a decision support system for efficient utilization of
TRANSCRIPT
University of Calgary
PRISM: University of Calgary's Digital Repository
Graduate Studies The Vault: Electronic Theses and Dissertations
2012-12-12
A Decision Support System for Efficient Utilization of
Overdesign as a Fast
Tracking Technique in Modular Steel Pipe Racks
Khoramshahi, Fereshteh
Khoramshahi, F. (2012). A Decision Support System for Efficient Utilization of Overdesign
as a Fast Tracking Technique in Modular Steel Pipe Racks (Unpublished
doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/24705
http://hdl.handle.net/11023/348
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UNIVERSITY OF CALGARY
A Decision Support System for Efficient Utilization of Overdesign
as a Fast Tracking Technique in Modular Steel Pipe Racks
by
Fereshteh Khoramshahi
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF CIVIL ENGINEERING
CALGARY, ALBERTA
DECEMBER, 2012
© Fereshteh Khoramshahi 2012
Abstract
To effectively address today’s aggressive schedule demands in the oil and gas industry,
engineering and construction activities are usually overlapped to some extent to attain
schedule compression. Overdesign is one of the techniques used in a project’s
engineering phase to reduce the information dependency between activities, which results
in overlapping. When there is insufficient design information, designers usually adopt
more conservative assumptions in their designs than would normally be the case. This
overdesign means successor activities can start and progress well ahead of and long
before accurate details can be determined. Although this helps to reduce the overall
schedule, it is not a risk-free process on its own. One of the major concerns in overdesign
is the lack of design optimization, which will be directly translated to extra costs and
increased materials wastage. Likewise, the assumptions made might not be conservative
enough, necessitating rework.
A review of the overdesign literature pointed to a lack of explicit research about
overdesign as a schedule compression technique. This research study was designed in a
way to address some of the gaps in the current overdesign literature. The purpose of the
research presented in this thesis was to develop a decision support system for choosing
the best opportunities to apply overdesign in oil and gas projects that provide the greatest
schedule compression for the least incremental cost. The scope of the research is limited
to the modular steel pipe racks.
A mixed methods approach was taken to conduct this research. The purpose of the
qualitative part was to build the overdesign conceptual framework, which further formed
the basis for formulating the overdesign time-cost trade-off problem. This helped model
ii
the overdesign problem using stochastic decision tree principles. The entire process laid
the foundation for developing the decision support system. The quantitative part of the
research involved gathering real project information, which was used to relate the degree
of conservativeness of the assumptions in overdesign to the probability of rework
associated with any overdesign decision.
This research provides contributions in four distinct categories: theoretical,
literature, methodological and finally industry contribution.
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Acknowledgements
At the end of my PhD studies, it is a great pleasure for me to give respect to those who
made this thesis both possible and an unforgettable experience for me. Apart from the
academic achievement, the best outcome from these past five years is finding good
friends who helped me in numerous ways. I love you all dearly. Particularly:
I owe sincere thankfulness to my research supervisor, Dr. Janaka Ruwanpura, who made
me believe in myself. I am sure that this dissertation would not have been possible
without his valuable guidance, support, understanding and encouragement.
I give my sincere gratitude to my supervisory committee members, Dr. Francis Hartman
and Dr. George Jergeas, for their valuable advice and support during the whole duration
of my PhD studies. Francis’ attendance at my candidacy exam right after his surgery was
an indication of high commitment and support that I will never forget; and lovely George,
who was not only a scientific advisor but also my great morale supporter.
I am extremely indebted to John Lacroix, who was absolutely my key supporter. John
spent a lot of time with me and showed unbelievable patience and support. He guided me
through my whole research journey and provided insightful discussions and suggestions.
John also helped me connect with many people who had key contributions to my
research. I will forever be heartily thankful to John.
My sincerest gratitude goes to Dr. Kam Jugdev, who provided me with her precious
comments. I am very happy that this thesis connected me to her as she is one of the nicest
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persons that I have ever met. I would also like to show my appreciation to Dr. Tak Fung
for his help on this study.
My warmest appreciation goes to my friends:
- Dr. Reza Dehghan, for his insightful discussions and suggestions
- Mario Rubi, who trained me to work with structural software and provided advice
- Tomson Chan, Sheldon Bennett, Debbie Young, Marcel St. Louis, Frank Van Der
Voet, and Sumeet Gill for their help
- Rhonda Greenaway for editing this thesis
And finally to my family, I can find no words to express my feelings when I talk about
them. Love you forever.
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Dedication
With all of my heart, I would like to dedicate this doctoral dissertation to my parents,
who suffered a lot from being so far away from me but still wished me to do this PhD.
Mom and Dad, you have been my true and great supporters during my good and bad
times and have always unconditionally loved me. You have been non-judgmental of me
and instrumental in instilling confidence. Thank you for all of these. You are the sole
reason I have survived.
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Table of Contents
Abstract ............................................................................................................................... iiAcknowledgements ............................................................................................................ ivDedication .......................................................................................................................... viTable of Contents.............................................................................................................. viiList of Tables .......................................................................................................................xList of Figures and Illustrations ......................................................................................... xiList of Symbols, Abbreviations and Nomenclature ...........................................................xvEpigraph........................................................................................................................... xix
CHAPTER ONE: INTRODUCTION..............................................................................11.1 Problem Statement .....................................................................................................31.2 Objectives ..................................................................................................................61.3 Literature review........................................................................................................71.4 Scope..........................................................................................................................91.5 Out of Scope ............................................................................................................101.6 Research assumptions ..............................................................................................111.7 Research deliverables ..............................................................................................111.8 Research methodology .............................................................................................121.9 Research contributions.............................................................................................141.10 Thesis structure ......................................................................................................15
CHAPTER TWO: LITERATURE REVIEW...............................................................172.1 Overdesign ...............................................................................................................212.2 Time-Cost Trade-Off and Optimization Techniques ...............................................35
2.2.1 Decision Tree...................................................................................................412.3 Project Development and the Execution Process ....................................................472.4 Modular Steel Pipe Racks........................................................................................642.5 Information Management Systems ..........................................................................64
CHAPTER THREE: RESEARCH METHODOLOGY ..............................................733.1 Part A – Conceptual Study.......................................................................................77
3.1.1 Part A: Data Collection Strategy .....................................................................803.1.2 Part A – Data Analysis Strategy......................................................................83
3.2 Part B - Formulating the Research Problem ............................................................843.3 Part C – Quantitative Research................................................................................86
3.3.1 Part C- Data Collection Strategy .....................................................................86
CHAPTER FOUR: OVERDESIGN ..............................................................................904.1 Overdesign Definition ..............................................................................................904.2 Main Drivers of Overdesign ....................................................................................924.3 Consequences of Overdesign...................................................................................974.4 Overdesign Theory ................................................................................................101
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CHAPTER FIVE: OVERDESIGN ON MODULAR PIPE RACKS ........................1095.1 Introduction to Modular Steel Pipe Rack Design ..................................................113
5.1.1 Structural Frame ............................................................................................1145.1.2 Pipe Rack Foundation....................................................................................119
5.2 Application of Overdesign on Modular Steel Pipe Racks .....................................1215.2.1 Tracking the Pipe Load Changes along the Pipe Rack ..................................1245.2.2 Effect of Pipe Load Change on the Steel Design ..........................................141
CHAPTER SIX: FORMULATING TIME-COST TRADE-OFF FOR OVERDESIGNING MODULAR STEEL PIPE RACKS .................................156
6.1 Time Impact Study in Overdesigning Modular Pipe Racks ..................................1566.2 Cost Impact Study in Overdesigning Modular Pipe Racks ...................................1626.3 Rework Study in Overdesigning Modular Pipe Racks ..........................................163
6.3.1 Probability of Rework ...................................................................................1656.3.2 Rework ..........................................................................................................173
6.4 Overdesign Time-Cost Trade-Off ..........................................................................176
CHAPTER SEVEN: DECISION SUPPORT SYSTEM FOR OVERDESIGNING MODULAR STEEL PIPE RACKS .................................179
7.1 Modeling the Research Problem............................................................................1827.2 Identifying Input Variables....................................................................................1857.3 Building Processing Modules ................................................................................188
7.3.1 Decision Tree Calculation Module................................................................1897.3.2 Sensitivity Analysis Module..........................................................................198
7.4 Defining Required Outputs....................................................................................2087.5 Designing the User Interface .................................................................................2117.6 Verification ............................................................................................................2117.7 Validation...............................................................................................................215
7.7.1 Pipe Rack DSS Input Variables for the Validation Project ...........................2287.7.2 Pipe Rack DSS Outputs for the Validation Project .......................................231
CHAPTER EIGHT: CONCLUSION ..........................................................................2438.1 Research Contributions..........................................................................................245
8.1.1 Theoretical contributions...............................................................................2458.1.2 Literature contributions .................................................................................2468.1.3 Methodological contributions ........................................................................2478.1.4 Industry contributions....................................................................................248
8.2 Research Limitations and Areas for Future Research ...........................................2508.3 Final Words............................................................................................................252
REFERENCES ................................................................................................................253
APPENDIX A: INTERVIEW QUESTIONS ..................................................................270
APPENDIX B: SAMPLE OF THE PIPE RACK GENERAL ARRANGEMENT DRAWING .............................................................................................................273
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APPENDIX C: PIPE RACK DSS INPUT SHEETS .......................................................275
APPENDIX D: VERIFICATION RESPONSES.............................................................281
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List of Tables
Table 3-1: Sample Pipe Rack Projects .............................................................................. 88
Table 5-1: Raw Data Collected from the General Arrangement Drawings Showing Preliminary Loads, Final Loads and Line Spaces ......................................... 128
Table 5-2: Moment and Support Reactions Calculated from Preliminary and Final Pipe Loads..................................................................................................... 149
Table 5-3: Pair Wise T-Test Results ............................................................................... 154
Table 6-1: Ideal Overdesign Factors for All Beams under the Study ............................. 168
Table 6-2: Steps for Calculating the Cumulative Density Function for Ideal Overdesign Factors ....................................................................................... 170
Table 7-1: Input Variables .............................................................................................. 185
Table 7-2: Project Information at the Preliminary Stage ................................................ 220
Table 7-3: Change in Project Information after Increasing the Loads by 100% ............ 223
Table 7-4: Increase in Cost of Steel Fabrication and Erection for One Module after Increasing the Loads by 100%...................................................................... 226
Table 7-5: Rough Estimate of Increase in Cost of Steel Fabrication and Erection for the Entire Pipe Rack after Increasing the Loads by 100% ............................ 226
Table 7-6: Ideal Overdesign Factor for Beams of the Validation Project ...................... 229
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List of Figures and Illustrations
Figure 1-1: Project Life Cycle Showing Potential to Add Value and Cost of Change – Burke (2003) ................................................................ 2
Figure 2-4: Sensitivity Characteristics of Downstream Activities – Krishnan et al.
