netherlands Voortman notes that a complete probabilisticanalysis considering all variables was explored for dike design in the 1980sbut that ldquothe legislative safety requirements are still prescribed as probabil-ities of exceedance of the design water level and thus the full probabilisticapproach has not been officially adopted to date Probabilistic methods aresometimes applied but a required failure probability for flood defenses is notdefined in Dutch lawrdquo The required level of safety is still couched in termsof a dike or levee being ldquosaferdquo against a flood elevation of some statisticalfrequency

In summary the literature to date largely consists of probabilistic modelsfor evaluation of the most tractable levee failure modes such as slope stabilityand underseepage Rudimentary methods of systems probability have beenused to consider multiple failure modes and long systems of levee reachesWork remains to better model throughseepage spatial variability systemsreliability and duration of flooding and to calibrate predicted probabilitiesof failure to the frequency of actual failures

124 A framework for levee reliability analysis

1241 Accuracy of probabilistic measures

Before proceeding a note of caution is in order The application of proba-bilistic analysis in civil engineering is still an emerging technology more soin geotechnical engineering and even more so in application to levees Muchexperience remains to be gained and the appropriate form and shape of

Reliability of levee systems 459

probability distributions for most of the relevant parameters are not knownwith certainty The methods described herein should not be expected toprovide ldquotruerdquo or ldquoabsoluterdquo probability-of-failure values but can provideconsistent measures of relative reliability when reasonable assumptions areemployed Such comparative measures can be used to indicate for examplewhich reach (or length) of levee which typical section or which alternativedesign may be more reliable than another They also can be used to judgewhich of several performance modes (seepage slope stability etc) governsthe reliability of a particular levee

1242 Calibration of models

Any reliability-based evaluation must be calibrated ie tested against a suf-ficient number of well-understood engineering problems to ensure that itproduces reasonable results Performance modes known to be problematical(such as seepage) should be found to have a lower reliability than those forwhich problems are seldom observed larger and more stable sections shouldbe found to be more reliable than smaller less stable sections etc As rec-ommended by the National Research Council (2000) as additional analysesare performed by researchers and practitioners on a wide range of real leveecross sections using real data refinements in the procedures may be neededto produce reasonable results

1243 The conditional probability-of-failure function

For the purposes at hand failure will be defined as the unintended floodingof the protected area and thus includes both overtopping and breaching ofthe levee at water elevations below the crown For an existing levee subjectedto a flood the probability of failure pf can be expressed as a function of theflood water elevation and other factors including soil strength permeabilityembankment geometry foundation stratigraphy etc The analysis of leveereliability will start with the development of a conditional probability offailure function given the flood water elevation which will be constructedusing engineering estimates of the probability functions or moments of theother relevant variables

The conditional probability of failure can be written as

pf = Pr(failure |FWE) = f (FWEX1X2 Xn) (121)

In the above equation the first expression (denoting probability of failure)will be used as a shorthand version of the second term The symbol ldquo|rdquois read given and the variable FWE is the flood water elevation In thesecond expression the random variables X1 through Xn denote relevantparameters such as soil strength conductivity top stratum thickness etc

460 Thomas F Wolff

Equation (121) can be restated as follows ldquoThe probability of failure giventhe flood water elevation is a function of the flood water elevation and otherrandom variablesrdquo

Two extreme values of the function can be readily estimated by engineeringjudgment

bull For flood water at the same level as the landside toe (base elevation) ofthe levee the levee is not loaded hence pf = 0

bull For flood water at or near the levee crown (top elevation) pf rarr 100

In principle the probability of failure value may be something less than 10with water at the crown elevation as additional protection can be providedby emergency measures

The question of primary interest however is the shape of the functionbetween these extremes Quantifying this shape is the focus of the proceduresto follow

Reliability (R) is defined as

R = 1 minus pf (122)

hence for any flood water elevation the probability of failure and reliabilitymust sum to unity

For the case of flood water partway up a levee pf could be very near zeroor very near unity depending on engineering factors such as levee geometrysoil strength hydraulic conductivity foundation stratigraphy etc In turnthese differences in the conditional probability of failure function could resultin very different scenarios Four possible shapes of the pf and R functionsare illustrated in Figure 127 For a well-designed and constructed ldquogoodrdquo

000 100Probability of Failure

000100 Reliability

ldquopoorrdquo levee

ldquogoodrdquo levee

Flood waterElevationLevee

Figure 127 Possibble probability of failure vs flood water elevation functions

Reliability of levee systems 461

levee the probability of failure may remain low and the reliability remainhigh until the flood water elevation is rather high In contrast a ldquopoorrdquolevee may experience greatly reduced reliability when subjected to even asmall flood head It is hypothesized that some real levees may follow theintermediate curve which is similar in shape to the ldquogoodrdquo case for smallfloods but reverses to approach the ldquopoorrdquo case for floods of significantheight

1244 Steps in levee system evaluation

To evaluate the annual probability of failure for an entire levee system foursteps are required

Step 1

A set of conditional probability of failure versus flood water elevation func-tions is developed For each levee reach this requires a separate function foreach of the following failure modes slope stability underseepage through-seepage and surface erosion Overtopping and other failure modes (eghuman error failure to close a gate) may be added as desired This leadsto N functions where N = rm r is the number of reaches and m is thenumber of failure modes considered

Step 2

For each reach the functions for the various failure modes developed in step 1are systematically combined into one composite conditional probability offailure function for each reach This leads to r composite functions

Step 3

Given the composite reach function and a probability of exceedance functionfor the flood water elevation the annual probability of levee failure for thereach can be determined by integrating the product of flood probability andconditional probability of failure over the range of water elevations from thelevee toe to the crown

Step 4

Using the results of step 3 an overall annual probability of levee systemfailure can be developed by combining the annual probabilities for the entireset of reaches If only a few reaches dominate (by having comparatively highprobabilities of failure) this step might be simplified by considering onlythose selected reaches In any case length effects must first be consideredbefore the number of reaches is finalized

462 Thomas F Wolff

125 Division into reaches

The first step in any levee analysis or design deterministic or probabilistic isdivision of the levee into reaches for geotechnical analysis Note that thoseanalyzing the economic benefits of a levee may divide the levee into ldquodamagereachesrdquo based on interior topography and value of property at risk andthose studying interior drainage may use another system of dividing a leveeinto reaches for their purposes In a comprehensive risk analysis reach iden-tification may require careful coordination among an analysis team to ensureproper combination of probabilities of failure and associated consequences

Analysis of slope stability and seepage conditions is usually performedusing a two-dimensional perpendicular cross-section of the levee and foun-dation topography and stratigraphy These are in fact an idealization ofthe actual conditions which have some uncertainty at any cross-sectionconsidered and furthermore are variable in the direction of the leveeA geotechnical reach is a length of levee for which the topography andsoil conditions are sufficiently similar that the engineer considers they canbe represented by a single cross-section for analysis This is illustrated inFigure 128 where the solid lines represent the best understanding (alsouncertain) of the foundation soil profile under the levee (often along thelandside toe) and the dashed lines represent the idealization into reachesfor analysis For each reach a cross-section is developed and assumed to berepresentative of the conditions along the reach (See Figure 129) Calculatedperformance measures (factor of safety exit gradients) for the section areassumed to be valid for the length of the reach In design practice reachesare typically several hundred meters long but the lengths and endpointsshould be carefully selected based on topographic and geotechnical infor-mation not on a specified length Within a reach the design cross-sectionis often selected at a point considered to be the most critical such as thegreatest height

In a probabilistic analysis it may be required to further subdivide leveereaches into statistically independent shorter reaches for analysis of overallsystem reliability This will be further discussed later under length effects

Figure 128 Division into reaches

Reliability of levee systems 463

440

420

400

380

360

0minus100 100

10 crown at el 420

1V on 2H side slopes

Clay top blanket

Pervious sand substratum extends to el 3120

Clay levee el 395

Figure 129 Two-dimensional cross-section representing a reach (from Wolff 1994)

126 Step 1 failure mode models

1261 Slope instability

It is desired to define the conditional probability of slope failure as a functionof the flood water elevation FWE To do so the relevant parameters arecharacterized as random variables a performance function is defined thatexpresses the slope safety as a function of those variables and a probabilisticmodel is selected to determine the probability of failure

12611 Random variables

Probabilistic slope stability analysis typically involves modeling most orall of the following parameters as random variables unit weight drainedstrength of sand and other pervious materials and drained and undrainedstrength of cohesive materials such as clays In addition some researchershave included geometric uncertainty of the soil boundaries and model uncer-tainty Table 121 summarizes some typical coefficients of variation for theseparameters The coefficients of variation shown are for the point strengthcharacterizing the strength at a random point However the uncertaintyin the average strength over some spatial distance may govern the stabilitymore than the point strength To consider the effects of spatial correlationin a slope stability analysis it may be necessary to reduce these variances orprovide some other modification in the analysis model

In slope stability problems uncertainty in unit weight usually pro-vides little contribution to the overall uncertainty which is dominatedby soil strength For stability problems it can usually be taken as a

464 Thomas F Wolff

Table 121 Typical coefficients of variation for soil properties used in slope stabilityanalysis

Parameter Coefficient ofvariation ()

Reference

Unit weight γ 3 Hammitt (1966) cited by Harr (1987)4ndash8 Assumed by Shannon and Wilson (1994)

Drained strength ofsand φprime

37ndash93 Direct shear tests Mississippi River Lock andDam No 2 Shannon and Wilson (1994)

12 Schultze (1972) cited by Harr (1987)Drained strength of

clay φprime75ndash101 S tests on compacted clay at Cannon Dam

(Wolff 1985)Undrained strength

of clays su40 Fredlund and Dahlman (1972) cited by

Harr (1987)30ndash40 Assumed by Shannon and Wilson (1994)11ndash45 UU tests on compacted clay at Cannon Dam

Wolff (1985)Strength-to-effective

stress ratio suσ primevo

31 Clay at Mississippi River Lock and Dam No 2Shannon and Wilson (1994)

deterministic variable to reduce the number of random variables and simplifycalculations

Reported coefficients of variation for the drained friction angle (φ) ofsands are in the range of 3ndash12 Lower values can be used where thereis some confidence that the materials considered are of consistent qualityand relative density and the higher values should be used where there isconsiderable uncertainty regarding material type or density For the directshear tests on sands from Lock and Dam No 2 cited in Table 121 (Shannonand Wilson and Wolff 1994) the lower coefficients of variation correspondto higher confining stresses and vice versa

Ladd et al (1977) and others have shown that the undrained strength su(or c) of clays of common geologic origin can be normalized with respect toeffective overburden stress σ prime

vo and overconsolidation ratio OCR and definedin terms of the ratio suσ prime

vo Analysis of test data on clay at Mississippi RiverLock and Dam No 2 (Shannon and Wilson and Wolff 1994) showed thatit was reasonable to characterize uncertainty in clay strength in terms of theprobabilistic moments of the suσ prime

v parameter The ratio of suσ primevo for 24 tested

samples was found to have a mean value of 035 a standard deviation of011 and a coefficient of variation of 31

12612 Deterministic model and performance function

For slope stability the choice of a deterministic analysis model is straight-forward A conventional limit-equilibrium slope stability model is used thatyields a factor of safety defined in terms of the shear strength of the soil

Reliability of levee systems 465

Many well documented programs are available to run the most popularslope stability analysis methods (eg Bishoprsquos method Spencerrsquos methodand the MorgensternndashPrice method) For a specific failure surface manystudies have shown that resulting factors of safety differ little for any of themethods that satisfy moment equilibrium or complete equilibrium How-ever finding the geometry of the critical failure surface is another matter Aslevees are typically located over stratified alluvial foundations the selecteddeterministic model and computer program should be capable of analyzingfailure surfaces with planar bases (eg ldquowedgerdquo surfaces) and other generalshapes not only circular arcs Furthermore there remains a philosophicalquestion as to what exactly is the ldquocritical surfacerdquo In many probabilisticslope stability studies the critical deterministic surface (lowest FS) has beenfound first and the probability of failure for that surface has been calcu-lated However it can be argued that one should locate the surface with thehighest probability of failure which can be greatly different when soils withdifferent levels of uncertainty are present Hassan and Wolff (1999) haveinvestigated this problem and presented an approximate empirical methodto locate the critical probabilistic surface when the available programs cansearch only for the surface of minimum FS

Depending on the probabilistic model being used it may be sufficient tobe able to simply determine the factor of safety for various realizations of therandom variables and use these to determine the probability that the factorof safety is less than one However some methods such as the advancedfirst-order second-moment method (AFOSM) may require a performancefunction that assumes a value of zero at the limit state For the latter theperformance function for slope stability can be taken as

FS minus 1 = 0 (123)

or

In FS = 0 (124)

The second expression is preferred as it works consistently with the assump-tion that the factor of safety is lognormally distributed which is appealingas FS cannot assume negative values

12613 Probabilistic modeling

Having defined a set of random variables a deterministic model and per-formance function a probabilistic model is then needed to determine theprobability of failure (or probability that the performance function assumesa negative value) Given the probability distributions or at least the proba-bilistic moments of the random variables the probabilistic model determines

466 Thomas F Wolff

the distribution or at least the probabilistic moments of the factor of safety orperformance function Details are described elsewhere in this book but com-mon models include the first-order reliability method (FORM) the advancedfirst-order second-moment method (AFOSM) both based on Taylorrsquos seriesexpansion and simulation (or Monte Carlo) methods

In the examples in this chapter a variation called the Taylorrsquos series ndashfinite difference method used by the Corps of Engineers (USACE 1995) isused to calculate the mean and standard deviation of the factor of safetyknowing the mean and standard deviations of the random variables TheTaylorrsquos series is expanded around the mean value of the FS function not theldquodesign pointrdquo found by iteration in the AFOSM Hence the nonlinearity ofthe performance function is not directly considered This deficiency is partlymitigated by determining the required partial derivatives numerically over arange of plus to minus one standard deviation rather than a tangent at themean Using this large increment captures some of the information aboutthe functional shape

12614 Example problem

The example to follow is taken from Wolff (1994) The assumed levee cross-section is shown in Figure 1210 and consists of a sand levee on a thin clay topstratum overlaying thick foundation sands Units are in English system feetconsistent with the original reference (1 ft = 03048 m) Despite their highconductivity sand levees have been used in a number of locations where claymaterials are scarce a notable example is along the middle Mississippi Riverin western Illinois Three random variables were defined and their assumedprobabilistic moments were assigned as shown in Table 122

440

420

400

380

360

0minus100 100

10 crown at el 420

1V on 25 side slopes8 ft clay top blanket

80 ft thick pervious sand substratumExtends to el 3120

Sand levee with clay face

Figure 1210 Cross-section for slope stability and underseepage example (elevations anddistances in feet)

Reliability of levee systems 467

Table 122 Random variables for slope stability example problem

Parameter Expectedvalue

Standarddeviation

Coefficient ofvariation ()

Friction angle of sand levee embankment φemb 32 deg 2 deg 67Undrained strength of clay foundation c or su 800 lbft2 320 lbft2 40Friction angle of sand foundation φfound 34 deg 2 deg 59

For slope stability analysis the piezometric surface in the embankmentsand was approximated as a straight line from the point where the floodwater intersects the riverside slope to the landside levee toe The piezometricsurface in the foundation sands was taken as that obtained for the expectedvalue condition in the underseepage analysis reported later in this chapterIf desired the piezometric surface could be modeled as an additional randomvariable using the results of a probabilistic seepage analysis

Using the Taylorrsquos seriesndashfinite difference method seven runs of a slopestability program are required for each flood water level considered one forthe expected value case and two runs to determine the variance componentof each random variable

The seven resulting solutions and partial variance terms are summarizedfor FWE = 400 ft (base of the levee) in Table 123 The seven failure surfacesare nearly coincident as seen in Figure 1211 The expected value of thefactor of safety is the factor of safety calculated using the expected values ofall variables

E[FS] = 1568 (125)

The variance of the factor of safety is calculated per USACE (1995) usinga finite-difference increment of 2σ

VarFS=(partFSpartφe

)2

σ 2φe

+(partFSpartc

)2

σ 2c +(partFSpartφf

)2

σ 2φf

=(

FS+minusFSminus2σφe

)2

σ 2φe

+(

FS+minusFSminus2σc

)2

σ 2c +(

FS+minusFSminus2σφf

)2

σ 2φf

=(

FS+minusFSminus2

)2

+(

FS+minusFSminus2

)2

+(

FS+minusFSminus2

)2

=(

1448minus16932

)2

+(

1365minus15682

)2

+(

1568minus15672

)2

=0025309 (126)

468 Thomas F Wolff

Water Elev = 400

385

390

395

400

405

410

415

420

425

minus10 0 10 20 30 40 50 60

Horizontal Distance (feet)

Ele

vati

on

(fe

et)

Figure 1211 Failure surfaces for slope stability example

Table 123 Example slope stability problem undrained conditions water at elevation 400(H = 0 ft)

Run (levee) c (clay) (found) FS Variance Percent of total variance

1 32 800 34 15682 30 800 34 14483 34 800 34 1693 0015006 59294 32 480 34 13655 32 1120 34 1568 00010302 40716 32 800 32 15687 32 800 36 1567 25 times 10minus7 00

Total 025309 1000

In the above expression φe is the friction angle of the embankment sand cis the undrained strength (or cohesion) of the top blanket clay and φf is thefriction angle of the foundation sand