Figure 2-12: Major Deliverables of the Process Discipline and their Interdisciplinary
Figure 2-13: Major Deliverables of the Mechanical Discipline and their
Figure 2-14: Major Deliverables of the Piping Discipline and their Interdisciplinary
Figure 2-15: Major Deliverables of the Civil/Structural Discipline and their
Figure 2-16: Major Deliverables of the Electrical Discipline and their
Figure 2-17: Major Deliverables of the Instrumentation and Control Discipline and
Figure 2-1: Literature Review Steps Extracted from Leedy & Ormrod (2005) ............... 17
Figure 2-2: Areas of Literature Review ............................................................................ 20
Figure 2-3: Evolution Characteristics of Upstream Activities – Krishnan et al. (1997) ... 25
(1997) ............................................................................................................ 26
Figure 2-5: Existing Techniques for Time-Cost Trade-off Problems............................... 37
Figure 2-6: Sample Decision Tree (Moussa et al., 2006) ................................................. 43
Figure 2-7: Project Development and Execution Process (Jergeas, 2008) ....................... 50
Figure 2-8: Engineering Input to Procurement and Construction ..................................... 51
Figure 2-9: Engineering Disciplines ................................................................................. 52
Figure 2-10: Relationship between Engineering Deliverables (Baron, 2010) .................. 55
Figure 2-11: Engineering Discipline Interface Map in Oil and Gas Projects ................... 57
Relationship ................................................................................................. 58
Interdisciplinary Relationship...................................................................... 59
Relationships................................................................................................ 60
Interdisciplinary Relationships .................................................................... 61
Interdisciplinary Relationships .................................................................... 62
their Interdisciplinary Relationships ............................................................ 63
Figure 2-18: Different Types of Information Management Systems................................ 67
xi
Figure 2-19: Concepts of Different Information Management Systems .......................... 72
Figure 3-1: Research Steps ............................................................................................... 74
Figure 3-2: Categorization of Research Steps from the Research Methodology Standpoint...................................................................................................... 75
Figure 3-3: Parts A and B Research Methodology ........................................................... 76
Figure 3-4: Qualitative Research Designs Extracted from Leedy and Ormrod (2005) .... 78
Figure 3-5: Research Participants’ Demographic Information ......................................... 82
Figure 3-6: Overview of the Research Problem ............................................................... 85
Figure 3-7: Overview of the Sampling Process for the Quantitative Part ........................ 87
Figure 4-1: Different Types of Activity Dependencies (Bogus, 2004) .......................... 102
Figure 4-2: Overdesign Theory (Adapted from Dehghan & Ruwanpura, 2011 and modified for overdesign) .................................................................................................. 105
Figure 4-3: Relationship of Overdesign Factor, Time Savings and Extra Cost .............. 107
Figure 5-1: Typical Steel Pipe Rack ............................................................................... 110
Figure 5-2: Modular Pipe Rack....................................................................................... 110
Figure 5-3: Different Components of the Pipe Rack Module ......................................... 114
Figure 5-4: Pipe Routing Diagram .................................................................................. 115
Figure 5-5: Pipe Rack Bent Spacing (Bausbacher & Hunt, 1993) ................................. 116
Figure 5-6: Pile Foundation ............................................................................................ 120
Figure 5-7: High Level Design Process of Pipe Rack Modules ..................................... 121
Figure 5-8: Application of Overdesign on Pipe Rack Modules ...................................... 123
Figure 5-9: Pipe Loads and Spaces in the First Sample Beam at the Preliminary Design Stage................................................................................................ 127
Figure 5-10: Pipe Loads and Spaces in the First Sample Beam at the Final Design Stage ..................................................................................... 127
Figure 5-11: A Typical Fixed-fixed Beam ...................................................................... 143
xii
Figure 5-12: Free Body Diagram of a Typical Fixed Beam Under the Study ................ 144
Figure 5-17: Preliminary and Final Maximum Bending Moments in all Sample
Figure 5-18: Range of the Differences between Preliminary and Final Maximum
Figure 6-2: Engineering Time saving (in Focus Activities) in Overdesigning Modular
Figure 6-3: Information Exchange between Stress Analysis and Structural Calculation
Figure 6-6: Graphical Presentation of the Required Overdesign Factor in Sample
Figure 6-7: Graphical Representation of the Overdesign Time-Cost Trade-Off
Figure 7-4: Expected Value Graphs of Two Hypothetical Overdesign Decisions and
Figure 7-5: One-Way Sensitivity Analysis for Time Saving Vs. Expected Value for a
Figure 5-13: Released Structure in the Form of a Simple Beam .................................... 145
Figure 5-14: Applying the Redundant Moment M1 as Load on the Released Structure . 146
Figure 5-15: Applying the Redundant Moment M2 as Load on the Released Structure . 146
Figure 5-16: Screenshot of the RISA Software used for Structural Calculations........... 148
Beams ......................................................................................................... 152
Bending Moments...................................................................................... 153
Figure 6-1: Focus Activities in Overdesigning Modular Steel Pipe Racks .................... 157
Pipe Racks ................................................................................................... 158
and Design (Adapted and Modified from Dehghan & Ruwanpura, 2011) . 164
Figure 6-4: Concept of Ideal Overdesign Factor ............................................................ 165
Figure 6-5: Cumulative Density Function for Ideal Overdesign Factors ....................... 171
Beams .......................................................................................................... 172
Problem for the Modular Steel Pipe Racks ................................................. 177
Figure 7-1: General Overview of Pipe Rack Overdesign DSS ....................................... 181
Figure 7-2: Decision Tree Model of the Overdesign Problem ....................................... 184
Figure 7-3: Steps for Performing Stochastic Analysis .................................................... 195
Associated Statistics .................................................................................... 197
Hypothetical Overdesign Case .................................................................... 200
Figure 7-6: Tornado Diagram for a Hypothetical Overdesign Case ............................... 201
xiii
Figure 7-7: Output of the Two-Way Sensitivity Analysis Module for a Hypothetical Overdesign Decision ................................................................................... 205
Figure 7-8: Analysis on the Output of the Two-Way Sensitivity Analysis Module for a Hypothetical Overdesign Decision........................................................... 207
Figure 7-9: Different Kinds of Pipe Rack Overdesign DSS Reports .............................. 210
Figure 7-10: Demographic Information of the Respondents to Veification Questions .. 215
Figure 7-11: Overview of the Validation Scenario ......................................................... 218
Figure 7-12: Screenshot of the Selected Project for Validation in STADD Software .... 219
Figure 7-13: Cumulative Density Function for Ideal Overdesign Factors showing the Probability of Rework for 56% Overdesign Factor (Refer to Figures 6-5 and 6-6) ...................................................................................................... 229
Figure 7-14: Cumulative Density Function for the Real Overdesign Case (Decision to Increase the Preliminary Loads by 100%) ................................................. 232
Figure 7-15: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Time Savings, Other Decision Variables Fixed at their Base Value).......................................................................... 233
Figure 7-16: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Extra Cost of Steel Fabrication, Other Decision Variables Fixed at their Base Value) .......................................... 234
Figure 7-17: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Extra Cost of Steel Erection, Other Decision Variables Fixed at their Base Value) .......................................... 234
Figure 7-18: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Steel Structure Calculation Rework, Other Decision Variables Fixed at their Base Value) ................................ 235
Figure 7-19: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Cost of Engineering Rework, Other Decision Variables Fixed at their Base Value) .......................................... 235
Figure 7-20: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Cost of Procurement Rework, Other Decision Variables Fixed at their Base Value) .......................................... 236
Figure 7-21: Tornado Diagram for the Validation Project ............................................. 237
Figure 7-22: Two-Way Sensitivity Analysis Report for the Real Validation Project .... 239
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List of Symbols, Abbreviations and Nomenclature
Symbol Definition
AOD Assumed overdesign factor
Bec Daily benefits of project early completion,
including revenue and daily incentives for early
completion
Bd1 Benefits of making no overdesign decision
Bod Benefit of overdesign
Bod-r Benefits of overdesign in case of rework
Cd1 Cost of making no overdesign decision
Clc Daily costs of project late completion, including
loss and daily penalties for late completion
Cod Cost of overdesign
Cod-r Total cost of overdesign in case of rework
Cpi All possible costs of installation of additional
piles required as a result of overdesign
including wages, equipment cost, overhead, etc.
Cpp All possible costs of purchasing additional piles
required as a result of overdesign
CRW Total cost of rework
Crwe All possible costs of rework in the engineering
phase including daily wages and overhead
xv
Crwp All possible costs of rework in the procurement
phase including daily salaries, overhead, change
in material, etc.
Crwc All possible costs of rework in the construction
phase including daily salaries, overhead,
demolition, reconstruction/reinstallation, etc.
Csf All possible costs of fabricating additional steel
required as a result of overdesign
Cse All possible costs of erection of additional steel
required as a result of overdesign including
wages, equipment cost, overhead, etc.
Dpi Increase in duration of piling installation after
overdesign
Dpp Increase in duration of piling purchase after
overdesign
Dse Increase in duration of steel erection after
overdesign
Dsf Increase in duration of steel fabrication after
overdesign
DSS Decision Support System
EV Expected value
EV (AOD) Expected value of overdesign with any assumed
value
xvi
EV (NOD) Expected value in normal execution and with
no overdesign
FEED Front-End Engineering Design
FMx Final moment for beam x
GA General Arrangement drawing
IODx Ideal overdesign factor for beam x
LDT Line Designation Tables
LM Lineal meter
MT Metric Ton
NTIod Net time impact of overdesign
OD Overdesign factor
Pay-off dn Pay-off of the decision n
Pay-off d1 Pay-off of making no overdesign decision
PFD Process Flow Diagrams
PMx Preliminary moment for beam x
Prrw Probability of rework
P&ID Piping and Instrumentation Diagrams
RW Total rework duration
RWc The equivalent rework duration for
Construction, as a result of change in pipe loads
RWe Engineering Rework
RWp The equivalent rework duration for
Procurement, as a result of change in pipe loads
xvii
RWpd The equivalent rework duration for piling
design & drawings, as a result of change in pipe
loads
RWsc The equivalent rework duration for structural
calculations & drawings, as a result of change
in pipe loads
TIod Overdesign time impact
TN Project duration in normal execution
Tod Project duration after overdesign
Tod-r Project duration after overdesign (in case of
rework)
TSeg Engineering time savings
Tt Target duration
UFD Utility Flow Diagrams
xviii
Epigraph
Yesterday I was Clever. So I wanted to change the world. Today I am wise. So I am
changing myself. – Rumi
xix
CHAPTER ONE: INTRODUCTION
Reducing project duration has significant strategic implications for the market share and
revenue stream of owners and contractors in the oil and gas industry. Owners tend to
reduce the duration of their projects because shorter project duration leads to a reduced
period of risk exposure and investment payback time and early income generation.
Owner companies in the oil and gas industry can generate significant profits when their
plants are on-stream. On the contractor side, reduced project duration results in early
income generation, the earlier deployment of resources to other jobs, possible
opportunities to earn incentives and an enhanced reputation, which leads to opportunities
with other owners.
The business benefits of early completion mentioned above challenge project
managers to employ fast tracking techniques to achieve a shorter project duration. Some
project managers may have to change their strategy from normal execution to fast track
execution in the middle of their projects to overtake delays which may result in millions
of dollars per day in lost profit. However, companies still face serious challenges in fast
tracking their projects; for many, the term fast track creates a negative impression. This is
because some fast tracking techniques may negatively impact project performance by
imposing additional risks and driving up costs. However, if the effects of applying fast
tracking techniques on project performance can be theoretically and practically identified,
monitored and controlled throughout the project, fast tracking can result in better
outcomes (Park 1999).
Among the techniques applied to expedite project execution, those applied in the
engineering phase are of vital importance. The opportunity to influence project
1
acceleration falls off rapidly over the project stages, so the greatest opportunity to ensure
the project is completed in the minimum time exists at the beginning of the project
(Figure1-1). Furthermore, engineering acceleration techniques have a cascading effect on
subsequent phases and may generate other opportunities for schedule reduction in
procurement and construction.
Figure 1-1: Project Life Cycle Showing Potential to Add Value
and Cost of Change – Burke (2003)
To effectively address aggressive engineering schedule demands, engineering and
construction activities are usually overlapped to some extent to attain more schedule
compression. To achieve overlapping, companies try to reduce dependencies between
activities using various techniques such as early freezing of design criteria, early release
of preliminary information and overdesign. Overdesign is one of the methods used to
reduce information dependency between dependent activities; this helps start the work of
the successor activity before finishing the work of the predecessor activity. In the absence
of sufficient design information, designers usually make more generous allowances and
adopt conservative assumptions in their designs than would normally be the case,
2
especially where the structural integrity of the asset is concerned. With this overdesign,
successor activities can start and progress well ahead and long before accurate details can
be determined. This process results in overlapping and schedule compression; however, it
also causes additional costs to be incurred in subsequent project phases due to lack of
design optimization and increased materials wastage.
The purpose of the research presented in this thesis is to develop a decision support
system for choosing the best opportunities for the application of overdesign in oil and gas
projects that provide the greatest schedule compression for the least incremental cost. The
focus of this research is the detailed design phase and the scope is limited to modular
steel pipe rack, which represent the spine or main artery of the plant and consist of an
overhead structure supporting the process pipes, utility lines, instrument lines and
electrical cables. Pipe rack usually must be erected first before it becomes obstructed by
rows of equipment. The corresponding piping drawings are also required early on for the
same reason. However, because little information usually exists at that time, pipe rack is
one of the best candidates for the application of overdesign. This is discussed in more
detail in Chapter 5.
1.1 Problem Statement
If companies want to survive in a competitive environment, they have to develop new
strategies that will provide them with a much-needed competitive edge over their
principal market competitors. For many companies, time is the most critical variable in
today’s competitive arena (Hastak et al. 1993). This is especially true for oil and gas
companies. Therefore, the extent of fast tracking has been increasing in these projects.
Despite this, fast tracking is still associated with many problems. Although utilizing fast
3
tracking techniques helps to reduce project duration, it may adversely affect other project
objectives, i.e., cost and quality, because these objectives are interrelated, interdependent
and essentially incompatible (Ruwanpura, 1992). Furthermore, the general belief is that
adopting fast tracking techniques may impose extra costs on the project and generate
higher level and newer risks, change, rework, and many other challenges. According to
Project Management Body of Knowledge (PMBOK Guide®), a fast tracking approach
can require work to be performed without completed detailed information, which results
in trading cost for time and increases the risk of achieving the shortened project schedule
(PMBOK®, 2008).
On the other hand, there may be many cost saving opportunities using this
approach, and some of the extra costs can be compensated by the business benefits of
early completion and/or performance bonuses. Pedwell et al. (1998) confirms this by
stating that fast tracking results in increased front-end engineering and construction costs.
However, the expectation is that this will result in overall cost savings to the project
(Pedwell et al. 1998). Therefore, the art of this approach is to maintain a proper balance
between the project objectives, while the question to be answered is how much of a
reduction in project duration keeps the project profitable. This dilemma generally exists
for all fast tracking techniques.
Focusing attention specifically on overdesign, we can see that application of
overdesign to speed up project delivery has its own pros and cons, like all the other fast
tracking techniques. Overdesign helps to reduce the overall schedule and gain the
associated business benefits. In some cases, it is also used to mitigate the risk of
information change in dependent activities. However, it is not a risk-free process on its
4
own. One of the major concerns in overdesign is the lack of design optimization, which
will be directly translated to extra costs and increased materials wastage. In a chain of
dependent activities, if each activity is designed conservatively, then the final asset will
be far away from the optimum design and this may create problems in the operational
stages. Also, the assumptions made might not be conservative enough, necessitating
redesign and rework on downstream activities. Therefore, overdesign presents the risk of
increasing project cost and rework.
As mentioned earlier, the scope of this research is limited to modular steel pipe
racks. In a modular approach, meeting the module program dates is crucial for project
managers, as the cost of missing the schedule is very significant. In regions with harsh
weather conditions such as Alberta, Canada, if a delay in the module program causes the
construction operations to extend to winter, the cost overrun will be even higher. On the
other hand, because the size of the temporary residence in the construction field is
limited, a module program should be completed early enough to avoid interfering with
peak construction activities. Overdesign can enable project managers to compress the
schedule to meet the module program dates. However, while industry practitioners are
using overdesign to help meet schedule dates, few of them are following a systematic and
scientific approach to overdesign. Most rely heavily on their own judgment and
experience as well as input from peers in other similar projects. In order to limit the
extent of the negative impacts of overdesign, managers need to make a sound decision
about overdesign based on analysis, rather than gut feelings. Both the extent and timing
of overdesign have cost implications. The extra cost of overdesign and material wastage
can ruin the project’s profit, which is why many engineers prefer not to overdesign – the
5
up-front cost may not look justifiable. For example, steel prices are sometimes subject to
wild fluctuations that may result in significant cost overruns. Therefore, it is of vital
importance to analyze whether the extra cost associated with overdesign overweighs its
benefits of early completion.
Having discussed the general advantages and disadvantages of overdesign, the
main question is: What are the best overdesign options in terms of degrees of
overdesign and timing that provide the maximum schedule benefit versus the minimum
extra costs in modular steel pipe racks?
The researcher believes these questions are worth attention and that answering
them can contribute to the existing knowledge of overdesign both in academics and in
industry. Because, prior to undertaking the current research study, the researcher had
real-world experience in project engineering and project planning and control. She has
been involved in solving real overdesign problems and has seen many project delays and
cost overruns stemming from wait times for receiving information and ineffective
decision-making caused by a lack of understanding of both the big picture and long-term
implications.
1.2 Objectives
The main purpose of this research is to develop a decision support system for discovering
the best overdesign options in modular steel pipe racks in oil and gas projects that
provide the greatest schedule compression for the least incremental cost. The sub-
objectives include:
1. Providing an overview of the engineering phase of oil and gas projects and the
function and deliverables of each engineering discipline
6
2. Investigating the overdesign practice in the engineering phase
2-1 Defining overdesign theory
2-2 Identifying main drivers for overdesign
2-3 Identifying consequences of overdesign
3. Investigating the application of overdesign as a fast tracking technique on modular
steel pipe racks of oil and gas projects
3-1 Definition of and introduction to modular steel pipe racks
3-2 Introduction to steel pipe rack design
3-3 Application of overdesign to the pipe rack module
4. Analyzing the sensitivity of project schedule and cost to different types and degrees
of overdesign in the pipe rack module of oil and gas projects
4-1 Investigating the consequences of the application of overdesign in a pipe rack
module in terms of time, cost and rework
4-2 Formulating the overdesign time-cost trade-off problem
4-3 Modeling the research problem
5. Developing a computerized decision support system that can assist in determining the
best options in terms of degree and timing for overdesigning modular steel pipe racks
that provide maximum schedule benefit versus minimum extra costs.
6. Verification and validation of the decision support system
1.3 Literature review
The research problem stated in section 1.1 of this chapter, as a whole, is fairly complex
and it is difficult to find a particular body of literature that can address all of its aspects.
However, dividing the main problem into different topics provides a way to focus
7
attention when reading literature. The following are different topics of the literature
review of this research which were defined based on the sub-objectives explained in
section 1.2.
• Application of overdesign as a fast tracking technique
• Project development and execution process in general and Engineering phase in
particular
• Modular steel pipe racks
• Time-Cost trade-off and optimization techniques in general and decision tree in
particular
• Information management systems in general and decision support systems in
particular
After determining the literature review topics, the researcher tried to find relevant
information with regards to each topic from various sources, e.g. peer-reviewed journal
papers, books, conference proceedings and Internet. The researcher had different goals
when reviewing the literature in each topic.
Reviewing the literature related to Application of overdesign as a fast tracking
technique was primary to the current research. It helped the researcher gain a better
understanding of the research problem and explore previous research findings as well as
the unknowns and gaps in the hopes of providing fresh insight. Literature review of this
topic helped the researcher learn what other researchers have done in situations with
difficulties similar to the current research. This also helped the researcher formulate the
8
research problem into a time-cost trade-off equation and further model it using the
decision tree principles.
Other literature review topics – i.e., Project development and execution process,
Modular steel pipe rack, Information management systems and Optimization techniques –
were secondary to the current research. They helped to increase the researcher’s
knowledge in areas relevant to her study and enabled her to more effectively tackle the
research problem. Studying the relevant literature in Project development and execution
process and Engineering phase of the projects helped the researcher narrow the scope to
the detailed design phase. Studying the Modular steel pipe racks helped researcher gain
knowledge about the high level design aspect of modular steel pipe racks. Studying the
optimization technique and analyzing the advantages and disadvantages of current
optimization techniques enabled the researcher to find a suitable way of dealing with the
current research problem. Finally, Information management systems study helped the
researcher develop her decision support system.
1.4 Scope
The scope of the research includes satisfying the objectives of the research clearly stated
in section 1.2. This means the following are specifically within the scope of this research.
• Developing the overdesign theory
• Investigating the application of overdesign as a fast tracking technique on
modular pipe racks in oil and gas projects
• Investigating the consequences of the application of overdesign in modular pipe
racks in terms of time, cost and rework
• Formulating the overdesign time-cost trade-off problem
9
• Modeling the research problem
• Developing a computerized decision support system that can help in choosing the
best overdesign options
The focus of the study is the detailed design phase of oil and gas projects and the
scope is limited to modular steel pipe racks with pile foundations.
It is important to note that this study will investigate overdesign on pipe loads
which will be manifested in the steel design. Likewise, the focus is on investigating the
effect of change in pipe operating loads (defined later in Chapter 5) on the beam design.
1.5 Out of Scope
The following items are excluded from the scope of this research.
1. The current research evaluates the effect of overdesign on project time and cost.
Evaluating potential adverse impacts on other project objectives such as quality is
excluded from the scope of this research. The rationale behind this decision is that in
projects under this study, time is the main driver. Therefore, there is no room or
incentive to incur additional time and cost to provide nice-to-have features and
greater quality. Therefore, it is assumed that the quality is at the level necessary to
meet contractual quality requirements and this level cannot be infringed.
2. When formulating time-cost trade-off, two parameters – daily benefits of early
completion and Daily cost of late completion – will be used to compare with the extra
costs imposed on the project as a result of overdesign. These parameters are related to
the project’s business objective and will be determined by senior management.
However, senior management should consider a variety of determinant factors such
10
as time value of money and competitive positioning in the market when determining
these parameters. In this research, it is assumed that senior management considers all
of these factors when determining “daily benefits of early completion and daily cost
of late completion”. Determining these values while considering all strategic and
contextual factors is beyond the scope of this research.
1.6 Research assumptions
The following are the main research assumptions:
• This study will investigate overdesign on pipe loads which will be manifested in
the steel design. The assumption is that the same overdesign will be carried over
to piling design, without a separate overdesign on the structural load.
• It is assumed that beams on the pipe rack are fixed at both ends.
1.7 Research deliverables
The main expected deliverable of the research is a decision support system in the
form of a computer software package, which is used to assist managers in evaluating the
cost and time impact of different overdesign options in pipe rack modules of oil and gas
projects. These evaluations help managers make a reasonable decision based on the
analysis. The decision support system has been designed based on formulating the
overdesign problem under the stochastic decision tree model and has a built-in sensitivity
analysis module to help managers obtain a fuller understanding of the dynamics of the
overdesign decision problem. It also helps them identify the important elements in the
overdesign problem.
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1.8 Research methodology
From a research methodology standpoint, this research study has a mixed methods
approach and is divided into three parts: A, B and C. The first part (Part A) provides the
conceptual framework of the research and is explanatory in nature. From this section, the
researcher sought a better understanding of the concept of overdesign, its drivers,
consequences and areas of application in general and in modular steel pipe racks in
particular. This part comprises qualitative research. Among the various existing research
designs with a qualitative approach, a Phenomenological study seemed to be the most
appropriate design for the first part of the research, because the researcher attempted to
understand the expert’s perception, perspectives and understanding of a particular
situation (Leedy & Ormrod, 2005). In the current research, the researcher sought to
enrich her understanding of the overdesign concept by taking advantage of the experience
of other professionals. By looking at multiple perspectives of the same subject, the
researcher could then build a better conceptual framework for her study.
The primary source of data collection for Part A of the research was interviews.
This part started with asking general questions, followed by collecting an extensive
amount of verbal data from participants and then organizing the data and using verbal
descriptions to portray the overall picture and situation. Therefore, the bulk of data
collection for this part was dependent on the researcher’s personal involvement, including
interviews in which the researcher and interviewees worked together to come to a
conclusion and to achieve a shared understanding of the situation. The interviewees were
purposefully selected as experienced individuals, from project managers and project
engineering managers to senior project engineers and engineering discipline leads of
12
owners and engineering, procurement and construction companies known as EPC firms
in order to obtain more reliable information about the subject. Overall, 37 industry
experts from 10 owners and EPC firms participated in the interviews, and each
participant was interviewed at least two times. The interviews were semi-structured, with
general, open-ended questions about the concept of overdesign. Each interview took
between 60 to 90 minutes. To support interviews, the literature was reviewed to enrich
the researcher’s basic understanding of overdesign and help her design the semi-
structured interview questions.
The next part of the research is Part B and deals with formulating the research
problem and is fed from Part A of the research. Following data collection, literature
review and interviews, as well as data analysis that helped portray the overall concept of
overdesign, focus groups were formed consisting of two highly experienced individuals
plus the researcher for brainstorming sessions. During these sessions, the researcher and
participants worked together to review, discuss and analyze the results of the data
collection and data analysis obtained from interviews. Overall, 15 focus group sessions
were held for data analysis and designing the research direction. The outcome of these
brainstorming sessions formulated the overdesign time-cost trade-off problem, which was
further customized for modular steel pipe racks.
By reviewing the literature about existing optimization techniques for solving
time-cost trade-off problems, a Stochastic Decision Tree was chosen for this research.
Therefore, the research problem was modeled using the stochastic decision tree
principles. This built the foundation for the decision support system.
13
The last part (Part C) is the quantitative part of the research. When investigating
rework as one of the potential consequences of the application of overdesign in modular
steel pipe racks, the researcher tried to use historical project information to establish a
relationship between the level of overdesign and the probability of rework. The intention
was to examine the situation as it exists while remaining detached from the research
participants to be able to draw unbiased conclusions.
For this quantitative part, research data included information from real projects
gathered from drawings of six modular steel pipe racks that were made available to the
researcher. These pipe racks are from SAGD projects in the oil and gas industry in
Alberta, Canada. Therefore, the selection of the projects was based on the concept of
convenient sampling. However, within those six projects, 774 individual pipe lines
located on 130 beams on the pipe racks were quite randomly selected for the study.