The factor of safety is assumed to be a lognormally distributed randomvariable with E[FS] = 1568 and σFS = 002530912 = 0159 From the prop-erties of the lognormal distribution the coefficient of variation of the factorof safety is

VFS = σFS

E[FS] = 01591568

= 01015 (127)

Reliability of levee systems 469

The standard deviation of ln FS is

σlnFS =radic

ln(1 + V2FS) =

radicln(1 + 010152) = 01012 (128)

The expected value of ln FS is

E[lnFS] = lnE[FS]minus σ 2lnFS

2= ln1568 minus 00102

2= 0447 (129)

The reliability index is then

β = E[lnFS]σlnFS

= 044701012

= 4394 (1210)

As FS is assumed lognormally distributed ln FS is normally distributed Fromthe cumulative distribution function of the standard normal distributionevaluated at minusβ the conditional probability of failure for water at elevation400 is

Prf = 6 times 10minus6

This is illustrated in Figure 1212 The results for all water elevations aresummarized in Table 124

12615 Interpretation

Note that the calculated probability of failure infers that the existinglevee is taken to have approximately a six in one million probability of

E[ln FS] = 0445

ln FS

βσln FS = (4394)(01012)

Pr(f) = 0000006

limit stateln 1 = 00

Figure 1212 Probability of failure for slope stability example water at elevation 400

470 Thomas F Wolff

Table 124 Example slope stability problem undrained conditions conditional probabilityof failure function

Water elevation E[FS] σFS Prf

4000 1568 0159 4394 6 times10minus6

4050 1568 0161 4351 7 times10minus6

4100 1568 0170 4114 19 times10minus5

4150 1502 0107 5699 6 times10minus9

4200 1044 0084 0499 03087

instability with flood water to its base elevation of 400 even though itmay in fact be existing and observed stable under such conditions Thereliability index model was developed for the analysis of yet-unconstructedstructures When applied to existing structures it will provide probabili-ties of failure greater than zero This can be interpreted as follows givena large number of different levee reaches each with the same geometryand with the variability in the strength of their soils distributed accord-ing to the same density functions about six in one million of those leveesmight be expected to have slope stability problems Expressing the reliabilityof existing structures in this manner provides a consistent probabilis-tic framework for use in economic evaluation of improvements to thosestructures

12616 Conditional probability of failure function

The probabilities of failure for all water elevations are summarized inTable 124 and plotted in Figure 1213 Contrary to what might beexpected the reliability index reaches a maximum and the probabilityof failure reaches a minimum at a flood water elevation of 4150 orthree-quarters the levee height For water above 410 the critical failuresurface moves from the clay foundation materials up into the embank-ment sands As this occurs the factor of safety becomes more dependenton the shear strength of the embankment sands and less dependent onthe shear strength of the foundation clays Although the factor of safetydrops as water rises from 410 to 415 the reliability index increases Asthere is more certainty regarding the strength of the sand (the coeffi-cient of variations are about 6versus 40 for the clay) this indicatesthat a sand embankment with a low factor of safety can be more reli-able than a clay embankment with a higher factor of safety Finally asthe water surface reaches the top of the levee at 420 the increasing seep-age forces and reduction in effective stress leads to the lowest values forFS and β

Reliability of levee systems 471

0000000001

000000001

00000001

0000001

000001

00001

0001

001

01

10 5 10 15 20 25

H ft

Pr(

failu

re)

Figure 1213 Conditional probability of failure function for slope stability

1262 Underseepage

12621 Deterministic model

Underseepage is a common failure mode for levees and floodwalls that hasbeen widely researched and for which analysis methods have been widelypublished (Peter 1982 USACE 2000) The example presented here uses anequation-based deterministic method developed by the Corps of Engineers(USACE 2000) for a specific but common set of idealized foundation con-ditions a relatively thin top semi-pervious top stratum of uniform thicknessoverlying a thick pervious substratum of uniform thickness A typical cross-section is shown in Figure 1214 For this case the exit gradient io at thelandside toe can be calculated as

io = ho

z(1212)

where

ho is the residual head at the levee toe andz is the effective thickness of the landside top blanket

The residual head can be calculated as

ho = H x3

x1 + x2 + x3(1213)

472 Thomas F Wolff

Landside ho

z

d

Riverside

Top blanket

Pervious substratum

piezometricsurface

X1 X2 X3

Figure 1214 Terms used in Crops of Engineersrsquo underseepage model

where

H is the net height of water on leveex1 is the effective seepage entrance distancex2 is the base width of levee andx3 is the effective seepage exit distance

For uniform blanket conditions of infinite landside length x3 can becalculated as

x3 =radic

kf

kbzd (1214)

where kf is the horizontal permeability of the pervious substratum kb isthe vertical permeability of the semi-pervious top blanket z is the thicknessof the top blanket and d is the thickness of the substratum The coefficientof permeability k is a combined property of the soil and the permeant andis now more commonly referred to as hydraulic conductivity The earliernomenclature is retained for consistency with the original references

Procedures for determining these values for a variety of conditions can befound in USACE (2000)

The exit gradient io is compared to the critical gradient ic which can becalculated as follows

ic = γ primeγw

(1215)

Reliability of levee systems 473

where

γ prime is the effective of submerged unit weight of the top stratum andγw is unit weight of water

An exit gradient in excess of the critical gradient implies failure Exit gra-dients can also be calculated for much more complex foundation conditionsusing methods such as finite element analysis

The formulation above considers the initiation of sand boils or piping tobe related only to gradient conditions Other researchers (eg Peter 1982)have proposed analysis models that include other parameters notably grainsize and grain size distribution

12622 Random variables

Table 125 provides some typical coefficients of variation for the parametersused in underseepage analysis

12623 Example problem

The levee cross section for this example is the same as that previouslyshown in Figure 1210 Four random variables are considered kf kb zand d The assigned probabilistic moments for these variables are given inTable 126

As borings are not available at every possible cross-section there is someuncertainty regarding the thicknesses of the soil strata at the critical locationHence z and d are modeled as random variables Their standard deviationsare set by engineering judgment regarding the probable range of actual valuesat the site For the blanket thickness z assigning the standard deviation at20 ft models a high probability that the actual blanket thickness will bebetween 40 and 120 ft ( plusmn2 standard deviations) and a very high probabilitythat the blanket thickness will be between 20 and 140 ft ( plusmn3 standard

Table 125 Typical coefficients of variation for soil parameters used in underseepageanalysis

Parameter Coefficient ofvariation ()

Reference

Coefficient ofpermeability k

90 For saturated soils Nielson et al (1973) citedby Harr (1987)

Permeability of topblanket clay kb

20 Derived from assumed distribution Shannonand Wilson (1994)

Permeability offoundation sands kf

20ndash275 For average permeability over thickness ofaquifer Shannon and Wilson (1994)

Permeability ratiokf kb

40 Derived using 30 for kf and kb seeAppendix A

474 Thomas F Wolff

Table 126 Random variables for underseepage example problem

Parameter Expected value Standard deviation Coefficient ofvariation()

Substratum permeability kf 1000 times 10minus4 cms 300 times 10minus4cms 30Top blanket permeability kb 1 times 10minus4 cms 03 times 10minus4cm s 30Blanket thickness z 80 ft 20 ft 25Substratum thickness d 80 ft 5 ft 625

deviations) For the substratum thickness d the two standard deviation rangeis 70ndash90 ft and the three standard deviation range is 65ndash95 ft For analysis ofreal levee systems it is suggested that the engineer review the geologic historyand stratigraphy of the area and assign a range of likely strata thicknessesthat are considered the thickest and thinnest probable values These can thenbe taken to correspond to plus and minus 25 or 30 standard deviations fromthe expected value

As the exit gradient is a function of the permeability ratio kf kb and notthe absolute magnitude of the values the number of analyses can be reducedby treating the permeability ratio as a single random variable To do so it isnecessary to determine the coefficient of variation of the permeability ratiogiven the coefficient of variation of the two permeability values Using severalmethods to determine the probabilistic moments of the ratio of two randomvariables it appears reasonable to take the expected value of the permeabilityratio as 1000 and its coefficient of variation as 40 This corresponds to astandard deviation of 400 for kf kb

The performance function is taken as the exit gradient landside of thelevee and the value of the critical gradient assumed to be 085 is taken asthe limit state

For each water elevation the exit gradient is calculated using the Taylorrsquosseriesndashfinite difference method This requires considering seven combinationsof the input parameters Results for a 20 ft head on the levee are summarizedin Table 127

For Run 1 the three random variables are all taken at their expectedvalues First the effective exit distance x3 is calculated as

x3 =radic

kf

kbmiddot z middot d = radic

1000 middot 8 middot 80 = 800ft (1216)

As the problem is symmetrical the distance from the riverside toe to theeffective source of seepage entrance x1 is also 800 ft

From the geometry of the given problem the base width of the levee x2is 110 ft

Reliability of levee systems 475

Table 127 Underseepage example Taylorrsquos series method water at elevation 420 (H =20 ft)

Run kf kb z d ho i Variance Percent of total variance

1 1000 80 800 9357 11702 600 80 800 9185 11483 1400 80 800 9451 1181 0000276 0304 1000 60 800 9265 15445 1000 100 800 9421 0942 0090606 99696 1000 80 750 9337 11677 1000 80 850 9375 1172 0000006 001

Total 0090888 1000

The net residual head at the levee toe is

ho = Hx3

x1 + x2 + x3= 20 middot 800

800 + 110 + 800= 9357ft (1217)

and the landside toe exit gradient is

i = ho

z= 9357

80= 1170 (1218)

For the second and third analyses the permeability ratio is adjusted to theexpected value plus and minus one standard deviation while the other twovariables are held at their expected values These are used to determine thecomponent of the total variance related to the permeability ratio

(parti

part(kfkb)

)2

σ 2kfkb

asymp(

i+ minus iminus2σkfkb

)2

σ 2kfkb

=(

i+ minus iminus2

)2

=(

1181 minus 11482

)2

= 0000276

(1219)

A similar calculation is performed to determine the variance componentscontributed by the other random variables

When the variance components are summed the total variance of the exitgradient is obtained as 0090888 Taking the square root of the variancegives the standard deviation of 0301

The exit gradient is assumed to be a lognormally distributed random vari-able with probabilistic moments E[i] = 1170 and σi = 0301 Using theproperties of the lognormal distribution the equivalent normally distributedrandom variable has moments E[ln i] = 0124 and σln i = 0254

476 Thomas F Wolff

The critical exit gradient is assumed to be 085 The probability of failureis then

Prf = Pr (ln i gt ln 085) (1220)

This probability can be found by first calculating the standard normalizedvariate z

z = ln icrit minus E[ln i]σln i

= minus016252 minus 0124490253629

= minus1132 (1221)

For this value the cumulative distribution function F(z) is 0129 and repre-sents the probability that the gradient is below critical The probability thatthe gradient is above critical is

Prf = 1 minus F(z) = 1 minus 0129 = 0871 (1222)

Note that the z value is analogous to the reliability index β and it couldbe stated that β = minus113 The probability calculation is illustrated inFigure 1215

Repeating this procedure for a range of flood water elevations the condi-tional probability of failure function can be plotted as shown in Figure 1216The probability of failure is very low until the head on the levee exceeds about8 ft after which it curves up sharply It reverses curvature when the headreaches about 15 ft and the probability of failure is near 50 When theflood water elevation is near the top of the levee the conditional probabilityof failure approaches 87

The results of one intermediate calculation should be noted As indicatedby the relative size of the variance components shown in Table 127 virtu-ally all of the uncertainty is in the top blanket thickness A similar effect wasfound in other underseepage analyses by the writer reported in the UpperMississippi River report (Shannon and Wilson and Wolff 1994) where

E[ln i] = 0124

ln i crit = minus0163

ln i

minus1132 sigma ln i

Figure 1215 Probability of failure for underseepage example water at el 420

Reliability of levee systems 477

0

01

02

03

04

05

06

07

08

09

1

0 5 10 15 20 25H ft

Pr(

failu

re)

Figure 1216 Conditional probability of failure function for underseepage example

the top blanket thickness was treated as a random variable its uncertaintydominated the problem This has two implications

bull Probability of failure functions for preliminary economic analysis mightbe developed using a single random variable the top blanket thickness z

bull In expending resources to design levees against underseepage failureobtaining more data to determine the blanket thickness profile may bebetter justified than obtaining more data on material properties

1263 Throughseepage

Three types of internal erosion or piping can occur as a result of seepagethrough a levee

bull Cracks in a levee due to hydraulic fracturing tensile stresses decay ofvegetation animal activity along the contours of hydraulic structuresetc can all provide a preferential seepage path along which piping mayoccur For piping (movement of soil material) to occur the tractive shearstress exerted by the flowing water must exceed the critical tractive shearstress of the soil

bull High exit gradients on the downstream face of the levee may cause pipingand possible progressive backward erosion

bull Internal erosion or removal of fine grains by excessive seepage forces mayoccur This type of piping occurs when the seepage gradient exceeds acritical value

478 Thomas F Wolff

Well-constructed clay levees are generally considered resistant againstinternal erosion but such erosion can occur where there are defects suchas above For sand levees throughseepage is a common occurrence duringflood Water flowing through the embankment will cut small channels onthe landside slope If the slope is sufficiently flat the levee will hold and theslope can be re-dressed after the flood Construction of sand levees is rela-tively common on the middle Mississippi River for levees constructed by theRock Island District of the US Army Corps of Engineers

12631 Deterministic models

There is no single widely accepted analytical technique or performance func-tion in common use for predicting internal erosion Review of various modelsindicates that erosion susceptibility may be taken to be a function of somecombination of the following parameters

bull permeability or hydraulic conductivity kbull hydraulic gradient ibull porosity nbull critical tractive stress τc (the shear stress required for flowing water to

dislodge a soil particle)bull particle size expressed as some representative size such as D50 or

D85 andbull friction angle φ or angle of repose

Essentially the models use the gradient critical tractive stress and particlesize to determine whether the shear stresses induced by seepage head lossare sufficient to dislodge soil particles and use the gradient permeabilityand porosity to determine whether the seepage flow rate is sufficient to carryaway or transport the particles once they have been dislodged

Very fine sands and silt-sized materials are among the most erosion-susceptible soils This arises from their having a critical balance of relativelyhigh permeability low particle weight and low critical tractive stress Parti-cles larger than fine sand sizes are generally too heavy to be moved easilyas particle weight increases with the cube of the diameter Particles smallerthan silts (ie clay sizes) although of light weight may have relatively largeelectro-chemical forces acting on them which can substantially increase thecritical tractive stress τc and also have sufficiently small permeability as toinhibit particle transport in significant quantity

12632 Schwartzrsquos method

As analytical models for throughseepage are complex and not well provenan illustrative example based on a design procedure for the landside slope of

Reliability of levee systems 479

sand levees developed by Schwartz (1976) will be used herein Where thereis experience with other erosion models they could be substituted for theillustrated method using the same approach of defining the probability offailure as the probability that the performance function crosses the limit stateSchwartzrsquos method includes some elements of erosion analysis However theresult of the method is a parameter to determine the need for providing toeberms according to semi-empirical criteria rather than to directly determinethe threshold of erosion conditions or predict whether erosion will occurPresumably some conservatism is present in the berm criteria and thus thecriteria do not represent a true limit state Accordingly the actual probabilityof failure should be somewhat lower than calculated

The procedure involves the calculation of two parameters the maximumerosion susceptibility M and the relative erosion susceptibility R The cal-culated values are compared to critical combinations for which toe bermsare considered necessary The parameters are functions of the embankmentgeometry and soil properties First the vertical distance of the seepage exitpoint on the downstream slope yc is determined using the well-known solu-tion for the ldquobasic parabolardquo by L Casagrande Two parameters λ1 and λ2are then calculated as

λ1 =cosβ minus γw

γbsinβ tan(β minus δ) minus γsat

λb

sinβ

tanφ(1223)

λ2 =γw sin07 β( n

149

)06 [k tan(β minus δ)

]06 (1224)

where β is the downstream slope angle

δ is taken as zero for a horizontal exit gradientn is Manningrsquos coefficient for sand typically 002γsat is the saturated density of the sand in lbft3

γb is the submerged effective density of the sand in lbft3

k is the sand permeability in ftsφ is the friction angle

It is important to note that the parameter λ2 is not dimensionless and theunits stated above must be used

The erosion susceptibility parameters are then calculated as

M =λ2y06e

λ1τco(1225)

R =ye minus(λ1τco

λ2

)167

H(1226)

480 Thomas F Wolff

In the above equations τco is the critical tractive stress which can be takenas about 003 lbft2 (1436 dynescm2) for medium sand and H is thefull embankment height measured in feet According to Schwartzrsquos crite-ria toe berms are recommended when M and R values fall above the shadedregion shown in Figure 1217 To simplify probabilistic analysis Shannonand Wilson and Wolff (1994) suggested replacing this region with a linearapproximation (also shown in Figure 1217) and taken to be the limit stateThe approximation is

M + 144R minus 130 = 0 (1227)

Positive values of the expression to the left of the equals sign indicate theneed for toe berms

12633 Example problem

The same embankment section previously analyzed will be used as an exam-ple of a throughseepage analysis Random variables were characterized asshown in Table 128 The method which assesses erosion at the landsideseepage face was numerically unstable (λ1 becomes negative) for the slopespreviously assumed To make the problem stable for purposes of illustra-tion the slopes had to be flattened to 1V on 3H riverside and 1V on 5Hlandside