Chapter 3 elaborates on the research methodology; however, details of data collection
and this process are explained in Chapter 5.
1.9 Research contributions
The contributions of this research are categorized under the following four distinct
categories.
• Theoretical contribution: this study contributes to overdesign theory and literature
by conducting a comprehensive study about the overdesign as well as by
developing the decision support system.
• Literature contribution: addressing the gaps in the current overdesign literature
and providing useful engineering multidisciplinary and interdisciplinary
information are considered the literature contribution of this research.
14
• Methodological contribution: involves addressing a unique method to relate the
level of overdesign to the probability of rework associated with any overdesign
decision as well as by modeling the overdesign problem under the stochastic
decision tree principles.
• Industry contribution: the decision support system developed in this study is
considered the industry contribution as it helps managers, cost engineers and
designers in different ways. Likewise, historical information about pipe load
changes on pipe racks and their effects on steel design is a unique source of
information for industry practitioners.
The details of each of these contributions are discussed in Chapter 8.
1.10 Thesis structure
This thesis is organized in the following order: The current chapter, chapter 1, is an
introduction to the overdesign problem. Chapter 2 is dedicated to the literature review of
different subjects related to the study, finding the gaps and making suggestions to fill
them. Chapter 3 describes the research methodology under which this research has been
conducted. Chapter 4 introduces the overdesign theory and provides in-depth information
about the overdesign concept, drivers and consequences of overdesign. Chapter 5
explains the modular steel pipe rack projects and discusses the application of overdesign
on modular steel pipe racks. Chapter 6 formulates the time-cost trade-off required for
solving the decision problem. It can be said that Chapters 5 and 6 provide the foundation
for constructing the decision tree model and decision support system. Chapter 7 models
the overdesign problem under the decision tree principles. Also, it explains different steps
15
required to develop the decision support system as well as inputs, processing modules
and output of the decision support system. Finally, Chapter 8 discusses the conclusions,
research contribution and future research.
16
CHAPTER TWO: LITERATURE REVIEW
Generally speaking, “literature review is looking again at what others have done
in areas that are similar, though not necessarily identical to one’s own area of
investigation” (Leedy & Ormrod, 2005). The research problem stated in section 1.1 of
Chapter 1 is fairly complex as a whole and it is difficult to find a particular literature that
can address all of its aspects. However, dividing the main problem into different subjects
provides a way to focus attention when reading the literature. Figure 2-1 presents
sequential steps to be taken to identify different subjects within the main problem that
need literature review. These steps have been extracted from Leedy and Ormrod (2005,
pages 71-72).
Figure 2-1: Literature Review Steps Extracted from Leedy & Ormrod (2005)
This research attempts to follow the steps for literature review presented in Figure 2-1.
Section 1-3 of Chapter 1 defined the main purpose and sub-objectives that led to a
determination of the main problem and sub-problems. This helped identify the important
words and phrases within each sub-problem, as follows.
17
• Project Development and Execution Process
• Engineering Phase
• Engineering Disciplines
• Overdesign
• Design under Uncertainty
• Fast-Tracking Techniques
• Project Acceleration
• Schedule Compression
• Agile Project Management
• Modular Pipe Racks
• Time-Cost Trade-Off
• Decision Tree and Optimization Techniques
• Decision Support System
These words and phrases have been translated into specific topics that the researcher
needed to study. These topics are broadly categorized, in no specific order, as follows.
• Application of overdesign as a fast tracking technique
• The project development and execution process in general and the project
engineering phase in particular
• Modular steel pipe racks
• Time-Cost trade-off and optimization techniques in general and the decision tree
in particular
18
• Information management systems in general and decision support systems in
particular
Since the literature review is an ongoing process throughout the research, many other
areas were identified within each category during the course of the research about which
the researcher needed to learn more. Figure 2-2 provides an overview of the literature
review areas of this research study. As illustrated in Figure 2-2, each area is narrowed
down to sub-areas. These sub-areas are the main focus of the research. For example, in
Figure 2-2, Fast tracking techniques is a broad topic which is condensed to the
Overdesign area, which is the heart of the research. This area is indicated with a colourful
background to show that its literature review is primary to the research. Areas with a
white background are topics that helped increase the researcher’s knowledge in the areas
relevant to her study and enabled her to more effectively tackle the research problem.
They also helped the researcher in formulating the research problem.
19
Figure 2-2: Areas of Literature Review
After determining the literature review topics, the researcher tried to find the
relevant information regarding each topic from various sources, e.g., peer-reviewed
journal papers, books, conference proceedings and Internet. The researcher mainly used
University of Calgary library databases, as they provide access to the journals, books and
online resources of major research institutions. Overall, she reviewed almost 89 peer-
reviewed journal and conference papers as well as 23 books. However, the researcher
sought different goals from reviewing the literature in each topic. This will be elaborated
when describing the literature review specific to each topic in the following sections.
It is important to note that for the areas that are primary to the current research,
such as overdesign, mostly peer-review journal and conference papers published up to 15
20
years ago were reviewed. Books were studied mostly in the areas in which the researcher
sought to increase her knowledge about a known subject. In these situations, the
researcher chose reference and bestselling books and tried to use the latest available
editions.
2.1 Overdesign
The literature review of Overdesign is primary to the research. By undertaking this
review, the researcher aimed to increase her understanding and knowledge of overdesign
concepts and principles. She also tried to discover what other scholars have done in this
area in order to uncover gaps in the literature as well as any room for improvement. A
review of the relevant literature revealed that overdesign literature is classified into two
broad categories: technical and managerial.
In technical literature, the studies are limited to investigating the effects of
overdesign on specific types of equipment in a purely technical context. Overdesign in
this context means to incorporate an allowance in the design to offset the effects of
uncertainties in design model parameters and to ensure feasible operations over a range
of operating conditions. Examples of these research works include: Soliman (2012), Van
der Merwe (2011), Lieberman (2010), Grondzik et al. (2010), Towler and Sinnott (2007),
Serth (2007), Bennett et al. (2007), Sharlow (2000), Mukherjee (1998), Fisher et al.
(1988 and 1985), Swaney and Grossman (1982), Malik and Hughes (1979),
Ramachandran and Jaydev Sharma (1979), Nishida et al. (1972).
The technical aspect of overdesign is not the focus of current research. The
current research investigates overdesign only as a schedule compression technique which
imposes extra costs and risks that need to be actively managed in order to benefit from
21
overdesign. In other words, the current research investigates overdesign from a
managerial standpoint rather than from a technical standpoint.
The literature contains research studies that look at overdesign from this
perspective. The following two principal sources discuss the application of overdesign as
a fast tracking technique. Although these two sources are relatively old, their importance
lies in the fact that numerous companies were involved in their study.
The first source is a study of schedule compression techniques undertaken by the
Construction Industry Institute (CII)-Cost/Schedule task force through Colorado State
University in 1986. Following many interviews with experienced engineering and
construction personnel, a catalogue of schedule compression techniques was compiled
and published. In this publication, “schedule compression” refers to the shortening of the
required time for accomplishing one or more engineering, procurement, construction or
start-up tasks (or a total project). This is exactly what overdesign intends to achieve.
The second study is a Fast Track Manual by Eastham (2002). In this study, a
taskforce of European Construction Institute members and other interested companies
discussed different aspects of fast tracking in various phases of projects, and listed
overdesign under the fast tracking techniques in the engineering phase of the projects.
However, studies performed by the Construction Industry Institute and the
European Construction Institute do not provide feedback on the project consequences
after applying the introduced fast tracking techniques (Khoramshahi and Ruwanpura,
2011).
As discussed above, overdesign-related literature correlates closely to fast tracking
techniques. More investigation revealed that among all other fast tracking techniques,
22
overdesign is most associated with overlapping. Overlapping is a schedule compression
technique in which phases or activities normally performed in sequence are performed in
parallel, in such a way that work on the downstream activity starts before work on the
upstream activity is finished (Dehghan et al., 2010). Overlapping is one of the techniques
that have a potential for cost and/or schedule reduction in construction projects
(Bayraktar et al., 2011).
The researcher believes overlapping is considered as a scheduling concept; in
practice, however, in order to actually achieve overlapping, other techniques should be
employed. Overdesign is one of the most important of these techniques. By definition, the
purpose of overdesign is to remove or reduce the logical link between dependent
activities by making educated guesses in the absence of accurate information. With
overdesign, dependent activities can proceed well ahead and long before accurate details
can be determined; this results in overlapping. This is also confirmed by Krishnan et al.
(1995, 1997), who say that to overlap activities, the flow of information should be either
removed or changed. This is what overdesign aims to perform to allow for overlapping
and reduce the project’s duration. Bogus et al. (2005, 2006) also named overdesign as a
strategy available for overlapping dependent activities. The Bogus and Krishnan studies
will be explained in more detail later in this section. In addition to the above, Ballard
(2000) names overdesign as a technique for reducing or eliminating unnecessary iteration
in projects (Ballard, 2000).
Based on the discussion so far, the researcher searched for studies related to
overlapping and, specifically, information exchange, which plays an important role in
overdesign. The summary of the findings is as follows.
23
Bogus et al. (2005, 2006) have defined four types of dependencies between
activities: Information dependency, resource dependency, permission dependency and
physical dependency. Overdesign is all about information dependency and its application
is in the design phase of projects. Prasad (1996) has worked on the relationship between
design activities and has defined four types of relationship between activities:
1. If no information dependency exists between two activities, they are called
independent activities
2. If one activity requires final information from another activity, then two activities
are called dependent
3. If one activity requires only partial information from other activities, they are
semi-independent
4. And finally, if a two-way information exchange between the activities occurs until
they are complete, they are called interdependent activities (Prasad, 1996)
Prasad’s categorization of activity dependencies (although it has been used by many
researchers) will be elaborated in the overdesign context in Chapter 4. Dehgahn and
Ruwanpura (2011), Bogus et al. (2005), Roemer and Ahmadi. (2004), Loch and
Terwiesch (1998) and Krishnan et al. (1995, 1997) have done some research on
information exchange between dependent and semi-independent activities. Other
researchers, e.g., Terwiesch et al. (2002), Yassine et al. (1999), Nicoletti and Nicolo
(1998) and Smith and Eppinger (1997), researched the information exchange in
interdependent activities. The following discusses some of these research works.
24
One of the most important research works in the area of information exchange
was conducted by Krishnan et al. (1995, 1997). This work has been referenced by many
other researchers. Before talking about their work, it is important to define upstream and
downstream activities that were repeatedly used in their research. When, two activities
have information dependencies, the upstream activity is the provider of the information
and downstream activity is the recipient of information. Krishnan et al. (1997) introduce
two characteristics for upstream and downstream activities that are important in
enhancing information exchange.
The first is upstream activity evolution characteristics, which describe the rate at
which information is developed and finalized in an activity. Activities can be either fast
evolution or slow evolution (Figure 2-3).
Figure 2-3: Evolution Characteristics of Upstream Activities – Krishnan et al. (1997)
The second characteristic is downstream activity sensitivity, which refers to how
sensitive the downstream activity is to possible changes in the upstream activity.
Activities can be either high sensitive or low sensitive (Figure 2-4).
25
Figure 2-4: Sensitivity Characteristics of Downstream Activities – Krishnan et al.
(1997)
Krishnan et al. (1997) further explain four extreme situations that can happen in two
dependent activities:
• Slow evolution upstream activity and low sensitive downstream activity: in this
case, downstream activity is not sensitive to changes in upstream activity and can
start upon receiving the preliminary information from upstream activity.
However, it should adjust itself on an iterative basis with the changes from
upstream activity, until receiving the final information.
• Fast evolution upstream activity and high sensitive downstream activity: in this
case, downstream activity starts earlier by receiving the finalized information
from the upstream activity, but there is no subsequent iterations.
• Slow evolution upstream activity and high sensitive downstream activity: this
situation is the riskiest and least desirable among all situations.
• Fast evolution upstream activity and low sensitive downstream activity: this
situation is the least risky situation in which it is possible to start downstream
26
activity with advanced information from upstream activity, and to modify later if
changes happen in the upstream information.
The concepts of evolution and sensitivity and their combination to define different
situations are used by many other researchers when investigating the mechanism of
information exchange between activities. These researchers include Dehghan and
Ruwanpura (2011), De la Garza and Hidrobo (2006), Bogus et al. (2005, 2006), Peña-
Mora and Li (2001) and Roemer and Ahmadi (2004). In Chapter 4, cost and time impacts
of overdesign as well as potential rework will be elaborated on, given the evolution and
sensitivity characteristics defined by Krsihnan et al. (1997).