The results for a 20 ft water height are summarized in Table 129 The mostsignificant random variables based on descending order of their variancecomponents are the unit weight the friction angle and the permeability

01 02 03 04 05 06 07 08 09 10

Relative Erosion Susceptibility R

0

5

10

15

20

0

Maximum Erosion Susceptibility M

No bermNo bermNo berm

Figure 1217 Schwartzrsquos method berm criteria and assumed linear limit state

Reliability of levee systems 481

Table 128 Random variables for throughseepage example

Variable Expected value Coefficient of variation()

Manningrsquos coefficient n 002 10Unit weight γsat 125 lbft3 8Friction angle φ 30 deg 67Coefficient of permeability k 2000 times 10minus4cms 30Critical tractive stress τ 18 dynescm2 10

Table 129 Results of internal erosion analysis example problem (modified to flatterslopes) H = 20 ft

n γsat φ k τ Performancefunction

Variancecomponent

Percent of totalvariance

002 125 30 2000 18 175240022 125 30 2000 18 184910018 125 30 2000 18 16515 09761 21002 135 30 2000 18 14798002 115 30 2000 18 23667 196648 429002 125 32 2000 18 14817002 125 28 2000 18 22179 135498 295002 125 30 2600 18 20321002 125 30 1400 18 14339 8961 195002 125 30 2000 198 16046002 125 30 2000 162 19369 27606 60

Total 458974 1000

The effects of Manningrsquos coefficient and the critical tractive stress at leastfor the coefficients of variation assumed are relatively insignificant

The reliability index was calculated as the expected value of the per-formance function divided by the standard deviation of the performancefunction The probability of failure was calculated using the reliability indexand assuming the performance function was normally distributed (Note thatthe assumption of a lognormally distributed performance function was notused for this failure mode as negative values of the performance function arepermitted)

When the probabilities of failure for various flood water elevations areplotted the conditional probability of failure function shown in Figure 1218is obtained Again it takes the expected reverse-curve shape Below heads10 ft or about half the levee height the probability of failure against throughseepage failure is virtually nil The probability of failure becomes greater than05 for a head of about 165 feet and approaches unity at the full head of20 ft

482 Thomas F Wolff

00

01

02

03

04

05

06

07

08

09

10

0 5 10 15 20 25

H ft

Pr(

failu

re)

Figure 1218 Conditional probability of failure function for throughseepage example

12634 Surface erosion

As flood stages increase the potential increases for surface erosion from twosources

bull erosion due to excessive current velocities parallel to the levee slope andbull erosion due to wave attack directly against the levee slope

Preventing surface erosion requires providing adequate slope protection typ-ically a thick grass cover and stone revetment at locations expected to besusceptible to wave attack During flood emergencies additional protectionmay be provided where necessary using dumped rock snow fence or plasticsheeting

12635 Analytical model

Although there are established criteria for determining the need for slopeprotection and for designing slope protection they are not in the form of alimit state or performance function (ie one does not typically calculate afactor of safety against scour) To perform a reliability analysis one needs todefine the problem as a comparison between the probable velocity or waveheight and the velocity or wave height that will result in damaging scourAs a first approximation for the purpose of illustration this section will usea simple adaptation of Manningrsquos formula for average flow velocity andassume that the critical velocity for a grassed slope can be expressed by anexpected value and coefficient of variation

Reliability of levee systems 483

For channels that are very wide relative to their depth (width gt 10timesdepth) the flow velocity can be expressed as

V = 1486y23S12

n(1228)

where y is the depth of flowS is the slope of the energy line andn is Manningrsquos roughness coefficient

It will be assumed that the velocity of flow parallel to a levee slope forwater heights from 0 to 20 ft can be approximated using the above formulawith y taken from 0 to 20 ft For real levees in the field it is likely thatbetter estimates of flow velocities at the location of the riverside slope canbe obtained by more detailed hydraulic models

The following probabilistic moments are assumed More detailed and site-specific studies would be necessary to determine appropriate values

E[S] =00001 VS = 10

E[n] =003 Vn = 10

It is assumed that the critical velocity that will result in damaging scour canbe expressed as

E[Vcrit] = 50fts Vvcrit = 20

Further research is necessary to develop guidance on appropriate values forprototype structures

The Manning equation is of the form

G(x1x2x3 ) = axg11 xg2

2 xg33 (1229)

For equations of this form Harr (1987) shows that the probabilisticmoments can be determined using a special form of Taylorrsquos series approx-imation he refers to as the vector equation In such cases the expectedvalue of the function is evaluated as the function of the expected valuesThe coefficient of variation of the function can be calculated as

V2G = g2

1V2(x1) + g22V2(x2) + g2

3V2(x3) +middotmiddot middot (1230)

For the case considered the coefficient of variation of the flow velocityis then

VV =radic

V2n + ( 1

4 )V2S (1231)

Note that although the velocity increases with flood water height y thecoefficient of variation of the velocity is constant for all heights

484 Thomas F Wolff

Knowing the expected value and standard deviation of the velocity andthe critical velocity a performance function can be defined as the ratio ofcritical velocity to the actual velocity (ie the factor of safety) and the limitstate can be taken as this ratio equaling the value 10 If the ratio is assumedto be lognormally distributed the reliability index is

β =ln(

E [C]E [D]

)radic

V2C + V2

D

=ln

(E[Vcrit]

E [V]

)radic

V2Vcrit + V2

V

(1232)

and the probability of failure can be determined from the cumulativedistribution function for the normal distribution

The assumed model and probabilistic moments were used to constructthe conditional probability of failure function in Figure 1219 It is againobserved that a typical levee may be highly reliable for water levels up toabout one-half the height and then the probability of failure may increaserapidly

127 Step 2 Reliability of a reach ndash combiningthe failure modes

Having developed a conditional probability of failure function for each con-sidered failure mode the next step is to combine them to obtain a totalor composite conditional probability of failure function for the reach that

0

001

002

003

004

005

006

007

008

009

0 5 10 15 20

H (feet)

Pr(

failu

re)

Figure 1219 Conditional probability of failure function for surface erosion example

Reliability of levee systems 485

combines all modes As a first approximation it may be assumed that thefailure modes are independent and hence uncorrelated This is not necessar-ily true as some of the conditions increasing the probability of failure forone mode may likely increase the probability of failure by another Notablyincreasing pressures due to underseepage will adversely affect slope stabilityHowever there is insufficient research to better quantify such possible cor-relation between modes Assuming independence considerably simplifies themathematics involved and may be as good a model as can be expected atpresent

1271 Judgmental evaluation of other modes

Before combining failure mode probabilities consider the probability of fail-ure due to other circumstances not readily treated by analytical modelsDuring a field inspection one might observe other items and features thatmight compromise the levee during a flood event These might include animalburrows cracks roots and poor maintenance that might impede detectionof defects or execution of flood-fighting activities To factor in such infor-mation a judgment-based conditional probability function could be addedby answering the following question

Discounting the likelihood of failure accounted for in the quantitativeanalyses but considering observed conditions what would an experi-enced levee engineer consider the probability of failure of this levee fora range of water elevations

For the example problem considered herein the function in Table 1210was assumed While this may appear to be guessing leaving out such infor-mation has the greater danger of not considering the obvious Formalizedtechniques for quantifying expert opinion exist and merit further researchfor application to levees

Table 1210 Assumed conditional probabilityof failure function for judgmental evaluationof observed conditions

Flood water elevation Probability of failure

4000 04050 0014100 0024150 0204175 0404200 080

486 Thomas F Wolff

1272 Composite conditional probability of failurefunction for a reach

For N independent failure modes the reliability or probability of no failureinvolving any mode is the probability of no failure due to mode 1 and nofailure due to mode 2 and no failure due to mode 3 etc As and impliesmultiplication the overall reliability at a given flood water elevation is theproduct of the modal reliability values for that flood elevation or

R = RSSRUSRTSRSERJ (1233)

where the subscripts refer to the identified failure modes Hence theprobability of failure at any flood water elevation is

Pr(f) =1 minus R

=1 minus (1 minus pSS)(1 minus pUS)(1 minus pTS)(1 minus pSE)(1 minus pJ)(1234)

The total conditional probability of failure function is shown in Figure 1220It is observed that probabilities of failure are generally quite low for waterelevations less than one-half the levee height then rise sharply as water levelsapproach the levee crest While there is insufficient data to judge whetherthis shape is a general trend for all levees it has some basis in experienceand intuition

00

02

04

06

08

10

12

395 400 405 410 415 420 425

Flood Water Elevation

Pro

bab

ility

of

Fai

lure

Underseepage

Slope Stability

Through Seepage

Surface Erosion

Judgment

Combined

Figure 1220 Combined conditional probability of failure function for a reach

Reliability of levee systems 487

128 Step 3 Annualizing the probability of failure

Returning to Equation (121) the probability of levee failure is conditionedon the flood water elevation FWE which is a random variable that varieswith time Flood water elevation is usually characterized by an annualexceedance probability function which gives the probability that the highestwater level in a given year will exceed a given elevation

It is desired to find the annual probability of the joint event of floodingto any and all levels and a levee failure given that level An example ofthe methodology is provided in Table 1211 The first column shows thereturn period and the second shows the annual probability of exceedancefor the water level shown in the third column The return period is simplythe inverse of the annual exceedance probability These values are obtainedfrom hydrologic studies In the fourth column titled lumped increment allwater elevations between those in the row above and the row below areassumed to be lumped as a single elevation at the increment midpoint Theannual probability of the maximum water level in a given year being withinthat increment can be taken as the difference of the annual exceedance prob-abilities for the top and bottom of the increment For example the annualprobability of the maximum flood water elevation being between 408 and410 is

Pr(408 lt FWE lt 410) =Pr(FWE gt 408) minus Pr(FWE gt 410)

=0500 minus 0200

=0300

(1235)

Table 1211 Annual probability of flooding for a reach

Returnperiod (yr)

AnnualPr(Exceed)

Water elev Lumpedincrement

AnnualPr(FWE)

Pr(F |FWE) Pr (F)

lt= 408 0505 002 0010102 0500 408

409 +minus 1 0300 003 0009005 0200 410

411 +minus 1 0100 008 00080010 0100 412

413 +minus 1 0050 045 00225020 0050 414

415 +minus 1 0030 072 00216050 0020 416

417 +minus 1 0010 086 000860100 0010 418

419 +minus 1 0005 097 000485200 0005 420

Sum 1000 008465

488 Thomas F Wolff

The flood water elevations for this increment are lumped at elevation 4090and hence the annual probability of the maximum flood water being eleva-tion 4090 is 0300 The annual probability of each flood water elevationis multiplied by the conditional probability of failure given that flood waterelevation (sixth column) to obtain the annual probability of levee failure forthat FWE (seventh column) For this example the values in the sixth columnare taken from Figure 1216 The annual probability of the joint event ofmaximum flood being between 408 and 410 and the levee failing under thisload is

Pr(408 lt FWE lt 410) times Pr(failure|FWE) = 0300 times 003 = 00090(1236)

Finally the values in column seven are summed to obtain the annual prob-ability of the joint event of flooding and failure for all possible waterelevations This can be expressed as

AnnualPr(f) =sum

all FWE

AnnualPr(FWE) times Pr(failure|FWE)FWE

(1237)

As the water level increment becomes small the equation above approaches

AnnualPr(f) =int

all FWE

AnnualPr(FWE) times Pf (failure|FWE)dFWE

(1238)

From Table 1211 the annual probability of failure for this reach is 008465which considers all flood levels (for which annual probabilities decrease withheight) and the probability of failure associated with each flood level (whichincrease with height) Note that this is a rather high probability the exampleproblem was deliberately selected to be of marginal safety to provide relativelarge numbers to easily view the mathematics

Baecher and Christian (2003 488ndash500) use an event-tree approach toillustrate an approach similar to that of Equations (1237) and (1238) Theprocess is started with probabilities of different river discharges Q For eachdischarge river stage may be uncertain For each river stage the levee mayor may not fail by various modes Following all branches of the event treeleads to one of two events failure or non-failure Summing up all the branchprobabilities leading to failure is equivalent to the equations above

Reliability of levee systems 489

129 Step 4 The Levee as a system ndash combiningthe reaches

Having an annual probability of failure for each reach the final step is todetermine the overall annual probability of failure for the entire levee systemwhich is a set of reaches

If a levee were ldquoperfectly uniformrdquo in cross-section and soil profile alongits length intuition would dictate that the probability of a failure for a verylong levee should be greater than that for a short identical levee as morelength of levee is at risk

First it will be assumed that each reach is statistically independent of allothers and then that assumption will be examined further For each reach anannual probability of failure has been determined Subtracting these valuesfrom unity the annual reach reliability Ri is obtained If the levee systemis considered to be a series system of discrete independent reaches such aslinks in a chain the system reliability is the product of the reliabilities foreach link and the same methods can be used for combining probabilities forreaches as was used for combining modes hence

R = R1R2R3 RN (1239)

where the subscripts refer to the separate reaches Hence for a given waterlevel the probability of failure for the system is

Pr(f) =1 minus R

=1 minus (1 minus p1)(1 minus p2)(1 minus p3) (1 minus pN)(1240)

Where the p values are ldquovery smallrdquo the above equation approaches the sumof the pi values

The problem remaining is to determine what is the distance along thelevee beyond which soil properties are statistically independent At shortdistances soil properties are highly correlated and if failure conditions arerealized they might occur over the entire length At long distances in a verylong statistically homogeneous reach soil properties may be very differentfrom each other due to inherent randomness There could be a failure due toweak strength values in one area but values could be very strong in anotherThe long reach may need to be subdivided into an equivalent number ofstatistically independent reaches to model the appropriate total number ofstatistically independent reaches in Equations (1239) and (1240)

Much research has been done in the areas of spatial correlation autocor-relation functions variance reduction functions etc which have a directbearing on this problem However there are seldom sufficient data toaccurately quantify such functions Spatial variability can be most simplyexpressed by using a ldquocorrelation distancerdquo δ discussed by a number of

490 Thomas F Wolff

researchers notably VanMarcke (1977a) For distances less than δ soilproperties can be considered highly correlated ie values are uncertainbut nearly the same from point to point For points separated by distancesconsiderably greater than δ soil property values are independent and uncor-related The degree of correlation between soil properties at separated pointsis assumed to follow some function which is scaled by the δ distance For alevee the longer a ldquostatistically homogeneousrdquo levee is as a multiple of thecorrelation distance the greater the probability of failure

For a levee with non-homogeneous foundation conditions already dividedinto a set discrete reaches based on the local geology the concept ofcorrelation distance can be included as follows

bull Where the reach length is less than the correlation distance δ theprobability of failure for the two-dimensional section could simply betaken as the probability of failure for the reach Alternatively if thereach length is quite small relative to the correlation distance (not com-mon) reaches could be combined and represented by the conditionalprobability function for the most critical reach

bull Where the reach length is greater than δ the probability of failure ofthat reach must be increased to account for length effects This can beapproximated by subdividing the reach into an additional integral num-ber of equivalent reaches with each reach of length δ and one additionalreach for the fractional remainder

The required correlation distance δ is a difficult parameter to estimateas it requires a detailed statistical analysis of a relatively large set ofequally spaced data which is generally not available The reported val-ues for soil strength in the horizontal direction are typically a few hundredfeet (eg VanMarcke 1977b uses 150 ft) On the other hand for leveesVrouwenvelder (1987) uses 500 m for Dutch levees While this is a con-siderable difference maximum reach lengths of 100ndash300 m would seemto be reasonable in levee systems analysis there should always be a newreach assumed when the levee geometry or foundation conditions changesignificantly

1210 Remaining shortcomings

One significant shortcoming remaining is the effect of flood duration As theduration of a flood increases the probability of failure inevitably increasesas extended flooding increases pore pressures and increases the likelihoodand intensity of damaging erosion The analyses herein essentially assumethat the flood has been of sufficient duration that steady-state seepageconditions have developed in pervious substratum materials and perviousembankment materials but no pore pressure adjustment has occurred in

Reliability of levee systems 491

impervious clayey foundation and embankment materials These are rea-sonable assumptions for economic analysis of most levees Further researchwill be required to provide a rational basis for modifying these functions forflood duration

Other shortcomings are enumerated by the National Research Council(2000) and have been discussed earlier

1211 Epilogue New Orleans 2005

During the preparation of this chapter Hurricane Katrina struck the GulfCoast of the United States creating storm surges that led to numerous leveebreaks flooding much of New Orleans Louisiana causing over 100 billiondollars of damage and the loss of over 1000 lives Much of New Orleansis situated below sea level protected by a system of levees (more properlycalled dikes) and pumping stations

A preliminary joint report on the performance of the levees and floodwallswas prepared by two field investigation teams one from the American Soci-ety of Civil Engineers and one sponsored by the National Science Foundationand led by civil engineering faculty at the University of California at Berkeley(Seed et al 2005) The report noted that three leveefloodwall failures (oneat the 17th St Canal and two along the London Avenue Canal) occurredat water levels lower than the top of the protection These were attributedto some combination of sliding instability and inadequate seepage controlThree additional major breaks along the Inner Harbor Navigation Canalwere attributed to overtopping and a large number of additional failuresmore remote from the urban part of the city occurred due to overtoppingandor wave erosion

Subsequently an extensive analysis of the New Orleans levee failures wasconducted by the Interagency Project Evaluation Team (IPET 2006) whichinvolved a number of government agencies academics and consultants ledby the US Army Corps of Engineers The IPET report was in turn reviewedby a second team from the American Society Society of Civil Engineers(ASCE) the External Review Panel (ERP) The IPET report found that oneof the breaches on the Inner Harbor Navigation Canal also likely failed priorto overtopping