Besides evolution and sensitivity characteristics, Krishnan et al. (1997) also
discuss the consequences of removing or reducing information dependencies between
activities. They argue that removing or reducing the information exchange does not
always result in time saving, as the downstream activity needs to be reworked to some
extent if upstream activity information changes. This leads to an increase in duration of
the downstream activity. The other disadvantage is the possibility of quality loss, as
changes in upstream activity should be limited to avoid reworking downstream activity.
Therefore, upstream activity loses its flexibility in terms of modifications and
optimization.
Other studies in the area of information exchange are as follows. Terwiesch et al.
(2002) performed a study in which they discussed important aspects of information
exchange in two interdependent activities. In their study, they discussed the subject of
information exchange by considering the characteristics of the information itself. They
27
determined two characteristics of information: information accuracy and information
stability. Information accuracy refers to the precision of the information, while
information stability refers to the likelihood that upstream activity information remains
unchanged during the overlap. Taking into account the tension between information
precision and information stability, they suggest different coordination strategies.
Terwiesch et al. (2002) also discussed the cost of adjustments in downstream
activity to changes in upstream activity. According to them, if stability in preliminary
information is low, there will be more risk of rework for the downstream activity and
therefore more cost of adjustment. They also argue that if upstream activity is not
sufficiently precise, upstream work cannot proceed, which means idle downstream
activity time.
In addition, Terwiesch et al. (2002) investigated the possibility of weakening the
interdependency between upstream and downstream activities by making the downstream
activity flexible to changes to upstream activity.
Nicoletti and Nicolo (1998) looked at ways to enhance the information exchange
between interdependent activities by defining an information link coefficient between
two activities. According to them, each activity consists of several operations and each
operation generates some information that can impact operations of other activities.
Therefore, a flow of information exists between activities. They defined an information
link coefficient as the number of operations of activity A that impact activity B plus the
number of operations of activity B that impact activity A. They used this coefficient to
enhance the information exchange between two activities. Their research contributes to
planning; however, it does not take into account the cost and rework.
28
The following research works also discuss information dependency, but they
focus mainly on the use and exchange of preliminary information as the means to achieve
overlapping. They have developed models for optimizing the extent of overlapping.
Loch and Terwiesch (1998) studied the role of communication when removing or
reducing the information dependency between two activities. In their study, they aimed at
developing a model for finding the optimum level of overlap between two activities by
reducing their information dependency. In their model, they have considered some cost
impacts such as cost of frequent communication. However, they did not consider the cost
of rework, which is a determinant parameter in finding the optimum level of overlap.
Dehghan et al. (2011) developed an overlapping optimization algorithm to
determine which activities have to be overlapped and to which extent to reduce the
project duration at the minimum cost. In their research, they discuss different overlapping
strategies; however, the cost of these strategies varies significantly depending on the total
rework and complexity they generate. Based on this discussion, they formulated the
overlapping time-cost trade-off model as well as an overlapping optimization algorithm.
Dehghan et al. (2011) further used their optimization algorithm to develop a computer
tool using Genetic Algorithms to optimize the amount of overlapping. Their research
work is a major contribution to overlapping knowledge since unlike many other similar
studies, they consider overlapping in the context of project schedule and with regard to
other overlaps. Dehghan et al. research was, to some extent, inspired by the models
developed in research studies by thier predecessors Roemer et al. (2000), Roemer and
Ahamdi (2004) and Gerk and Qassim (2008). These researchers also developed models
for optimizing activity overlapping in the context of project schedule. However,
29
Dehghan et al. (2011) differentiated their research from the others by taking all activities,
critical and non-critical, into account; handling multi-path networks; considering changes
in the project’s critical path; and accounting for resource limitations and schedule
constraints. In addition, their computer tool is both new and unique as no similar tools
exist in industry or academia so far, to the best knowledge of the researcher.
Chakravarty (2001) developed an overlap model for design and build cycles that
focuses on maximizing project profit. In the model, the financial viability of overlapping
design and build activities depended on two factors:
1. The relationship between the time it takes to design an element and the time it
takes to build that same element
2. The risk of changes in the design work after building activities have begun
(Chakravarty, 2001)
Yassine et al. (1999) developed a decision framework for identifying the level of
overlap between activities and the potential time savings based on risk analysis
principles.
All of the research works named so far either do not address specific strategies for
achieving the suggested overlap or limited their research to one strategy. Bogus et al.
(2006) attempted to address this gap and distinguish their study from the others by
identifying and classifying overlapping strategies. They name these strategies as:
• Early freezing of design criteria
• Overdesign
• Early release of preliminary information
30
• Prototyping
• Allowing no iteration or optimization
• Standardization
• Set-based design
• Decomposition
They suggest a planning framework which uses activity evolution and sensitivity
characteristics, defined by Krishnan et al. (1997), to suggest opportunities for using
overlapping strategies she defined earlier. In other words, they categorized the
overlapping strategies, including overdesign, in terms of their applicability to activity
pairs with different evolution and sensitivity characterizations. They greatly contributed
to the existing knowledge of both overlapping and overlapping strategies, since according
to them a framework for deciding among multiple overlapping strategies is currently
absent from the literature. In addition, they discuss the potential consequences associated
with applying each overlapping strategy. These consequences include the potential for
rework in downstream activity due to changes in upstream information as well as the
potential for additional design and construction costs (Bogus et al., 2006).
Bogus (2004) defines overdesign as a “strategy which acts on the downstream
activity by reducing the sensitivity of downstream activities to changes in upstream
information. Reducing the sensitivity to changes in upstream information effectively
removes the dependency of the downstream activity on upstream information” (p 155).
The researcher would like to argue this definition. She believes overdesign can also be
applied to upstream activity. If the upstream activity is overdesigned, there will be a
31
lower probability of change in the information related to the upstream activity; on the
other hand, if the downstream activity is overdesigned, it will be less impacted by change
in the upstream activity information. In other words, the sensitivity of the downstream
activity to changes in the upstream activity information reduces.
Bogus (2004) concludes that “overdesign strategy is most appropriate when a
downstream activity is sensitive to input information from an upstream activity. By
making conservative assumptions regarding the required input information, the
downstream designer can reduce the sensitivity of the downstream design to the final
input value. Conversely, overdesign is not as appropriate when the downstream activity is
constraint sensitive. In this situation, constraints must be met and overdesigning the
downstream activity may exceed the given constraints” (p 159).
Bogus’ comments on the consequences of the application of overdesign are
another valuable part of her work. She discusses conceptually the cost of overdesign and
potential rework given the evolution and sensitivity characteristics of activities. However,
she believes overdesign mainly affects the cost of construction, as by definition,
overdesigned projects produce designs that are overly conservative for the actual
conditions (Bogus, 2004). The researcher would like to comment on this, as the cost of
procurement comes before construction because the magnitude of cost increase in
procurement is sometimes more than that of construction. The detailed cost impact of
overdesign in each phase of the project is discussed in Chapter 6.
The preceding was a review and critique of the overdesign literature, which served
the following general purposes.
• It helped the researcher gain a better understanding of the concept of overdesign
32
• It explored previous research findings regarding the problem as well as the
unknowns with the intent of gaining insight
• It showed how other researchers have handled methodological issues in studies
similar to the current research
• It offered new ideas, perspectives and approaches that may not have occurred to
the researcher
• It helped the researcher discover what other researchers have done in situations
and difficulties similar to the current research
• It helped to tie the research results to the work of predecessors
• And finally, finding gaps in the literature helped the researcher define the point of
departure of this research, which will be explained in the next section
Point of Departure
A review of the above mentioned literature in the area of information exchange and
overlapping points to several areas where explicit research is lacking; specifically, a
comprehensive study of overdesign as a specific strategy to reduce information
dependency between activities.
Most of the research studies discussed above do not address how to reduce or
remove information dependencies between activities. Bogus et al. (2006) work is
valuable because they fill this gap; however, their study investigates the appropriateness
of different overlapping strategies, including overdesign, from various combinations of
evolution and sensitivity characteristics of activities. Therefore, their study is not specific
to overdesign; however, they present a conceptual discussion about the cost of overdesign
33
and rework for a pair of activities with different evolution and sensitivity characteristics.
Bogus et al. also mention that there should be a balance between increasing the
conservativeness of the assumptions in overdesign and maintaining a reasonable cost for
the project, but they neither address it in more detail nor provide a model for analyzing
this issue. This is exactly the point of departure for this research. The main purpose of
this research is to realize the best overdesign options that provide the greatest schedule
compression for the least incremental cost. The researcher purposefully limited her scope
to modular steel pipe racks in oil and gas projects because they provide one of the best
candidates for overdesign, as will be discussed in Chapter 5.
Other than the research conducted by Bogus (2004) and Bogus et al. (2006), the
current research was also inspired by Dehghan’s (2011) work, as his research is one of
the most valuable studies in formulating the overlapping time-cost trade-off model. In
fact, it can be said that Dehghan’s work was inspirational in formulating the time-cost
trade-off model (TCT) for the current research. He built his TCT model for overlapping;
the current research, though, built TCT for overdesign. In addition, although Dehghan’s
work also focuses on the construction industry, his model is general. However, since the
scope of the current research has been narrowed to modular steel pipe racks in oil and gas
projects, the researcher was able to build a detailed and specific time-cost trade-off model
to consider the relevant activities.
Both Dehghan (2011) and Bogus (2004) discussed the rework conceptually.
Bogus (2004) conceptually evaluated different overlapping strategies, including
overdesign based on the amount of overlap and the resulting rework for different
combinations of activity evolution and sensitivity characteristics. She states that an
34
alternative to her evaluation is to assign uncertainty to the potential for rework; however,
she reserved detailed evaluation of uncertainty for future work. Likewise, Dehghan
(2011) predicted the rework parameter in his time-cost trade-off model but did not
determine the rework functions as well as the probabilities associated with rework,
because both of these parameters are activity specific and therefore out of his scope. The
current research is different from both of the above mentioned studies as well as from
other predecessors as it tries to determine the probability of rework using historical
information. Narrowing the scope of the research enabled the researcher to achieve this
purpose as she investigated specific activities.
Another aspect contributed by this research was developing a decision support
system for analyzing the sensitivity of cost and schedules for different degrees of
overdesign. This decision support system was built based on modeling the research
problem under the Stochastic Decision Tree, which is one of the most effective
techniques for handling decision problems such as the current problem.
2.2 Time-Cost Trade-Off and Optimization Techniques
As discussed in section 1-3 of Chapter 1, the purpose of this research is to discover the
best opportunities for the application of overdesign in pipe rack modules of oil and gas
projects that provide the greatest schedule compression for the least incremental cost. To
achieve this purpose, sensitivities of project schedules and costs for different types and
degrees of overdesign in modular pipe racks will be analyzed. As a precursor to this
process, a time-cost trade-off problem was formulated to provide the basis for the
sensitivity analysis.
35
Time-cost trade-off problems are viewed as one of the most important aspects of
construction decision-making (Feng et al., 1997). “Decision makers are often faced with
deciding between a project execution strategy that emphasizes either cost or schedule. An
apparent trade-off condition presents itself on almost any project. This paradigm demands
a close examination of existing practices and development of new guidelines and decision
support tools that can be deployed during project development and delivery, focusing
exclusively on how to achieve the desired trade-off between cost and schedule ”
(Bayraktar et al., 2011, p 645).
Construction time -cost trade-off problems have been the subject of extensive
research e.g. Bayraktar et al. (2011), Dehghan and Ruwanpura (2011), Roemer and
Ahmadi (2004), Feng et al. (2000), Hegazy, (1999).
Existing optimization techniques for time-cost trade-off problems have been
categorized into four areas: heuristics, mathematical programming, simulation and
evolutionary-based optimization algorithms (EOAs) or meta-heuristic algorithms (Figure
2-5) (Hegazy 1999; Zheng et al. 2005; El-Gafy 2007; Xiong and Kuang 2008; Ng and
Zhang 2008; Dehghan et al. 2011).
36
Figure 2-5: Existing Techniques for Time-Cost Trade-off Problems
Heuristic methods are based on rules of thumb or simple common sense. They are
experience-based methods and generally lack the precision of mathematical solutions.
Examples of heuristic methods include Vanhoucke’s (2007), Moselhi’s structural
stiffness method (1993), Siemens’s effective cost slope model (1971), a structural model
(Prager, 1963) and Fondahl’s (1961) method. Heuristic methods are easy to use and
provide fairly good solutions; however, they do not guarantee an optimum solution; in
fact, even good solutions are not always guaranteed. Heuristic methods are problem-
dependent; they mostly assume a linear relationship between time and cost. In addition,
the solutions obtained by heuristic methods do not provide a range of possible solutions,
37
making it difficult to experiment with different scenarios for what-if analysis (Feng et al.,
2000).