This event the most catastrophic levee failure in recent history raisesquestions as to how the New Orleans levee system would have been viewedfrom a reliability perspective and how application of reliability-based prin-ciples might have possibly reduced the level of the damage The writerwas in the area shortly after the failures as a member of the first ASCElevee assessment team However the observations below should be con-strued only as the opinions of the writer and not to reflect any positionof the ASCE which has published its own recommendations at its web site(wwwasceorg)

492 Thomas F Wolff

12111 Level of protection and probability of systemfailure

The system of levees and floodwalls forming the New Orleans hurricaneprotection system were reported to be constructed to heights that wouldcorrespond to return periods of 200ndash300 years or an equivalent annualprobability of 0005ndash00033 While this is several times the expected lifetimeof a person the probability of such an event occurring in onersquos lifetime isnot negligible From the Poisson distribution the probability of at least oneevent in an interval of t years is given as

Pr(x gt 0) = 1 minus eminusλt (1241)

where x is the number of events in the interval t and λ is a parameter denotingthe expected number of events per unit time For an annual probability of 1in 200 λ = p = 0005

Hence the probability of getting at least one 200-year event in a 50-yeartime period (a reasonable estimate of the length of time one might residebehind the levee) is

Pr(x gt 0) = 1 minus e(minus0005)(50) = 0221

which is between 1 in 4 and 1 in 5 It is fair to say the that the publicperception of the following statement

There is between a 1 in 4 and 1 in 5 chance that your home will beflooded sometime in your life

is much different than

Your home is protected by a levee designed higher than the 200 yearflood

A 200-year level of protection for an urban area leaves a significant chanceof flood exceedance in any 50-year period In comparison the primary dikesprotecting the Netherlands are set to height corresponding to 10000 yearreturn period (Voortman 2003) and the interior levees protecting againstthe Rhine are set to a return period of about 1250 years (Vrouwenvelder1987)

In addition to the annual probability of the flood water exceeding thelevee there is some additional probability that the levee will fail at waterlevels below the top of the levee Hence flood return periods only provide anupper bound on reliability and perhaps a poor one Estimating conditionalfailure probabilities to determine an overall probability of levee failure (asopposed to probability of height exceedance) is the purpose of the methods

Reliability of levee systems 493

discussed in this chapter as well as by Wolff (1994) Vrouwenvelder (1987)the National Research Council (2000) and others The three or four failuresin New Orleans that occurred without overtopping illustrate the importanceof considering material variability model uncertainty and other sources ofuncertainty

Finally the levee system in New Orleans is extremely long Local pressreports indicated that system was nearly 170 miles long The probability ofat least one failure in the system can be calculated from Equation (1240)Using a large amount of conservatism assume that there are 340 statisticallyindependent reaches of one half mile each and that conditional probabilityof failure for each reach with water at the top of the levee for each reach is10minus3 The probability of system failure with water at the top of all leveesis then

Pr(f) =1 minus (1 minus 0001)340

=1 minus 07116

=02884

or about 29 But if the conditional probability of failure for each reachdrops to 10minus2 the probability of system failure rises to almost 97 It isevident from the above that very long levees protecting developed areas needto be designed to very high levels of reliability with conditional probabilitiesof failure on the order of 10minus3 or smaller to ensure a reasonably smallprobability of system failure

12112 Reliability considerations in project design

In hindsight applying several principles of reliability engineering may haveprevented some damage and enhanced response in the Katrina disaster

bull Parallel (redundant) systems are inherently much more reliable thanseries systems Long levee systems are primarily series systems a fail-ure at any one point is a failure of the system leading to widespreadflooding Had the interior of New Orleans been subdivided into a setof compartments by interior levees only a fraction of the area mayhave been flooded Levees forming containment compartments are com-mon around tanks in petroleum tank farms Such interior levees wouldundoubtedly run against public perception which would favor buildinga larger stronger levee that ldquocould not failrdquo over interior levees on dryland far from the water that would limit interior flooding

bull However small the probability of the design event one should considerthe consequences of even lower probability events The levee systems inNew Orleans included no provisions for passing water in a controlled

494 Thomas F Wolff

non-destructive manner for water heights exceeding the design event(Seed et al 2005) Had spillways or some other means of landside hard-ening been provided the landside area would have still been flooded butsome of the areas would not have breached This would have reducedthe number of buildings flattened by the walls of water coming throughthe breaches and facilitated the ability to begin pumping out the inte-rior areas after the storm surge had passed It can be perceived that the200ndash300-year level of protection may have been perceived as such a lowprobability event that it would never occur

bull Overall consequences can be reduced by designing critical facilities toa higher level of reliability With the exception of tall buildings essen-tially all facilities in those parts of New Orleans below sea level werelower than the tops of the levees Had critical facilities such as policeand fire stations military bases medical care facilities communicationsfacilities and pumping station operating floors been constructed on highfills or platforms the emergency response may have been significantlyimproved

References

Apel H Thieken A H Merz B and Bloschl G (2004) Flood risk assess-ment and associated uncertainty Natural Hazards and Earth System Sciences 4295ndash308

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering J Wiley and Sons Chichester UK

Buijs F A van Gelder H A J M and Hall J W (2003) Application ofreliability-based flood defence design in the UK ESREL 2003 ndash European Safetyand Reliability Conference 2003 Maastricht Netherlands Available online athttpherontudelftnl2004_1Art2pdf

Duncan J M and Houston W N (1983) Estimating failure probabilities forCalifornia levees Journal of Geotechnical Engineering ASCE 109 2

Fredlund D G and Dahlman A E (1972) Statistical geotechnical properties ofglacial lake edmonton sediments In Statistics and Probability in Civil EngineeringHong Kong University Press Hong Kong

Hammitt G M (1966) Statistical analysis of data from a comparative laboratorytest program sponsored by ACIL Miscellaneous Paper 4-785 US Army EngineerWaterways Experiment Station Corps of Engineers

Harr M E (1987) Reliability Based Design in Civil Engineering McGraw-HillNew York

Hassan A M and Wolff T F (1999) Search algorithm for minimum reliabil-ity index of slopes Journal of Geotechnical and Geoenvironmental EngineeringASCE 124(4) 301ndash8

Interagency Project Evaluation Team (IPET 2006) Performance Evaluation of theNew Orleans and Southeast Louisiana Hurricane Protection System Draft FinalReport of the Interagency Project Evaluation Team 1 June 2006 Available onlineat httpsipetwesarmymil

Reliability of levee systems 495

Ladd C C Foote R Ishihara K Schlosser F and Poulos H G (1977)Stress-deformation and strength characteristics State-of-the-Art report In Pro-ceedings of the Ninth International Conference on Soil Mechanics and Foun-dation Engineering Tokyo Soil Mechanics and Foundation Engineering 14(6)410ndash14

National Research Council (2000) Risk Analysis and Uncertainty in Flood DamageReduction Studies National Academy Press Washington DC httpwwwnapedubooks0309071364html

Peter P (1982) Canal and River Levees Elsevier Scientific Publishing CompanyAmsterdam

Schultze E (1972) ldquoFrequency Distributions and Correlations of Soil Propertiesrdquoin Statistics and Probability in Civil Engineering Hong Kong

Schwartz P (1976) Analysis and performance of hydraulic sand-hill levees PhDdissertation Iowa State University Anes IA

Seed R B Nicholson P G et al (2005) Preliminary Report on the Perfor-mance of the New Orleans Levee Systems in Hurricane Katrina on August29 2005 National Science Foundation and American Society of Civil Engi-neers University of California at Berkeley Report No UCBCITRIS ndash 050117 November 2005 Available online at httpwwwasceorgfilespdfkatrinateamdatareport1121pdf

Shannon and Wilson Inc and Wolff T F (1994) Probability models for geotech-nical aspects of navigation structures Report to the St Louis District US ArmyCorps of Engineers

US Army Corps of Engineers (USACE) (1991) Policy Guidance Letter No 26Benefit Determination Involving Existing Levees Department of the Army Officeof the Chief of Engineers Washington DC Available online at httpwwwusacearmymilinetfunctionscwcecwpbranchesguidance _devpglspgl26htm

US Army Corps of Engineers (USACE) (1995) Introduction to Probability andReliability Methods for Use in Geotechnical Engineering Engineering TechnicalLetter 1110-2-547 30 September 1995 Available online at httpwwwusacearmymilinetusace-docseng-tech-ltrsetl1110-2-547entirepdf

US Army Corps of Engineers (USACE) (1999) Risk-Based Analysis in GeotechnicalEngineering for Support of Planning Studies Engineering Technical Letter 1110-2-556 28 May 1999 Available online at httpwwwusacearmymilinetusace-docseng-tech-ltrsetl1110-2-556

US Army Corps of Engineers (USACE) (2000) Design and Construction of LeveesEngineering Manual 1110-2-1913 April 2000 Available online at httpwwwusacearmymilinetusace-docseng-manualsem1110-2-1913tochtm

VanMarcke E (1977a) Probabilistic modeling of soil profiles Journal of theGeotechnical Engineering Division ASCE 103 1227ndash46

VanMarcke E (1977b) Reliability of earth slopes Journal of the GeotechnicalEngineering Division ASCE 103 1247ndash65

Voortmann H G (2003) Risk-based design of flood defense systems Doctoral dis-sertation Delft Technical University Available online at httpwwwwaterbouwtudelftnlindexphpmenu_items_id=49

Vrouwenvelder A C W M (1987) Probabilistic Design of Flood Defenses ReportNo B-87-404 IBBC-TNO (Institute for Building Materials and Structures of theNetherlands Organization for Applied Scientific Research) The Netherlands

496 Thomas F Wolff

Wolff T F (1985) Analysis and design of embankment dam slopes a probabilisticapproach PhD thesis Purdue University Lafayette IN

Wolff T F (1994) Evaluating the Reliability of Existing Levees prepared forUS Army Engineer Waterways Experiment Station Geotechnical LaboratoryVicksburg MS September 1994 This is also Appendix B to US Army Corps ofEngineers ETL 1110-2-556 Risk-Based Analysis in Geotechnical Engineering forSupport of Planning Studies 28 May 1999

Wolff T F Demsky E C Schauer J and Perry E (1996) Reliability assessment ofdike and levee embankments In Uncertainty in the Geologic Environment FromTheory to Practice Proceedings of Uncertainty rsquo96 ASCE Geotechnical SpecialPublication No 58 eds C D Shackelford P P Nelson and M J S Roth ASCEReston VA pp 636ndash50

Chapter 13

Reliability analysis ofliquefaction potential of soilsusing standardpenetration test

Charng Hsein Juang Sunny Ye Fang and David Kun Li

131 Introduction

Earthquake-induced liquefaction of soils may cause ground failure such assurface settlement lateral spreading sand boils and flow failures whichin turn may cause damage to buildings bridges and lifelines Examplesof such structural damage due to soil liquefaction have been extensivelyreported in the last four decades As stated in Kramer (1996) ldquosome of themost spectacular examples of earthquake damage have occurred when soildeposits have lost their strength and appeared to flow as fluidsrdquo Duringliquefaction ldquothe strength of the soil is reduced often drastically to thepoint where it is unable to support structures or remain stablerdquo

Liquefaction is ldquothe act of process of transforming any substance intoa liquid In cohesionless soils the transformation is from a solid state toa liquefied state as a consequence of increased pore pressure and reducedeffective stressrdquo (Marcuson 1978) The basic mechanism of the initiation ofliquefaction may be elucidated from the observation of behavior of a sandsample undergoing cyclic loading in a cyclic triaxial test In such laboratorytests the pore water pressure builds up steadily as the cyclic deviatoric stressis applied and eventually approaches the initially applied confining pressureproducing an axial strain of about 5 in double amplitude (DA) Such astate has been referred to as ldquoinitial liquefactionrdquo or simply ldquoliquefactionrdquoThus the onset condition of liquefaction or cyclic softening is specified interms of the magnitude of cyclic stress ratio required to produce 5 DAaxial strain in 20 cycles of uniform load application (Seed and Lee 1966Ishihara 1993 Carraro et al 2003)

From an engineerrsquos perspective three aspects of liquefaction are ofparticular interest they include (1) the likelihood of liquefaction occurrenceor triggering of a soil deposit in a given earthquake referred to herein asliquefaction potential (2) the effect of liquefaction (ie the extent of ground

498 Charng Hsein Juang Sunny Ye Fang and David Kun Li

failure caused by liquefaction) and (3) the response of foundations in aliquefied soil In this chapter the focus is on the evaluation of liquefactionpotential

The primary factors controlling the liquefaction of a saturated cohe-sionless soil in level ground are the intensity and duration of earthquakeshaking and the density and effective confining pressure of the soil Sev-eral approaches are available for evaluating liquefaction potential includingthe cyclic stress-based approach the cyclic strain-based approach and theenergy-based approach In the cyclic strain-based approach (eg Dobry et al1982) both ldquoloadingrdquo and ldquoresistancerdquo are described in terms of cyclicshear strain Although the cyclic strain-based approach has an advantageover the cyclic stress-based approach in that pore water pressure gener-ation is more closely related to cyclic strains than cyclic stresses cyclicstrain amplitudes cannot be predicted as accurately as cyclic stress ampli-tudes and equipment for cyclic strain-controlled testing is less readilyavailable than equipment for cyclic stress-controlled testing (Kramer andElgamal 2001) Thus the cyclic strain-based approach is less commonlyused than the cyclic stress-based approach The energy-based approach isconceptually attractive as the dissipated energy reflects both cyclic stressand strain amplitudes Several investigators have established relationshipsbetween the pore pressure development and the dissipated energy dur-ing ground shaking (Davis and Berrill 1982 Berrill and Davis 1985Figueroa et al 1994 Ostadan et al 1996) The initiation of liquefac-tion can be formulated by comparing the calculated unit energy from thetime series record of a design earthquake with the resistance to liquefac-tion in terms of energy based on in-situ soil properties (Liang et al 1995Dief 2000) The energy-based methods however are also less commonlyused than the cyclic stress-based approach Thus the focus in this chapteris on the evaluation of liquefaction potential using the cyclic stress-basedmethods

Two general types of cyclic stress based-approach are available for assess-ing liquefaction potential One is by means of laboratory testing (eg cyclictriaxial test and cyclic simple shear test) of undisturbed samples and theother involves use of empirical relationships that relate observed field behav-ior with in situ tests such as standard penetration test (SPT) cone penetrationtest (CPT) shear wave velocity measurement (Vs) and the Becker penetrationtest (BPT) Because of the difficulties and costs associated with high-qualityundisturbed sampling and subsequent high-quality testing of granular soilsuse of in-situ tests along with the case histories-calibrated empirical relation-ships (ie liquefaction boundary curves) has been and is still the dominantapproach in engineering practise

The most widely used cyclic stress-based method for liquefactionpotential evaluation in North America and throughout much of theworld is the simplified procedure pioneered by Seed and Idriss (1971)

Analysis of liquefaction potential 499

The simplified procedure was developed based on field observations andfield and laboratory tests with a strong theoretical basis Case histo-ries of liquefactionno-liquefaction were collected from sites on level togently sloping ground underlain by Holocene alluvial or fluvial sedi-ments at shallow depths (lt 15 m) In a case history the occurrence ofliquefaction was primarily identified with surface manifestations such aslateral spread ground settlement and sand boils Because the simplifiedprocedure was eventually ldquocalibratedrdquo based on such case histories theldquooccurrence of liquefactionrdquo should be interpreted accordingly that isthe emphasis is on the surface manifestations This definition of liquefac-tion does not always correspond to the initiation of liquefaction definedbased on the 5 DA axial strain in 20 cycles of uniform load typi-cally adopted in the laboratory testing The stress-based approach thatfollows the simplified procedure by Seed and Idriss (1971) is consideredherein

The state of the art for evaluating liquefaction potential was reviewedin 1985 by a committee of the National Research Council The report ofthis committee became the standard reference for practicing engineers inNorth America (NRC 1985) About 10 years later another review wassponsored by the National Center for Earthquake Engineering Research(NCEER) at the State University of New York at Buffalo This workshopfocused on the stress-based simplified methods for liquefaction potentialevaluation The NCEER Committee issued a report in 1997 (Youd andIdriss 1997) but continued to re-assess the state of the art and in 2001published a summary paper (Youd et al 2001) which represents the cur-rent state of the art on the subject of liquefaction evaluation It focuseson the fundamental problem of evaluating the potential for liquefaction inlevel or nearly level ground using in-situ tests to characterize the resistanceto liquefaction and the Seed and Idriss (1971 1982) simplified method tocharacterize the duration and intensity of the earthquake shaking Amongthe methods recommended for determination of liquefaction resistanceonly the SPT-based method is examined herein as the primary purposeof this chapter is on the reliability analysis of soil liquefaction The SPT-based method is used only as an example to illustrate the probabilisticapproach

In summary the SPT-based method as described in Youd et al (2001) isadopted here as the deterministic model for liquefaction potential evaluationThis method is originated by Seed and Idriss (1971) but has gone throughseveral stages of modification (Seed and Idriss 1982 Seed et al 1985 Youdet al 2001) In this chapter a limit state of liquefaction triggering is definedbased on this SPT-based method and the issues of parameter and modeluncertainties are examined in detail followed by probabilistic analyses usingreliability theory Examples are presented to illustrate both the deterministicand the probabilistic approaches