In mathematical programming methods, time-cost trade-off problems will be
mathematically modeled, and then optimization techniques, such as linear programming,
integer programming or dynamic programming, will be utilized to solve them. Pagnoni
(1990), Hendrickson and Au (1989) and Kelly (1961) formulated time-cost trade-off
problems by assuming linear time-cost relationships within processes. Linear
programming techniques are suitable for problems with linear time-cost relationships but
fail to solve those with discrete time-cost relationships. Patterson and Huber (1974) and
Meyer and Shaffer (1963) solved time-cost problems – including both linear and discrete
relationships – by using mixed integer programming. However, integer programming
requires a prohibitive amount of computational effort once the number of options to
complete an activity becomes too large or the network becomes too complex. Burns et al.
(1996) and Liu et al. (1995) took a hybrid approach that used: (1) linear programming to
find a lower boundary of the trade-off curve; and (2) integer programming to find the
exact solution for any desired duration. De et al. (1995), Elmagraby (1993) and Robinson
(1975) used dynamic programming to solve time-cost trade-off problems. Overall, the
main criticism of mathematical programming methods has been their complex
formulations, computational-intensive nature, applicability to small size problems, and
local minimum solutions (Feng et al., 2000, Hegazy, 1999, Moselhi, 1993, Feng et al.,
1997, Li and Love, 1997).
Based on mathematical programming methods, simulation techniques have been
used to find optimal solutions for construction time-cost trade-off problems in stochastic
38
project networks. However, many studies focus only on estimating project duration or
cost (Weiss, 1986, Dobin, 1985). Few studies have been conducted to optimize the
project in a stochastic network, e.g., Wan (1994) and Kidd (1987). Simulation techniques
based on mathematical programming provide a good estimate for optimal solutions;
however, a guide must be provided to analyze the result of the simulation in order to
efficiently find solutions.
With recent advances in the artificial intelligence branch of computer science and
the fast growth in computer technology, a new breed of optimization techniques –
evolutionary-based optimization algorithms (EOAs) or meta-heuristic algorithms – has
emerged. “Evolutionary Optimization Algorithms (EOA) have been utilized by
researchers to cope with the weaknesses of mathematical and heuristic approaches. EOAs
are stochastic search methods that mimic the metaphor of natural biological evolution
and/or the social behaviour of species. The behaviour of such species is guided by
learning, adaptation, and evolution. EOAs are robust search algorithms that are applicable
to large and complex problems. However, EOAs typically have time consuming
calculation processes” (Dehghan, 2011).
Amongst various EOAs, genetic algorithms are best applied in deriving optimal
solutions for multi-objective problem domains. Genetic algorithms are a way of solving
problems by mimicking the same processes used by Mother Nature. They use the same
combination of selection, recombination and mutation to evolve a solution to a problem.
Genetic algorithms work very well on mixed (continuous and discrete), combinatorial
problems. They are less susceptible to getting 'stuck' at local optima than gradient search
methods. The following researchers have developed genetic algorithms models for
39
solving the time-cost trade-off problem: Dehghan and Ruwanpura (2011), Ng and Zhang
(2008), Xiong and Kuang (2008), El-Gafy (2007), Elbeltagi et al. (2005), Zheng et al.
(2005), Feng et al. (2000), Hegazy (1999), Feng et al. (1997) and Li and Love (1997).
In addition to Genetic Algorithms, other EOA techniques introduced by
researchers were inspired by different natural processes. Examples include the Ant
Colony Optimization (Dorigo et al., 1996), Memetic algorithms (Moscato, 1989), Particle
Swarm Optimization (Kennedy and Eberhart, 1995) and Shuffled Frog Leaping approach
(Eusuff and Lansey, 2003). Elbeltagi et al. (2005) employed all these techniques for
solving discrete time-cost trade-off problems, and compared their performance and
efficiency.
The main drawbacks of evolutionary-based optimization algorithms or meta-heuristic
algorithms are as follows.
• They require large computational time for the search (Hegazy, 1999)
• They do not have a rigid mathematical ground; therefore, prioritizing them in
terms of their performance, effectiveness and quality cannot be proved using solid
mathematical proofs
• They are not guaranteed to find the optimum or even a satisfactory near-optimal
solution. All meta-heuristics will eventually encounter problems on which they
perform poorly, and the practitioner must gain experience of which optimizers
work well on different classes of problems. Some researchers, e.g., Elbeltagi et al.
(2005), performed benchmark comparisons to investigate processing time,
convergence speed (success rate) and quality of the results of evolutionary-based
algorithms or meta-heuristic algorithms. Such empirical comparisons have been
40
criticized by Wolpert and Macready (1997), who prove that all optimizers
perform equally well when averaged over all problems (Dehghan, 2011).
Based on the advantages and disadvantages of each optimization technique discussed
above and used so far for time-cost trade-off problems, the researcher intends to choose
Decision Trees – classified under the category of mathematical methods – to solve the
time-cost trade-off problem presented in this research. In the following section, Decision
Trees are first introduced in detail and then the rationale behind choosing decision trees
for time-cost trade-off problems discussed in this research is presented.
2.2.1 Decision Tree
A decision tree is a diagram that describes a decision under consideration and
consequences of choosing one or another of the available alternatives. It incorporates the
probabilities of risks and the costs / rewards of each logical path of events and future
decisions (Project Management Institute [PMBOK®], 2008). In decision trees, the actual
payoff of each decision alternative is weighed by the relative frequency of occurrence,
producing a decision that is best in the long run.
Decision trees are one of the most attractive and easy-to-use tools in decision
making. They provide the opportunity to analyze decision alternatives in a systematic,
chronological way and provide an easy-to-read, graphical presentation of decisions under
consideration. Many consider decision trees to be the most powerful modeling and
calculation technique in decision analysis (Schuyler, 2001). Decision trees have a wide
range of applications such as strategy selection, project selection, investment decisions,
41
equipment selection, oil drilling, make or buy decisions and in general any problem
carrying risk and uncertainty.
Decision trees have been used for problems related to a wide range of knowledge
areas such as legal, research methods, financial, engineering and medical applications
(Moussa et al., 2006). Examples include Blodgett (1986), Mock (1972), Hespos and
Strassmann (1965), Benjamin and Cornell (1970), Hazen et al. (1998) .
The decision tree approach has been covered in a wide range of publications, e.g.,
Moussa et al. (2006), Bordley (2002), Hillier and Lieberman (2001), Schuyler (2001),
Revelle et al. (1997), Taha (1997), Meredith et al. (1973 and Raiffa et al. (1961).
According to Schuyler (2001), a Decision Tree consists of five main components:
• Decision Node: a square node that precedes variables or actions that the decision
maker controls; it represents a point at which a decision must be made; each line
leading from a square node represents a possible decision to be made
• Decision: a branch departing from a square node; it represents an alternative
decision available to decision-makers
• Chance Event Node: a circular node that precedes variables or events that cannot
be controlled by a decision maker; each line leading from a circular node
represents a possible outcome from a decision
• Chance outcome: a branch leading from a circular node; it represents a possible
outcome from a decision; chance event branches are assigned probability and may
be labeled by their probability and the value of the outcome
• Terminal / End Node: the end point where outcome values (payoff values) are
attached
42
A Decision Tree is drawn from left to right. It starts from a root node that precedes
current alternatives and branches afterwards based on possible alternatives. The branches
of Decision Trees proceed forward in time exactly as would steps in a real decision
process (Revelle et al, 1997). Figure 2-6 shows a simple DT with two decisions and three
chance-events resulting from each decision.
Figure 2-6: Sample Decision Tree (Moussa et al., 2006)
The common approach when using a Decision Tree is to calculate the Expected Value
(EV) based on single number estimates. This is called a deterministic decision tree. To
solve a DT, the analyst calculates the Expected Value at each node and makes the
decision that yields the maximum EV at the root. The following summarizes the EV
calculation procedure.
1. Identify alternative decisions and their cost
2. Identify the possible outcomes of each decision
43
3. Estimate the probability of the outcomes; if there are subsequent decisions or
outcomes, steps 1 to 3 are to be repeated. In calculating the probability of the
outcomes, note that if X , X , X ,....X n 1 2 3 are mutually exclusive chance events that
could possibly occur at a chance node, then
n
P Xi [2.1]∑ ( ) = 1 i=1
Where P(X) = Probability of the realization of chance “i” of the n’s chances
originated from a chance node.
4. Draw the tree chronologically from left to right and calculate the payoffs at the
end of each branch
5. Fold back the tree to calculate the EV and make the decision that has the optimum
EV. The EV is calculated so that
n
∑ ( ) [2.2]XiP X i=1
Where X = the value of the chance event # “i“, P(X) = the probability of the
realization of the chance event value for the n number of chance events.
At the decision nodes, the EV equals the optimum EV at the node (minimum or
maximum as per the utility optimization criterion required).
EV=Max (U) [2.3]
Where U = the utility of the decision maker.
A deterministic decision tree has the following disadvantages (Moussa et al., 2006,
Schuyler, 2001, Smith et al., 1983, Ferrara, 1970 and Hespos and Strassmann, 1965):
• The single number EV method has limited application in real-life situations
44
• The EV based on a single number estimate does not take into consideration the
risk attitude of the decision-maker nor what amount of money the decision maker
is willing to lose or needs to make.
• The EV based on a single number estimate does not show the risk involved in the
decisions and gives no information on the range of possible outcomes nor the
probability associated with these outcomes
To overcome the disadvantages of the deterministic trees explained in the above
section, stochastic decision trees are used. Hazen and Pellissier (1996) define a stochastic
tree as an “extension of a Decision Tree that facilitates the modeling of temporal
uncertainties.” Stochastic decision trees permit the use of frequency distributions for
some or all factors affecting decisions. “The use of the probability distribution functions
in Decision Trees was first introduced in Hespos and Strassmann (1965)” (Moussa et al.,
2006). This is a strong advantage of decision trees, as real-life estimates involve
decisions with several stochastic cost components. Furthermore, stochastic decision trees
provide the decision maker with more insights into decisions by giving information about
the results from any or all combination of decisions made at sequential points in time that
can be obtained in a probabilistic form. Deterministic trees give no information on the
range of possible outcomes or the probability associated with these outcomes.
However, very few attempts have been made to model using stochastic decision trees.
Hazen and Pillissier (1996) used stochastic trees in medical decision applications. Hespos
and Strassman (1965) used stochastic trees in a financial application. Moussa et al.
(2006) discuss the difficulties associated with modeling stochastic DTs as follows.
45
• The complexity involved in solving probabilistic problems. They suggest
simulation platforms that are able to model general types of DTs without the need
for reprogramming, such as Special Purpose Simulation (SPS) that allows an
experienced simulationist to build a template (a collection of modeling elements
that are targeted for a single domain) that can be used by inexperienced users
(Hajjar and AbouRizk, 2002).
• Difficulties related to the decision model’s ability to incorporate the complexity
of decision problems and be dynamic enough to incorporate more details. To
overcome this problem, they suggest the use of Multi Level Decision Trees in
which decisions can be broken down in a hierarchical scheme, allowing the tree to
expand vertically to accommodate decomposition of decisions, reducing the tree
size to manageable decision stages.
• Problems related to estimating the magnitude and probability of realizing chance
events when the magnitude, the number and the probability of realization of
chance events are based on judgment.
Conclusion: Decision trees are considered optimization networks. They provide a
powerful method of visualizing and analyzing decisions under risk and uncertainty
because of their ability to represent the probability of the consequences of decisions
(Moussa et al., 2006). The problem investigated by this research can be easily formulated
as a decision tree problem. The researcher tried to analyze different decision alternatives
while considering the possible outcomes of each decision, the probability of those
outcomes and the cost of each decision. The results of this analysis help the decision
maker choose a decision that yields the optimum expected value. Since conventional
46
methods of solving DTs do not respond to stochastic needs and are not suitable for most
real-life decisions, the researcher designed the decision support system in a way to also
allow for stochastic inputs. Furthermore, the researcher tried to overcome difficulties in
stochastic decision trees by providing the probability distribution function for the
probabilistic parameter of the research (i.e., pipe loads and beam loads which are
explained in Chapter 7) from real data. This has been done to stay detached from expert
judgment.
2.3 Project Development and the Execution Process
Projects proceed in phases, which can usually be defined by a milestone achievement that
ends the phase. Details of the phases are defined by Project Execution Processes. Project
processes also determine deliverables in each phase and the criteria for determining if a
phase is complete. The engineering phase is one of the phases defined by Project
Execution Processes.