500 Charng Hsein Juang Sunny Ye Fang and David Kun Li

132 Deterministic approach

1321 Formulation of the SPT-based method

In the current state of knowledge the seismic loading that could cause asoil to liquefy is generally expressed in terms of cyclic stress ratio (CSR)Because the simplified stress-based methods were all developed based oncalibration with field data with different earthquake magnitudes and over-burden stresses CSR is often ldquonormalizedrdquo to a reference state with momentmagnitude Mw = 75 and effective overburden stress σ prime

v = 100 kPa At thereference state the CSR is denoted as CSR75σ which may be expressed as(after Seed and Idriss 1971)

CSR75σ = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ (131)

where σv = the total overburden stress at the depth of interest (kPa) σ primev = the

effective stress at the depth of interest (kPa) g = the unit of the accelerationof gravity amax = the peak horizontal ground surface acceleration (amaxgis dimensionless) rd = the depth-dependent stress reduction factor (dimen-sionless) MSF = the magnitude scaling factor (dimensionless) and Kσ = theoverburden stress adjustment factor for the calculated CSR (dimensionless)For the peak horizontal ground surface acceleration the geometric meanis preferred for use in engineering practice although use of the larger ofthe two orthogonal peak accelerations is conservative and allowable (Youdet al 2001)

For routine practice and no critical projects the following equations maybe used to estimate the values of rd (Liao and Whitman 1986)

rd = 10 minus 000765d for d lt 915m (132a)

rd = 1174 minus 00267d for 915m lt d le 20m (132b)

where d = the depth of interest (m) The variable MSF may be calculatedwith the following equation (Youd et al 2001)

MSF = (Mw75)minus256 (133)

It should be noted that different formulas for rd and MSF have been pro-posed by many investigators (eg Youd et al 2001 Idriss and Boulanger2006 Cetin et al 2004) To be consistent with the SPT-based deter-ministic method presented herein use of Equations (132) and (133) isrecommended

As noted previously the variable Kσ is a stress adjustment factor usedto adjust CSR to the effective overburden stress of σ prime

v = 100 kPa This is

Analysis of liquefaction potential 501

different from the overburden stress correction factor (CN) that is applied tothe SPT blow count (N60) which is described later The adjustment factorKσ is defined as follows (Hynes and Olsen 1999)

Kσ = (σ primevPa)(f minus1) (134)

where f asymp 06 minus 08 and Pa is the atmosphere pressure (asymp100 kPa) Datafrom the existing database are insufficient for precise determination of thecoefficient f For routine practice and no critical projects f = 07 may beassumed and thus the exponent in Equation (134) would be minus03

For the convenience of presentation hereinafter the normalized cyclicstress ratio CSR75σ is simply labeled as CSR whenever no confusion wouldbe caused by such use For liquefaction potential evaluation CSR (as theseismic loading) is compared with liquefaction resistance expressed as cyclicresistance ratio (CRR) As noted previously the simplified stress-based meth-ods were all developed based on calibration with field observations Suchcalibration process is generally based on the concept that cyclic resistanceratio (CRR) is the limiting CSR beyond which the soil will liquefy Basedprimarily on this concept and with engineering judgment the followingequation is recommended by Youd et al (2001) for the determination ofCRR using SPT data

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

(135)

where N160cs (dimensionless) is the clean-sand equivalence of the overbur-den stress-corrected SPT blow count defined as follows

N160cs = α+βN160 (136)

where α and β are coefficients to account for the effect of fines contentdefined later and N160 is the SPT blow count normalized to the referencehammer energy efficiency of 60 and effective overburden stress of 100 kPadefined as

N160 = CNN60 (137)

where N60 = the SPT blow count at 60 hammer energy efficiency andcorrected for rod length sampler configuration and borehole diameter(Skempton 1986 Youd et al 2001)

CN = (Paσprimev)05 le 17 (138)

502 Charng Hsein Juang Sunny Ye Fang and David Kun Li

The coefficients α and β in Equation (136) are related to fines content (FC)as follows

α = 0 for FC le 5 (139a)

α = exp [176 minus (190FC2)] for 5 lt FC lt 35 (139b)

α = 50 for FC ge 35 (139c)

β = 10 for FC le 5 (1310a)

β = [099 + (FC151000)] for 5 lt FC lt 35 (1310b)

β = 12 for FC ge 35 (1310c)

Equations (131) through (1310) collectively represent the SPT-base deter-ministic model for liquefaction potential evaluation recommended by Youdet al (2001) This model is recognized as the current state of the art forliquefaction evaluation using SPT The reader is referred to Youd et al(2001) for additional details on this model and its parameters In a deter-ministic evaluation factor of safety (FS) defined as FS = CRRCSR is usedto ldquomeasurerdquo liquefaction potential In theory liquefaction is said to occurif FS le 1 and no liquefaction if FS gt 1 However caution must be exer-cised when interpreting the calculated FS In the back analysis of a casehistory or in a post-earthquake investigation analysis use of FS le 1 to judgewhether liquefaction had occurred could be misleading as the existing sim-plified methods tend to be conservative (in other words there could be modelbias toward the conservative side) Because of model and parameter uncer-tainties FS gt 1 does not always correspond to no-liquefaction and FS le 1does not always correspond to liquefaction

The selection of a minimum required FS value for a particular project ina design situation depends on factors such as the perceived level of modeland parameter uncertainties the consequence of liquefaction in terms ofground deformation and structures damage potential the importance of thestructures and the economic consideration Thus the process of selectingan appropriate FS is not a trivial exercise In a design situation a factor ofsafety of 12ndash15 is recommended by the Building Seismic Safety Council(1997) in conjunction with the use of the Seed et al (1985) method for liq-uefaction evaluation Since the Youd et al (2001) method is essentially anupdated version of and is perceived as conservative as the Seed et al (1985)method the recommended range of FS by the Building Seismic Safety Coun-cil (1997) should be applicable In recent years however there is growingtrend to assess liquefaction potential in terms of probability of liquefaction(Liao et al 1988 Juang et al 2000 2002 Cetin et al 2004) To facilitatethe use of probabilistic methods calibration of the calculated probabilityto the previous engineering experience is needed In a previous study by

Analysis of liquefaction potential 503

Juang et al (2002) a factor of safety of 12 in the Youd et al (2001) methodwas found to correspond approximately to a mean probability of 030 Inthis chapter further calibration of the calculated probability of liquefactionis presented later

1322 Example No 1 deterministic evaluation of anon-liquefied case

This example concerns a non-liquefied case Field observation of the sitewhich is designated as San Juan B-5 (Idriss et al as cited in Cetin 2000) indi-cated no occurrence of liquefaction during the 1977 Argentina earthquakeThe mean values of seismic and soil parameters at the critical depth (29 m)are given as follows N60 = 80 FC = 3 σ prime

v = 381 kPa σv = 456kPa amax = 02 g and Mw = 74 (Cetin 2000) First CRR is calculatedas follows

Using Equation (138)

CN = (Paσprimev)05 = (100381)05 = 162 lt 17

Using Equation (137)

N160 = CNN60 = (162)(80) = 130

Since FC = 3 lt 5 thus α = 0 and β = 1 according to Equations (139)and (1310) Thus according to Equation (136)

N160cs = α+βN160 = 130

Finally using Equation (135) we have

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

= 0141

Next the intermediate parameters of CSR are calculated as follows

MSF = (Mw75)minus256 = (7475)minus256 = 1035

Kσ = (σ primevPa)(f minus1) = (381100)minus03 = 1335

rd = 10 minus 000765d = 10 minus 000765(29) = 0978

504 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Finally using Equation (131) we have

CSR75σ = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ

= 065(

456381

)(02)(0978)[(1035)(1335)]

= 0110

The factor of safety is calculated as follows

FS = CRRCSR = 01410110 = 128

As a back analysis of a case history this FS value would suggest no liq-uefaction which agrees with the filed observation However a probabilisticanalysis might be necessary or desirable to complement the judgment basedon the calculated FS value

1323 Example No 2 deterministic evaluation of aliquefied case

This example concerns a liquefied case Field observation of the site des-ignated as Ishinomaki-2 (Ishihara et al as cited in Cetin 2000) indicatedoccurrence of liquefaction during the 1978 Miyagiken-Oki earthquake Themean values of seismic and soil parameters at the critical depth (37 m) aregiven as follows N160 = 5 FC = 10 σ prime

v = 3628 kPa σv = 5883 kPaamax = 02 g and Mw = 74 (Cetin 2000)

Similar to the analysis performed in Example 1 the calculations of theCRR CSR and FS are carried out as follows

α = exp [176 minus (190FC2)] = exp[176 minus (190102)] = 0869

β = [099 + (FC151000)] = [099 + (10151000)] = 1022

N160cs = α+βN160 = 598

CRR = 134 minus N160cs

+ N160cs

135+ 50

[10 middot N160cs + 45]2 minus 1200

= 134 minus 598

+ 598135

+ 50[10 times 598 + 45]2 minus 1

200

= 0080

MSF = (Mw75)minus256 = (7475)minus256 = 1035

Kσ = (σ primevPa)(f minus1) = (3628100)minus03 = 1356

Analysis of liquefaction potential 505

rd = 10 minus 000765d = 10 minus 000765(37) = 0972

CSR = 065(σv

σ primev

)(amax

g

)(rd)MSFKσ = 0146

FS = CRRCSR = 0545

The calculated FS value is much below 1 As a back analysis of a case his-tory the calculated FS value would confirm the field observation with agreat certainty However a probabilistic analysis might still be desirable tocomplement the judgment based on the calculated FS value

133 Probabilistic approach

Various models for estimating the probability of liquefaction have been pro-posed (Liao et al 1988 Juang et al 2000 2002 Cetin et al 2004) Thesemodels are all data-driven meaning that they are established based on statis-tical analyses of the databases of case histories To calculate the probabilityusing these empirical models only the best estimates (ie the mean values) ofthe input variables are required the uncertainty in the model termed modeluncertainty and the uncertainty in the input variables termed parameteruncertainty are excluded from the analysis Thus the calculated proba-bilities might be subject to error if the effect of model andor parameteruncertainty is significant A more fundamental approach to this problemwould be to adopt a reliability analysis that considers both model and param-eter uncertainties The formulation and procedure for conducting a rigorousreliability analysis is described in the sections that follow

1331 Limit state of liquefaction triggering

In the context of reliability analysis presented herein the limit state of lique-faction triggering is essentially the boundary curve that separates ldquoregionrdquoof liquefaction from the region of no-liquefaction An example of a limitstate is shown in Figure 131 where the SPT-based boundary curve recom-mended by Youd et al (2001) is shown with 148 case histories As reflectedin the scattered data shown in Figure 131 uncertainty exists as to wherethe boundary curve should be ldquopositionedrdquo This uncertainty is the modeluncertainty mentioned previously The issue of model uncertainty is dis-cussed later At this point the limit state may be expressed symbolically asfollows

h(x) = CRR minus CSR = 0 (1311)

where x is a vector of input variables that consist of soil and seismic param-eters that are required in the calculation of CRR and CSR and h(x) lt 0indicates liquefaction

506 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Figure 131 An example limit state of liquefaction triggering

As noted previously Equations (131) through (1310) collectively repre-sent the SPT-base deterministic model for liquefaction potential evaluationrecommended by Youd et al (2001) Two parameters N160 and FC arerequired in the calculation of CRR and both are assumed to be randomvariables Since MSF is a function of Mw and Kσ is a function of σ prime

v fiveparameters including amaxMw σ prime

vσv and rd are required for calculat-ing CSR The first four parameters amaxMw σ prime

v and σv are assumed tobe random variables The parameter rd is a function of depth (d) and is notconsidered as a random variable (since CSR is evaluated for soil at a given d)Based on the above discussions a total of six random variables are identi-fied in the deterministic model by Youd et al (2001) described previouslyThus the limit state of liquefaction based on this deterministic model maybe expressed as follows

h(x) = CRR minus CSR = h(N160FCMwamaxσprimev and σv) = 0 (1312)

It should be noted that while the parameter rd is not considered as a randomvariable the uncertainty does exist in the model for rd (Equation (132))just as the uncertainty exists in the model for MSF (Equation (133)) and inthe model for Kσ (Equation (134)) The uncertainty in these ldquocomponentrdquomodels contributes to the uncertainty of the calculated CSR which inturn contributes to the uncertainty of the CRR model since CRR is con-sidered as the limiting CSR beyond which the soil will liquefy Ratherthan dealing with the uncertainty of each component model where thereis a lack of data for calibration the uncertainty of the entire limit state

Analysis of liquefaction potential 507

model is characterized as a whole since the only data available for modelcalibration are field observations of liquefaction (indicated by h(x) le 0) orno liquefaction (indicated by h(x) gt 0) Thus the limit state model thatconsiders model uncertainty may be rewritten as

h(x) = c1CRR minus CSR = h(c1N160FCMwamaxσprimev and σv) = 0

(1313)

where random variable c1 represents the uncertainty of the limit state modelthat is yet to be characterized Use of a single random variable to characterizethe uncertainty of the entire limit state model is adequate since CRR isdefined as the limiting CSR beyond which the soil will liquefy as notedpreviously Thus only one random variable c1 applied to CRR is requiredIn fact Equation (1313) may be interpreted as h(x) = c1 CRR ndash CSR =c1 FS ndash1 = 0 Thus the uncertainty of the entire limit state model is seenhere as the uncertainty in the calculated FS where data (field observations)are available for calibration For convenience of presentation hereinafterthe random variable c1 is referred to as the model bias factor or simplymodel factor

1332 Parameter uncertainty

For a realistic estimate of liquefaction probability the reliability analy-sis must consider both parameter and model uncertainties The issue ofmodel uncertainty is discussed later Thus for reliability analysis of a futurecase the uncertainties of input random variables must first be assessedFor each input variable this process involves the estimation of the meanand standard deviation if the variable is assumed to follow normal orlognormal distribution The engineer usually can make a pretty good esti-mate of the mean of a variable even with limited data This probably hasto do with the well-established statistics theory that the ldquosample meanrdquois a best estimate of the ldquopopulation meanrdquo Thus the following discus-sion focuses on the estimation of standard deviation of each input randomvariable

Duncan (2000) suggested that the standard deviation of a random variablemay be obtained by one of the following three methods (1) direct calculationfrom data (2) estimate based on published coefficient of variation (COV)and (3) estimate based on the ldquothree-sigma rulerdquo (Dai and Wang 1992) Inthe last method the knowledge of the highest conceivable value (HCV) andthe lowest conceivable value (LCV) of the variable is used to calculate thestandard deviation σ as follows (Duncan 2000)

σ = HCV minus LCV6

(1314)

508 Charng Hsein Juang Sunny Ye Fang and David Kun Li

It should be noted that the engineer tends to under-estimate the range of agiven variable (and thus the standard deviation) particularly if the estimatewas based on very limited data and judgment was required Thus for asmall sample size a value of less than 6 should be used for the denominatorin Equation (1314) Whenever in doubt a sensitivity analysis should beconducted to investigate the effect of different levels of COV of a particularvariable on the results of reliability analysis

Typical ranges of COVs of the input variables according to the publisheddata are listed in Table 131 It should be noted that the COVs of the earth-quake parameters amax and Mw listed in Table 131 are based on valuesreported in the published databases of case histories where recorded strongground motions andor locally calibrated data were available The COVof amax based on general attenuation relationships could easily be as highas 050 (Haldar and Tang 1979) According to Youd et al (2001) for afuture case the variable amax may be estimated using one of the followingmethods

1 Using empirical correlations of amax with earthquake magnitude dis-tance from the seismic energy source and local site conditions

2 Performing local site response analysis (eg using SHAKE or othersoftware) to account for local site effects

3 Using the USGS National Seismic Hazard web pages and the NEHRPamplification factors

Table 131 Typical coefficients of variation of input random variables

Random variable Typical range of COVa References

N160 010ndash040 Harr (1987)Gutierrez et al (2003)Phoon and Kulhawy (1999)

FC 005ndash035 Gutierrez et al (2003)σ prime

v 005ndash020 Juang et al (1999)σv 005ndash020 Juang et al (1999)amax 010ndash020b Juang et al (1999)Mw 005ndash010 Juang et al (1999)

NoteaThe word ldquotypicalrdquo here implies the range approximately bounded by the 15th percentile andthe 85th percentile estimated from case histories in the existing databases such as Cetin (2000)Published COVs are also considered in the estimate given here The actual COV values could behigher or lower depending on the variability of the site and the quality and quantity of data that areavailablebThe range is based on values reported in the published databases of case histories where recordedstrong ground motions and locally calibrated data were available However the COV of amaxbased on general attenuation relationships or amplification factors could easily be as high as orover 050

Analysis of liquefaction potential 509

Use of the amplification factors approach is briefly summarized inthe following The USGS National Hazard Maps (Frankel et al 1997)provide rock peak ground acceleration (PGA) and spectral acceleration(SA) for a specified locality based on latitudelongitude or zip codeThe USGS web page (httpearthquakeusgsgovresearchhazmaps) pro-vides PGA value and SA values at selected spectral periods (For exam-ple T = 02 03 and 10 s) each with six levels of probability ofexceedance including 1 probability of exceedance in 50 years (annualrate of 00002) 2 probability of exceedance in 50 years (annualrate of 00004) 5 probability of exceedance in 50 years (annual rateof 0001) 10 probability of exceedance in 50 years (annual rate of0002) and 20 probability of exceedance in 50 years (annual rateof 0004) and 50 probability of exceedance in 75 years (annual rateof 0009) The six levels of probability of exceedance are often referredto as the six seismic hazard levels with corresponding earthquake returnperiods of 4975 2475 975 475 224 and 108 years respectively Fora given locality a PGA can be obtained for a specified probability ofexceedance in an exposure time from the USGS National Seismic HazardMaps