The current research concerns the subject of the overdesign which is applicable in
the engineering phase of projects. The engineering phase itself is divided into several
sub-phases. As discussed in section 1-5 of Chapter 1, the scope of the research is limited
to the detailed design phase of the engineering. This decision was made after a review of
the related literature about the project development and execution process. To be able to
formulate the current research problem about overdesigning the pipe rack module as well
as understanding the time and cost impacts afterwards, it was essential for the researcher
to have fairly good knowledge about the project development and execution process in
general and the engineering phase in particular. Therefore, the researcher reviewed the
relevant literature to obtain an understanding of the project execution processes and the
47
engineering phase of projects, which helped her formulate the research problem down the
road. Also, the results of this literature review helped the researcher narrow down the
scope of the research to the detailed design phase of the project. Hence, the purpose of
the literature review of the project development and execution process was to support the
whole research study by increasing the researcher’s general knowledge about the subject.
Below is a review of the literature regarding Project Execution Processes. The
major influence in defining Project Execution Processes was the Construction Industry
Institute (CII), founded in the early 1980s, and the Independent Project Analysis (IPA),
founded in 1987.
The CII defines project phases as follows:
0- Feasibility
1- Concept
2- Detailed Scope
Phases 0 – 2 together are often called Front-End Planning, Front-End Design
(FED), Front-End Engineering Design (FEED) or Project Development
3- Design
4- Construction
5-Commissioning & Start-up
6-Operations
Major oil and gas companies have defined their own execution processes, which
have been developed in response to poor project outcomes and a need to better steward
capital. The expected outcomes of defining project execution processes are defined as
follows.
48
• Significant reduction in recycle and rework for each subsequent phase through
better initial Scope definition; greater Stakeholder input in the earlier phases of
the project; and a more disciplined approach to providing key deliverables
through the role of the Gatekeeper
• Ability to share learnings to continuously improve the way projects are managed
by having a common “Process”
The following are some examples of project execution processes developed by large
companies.
• CPDEP-Chevron “Chip Dip”
• IPMS- Shell Global Integrated Project Management System
• SPIM- Suncor Project Implementation Methodology
• PDM-Petro Canada Project Delivery Model
• KASE-Bechtel/Bantrel Key Attributes for Successful Execution
• J Steps- Jacobs
• PEM-Aker Kvaerner
• Project Development & Implementation – NOVA Chemical
• Husky Project Development and Execution Processes
Project phases defined in the above-mentioned Project Execution Processes have not
been named consistently across the industry; however, they are very similar in nature.
The following are almost all common in project development and execution processes
(Figure 2-7).
1. Identify: Opportunity identification
49
2. Select: Concept screening and selection
3. Develop: Front end engineering
4. Execute: Detailed engineering, procurement and construction
5. Operate: Start up and operate, project closing
PHASE 1IDENTIFY &
AssessOpportunities
PHASE 2 SELECT
from Alternatives
PHASE 3 DEVELOP Preferred
Alternative
PHASE 4 EXECUTE
(Detail EPC)
PHASE 5 OPERATE
& Evaluate
Determine Project Feasibility and Alignment with Business Strategy
Select the Preferred Project Development Option
Finalize Project Scope, Cost and Schedule and Get the Project Funded
Produce an Operating Asset Consistent with Scope, Cost and Schedule
Evaluate Asset to Ensure Performance to Specifications and Maximum Return to the Shareholders
Figure 2-7: Project Development and Execution Process (Jergeas, 2008)
The engineering phase, which is the focus of this research, is part of the Develop
and Execute phase. After studying the project execution processes and project phases, the
next step was to focus on the Engineering phase, different engineering disciplines, their
interfaces and their major deliverables.
As outlined in Figure 2-8, the purpose of Engineering is to determine the list of
and specifications for all materials and equipment to be procured, as well as to provide all
drawings for equipment to be installed and construction work to be performed.
50
Engineering
List and Specification of all Equipmentand Materials
Construction Drawings
Procurement
Construction
Figure 2-8: Engineering Input to Procurement and Construction
In the Develop phase, the engineering function is at the conceptual level, while in
the Execute phase, it is at the detailed level. The Conceptual level, called the Conceptual
Study, Basic Engineering or Front End Engineering Design (FEED), is usually under an
engineering service contract and on a reimbursable basis. The scope of the FEED is to
define the facility at a conceptual rather than a detailed level. It entails defining the
process scheme, the main equipment, the overall plot plan, the architecture of the system,
etc. FEED stops with the issue of the main documents defining the plant, which are
mainly the piping and instrumentation diagram, the overall layout of the plant (plot plan),
the specification of the main equipment, the electrical distribution diagram and the
process control system architecture drawings. The FEED documents serve as the
technical part of the bid for the Engineering, Procurement and Construction (EPC)
contract (Baron, 2010).
The Execution level, called Detailed Engineering, is normally part of the EPC
contract. Detailed Engineering is included in the actual project execution phase (Figure 2
5) and consists of producing all documents necessary to purchase and install all plant
51
equipment. It therefore involves producing the specification and bill of quantities for all
equipment and materials, as well as producing all detailed installation drawings (Baron,
2010).
This research focuses on the Detailed Engineering phase. Engineering involves a
variety of specialties, usually called engineering disciplines, which include Process,
Mechanical/Equipment, Civil, Structural, Piping, Electrical, and Instrumentation &
Control (Figure 2-9).
Project
Engineering Procurement Construction
Process Mechanical / Civil Structural Piping Electrical Instrumentation Equipment & Control
Figure 2-9: Engineering Disciplines
Several information sources were studied to provide the researcher with an
overview of both the engineering phase of oil and gas projects and the function and
deliverables of each engineering discipline shown in Figure 2-9.
It is important to note that many books already exist that address purely technical
aspects of each engineering discipline. Some of the most important studies include:
Applied Process Design for Chemical and Petrochemical Plants (Ludwig, 1984);
Engineering Mechanics Statistics (Hibbeler, 2012); Mechanics of Materials (Timoshenko
52
and Gere, 1996); Handbook of Steel Construction (Canadian Institute of Steel
Construction, 2008); Handbook of Electrical Engineering - For Practitioners in the Oil,
Gas and Petrochemical Industry (Sheldrake, 2003); Instrument Engineers' Handbook:
Process Control and Optimization (Liptak, 2005); etc.
These studies were valuable sources of information; however, most simply
provided interdisciplinary information and did not comprehensively discuss the
relationship between different engineering disciplines. Therefore, they did not cover the
entire engineering phase.
Since it was not the intention of the researcher to master technical aspects, she
decided to selectively review literature to study each engineering discipline from a high-
level perspective. The intention was to find studies that address multidisciplinary
engineering skills. The Oil and Gas Engineering Guide by Baron (2010) fully served this
purpose. It was a major and valuable source of information which gave the researcher an
overview of how oil and gas facilities are engineered. The researcher believes this book is
a rarity because unlike engineering manuals which generally cover a specific discipline
such as process or civil engineering, this guide describes all disciplines. It also covers the
entire design cycle, from the high level functional duty to detailed design. Hence, the
book gives the reader comprehensive information he or she can easily refer to at any
point in the project life cycle. Contemporary engineering work for large projects is
usually carried out at multiple locations; this does not make it easy for newcomers to gain
an end-to-end knowledge of the process. However, Baron’s guide will fast-track the
acquisition of this knowledge. It will also meet the needs of anyone wishing to
understand technical factors driving the execution of a project. In Baron’s work (2010),
53
each engineering task is described and illustrated with a sample document taken from a
real project. The author has done a very good job of distilling the essence of engineering
disciplines and presenting them in a concise and precise fashion. Another advantage of
this book is that the author illustrated the relationship between different engineering
deliverables.
Since this book was so valuable as a guide to the engineering phase of projects,
the researcher wished to contact the author for further sources of information. She
successfully connected with the author through professional networks (i.e., Linkedin) and
received additional information.
As mentioned earlier, the purpose of the literature review in this section was to
gain an increased understanding of engineering functions; however, studies showed a gap
in the literature that the researcher tried to fill with information already obtained.
As discussed above, only Baron (2010) illustrated the relationship between
different engineering deliverables (Figure 2-10). Indeed, his work makes a great
contribution to the literature. The researcher is impressed and inspired by his work;
however, she found room for improvement, as shown below.
54
Figure 2-10: Relationship between Engineering Deliverables (Baron, 2010)
55
As shown in Figure 2-10, the intermediate process for providing engineering
deliverables is not described. Furthermore, the relationship between different engineering
deliverables for all disciplines is shown in one diagram, which is difficult to follow.
The researcher tried to build on Baron’s work by distinguishing the deliverables
of each specific engineering discipline and adding the intermediate process, which results
in the production of each deliverable. Also, in the overall diagram, the researcher
modified some of the relationships and added to them, based on information obtained
from both literature and an investigation of current industry practices in North America.
She also changed the terminology to that used in North America as Baron’s diagram uses
European terminology for engineering deliverables. This modified work was shared and
reviewed with industry practitioners and engineering discipline leads for the purpose of
verification.
The researcher believes the contribution of this literature research is to fill the
existing gap regarding the relationship between different engineering disciplines and
deliverables. Therefore, it provides useful information for those looking for engineering
multidisciplinary and interdisciplinary information. Figure 2-11 presents the researcher’s
interpretation of the relationship between engineering deliverables in the overall project,
called an Engineering Disciplines Interface Map. Likewise, Figures 2-12 to 2-17 present
the researcher’s interpretation of the same relationship within each specific engineering
discipline.
56
Figure 2-11: Engineering Discipline Interface Map in Oil and Gas Projects
57
Figure 2-12: Major Deliverables of the Process Discipline and their Interdisciplinary
Relationship
Legend
Deliverable
Process
58
Figure 2-13: Major Deliverables of the Mechanical Discipline and their
Interdisciplinary Relationship
59
Figure 2-14: Major Deliverables of the Piping Discipline and their Interdisciplinary
Relationships
60
Figure 2-15: Major Deliverables of the Civil/Structural Discipline and their
Interdisciplinary Relationships
61
Figure 2-16: Major Deliverables of the Electrical Discipline and their
Interdisciplinary Relationships
62
Figure 2-17: Major Deliverables of the Instrumentation and Control Discipline and
their Interdisciplinary Relationships
63
2.4 Modular Steel Pipe Racks
Since the scope of this research is limited to modular steel pipe racks, it was essential that
the researcher increase her understanding and knowledge of the high level design aspect
of modular steel pipe racks, which is relevant to her study. Therefore, the literature
review served the purpose of gaining knowledge, not finding a gap or judging what other
researchers have done. For the same reason, the researcher selected limited known and
credible reference books about piping and structural calculation that are used as text
books in educational institutions and universities. The most important of these include:
Process Plant Layout and Piping Design (Bausbacher and Hunt, 1993); Engineering
Mechanics Statistics, 13th edition (Hibbeler, 2012); Mechanics of Materials, 4th edition
(Timoshenko and Gere, 1997); Handbook of Steel Construction, 9th edition (Canadian
Institute of Steel Construction, 2008). There have been several editions of these books
since their first publications. They helped the researcher a great deal in gaining general
knowledge which was later enriched by investigating current industry practice. The
results are discussed in Chapter 5.
2.5 Information Management Systems
Information Management is the application of management techniques to collect
information, communicate it within and outside the organization, and process it to enable
managers to make quicker and better decisions. As stated in section 1.3 of Chapter 1, one
of the steps of the current research study is to analyze the sensitivity of project schedule
and cost for different types and degrees of overdesign in pipe rack modules in oil and gas
projects. To do this, information with regards to cost and time impacts for different
degrees of overdesign should be collected and analyzed to help managers make better
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decisions. The researcher believes this is part of Information Management. Therefore, she
had to learn more about different information management systems to enable her to find
the appropriate system for the purpose of this study. At the end of this section’s literature
review, the researcher expected to know the definitions of Information Management as
well as different types of Information Management Systems and their areas of application.