For liquefaction analysis the rock PGA needs to be converted to peakground surface acceleration at the site amax Ideally the conversion shouldbe carried out based on site response analysis Various simplified proce-dures are also available for an estimate of amax (eg Green 2001 Gutierrezet al 2003 Stewart et al 2003 Choi and Stewart 2005) As an examplea simplified procedure for estimating amax perhaps in the simplest form isexpressed as follows

amax = Fa (PGA) (1315)

where Fa is the amplification factor which in a simplest form may beexpressed as a function of rock PGA and the NEHRP site class (NEHRP1998) Figure 132 shows an example of a simplified chart for the ampli-fication factor The NEHRP site classes used in Figure 132 are basedon the mean shear wave velocity of soils in the top 30 m as listed inTable 132

Choi and Stewart (2005) developed a more sophisticated model for groundmotion amplification that is a function of the average shear wave velocityover the top 30 m of soils VS30 and ldquorockrdquo reference PGA The amplifica-tion factors are defined relative to ldquorockrdquo reference motions from severalattenuation relationships for active tectonic regions including those ofAbrahamson and Silva (1997) Sadigh et al (1997) and Campbell andBozorgnia (2003) The databases used in model development cover theparameter spaces VS30 = 130 sim 1300 ms and PGA = 002 sim 08 g andthe model is considered valid only in these ranges of parameters The Choi

510 Charng Hsein Juang Sunny Ye Fang and David Kun Li

0 01 02 03 04 05

Rock PGA (g)

0

1

2

3

4

Am

plifi

catio

n fa

ctor

Fa

Site B (Rock)

Site C (Very dense soil and soft rock)

Site D (Stiff soil)

Site E (Soft soil)

Figure 132 Amplification factor as a function of rock PGA and the NEHRP site class (re-produced from Gutierrez et al 2003)

Table 132 Site classes (categories) in NEHRP provisions

NEHRP category(soil profile type)

Description

A Hard rock with measured mean shear wave velocity in the top30 m vs gt 1500 ms

B Rock with 760 ms lt vs le 1500 msC Dense soil and soft rock with 360 ms lt vs le 760 msD Stiff soil with 180 ms lt vs le 360 msE Soil with vs le 180 ms or any profile with more than 3 m of soft

clay (plasticity index PI gt 20 water content w gt 40 andundrained shear strength su lt 25 kPa)

F Soils requiring a site-specific study eg liquefiable soils highlysensitive clays collapsible soils organic soils etc

and Stewart (2005) model for amplification factor (Fij) is expressed asfollows

ln(Fij) = c ln

(VS30ij

Vref

)+ b ln

(PGArij

01

)+ηi + εij (1316)

where PGAr is the rock PGA expressed in units of g b is a function ofregression parameters c and Vref are regression parameters ηi is a random

Analysis of liquefaction potential 511

effect term for earthquake event i and εij represents the intra-event modelresidual for motion j in event i

Choi and Stewart (2005) provided many sets of empirical constants foruse of Equation (1316) As an example for a site where the attenuationrelationship by Abrahamson and Silva (1997) is applicable and a spectralperiod T= 02 sec is specified Equation (1316) becomes

ln(Fa) = minus031 ln(

VS30

453

)+ b ln

(PGAr

01

)(1317)

In Equation (1317) Fa is the amplification factor VS30 is obtained fromsite characterization PGAr is obtained for reference rock conditions usingthe attenuation relationship by Abrahamson and Silva (1997) and b isdefined as follows (Choi and Stewart 2005)

b = minus052 for Site Category E (1318a)

b = minus019 minus 0000023 (VS30 minus 300)2 for 180 lt VS30 lt 300(ms)(1318b)

b = minus019 for 300 lt VS30 lt 520(ms) (1318c)

b = minus019 + 000079(VS30 minus 520) for 520 lt VS30 lt 760(ms)(1318d)

b = 0 for VS30 gt 760(ms) (1318e)

The total standard deviation for the amplification factor Fa obtainedfrom Equation (1317) came from two sources the inter-event standarddeviation of 027 and the intra-event standard deviation of 053 Thusfor the given scenario (the specified spectral period and the chosen atten-uation model) the total standard deviation is

radic(027)2 + (053)2 = 059

The peak ground surface acceleration amax can be obtained from Equation(1315)

For subsequent reliability analysis amax obtained from Equation (1315)may be considered as the mean value For a specified probability ofexceedance (and thus a given PGA) the variation of this mean amax isprimarily caused by the uncertainty in the amplification factor model Useof simplified amplification factors for estimating amax tends to result in alarge variation and thus for important projects concerted effort to reducethis uncertainty using more accurate methods andor better quality datashould be made whenever possible The reader is referred to Bazzurro andCornell (2004 ab) and Juang et al (2008) for detailed discussions of thissubject

The magnitude of Mw can also be derived from the USGS National SeismicHazard web pages through a de-aggregation procedure Detailed information

512 Charng Hsein Juang Sunny Ye Fang and David Kun Li

may be obtained from httpeqintcrusgsgovdeaggint2002indexphpA summary of the procedure is provided in the following

The task of seismic hazard de-aggregation involves the determination ofearthquake parameters principally magnitude and distance for use in aseismic-resistant design In particular calculations are made to determinethe statistical mean and modal sources for any given US site for the sixhazard levels (or the corresponding probabilities of exceedance)

The seismic hazard presented in the USGS Seismic Hazard web page is de-aggregated to examine the ldquocontribution to hazardrdquo (in terms of frequency)as a function of magnitude and distance These plots of ldquocontribution tohazardrdquo as a function of magnitude and distance are useful for specify-ing design earthquakes On the available de-aggregation plots from theUSGS website the height of each bar represents the percent contributionof that magnitude and distance pair (or bin) to the specified probabilities ofexceedance The distribution of the heights of these bars (ie frequencies)is essentially a joint probability mass function of magnitude and distanceWhen this joint mass function is ldquointegratedrdquo along the axis of distancethe ldquomarginalrdquo or conditional probability mass function of the magnitude isobtained This distribution of Mw is obtained for the same specified probabil-ity of exceedance as the one from which the PGA is derived The distribution(or the uncertainty) of Mw here is due primarily to the uncertainty in seismicsources

It should also be noted that for selection of a design earthquake in a deter-ministic approach the de-aggregation results are often described in termsof the mean magnitude However use of the modal magnitude is preferredby many engineers because the mode represents the most likely source inthe seismic-hazard model whereas the mean might represent an unlikelyor even unconsidered source especially in the case of a strongly bimodaldistribution

In summary a pair of PGA and Mw may be selected at a specified hazardlevel or probability of exceedance The selected PGA is converted to amaxand the pair of amax and Mw is then used in the liquefaction evaluationFor reliability analysis the values of amax and Mw determined as describedpreviously are taken as the mean values and the variations of these variablesare estimated and expressed in terms of the COVs The reader is referred toJuang et al (2008) for additional discussions on this subject

1333 Correlations among input random variables

The correlations among the input random variables should be considered in areliability analysis The correlation coefficients may be estimated empiricallyusing statistical methods Except for the pair of amax and Mw the correla-tion coefficient between each pair of input variables used in the limit statemodel is estimated based on an analysis of the actual data in the existing

Analysis of liquefaction potential 513

Table 133 Coefficients of correlation among the six input random variables

Variable Variable N160 FC primev v amax Mw

N160 1 0 03 03 0 0FC 0 1 0 0 0 0σ prime

v 03 0 1 09 0σv 03 0 09 1 0 0amax 0 0 0 0 1 09a

Mw 0 0 0 0 09a 1

NoteaThis is estimated based on local attenuation relationships calibrated to given historic earthquakesThis correlation may be used for back-analysis of a case history The correlation of the two parametersat a locality subject to uncertain sources as in the analysis of a future case could be much lower oreven negligible

databases of case histories The correlation coefficient between amax andMw is taken to be 09 which is based on statistical analysis of the datagenerated from the attenuation relationships (Juang et al 1999) The coef-ficients of correlation among the six input random variables are shown inTable 133 The correlation between the model uncertainty factor (c1 inEquation 1313) and each of the six input random variables is assumedto be 0

It should be noted that the correlation matrix as shown in Table 133 mustbe symmetric and ldquopositive definiterdquo (Phoon 2004) If this condition is notsatisfied a negative variance might be obtained which would contradict thedefinition of the variance In ExcelTM the condition can be easily checkedusing ldquoMAT_CHOLESKYrdquo It should be noted that MAT_CHOLESKYcan be executed with a free ExcelTM add-in ldquomatrixxlardquo which mustbe installed once by the user The file ldquomatrixxlardquo may be downloadedfrom httpdigilanderliberoitfoxesindexhtm For the correlation matrixshown in Table 133 the diagonal entries of the matrix of Choleskyfactors are all positive thus the condition of ldquopositive definitenessrdquo issatisfied

1334 Model uncertainty

The issue of model uncertainty is important but rarely emphasized in thegeotechnical engineering literature perhaps because it is difficult to addressInstead of addressing this issue directly Zhang et al (2004) suggested aprocedure for reducing the uncertainty of model prediction using Bayesianupdating techniques However since a large quantity of liquefaction casehistories (Cetin 2000) is available for calibration of the calculated relia-bility indexes an estimate of the model factor (c1 in Equation (1313)) is

514 Charng Hsein Juang Sunny Ye Fang and David Kun Li

possible as documented previously by Juang et al (2004) The procedurefor estimating model factor involves two steps (1) deriving a Bayesian map-ping function based on the database of case histories and (2) using thecalibrated Bayesian mapping function as a reference to back-figure the modelfactor c1 The detail of this procedure is not repeated herein only a briefsummary of the results obtained from the calibration of the limit state model(Equation (1313)) is provided and the reader is referred to Juang et al(2004 2006) for details of the procedure

The model factor c1 is assumed to follow lognormal distribution With theassumption of lognormal distribution the model factor c1 can be character-ized with a mean microc1 and a standard deviation (or coefficient of variationCOV) In a previous study (Juang et al 2004) the effect of the COV ofthe model factor (c1) on the final probability obtained through reliabilityanalysis was found to be insignificant relative to the effect of microc1 Thusfor the calibration (or estimation) of mean model factor microc1 an assump-tion of COV = 0 is made It should be noted however that because of theassumption of COV = 0 and the effect of other factors such as data scatterthere will be variation on the calibrated microc1 which would be reflected in itsstandard deviation σmicroc1

The first step in the model calibration process is to develop Bayesian map-ping functions based on the distributions of the values of reliability index β

for the group of liquefied cases and the group of non-liquefied cases (Juanget al 1999)

PL = P(L|β) = P(β|L)P(L)P(β|L)P(L) + P(β|NL)P(NL)

(1319)

where P(L|β) = probability of liquefaction for a given β P(β|L) = probabilityof β given that liquefaction did occur P(β|NL) = probability of β given thatliquefaction did not occur P(L) = prior probability of liquefaction P(NL) =prior probability of no-liquefaction

The second step in the model calibration process is to back-figure themodel factor microc1 using the developed Bayesian mapping functions Bymeans of a trial-and-error process with varying microc1 values the uncer-tainty of the limit state model (Equation (1313)) can be calibrated usingthe probabilities interpreted from Equation (1319) for a large number ofcase histories The essence of the calibration here is to find an ldquooptimumrdquomodel factor microc1 so that the calibrated nominal probabilities match thebest with the reference Bayesian mapping probabilities for all cases in thedatabase Using the database compiled by Cetin (2000) the mean modelfactor is calibrated to be microc1 = 096 and the variation of the calibratedmean model factor is reflected in the estimated standard deviation ofσmicroc1 = 004

Analysis of liquefaction potential 515

With the given limit state model (Equation (1313) and associatedequations) and the calibrated model factor reliability analysis of a futurecase can be performed once the mean and standard deviation of each inputrandom variable are obtained

1335 First-order reliability method

Because of the complexity of the limit state model (Equation (1313)) andthe fact that the basic variables of the model are non-normal and cor-related no closed-form solution for reliability index of a given case ispossible For reliability analysis of such problems numerical methods suchas the first-order reliability method (FORM) are often used The generalapproach undertaken by the FORM is to transform the original randomvariables into independent standard normal random variables and theoriginal limit state function into its counterpart in the transformed orldquostandardrdquo variable space The reliability index β is defined as the short-est distance between the limit state surface and the origin in the standardvariable space The point on the limit state surface that has the shortestdistance from the origin is referred to as the design point FORM requiresan optimization algorithm to locate the design point and to determine thereliability index Several algorithms are available and the reader is referredto the literature (eg Ang and Tang 1984 Melchers 1999 Baecher andChristian 2003) for details of these algorithms Once the reliability index β

is obtained using FORM the nominal probability of liquefaction PL canbe determined as

PL = 1 minus(β) (1320)

where is the standard normal cumulative distribution functionIn Microsoft ExcelTM numerical value of (β) can be obtained using thefunction NORMSDIST(β)

The FORM procedure can easily be programmed (eg Yang 2003)Efficient implementation of the FORM procedure in ExcelTM was firstintroduced by Low (1996) and many geotechnical applications are foundin the literature (eg Low and Tang 1997 Low 2005 Juang et al2006) The spreadsheet solution introduced by Low (1996) is a clever solu-tion of reliability index based on the formulation by Hasofer and Lind(1974) on the original variable space It utilized a feature of ExcelTMcalled ldquoSolverrdquo for performing the optimization process Phoon (2004)developed a similar spreadsheet solution using ldquoSolverrdquo however thesolution of reliability index was obtained in the standard variable spacewhich tends to produce more stable numerical results Both spreadsheetapproaches yield a solution (reliability index) that is practically identicalto each other and to the solution obtained by a dedicated computer program

516 Charng Hsein Juang Sunny Ye Fang and David Kun Li

(Yang 2003) that implement the well-accepted algorithms for reliabilityindex using FORM

The mean probability of liquefaction PL may be obtained by the FORManalysis considering the mean model factor microc1 To determine the variationof the estimated mean probability as a result of the variation in microc1 in termsof standard deviation σPL

a large number of microc1 values may be ldquosampledrdquowithin the range of microc1plusmn 3σmicroc1 The FORM analysis with each of thesemicroc1 values will yield a large number of corresponding PL values and thenthe standard deviation σPL

can be determined Alternatively a simplifiedmethod may be used to estimate σPL

Two additional FORM analyses maybe performed one with microc1+ 1σmicroc1 as the model factor and the other withmicroc1minus 1σmicroc1 as the model factor Assuming that the FORM analysis usingmicroc1+ 1σmicroc1 as the model factor yields a probability of P+

L and the FORManalysis using microc1minus 1σmicroc1 as the model factor yields a probability of Pminus

L then the standard deviation σPL

may be estimated approximately with thefollowing equation (after Gutierrez et al 2003)

σPL= (P+

L minus PminusL )2 (1321)

1336 Example No 3 probabilistic evaluation of anon-liquefied case

This example concerns a non-liquefied case that was analyzed previouslyusing the deterministic approach (See Example No 1 in Section 1322)As described previously field observation of the site indicated no occurrenceof liquefaction during the 1977 Argentina earthquake The mean values ofseismic and soil parameters at the critical depth (29 m) are given as followsN160 = 13 FC= 3 σvprime = 381 kPa σv = 456 kPa amax = 02 g andMw =74 and the corresponding coefficients of variation of these parametersare assumed to be 023 0333 0085 0107 0075 and 010 respectively(Cetin 2000)

In the reliability analysis based on the limit state model expressed inEquation (1313) the parameter uncertainty and the model uncertaintyare considered along with the correlation between each pair of input ran-dom variables However no correlation is assumed between the modelfactor c1 and each of the six input random variables of the limit statemodel This assumption is supported by the finding of a recent study byPhoon and Kulhawy (2005) that the model factor is weakly correlatedto the input variables To facilitate the use of this reliability analysis aspreadsheet that implements the procedure of the FORM is developedThis spreadsheet shown in Figure 133 is designed specifically for lique-faction evaluation using the SPT-based method by Youd et al (2001) andthus all the formulas of the limit state model (Equation (1313) along with

Analysis of liquefaction potential 517

Equations (131)ndash(1310)) are implemented For users of this spreadsheetthe input data consists of four categories

1 the depth of interest (d = 29 m in this example)2 the mean and standard deviation of each of the six random variables

N160 FC σ primev σ v amax and Mw

3 the matrix of the correlation coefficients (use of default values as listed inTable 133 is recommended however a user-specified matrix could beused if it is deemed more accurate and it satisfies the positive definitenessrequirement as described previously) and

4 the model factor statistics microc1 and COV (note COV is set to 0 here asexplained previously)

Figure 133 shows a spreadsheet solution that is adapted from thespreadsheet originally designed by Low and Tang (1997) The input datafor this example along with the intermediate calculations and the finaloutcome (reliability index and the corresponding nominal probability) areshown in this spreadsheet It should be noted that the solution shown in

Mean COV h l x mN sN

N160 1300

300

3814

4561

020

740

096

0231

0333

0085

0107

0075

0100

0000

0228

0325

0085

0107

0075

0100

0000

2539

1046

3638

3814

minus1612

1997

minus0041

1196

286

3790

4532

021

771

096

12647

2846

37999

45345

0199

7355

0960

2723

0927

3221

4853

0015

0769

0000

Depth 290 a 0

FC MSF 0932 b 1

svrsquo

sv

rd 0978 N160cs 1196

f 07 Kσ 1338

amax CSR75σ 0126 CRR 0131

Mw FS 1042

c1

Correlation Matrix r Results

1

0

03

03

0

0

0

0

1

0

0

0

0

0

03

0

1

09

0

0

0

03

0

09

1

0

0

0

0

0

0

0

1

09

0

0

0

0

0

09

1

0

0

0

0

0

0

0

1

minus02536

00119

minus00296

minus00059

04338

04595

00000

FSoriginal 1278 0533

h() -1E-09 PL 0297

Notes

For this case in solverrsquos option use automatic scaling

Quadratic for Estimates Central for Derivatives and Newton for Search

others as default options

Initially enter original mean values for x column followed by invoking Excel Solver to automaticallyminimize reliability index b by changing x column subject to h(x) = 0

Original inputEquivalent normalparameters Calculate CSR and CRREquivalent normal

parameters at design point

To enter ARRAY FORMULA hold down ldquoCtrlrdquo and ldquoShiftrdquo keys then press Enter

h () = c1sdot CRR ndash CSR

xi minus mi

T

xisinF

minus1

|b|

b = minsi

xi minus mi

si

r

(x minus mN)sN

Figure 133 A spreadsheet that implements the FORM analysis of liquefaction potential(after Low and Tang 1997)

518 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Figure 133 was obtained using the mean model factor microc1 = 096 Becausethe spreadsheet uses the ldquoSolverrdquo feature in ExcelTM to perform the optimiza-tion procedure as part of the FORM analysis the user needs to make sure thisfeature is selected in ldquoToolsrdquo Within the ldquoSolverrdquo screen various optionsmay be selected for numerical solutions and the user may want to experimentwith different choices In particular the option of ldquoUse Automatic Scalingrdquoin Solver should be activated if the optimization is carried out in the originalvariable space (Figure 133) After selecting the solution options the userreturns to the Solver screen and chooses ldquoSolverdquo to perform the optimiza-tion and when the convergence is achieved the reliability index and theprobability of liquefaction are obtained Because the results depend on theinitial guess of the ldquodesign pointrdquo which is generally assumed to be equalto the vector of the mean values of the input random variables the user maywant to repeat the solution process a few times using different initial trialvalues to make certain ldquostablerdquo results have indeed been obtained Usingmicroc1 = 096 the spreadsheet solution (Figure 133) yields a probability ofliquefaction of PL = 0297 asymp 030

As a comparison the spreadsheet solution that is adapted from the spread-sheet originally designed by Phoon (2004) is shown in Figure 134 Practicallyidentical solution (PL = 030) is obtained However experience with bothspreadsheet solutions one with optimization carried out in the original vari-able space and the other in the standard variable space indicates that thesolution with the latter approach is significantly more robust and is generallyrecommended

Following the procedure described previously the FORM analyses usingmicroc1 plusmn 1σmicroc1 as the model factors respectively are performed with thespreadsheet shown in Figure 133 (or Figure 134) The standard deviation ofthe computed mean probability is then estimated to be σPL

= 0039 accord-ing to Equation (1321) If the three sigma rule is applied the probability PLwill approximately be in the range of 018ndash042 with a mean of 030

As noted previously (Section 133) a preliminary estimate of the meanprobability may be obtained from empirical models Using the proceduredeveloped by Juang et al (2002) the following equation is developed forinterpreting FS determined by the adopted SPT-based method

PL = 1

1 +(

FS105

)38 (1322)

This equation is intended to be used only for a preliminary estimate of theprobability of liquefaction in the absence of the knowledge of parameteruncertainties For this non-liquefied case FS = 128 and thus PL = 032according to Equation (1322) This PL value falls in the range of 018ndash042determined by the probabilistic approach using FORM As noted previously

Analysis of liquefaction potential 519

Mean COV h l Y=H(x) x=LU trial values Ux1 N160 Depth 290 a 000x2 FC MSF 0932 b 100x3 svrsquo rd 0978 N160cs 1196

x4 sv f 07

0126

Kσ 1338

x5 amax CSR75σ CRR 0131

x6 Mw FS 1042x7 c1

Correlation Matrix r

Results FSorginal 1278 h() 5E-09 05333 PL 0297

H 0a 1 2 3 4 5 6 7

Y1

Y2

Y3

Y4

Y5

Y6

Y7

Cholesky L

Initially enter original mean values for x column followed by invoking Excel Solver to automatically minimizereliability index b by changing x column subject to h(x) = 0

Original inputEquivalent normalparameters

Equivalent normal parameters Calculate CSR and CRR

minus0254

0000

0049

0066

0434

0158

0000

minus0254

0000

minus0030

minus0006

0434

0460

0000

11956

2846

37903

45316

0206

7709

096

2539

1046

3638

3814

minus1612

1997

minus0041

0228

0325

0085

0107

0075

0100

0000

0231

0333

0085

0107

0075

0100

0000

1300

300

3814

4561

020

740

096

0

1

1

1

1

1

1

1

1

minus02536

0

minus00296

minus00059

04338

04595

-1E-07

2

minus09357

minus1

minus09991

minus1

minus08118

minus07889

minus1

3

07446

0

00889

00178

minus12198

minus12815

4E-07

4

26182

3

29947

29998

19063

17777

3000

5

minus36423

0

minus04445

minus0089

57061

59428

-2E-06

6

minus12167

minus15

minus1496

minus14998

minus7056

minus61579

minus15

7

minus2494

0

31104

06232

minus37298

minus38486

1E-05

13

3

38136

45606

02

74

096

29612

09738

32404

48841

0015

07382

1E-07

03373

0158

01377

02615

00006

00368

5E-15

00256

00171

00039

00093

1E-05

00012

2E-22

00015

00014

8E-05

00002

3E-07

3E-05

4E-30

7E-05

9E-05

1E-06

5E-06

4E-09

6E-07

8E-38

3E-06

5E-06

2E-08

1E-07

5E-11

1E-08

1E-45

8E-08

2E-07

2E-10

1E-09

5E-13

1E-10

2E-53

1

00

03

03

0

0

0

00

1

00

00

0

0

0

03

00

1

09

0

0

0

03

00

09

1

0

0

0

0

0

0

0

1

09

0

0

0

0

0

09

1

0

0

0

0

0

0

0

1

1

0

03

03

0

0

0

0

1

0

0

0

0

0

0

0

09539

08491

0

0

0

0

0

0

04348

0

0

0

0

0

0

0

1

09

0

0

0

0

0

0

04359

0

0

0

0

0

0

0

1

|b|

Figure 134 A spreadsheet that implements the FORM analysis of liquefaction potential(after Phoon 2004)

in Section 133 the models such as Equation (1322) require only the bestestimates (ie the mean values) of the input variables and thus the calculatedprobabilities might be subject to error if the effect of parameter uncertaintiesis significant

Table 134 summarizes the solutions obtained by using the deterministicand the probabilistic approaches for Example No 3 and for Example No 4which is presented later The results obtained for Example No 3 from bothapproaches confirm field observation of no liquefaction However the cal-culated probability of liquefaction could still be as high as 042 even witha factor of safety of FS = 128 which reflects significant uncertainties in theparameters as well as in the limit state model

1337 Example No 4 probabilistic evaluation of aliquefied case

This example concerns a liquefied case that was analyzed previouslyusing the deterministic approach (See Example No 2 in Section 1323)

520 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Table 134 Summary of the deterministic and probabilistic solutions

Case FS Mean Standard Range of PLprobability PL deviation of PL PL

San Juan B-5(Example Nos 1 and 3)

128 030 0039 018ndash042

Ishinomaki-2(Example Nos 2 and 4)

055 091 0015 086ndash095

Field observation of the site indicated occurrence of liquefaction duringthe 1978 Miyagiken-Oki earthquake The mean values of seismic and soilparameters at the critical depth (37 m) are given as follows N160 = 5FC = 10 σ prime

v = 3628 kPaσv = 5883 kPa amax = 02 g and Mw = 74and the corresponding coefficients of variation of these parameters are 01800200 0164 0217 02 and 01 respectively (Cetin 2000)

Using the spreadsheet and the same procedure as described in ExampleNo 3 the following results are obtained PL = 091 and σPL

= 0015Estimating with the three sigma rule PL falls approximately in the rangeof 086ndash095 with a mean of 091 Similarly a preliminary estimate of theprobability of liquefaction may be obtained by Equation (1322) using onlythe mean values of the input variables For this liquefied case FS = 055 andthus PL = 092 according to Equation (1322) This PL value falls within therange of 086ndash095 determined by the probabilistic approach using FORMAlthough the general trend of the results obtained from Equation (1322)appears to be correct based on the two examples presented use of simplifiedmodels such as Equation (1322) for estimating the probability of liquefac-tion should be limited to preliminary analysis of cases where there is a lackof the knowledge of parameter uncertainties

Table 134 summaries the solutions obtained by using the deterministicand the probabilistic approaches for this liquefied case (Example No 4)The results obtained from both approaches confirm field observation ofliquefaction

Finally one observation of the standard deviation of the estimated prob-ability obtained in Example Nos 3 and 4 is perhaps worth mentioningIn general the standard deviation of the estimated mean probability is muchsmaller in the cases with extremely high or low probability (PL gt 09 orPL lt 01) than those with medium probability (03 lt PL lt 07) In the caseswith extremely high or low probability the potential for liquefaction or no-liquefaction is almost certain it will either liquefy or not liquefy This lowerdegree of uncertainty is reflected in the smaller standard deviation In thecases with medium probability there is a higher degree of uncertainty as towhether liquefaction will occur and thus it tends to have a larger standarddeviation in the estimated mean probability

Analysis of liquefaction potential 521

134 Probability of liquefaction in a givenexposure time

For performance-based earthquake engineering (PBEE) design it is oftennecessary to determine the probability of liquefaction of a soil at a site in agiven exposure time The probability of liquefaction obtained by reliabilityanalysis as presented in Section 133 (in particular in Example Nos 3 and4) is a conditional probability for a given set of seismic parameters amaxand Mw at a specified hazard level For a future case with uncertain seismicsources the probability of liquefaction in a given exposure time (PLT) may beobtained by integrating the conditional probability over all possible groundmotions at all hazard levels

PLT =sum

All pairs of (amax Mw)

p[L|(amaxMw)] middot p(amaxMw)

(1323)

where the term p[L|(amaxMw)] is the conditional probability of lique-faction given a pair of seismic parameters amax and Mw and the termp(amaxMw) is the joint probability of amax and Mw It is noted that thejoint probability p(amaxMw) may be thought of as the likelihood of an event(amax Mw) and the conditional probability of liquefaction p[L|(amaxMw)]as the consequence of the event Thus the product of p[L|(amaxMw)] andp(amaxMw) can be thought of as the weighted consequence of a single eventSince all mutually exclusive and collectively exhaustive events [ie all pos-sible pairs of (amax Mw)] are considered in Equation 1323 the sum ofall weighted consequences yields the total probability of liquefaction at thegiven site

While Equation (1323) is conceptually straightforward the implemen-tation of this equation is a significant undertaking Care must be exercisedto evaluate the joint probability of the pair (amax and Mw) for a local sitefrom different earthquake sources using appropriate attenuation and ampli-fication models This matter however is beyond the scope of this chapterand the reader is referred to Kramer et al (2007) and Juang et al (2008) fordetailed treatment of this subject

135 Summary

Evaluation of liquefaction potential of soils is an important task in geotechni-cal earthquake engineering In this chapter the simplified procedure based onthe standard penetration test (SPT) as recommended by Youd et al (2001) isadopted as the deterministic model for liquefaction potential evaluation Thismodel was established originally by Seed and Idriss (1971) based on in-situand laboratory tests and field observations of liquefactionno-liquefactionin the seismic events Field evidence of liquefaction generally consistedof surficial observations of sand boils ground fissures or lateral spreads

522 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Case histories of liquefactionno-liquefaction were collected mostly fromsites on level to gently sloping ground underlain by Holocene alluvial orfluvial sediments at shallow depths (lt 15 m) Thus the models presented inthis chapter are applicable only to sites with similar conditions

The focus of this chapter is on the probabilistic evaluation of liquefactionpotential using the SPT-based boundary curve recommended by Youd et al(2001) as the limit state model The probability of liquefaction is obtainedthrough reliability analysis using the first-order reliability method (FORM)The FORM analysis is carried out considering both parameter and modeluncertainties as well as the correlations among the input variables Proce-dures for estimating the parameter uncertainties in terms of coefficient ofvariation are outlined In particular the estimation of the seismic parame-ters amax and Mw is discussed in detail The limit state model based on Youdet al (2001) is characterized with a mean model factor of microc1 = 096 and astandard deviation of the mean σmicroc1 = 004 Use of these mean model fac-tor statistics for the estimation of the mean probability PL and its standarddeviation σPL

is illustrated in two examples Spreadsheet solutions specifi-cally developed for this probabilistic liquefaction evaluation using FORMare presented The spreadsheets can facilitate the use of FORM for predictingthe probability of liquefaction considering the mean and standard deviationof the input variables It may also be used to investigate the effect of thedegree of uncertainty of individual parameters on the calculated probabilityto aid in design considerations in cases where there is insufficient knowledgeof parameter uncertainties

The probability of liquefaction obtained in this chapter is a conditionalprobability at a given set of seismic parameters amax and Mw correspond-ing to a specified hazard level For a future case with uncertain seismicsources the probability of liquefaction in a given exposure time (PLT) may beobtained by integrating the conditional probability over all possible groundmotions at all hazard levels

References

Abrahamson N A and Silva W J (1997) Empirical response spectral attenua-tion relations for shallow crustal earthquakes Seismological Research Letters 6894ndash127

Ang A H-S and Tang W H (1984) Probability Concepts in Engineering Plan-ning and Design Vol II Design Risk and Reliability John Wiley and SonsNew York

Baecher G B and Christian J T (2003) Reliability and Statistics in GeotechnicalEngineering John Wiley and Sons New York

Bazzurro P and Cornell C A (2004a) Ground-motion amplification in nonlin-ear soil sites with uncertain properties Bulletin of the Seismological Society ofAmerica 94(6) 2090ndash109

Analysis of liquefaction potential 523

Bazzurro P and Cornell C A (2004b) Nonlinear soil-site effects in probabilisticseismic-hazard analysis Bulletin of the Seismological Society of America 94(6)2110ndash23

Berrill J B and Davis R O (1985) Energy dissipation and seismic liquefaction ofsands Soils and Foundations 25(2) 106ndash18

Building Seismic Safety Council (1997) NEHRP Recommended Provisions forSeismic Regulations for New Buildings and Other Structures Part 2 CommentaryFoundation Design Requirements Washington DC

Campbell K W and Bozorgnia Y (2003) Updated near-source ground-motion(attenuation) relations for the horizontal and vertical components of peak groundacceleration and acceleration response spectra Bulletin of the SeismologicalSocoety of America 93 314ndash31

Carraro J A H Bandini P and Salgado R (2003) Liquefaction resistanceof clean and nonplastic silty sands based on cone penetration resistanceJournal of Geotechnical and Geoenvironmental Engineering ASCE 129(11)965ndash76

Cetin K O (2000) Reliability-based assessment of seismic soil liquefaction initiationhazard PhD dissertation University of California Berkeley CA

Cetin K O Seed R B Kiureghian A D Tokimatsu K Harder L FJr Kayen R E and Moss R E S (2004) Standard penetration test-basedprobabilistic and deterministic assessment of seismic soil liquefaction potentialJournal of Geotechnical and Geoenvironmental Engineering ASCE 130(12)1314ndash40

Choi Y and Stewart J P (2005) Nonlinear site amplification as function of 30 mshear wave velocity Earthquake Spectra 21(1) 1ndash30

Dai S H and Wang M O (1992) Reliability Analysis in Engineering ApplicationsVan Nostrand Reinhold New York

Davis R O and Berrill J B (1982) Energy dissipation and seismic liquefaction insands Earthquake Engineering and Structural Dynamics 10 59ndash68

Dief H M (2000) Evaluating the liquefaction potential of soils by the energymethod in the centrifuge PhD dissertation Case Western Reserve UniversityCleveland OH

Dobry R Ladd R S Yokel F Y Chung R M and Powell D (1982) Predictionof Pore Water Pressure Buildup and Liquefaction of Sands during Earthquakes bythe Cyclic Strain Method National Bureau of Standards Publication No NBS-138 Gaithersburg MD

Duncan J M (2000) Factors of safety and reliability in geotechnical engineer-ing Journal of Geotechnical and Geoenvironmental Engineering ASCE 126(4)307ndash16

Figueroa J L Saada A S Liang L and Dahisaria M N (1994) Evaluation ofsoil liquefaction by energy principles Journal of Geotechnical Engineering ASCE120(9) 1554ndash69

Frankel A Harmsen S Mueller C et al (1997) USGS national seismichazard maps uniform hazard spectra de-aggregation and uncertainty InProceedings of FHWANCEER Workshop on the National Representation ofSeismic Ground Motion for New and Existing Highway Facilities NCEERTechnical Report 97-0010 State University of New York at Buffalo New Yorkpp 39ndash73

524 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Green R (2001) The application of energy concepts to the evaluation of remedia-tion of liquefiable soils PhD dissertation Virginia Polytechnic Institute and StateUniversity Blacksburg VA

Gutierrez M Duncan J M Woods C and Eddy E (2003) Development of asimplified reliability-based method for liquefaction evaluation Final TechnicalReport USGS Grant No 02HQGR0058 Virginia Polytechnic Institute and StateUniversity Blacksburg VA

Haldar A and Tang W H (1979) Probabilistic evaluation of liquefaction potentialJournal of Geotechnical Engineering ASCE 104(2) 145ndash62

Hasofer A M and Lind N C (1974) Exact and invariant second momentcode format Journal of the Engineering Mechanics Division ASCE 100(EM1)111ndash21

Harr M E (1987) Reliability-based Design in Civil Engineering McGraw-HillNew York

Hynes M E and Olsen R S (1999) Influence of confining stress on liquefactionresistance In Proceedings International Workshop on Physics and Mechanics ofSoil Liquefaction Balkema Rotterdam pp 145ndash52

Idriss I M and Boulanger R W (2006) ldquoSemi-empirical procedures for evalu-ating liquefaction potential during earthquakesrdquo Soil Dynamics and EarthquakeEngineering Vol 26 115ndash130

Ishihara K (1993) Liquefaction and flow failure during earthquakes The 33rdRankine lecture Geacuteotechnique 43(3) 351ndash415

Juang C H Rosowsky D V and Tang W H (1999) Reliability-based methodfor assessing liquefaction potential of soils Journal of Geotechnical and Geoenvi-ronmental Engineering ASCE 125(8) 684ndash9

Juang C H Chen C J Rosowsky D V and Tang W H (2000) CPT-based liquefaction analysis Part 2 Reliability for design Geacuteotechnique 50(5)593ndash9

Juang C H Jiang T and Andrus R D (2002) Assessing probability-based meth-ods for liquefaction evaluation Journal of Geotechnical and GeoenvironmentalEngineering ASCE 128(7) 580ndash9

Juang C H Yang S H Yuan H and Khor E H (2004) Characterization ofthe uncertainty of the Robertson and Wride model for liquefaction potential SoilDynamics and Earthquake Engineering 24(9ndash10) 771ndash80

Juang C H Fang S Y and Khor E H (2006) First-order reliabilitymethod for probabilistic liquefaction triggering analysis using CPTJournal of Geotechnical and Geoenvironmental Engineering ASCE 132(3)337ndash50

Juang C H Li D K Fang S Y Liu Z and Khor E H (2008) A simplified proce-dure for developing joint distribution of amax and Mw for probabilistic liquefactionhazard analysis Journal of Geotechnical and Geoenvironmental EngineeringASCE in press

Kramer S L (1996) Geotechnical Earthquake Engineering Prentice-HallEnglewood Cliffs NJ

Kramer S L and Elgamal A (2001) Modeling Soil Liquefaction Hazards forPerformance-Based Earthquake Engineering Report No 200113 PacificEarthquake Engineering Research (PEER) Center University of CaliforniaBerkeley CA

Analysis of liquefaction potential 525

Kramer S L and Mayfield R T (2007) ldquoReturn period of soil liquefactionrdquoJournal of Geotechnical and Geoenvironmental Engineering ASCE 133(7)802ndash13

Liang L Figueroa J L and Saada A S (1995) Liquefaction under randomloading unit energy approach Journal of Geotechnical and GeoenvironmentalEngineering 121(11) 776ndash81

Liao S S C and Whitman R V (1986) Overburden correction factors for SPT insand Journal of Geotechnical Engineering ASCE 112(3) 373ndash7

Liao S S C Veneziano D and Whitman R V (1988) Regression model forevaluating liquefaction probability Journal of Geotechnical Engineering ASCE114(4) 389ndash410

Low B K (1996) Practical probabilistic approach using spreadsheet In Uncer-tainty in the Geologic Environment from Theory to Practice Geotechnical SpecialPublication No 58 Eds C D Shackelford P P Nelson and M J S Roth ASCEReston VA pp 1284ndash302

Low B K and Tang W H (1997) Efficient reliability evaluation using spreadsheetJournal of Engineering Mechanics ASCE 123(7) 749ndash52

Low B K (2005) Reliability-based design applied to retaining walls Geacuteotechnique55(1) 63ndash75

Marcuson W F III (1978) Definition of terms related to liquefaction JournalGeotechnical Engineering Division ASCE 104(9) 1197ndash200

Melchers R E (1999) Structural Reliability Analysis and Prediction 2nd edWiley Chichester UK

NEHRP (1998) NEHRP Recommended Provisions for Seismic Regulationsfor New Buildings and Other Structures Part 1 ndash Provisions FEMA 302Part 2 ndash Commentary FEMA 303 Federal Emergency Management AgencyWashington DC

National Research Council (1985) Liquefaction of Soil during Earthquake NationalResearch Council National Academy Press Washington DC

Ostadan F Deng N and Arango I (1996) Energy-based Method forLiquefaction Potential Evaluation Phase 1 Feasibility Study Report NoNIST GCR 96-701 National Institute of Standards and TechnologyGaithersburg MD

Phoon K K (2004) General Non-Gaussian Probability Models for First OrderReliability Method (FORM) A State-of-the Art Report ICG Report 2004-2-4 (NGI Report 20031091-4) International Center for Geohazards OsloNorway

Phoon K K and Kulhawy F H (1999) Characterization of geotechnical variabilityCanada Geotechnical Journal 36 612ndash24

Phoon K K and Kulhawy F H (2005) Characterization of modeluncertainties for laterally loaded rigid drilled shafts Geacuteotechnique 55(1)45ndash54

Sadigh K Chang C -Y Egan J A Makdisi F and Youngs R R (1997) Attenu-ation relations for shallow crustal earthquakes based on California strong motiondata Seismological Research Letters 68 180ndash9

Seed H B and Idriss I M (1971) Simplified procedure for evaluating soil lique-faction potential Journal of the Soil Mechanics and Foundation Division ASCE97(9) 1249ndash73

526 Charng Hsein Juang Sunny Ye Fang and David Kun Li

Seed H B and Idriss I M (1982) lsquoGround Motions and Soil Liquefactionduring Earthquakes Earthquake Engineering Research Center Monograph EERIBerkeley CA

Seed H B and Lee K L (1966) Liquefaction of saturated sands during cyclicloading Journal of the Soil Mechanics and Foundations Division ASCE 92(SM6)Proceedings Paper 4972 Nov 105ndash34

Seed H B Tokimatsu K Harder L F and Chung R (1985) Influence of SPTprocedures in soil liquefaction resistance evaluations Journal of GeotechnicalEngineering ASCE 111(12) 1425ndash45

Skempton A K (1986) Standard penetration test procedures and the effects in sandsof overburden pressure relative density particle size aging and overconsolidationGeacuteotechnique 36(3) 425ndash47

Stewart J P Liu A H and Choi Y (2003) Amplification factors for spectral accel-eration in tectonically active regions Bulletin of Seismological Society of America93(1) 332ndash52

Yang S H (2003) Reliability analysis of soil liquefaction using in situ tests PhDdissertation Clemson University Clemson SC

Youd T L and Idriss I M Eds (1997) Proceedings of the NCEER Workshopon Evaluation of Liquefaction Resistance of Soils Technical report NCEER-97-0022 National Center for Earthquake Engineering Research State University ofNew York at Buffalo Buffalo NY

Youd T L Idriss I M Andrus R D Arango I Castro G Christian J TDobry R Liam Finn W D Harder L F Jr Hynes M E Ishihara KKoester J P Laio S S C Marcuson W F III Martin G R Mitchell J KMoriwaki Y Power M S Robertson P K Seed R B and Stokoe K H II(2001) Liquefaction resistance of soils summary report from the 1996 NCEERand 1998 NCEERNSF workshops on evaluation of liquefaction resistance of soilsJournal of Geotechnical and Geoenvironmental Engineering ASCE 127(10)817ndash33

Zhang L Tang W H Zhang L and Zheng J (2004) Reducing uncer-tainty of prediction from empirical correlations Journal of Geotechnical andGeoenvironmental Engineering ASCE 130(5) 526ndash34

Index

17th St Canal 452 491

acceptance criteria 403ndash8action 299 300 307ndash10 316 327AFOSM 465ndash6allowable displacement 345ndash6 369anchored sheet pile wall 142 145 151Arias Intensity 247ndash50augered cast-in-place pile 346

349 357autocorrelation 82ndash6axial compression 346ndash8

Bayesian estimation 108ndash9Bayesian theory 101 108Bayesian updating 431ndash9bearing capacity 136ndash8 140ndash1 225ndash6

254ndash5 389ndash92bearing resistance 317 322 323 326berm 228ndash9 451 479ndash80bivariate probability model

357ndash9borrow pit 450breach 451 452 459 491 494

calibration of partial factors 319ndash21Case Pile Wave Analysis Program

(CAPWAP) 392Cauchy distribution 174characteristic value 147 244 309 316closed-form solutions 27ndash32coefficient of correlation 512ndash13coefficient of variation 219ndash20

238 247 252 283 287 387ndash8309 399 401 464 473ndash4482ndash3

combination factor 309ndash10conditional probability 521ndash2

conditional probability of failurefunction 459ndash61 470 471 477482 484 486

cone tip resistance 227 229consequences classes 321constrained optimization 135 137

139 163ndash4construction control 392correlation distance 87 217 232 236correlation factor 315correlation matrix 137 151 162ndash3covariance matrix 18ndash21 45crevasse 451crown 450ndash2 460ndash1 463cutoffs 451cyclic resistance ratio 501cyclic stress ratio 500ndash1

decision criteria 195ndash201decision making 192 202 204 210

213 220ndash1decision tree 193ndash5deformation factor 376ndash8Design Approaches 316ndash19

322ndash3design life 202 204 206ndash7 209design point 141ndash2 146ndash7 164ndash5

180ndash1design value 298 313 323ndash4deterministic approach 137 500ndash5deterministic model 281ndash2 464ndash5

471ndash3 478dikes 448ndash50 455ndash6displacement analysis 421ndash2drained strength 463ndash4drilled shaft 349 356ndash7 371 373

377ndash81

528 Index

earthquake 497ndash500 508ndash9511ndash13 521

erosion 423 454 477ndash84 486Eurocode 7 9 145 298ndash341Excel Solver 156 164excess pore water pressure 241ndash9expansion optimal linear estimation

method (EOLE) 265ndash6exponential distribution 174

factor of safety 99ndash100 104ndash5 160ndash1202ndash3 365 386 398 405 407

failure mechanism 225 238 254failure modes 37ndash9 452ndash4 463ndash84failure probability 177 182 184 288

417ndash21 439first-order reliability method (FORM)

32ndash9 58ndash9 261 278ndash80 364ndash5515ndash16

first-order second moment method261 413 416

flood water elevation 459ndash63 485486 487ndash8

floodwalls 448ndash50FORM analyses 280 285 287 325ndash9

429 517 519foundation displacement 361fragility curves 244ndash5 250ndash3Frechet distribution 174

Generalized Pareto distribution 175Geotechnical Categories 302ndash7geotechnical risk 302ndash7geotechnical uncertainties 6ndash9global sensitivity analysis 275ndash6Gumbel distribution 174

Hasofer-Lind reliability index 140ndash2Hermite polynomials 15ndash18 274 293

353 358horizontal correlation distance

216ndash17 249

importance sampling 179ndash81 280interpolation 116ndash17

Johnson system 10ndash15

Karhunen-Loegraveve expansion 264ndash5kurtosis 10 16 290

landside 450ndash3levees 448ndash51

level crossing 123ndash5limit equilibrium analysis 415ndash20limit state 140ndash2 283ndash7 505ndash7limit state design concept 300ndash1line of protection 451liquefaction 240ndash4 245ndash9 505ndash7 521liquefaction potential 227 497ndash522Load and Resistance Factor Design

(LRFD) 376ndash9 56ndash8load factor 338ndash40load tests 389ndash92 402ndash8load-displacement curves 345ndash9 372

374 379ndash80low discrepancy sequences 170ndash2lower-bound capacity 205ndash8 213ndash15lumped factor of safety 137ndash9 143ndash4

marginal distribution 351ndash4Markov chain Monte Carlo (MCMC)

simulation 181ndash7materials factor approach (MFA) 318MATLAB codes 50ndash63maximum likelihood estimation 103

106ndash8measurement noise 78 90ndash3 97Metropolis-Hastings algorithm 47

189ndash91model calibration 210ndash20model errors 337ndash8model factor 329 365ndash7 507 513ndash18model uncertainty 96 319 513ndash15moment estimation 102ndash3Monte Carlo analysis 331ndash4Monte Carlo simulation 160ndash1

169ndash90 176ndash9 233ndash4 240 242277ndash8

most probable failure point 137140ndash1 158

New Orleans 125ndash9 452 491ndash4non-intrusive methods 261 271ndash4 292nonnormal distributions 149ndash51

offshore structures 192ndash4 199overtopping 452

parameter uncertainty 507ndash12partial action factor 308partial factor method 302 307ndash8

322ndash5partial factors 137ndash9 316ndash21partial material factor 299 308 318

Index 529

partial resistance factor 219ndash20317ndash18

peak ground acceleration (PGA)247ndash8 509ndash12

Pearson system 10ndash11permanent action 318permeability 472ndash5 480ndash1pile capacity 197 206ndash7 213ndash20 386

387ndash92pile foundations 402 407 408pile resistance 315ndash16polynomial chaos expansions 267ndash9pore pressure 423ndash4positive definite correlation matrix

162ndash3pressure-injected footing 349 354

356ndash7probabilistic approach 505ndash20probabilistic hyperbolic model 345

346ndash51 361ndash70 379probabilistic model 4 465ndash6probability 479 492ndash3 502ndash3 509

514ndash20 521probability density function 160ndash1

182ndash3 229 261ndash2probability of failure 26ndash32 66

69 78 125ndash6 141ndash2 160ndash1 321326 328ndash9 331 333 338ndash41470ndash1 486

product-moment correlation 349 359proof load tests 389ndash92 398 402ndash8pseudo random numbers 169ndash70 173

quality assurance 392quasi random numbers 170ndash2

random field 111ndash29 263ndash4random number generation 169ndash76random process 22ndash6random variables 6ndash18 52ndash3 66

463ndash4 467 473 480ndash1random vector 18ndash22 262ndash3 266

267ndash8rank model 359ndash61reach 462ndash91reliability 125ndash8 497 522reliability index 137ndash42 151ndash2reliability-based design 142ndash51 301ndash2

386ndash7 402ndash3 370relief wells 451representative value 309resistance factor approach (RFA) 318

response surface 163 274riprap 451riverside 450ndash3 471ndash2

sample moments 10 104 112sand boil 453ndash4 473scale of fluctuation 22ndash3 119ndash20Schwartzrsquos method 478ndash80second-order reliability method

(SORM) 69ndash70 66 261seepage berm 451serviceability limit state (SLS) 300

320ndash1 344ndash80SHANSEP 95ndash101skewness 10 16slope stability 152ndash61 463ndash71Sobolrsquo indices 275ndash6soil borings 215ndash20soil liquefaction 240ndash4soil properties 11 76ndash95 127 224ndash33

236 424ndash6soil property variability 6 377 379ndash80soil variability 225 232 244 251ndash3spatial variability 78ndash9 82 96ndash7 129

215ndash20spectral approach 22ndash3Spencer method of slices 134

152ndash9spread foundation 311 322ndash5

331ndash4spreadsheet 134ndash66 515ndash20stability berm 451standard penetration test 228

497ndash522stochastic data 6ndash26stochastic finite element methods

260ndash94strength 463ndash4structural reliability 32 261 276ndash80subset MCMC 61ndash3 169 181ndash7system reliability 36ndash9 208ndash10

target reliability index 321369ndash70 402

Taylorrsquos series - finite differencemethod 466ndash7 474

throughseepage 453ndash4 477ndash84toe 313ndash14 450ndash1 453 460ndash2 467

471 474ndash5 479ndash80translation model 12 13 20ndash2 49

357ndash9trend analysis 79ndash82

530 Index

ultimate limit state (ULS) 300308 316 321ndash3 332ndash3338ndash40 344 363 365370ndash1

underseepage 451 453 455 466471ndash7 485

undrained strength 93 126ndash8 463464 467

undrained uplift 377ndash8

US Army Corps of Engineers 456478 491

variable action 309variance reduction function

119ndash20variogram 109ndash11 113 117

Weibull distribution 175

  • Book Cover
  • Title
  • Copyright
  • Contents
  • List of contributors
  • Chapter 1 Numerical recipes for reliability analysis ndash a primer
  • Chapter 2 Spatial variability and geotechnical reliability
  • Chapter 3 Practical reliability approach using spreadsheet
  • Chapter 4 Monte Carlo simulation in reliability analysis
  • Chapter 5 Practical application of reliability-based design in decision-making
  • Chapter 6 Randomly heterogeneous soils under static and dynamic loads
  • Chapter 7 Stochastic finite element methods in geotechnical engineering
  • Chapter 8 Eurocode 7 and reliability-based design
  • Chapter 9 Serviceability limit state reliability-based design
  • Chapter 10 Reliability verification using pile load tests
  • Chapter 11 Reliability analysis of slopes
  • Chapter 12 Reliability of levee systems
  • Chapter 13 Reliability analysis of liquefaction potential of soils using standard penetration test
  • Index
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