Various sources of information regarding this topic were selected among the bestselling
books in the area of information management systems and studied to achieve this
purpose. Examples include: Decision Support and Business Intelligence Systems, 9th
edition (Turban et al., 2010); Management Information Systems: Managing Information
Technology in the Networked Enterprise, 3rd edition (O'Brien, 2010); Management
Information Systems: Texts and Cases (Jawadekar, 2006); Handbook on Decision
Support Systems (Burstein & Holsapple, 2008); Management Information Systems, 9th
edition (Lucey, 2005); Management Information Systems: Managing the Digital Firm, 7th
edition (Laudon & Laudon, 2002); Decision Support Systems: Concepts and Resources
for Managers (Power, 2002); Management Information Systems: For the Information
Age, 5th edition (Haag et al., 2004); Essentials of Management Information Systems:
Transforming Business and Management, 10th edition (Laudon & Laudon, 1999);
Decision Support Systems in the Twenty-first Century, 2nd edition (Marakas, 1999);
Information Management: The Organizational Dimension (Earl, 1998); Principles of
Transaction Processing for the Systems Professional, 2nd edition (Bernstein &
Newcomer, 2009); Expert Systems: Introduction to First and Second Generation and
Hybrid Knowledge-based Systems (Nikolopoulos, 1997); Decision Support Systems: A
Knowledge-based Approach (Holsapple & Whinston, 1996).
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As stated earlier, the purpose of the researcher reviewing Information
Management literature was to gain a better understanding of the subject while neither
judging other researchers’ works nor finding a gap in their studies. However, the
researcher found the following very instructive.
Laudon and Laudon (2002) in Management Information Systems: Managing the
Digital Firm provide a valuable source of information about Management Information
Systems, Information Technology and Information Systems. With relevant coverage of
today's digital firms, the authors clearly illustrate the impact of information technology
on business through vivid examples and the most current information. Laudon and
Laudon (1999) also have another valuable book in the area of information management,
Essentials of Management Information Systems: Transforming Business and
Management. This book addresses constantly changing demands of using information
systems in today's fast-paced organizations by relating MIS to management, the
organization and technology and focusing on the importance of integrating these
elements. Finally, Lucey (2005) in Management Information Systems presents thorough
coverage of the principles, application and design of management information systems.
Apart from the above mentioned studies, the following two studies that are
specific to Decision Support Systems were particularly inspiring and instructive for the
researcher. Power’s book, Decision Support Systems: Concepts and Resources for the
Manager (2002), provides a readable, comprehensive and understandable guide to the
concepts and applications of decision support systems. Likewise, Turban et al. (2010)
give a comprehensive, up-to-date guide to today's revolutionary management support
system technologies in Decision Support and Business Intelligence Systems. Coverage by
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Turban et al. on decision support systems, modeling, analysis and expert systems is very
thorough.
The results of the literature review for this section are summarized as follows.
This summary has been provided by the researcher using the concepts presented in the
above mentioned studies. Figure 2-18 shows the different types of Information
Management Systems.
Figure 2-18: Different Types of Information Management Systems
The following provides a description of each category of information management
system.
Transaction Processing Systems (TPS)
A business transaction is an interaction in the real world, usually between an enterprise
and a person or another enterprise, where something is exchanged, for example money,
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products, information or services. Some bookkeeping is usually required to record the
transaction; this is often done by computer for better scalability, reliability and cost.
Communication between the parties involved in the business transaction occurs over a
computer network. This is known as Transaction Processing - the processing of business
transactions by computers connected by computer networks (Bernstein and Newcomer,
1997). In other words, Transaction Processing Systems record and track an organization's
transactions, such as sales or inventory of items, from the moment each is first created
until it leaves the system. This helps managers at the day-to-day operational level keep
track of daily transactions as well as make decisions on when to place orders, make
shipments, and so on.
Management Information Systems (MIS)
The phrase Management Information Systems (MIS) arose to describe the information
processing support for management activities. However, it has been described and
understood in several ways, including the following:
• Laudon and Laudon (2002) define MIS as the use of information systems in
business and management. This definition provides a broad platform for
application.
• MIS is an interaction of business strategy, people, procedures, hardware,
databases, software, telecommunications. (Laudon and Laudon, 2002); Mejabi
(2008).
• MIS is an integrated system of man and machine for providing the information to
support the operation, management and decision-making functions in the
organization (Jawadekar, 2006).
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• MIS is a computer-based information system (Jawadekar, 2006).
• MIS is defined as a system based on the database of the organization evolved for
the purpose of providing information (Jawadekar, 2006).
Though there are several definitions for MIS, all of them converge at one point: that
MIS supports decision making by providing relevant information for decision makers.
The difference lies in defining the MIS elements (Jawadekar, 2006).
Although MIS provides information for whoever needs it, its strength is in
providing mid-level and senior managers with periodic and often summarized reports that
help these managers assess performance and make appropriate decisions based on that
information.
Decision Support Systems (DSS)
According to Sprague and Carlson (1982), DSS comprise a class of information systems
aimed at supporting the decision-making activities of managers and other knowledge
workers in organizations (as cited in Power, 2002). Power (2002) defines DSS broadly as
interactive computer-based systems that help people use computer communications, data,
documents, knowledge and models to solve problems and make decisions (Power, 2002).
In pursuing the goal of improving decision making, many different types of DSS
have been built to help decision makers. Some systems provide structured information
directly to managers. These systems draw on Transaction Processing Systems. Other
systems help managers analyze situations using various types of models. Some DSS store
the knowledge and make it continuously and/or selectively available to managers
(Power, 2002).
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In general, decision support systems are designed to help mid-level and senior
managers make difficult decisions about which not every relevant parameter is known.
These decisions, referred to as semi structured decisions, are characteristic of the types of
decisions made at higher levels of management. The value of a decision support system
(DSS) is in its ability to permit "what-if" analyses. That is, a DSS helps the user (decision
maker) to model and analyze different scenarios in order to arrive at a final, reasonable
decision, based on the analysis.
One type of decision support system geared primarily toward high-level senior
managers is the executive information system (EIS) or executive support system (ESS).
While this has the capability to do very detailed analyses just like a regular DSS, it is
designed primarily to help executives keep track of a few selected items that are critical
to their day-to-day high-level decisions. Examples of such items include performance
trends for selected product or customer groups, interest rate yields and the market
performance of major competitors.
Expert Systems (ES)
The knowledge contained within expert systems consists of facts and heuristics
(Nikolopoulos, 1997). An expert system is built by modeling into the computer the
thought processes and decision-making heuristics of a recognized expert in a particular
field. Thus, this type of information system is theoretically capable of making decisions
for a user based on input received from the user. However, expert systems solve the
problem in a very narrow domain of expertise and cannot be a general problem solver.
A computer program is not an expert system simply because of its ability to
perform like an expert in a particular domain. Rather, the characteristics of the system
70
place it in the Expert System category. These characteristics include the system
architecture, the encoding of the knowledge in a knowledge base, the ability to reason
under uncertainty, knowledge acquisition tools and the ability of explanation facilities,
etc. Another differentiation is that they drive solutions by using a heuristic rather than an
algorithmic approach (Nikolopoulos, 1997).
Due to the complex and uncertain nature of most business decision environments,
expert system technology has traditionally been used in these environments primarily like
decision support systems; that is, to help a human decision maker arrive at a reasonable
decision.
Based on the discussion so far with regards to different information management
systems, it can be concluded that as we move from Transaction Processing Systems to
Expert Systems, the level of structuredness is decreased. While users of Transaction
Processing Systems will be operational level management, as we move towards Expert
Systems, users will be more high-level managers. Figure 2-19 presents this concept.
The researcher believes that based on the various Information Management
Systems explained above, the problem of this research is categorised under Decision
Support Systems.
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Transaction Processing Systems (TPS)
Management Information Systems (MIS)
Decision Support Systems (DSS)
Executive Information System (EIS)
Leve
l of M
anag
emen
t
Stru
ctur
edne
ss
Low
High Low
High
Figure 2-19: Concepts of Different Information Management Systems
In order for the current research to analyze the sensitivity of project schedule and
cost to different degrees of overdesign in pipe rack modules in oil and gas projects, the
researcher aimed to help managers model and analyze different scenarios regarding
overdesign in order to arrive at a final, reasonable decision, based on the analysis. Hence,
the purpose here is to help a human decision maker arrive at a reasonable decision, rather
than to actually make the decision for the user. Also, the decisions will be more or less
semi-structured and all relevant parameters are not known. As explained above, this is
categorized as Decision Support Systems. In Chapter 8, steps required for building a
decision support system are explained.
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CHAPTER THREE: RESEARCH METHODOLOGY
As discussed in Chapter 1, the purpose of this research was to develop a decision support
system for discovering the best opportunities for the application of overdesign in pipe
rack modules in oil and gas projects that provide the greatest schedule compression for
the least incremental cost. This is the axis around which the whole of this research effort
revolves. At the beginning, literature was preliminarily reviewed to discover what is
already known about this topic of interest. The results revealed that although overdesign
in general and overdesigning of pipe rack modules in particular are very common
dilemmas in industry at the management level, it is both less defined and less heeded in
academia. Therefore, the literature review helped identify the unknowns. In parallel,
expert advice was sought to help develop the goals and directions of this entire research.
These efforts resulted in dividing the whole research problem into more manageable
steps, as outlined in section 1.2 of Chapter 1, and helped provide better potential research
methodologies. These steps are revisited in this section to best present the research
methodologies (Figure 3-1).
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Figure 3-1: Research Steps
Using Figure 3-1, the researcher grouped the research steps into three main categories
from the research methodology standpoint (Figure 3-2).
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Figure 3-2: Categorization of Research Steps from the Research Methodology
Standpoint
As seen in Figure 3-2, a mixed qualitative and quantitative approach was taken to
conduct the current research. Figure 3-3 provides an overview of the research
methodology of Parts A and B of the research, including research approach, research
design, research tools and finally verification and validation methods.
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Figure 3-3: Parts A and B Research Methodology
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In the following sections, the research methodology of each part of the research shown in
Figure 3-2 is detailed.
3.1 Part A – Conceptual Study
This part of the research comprises a conceptual study which is explanatory in nature;
from it, the researcher sought a better understanding of and new insights about the
concept of overdesign. As Leedy and Ormrod (2005) state, in these situations, taking the
qualitative approach is appropriate in conducting the research. Peshkin (1993) states that
qualitative research studies typically serve one or more of the following purposes (as
cited in Leedy & Ormrod, 2005, p. 134).
- Description - they can reveal the nature of certain situations, relationships,
systems or people.
- Interpretations - they enable a researcher to a) gain new insights about a
particular phenomenon b) develop new concepts or theoretical perspectives about
the phenomenon, and/or c) discover the problems that exist within the
phenomenon.
- Verification - they allow a researcher to test the validity of certain assumptions,
claims, theories or generalizations within real-world contexts.
- Evaluation – they provide a means through which a researcher can judge the
effectiveness of particular policies or practices.
The first three steps of this research, shown in Figure 3-1, are used for the purposes of
Description and Interpretations, as described above. Therefore, according to Leedy and
Ormrod (2005) as well as Peshkin (1993), a qualitative approach seems to be the best
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approach for this part of the research. This is also confirmed when considering the
following facts.
a) As discussed in Chapter 2, little information exists on the topic of overdesign,
especially from the managerial viewpoint.
b) Many unknown variables and factors affect the timing and cost of the overdesign
as well as the probability of rework.
c) A relevant theory base for addressing the concept of overdesign and its
consequences is missing.
After determining the overall research approach for this part, the next step is to
determine the appropriate qualitative research design. Figure 3-4 presents qualitative
research designs based on Leedy and Ormrod (2005).
Figure 3-4: Qualitative Research Designs Extracted from Leedy and Ormrod (2005)
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The following presents a brief description of each of these methods (Leedy &
Ormrod, 2005).
a) In the case study approach, a particular program or event is studied in-depth for a
defined period of time.
b) In an Ethnography, the researcher looks at an entire group in its natural setting for
a lengthy period of time, often several months or even several years.
c) A phenomenological study attempts to understand experts’ perceptions,
perspectives and understanding of a particular situation.
d) The grounded theory aims at beginning with the data and using them to develop
the theory.
e) Content analysis is a detailed and systematic examination of the contents of a
particular body of material for the purpose of identifying patterns, themes or
biases.
Among the research designs shown in Figure 3-4 and described afterwards, the
phenomenological study seems to be the most appropriate research design for part A
of the research shown in Figure 3-2. As stated earlier, the concept of overdesign is
immature in the current literature due to a lack of previous research. Therefore, there
is a need to explore, describe and develop this concept.
Prior to undertaking the current research study, the researcher had real-world
experience in project engineering and project planning and control. She has seen
many project delays and cost overruns stemming from wait times for receiving
information and ineffective decision-making caused by a lack of understanding of
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both the big picture and long-term implications. She has also been involved in solving
real overdesign problems, which gave her a basic knowledge about the issue.
However, she seeks to enrich her understanding of the overdesign concept by taking
advantage of the experience of other professionals – listening to them and building an
understanding based on what she heard. By looking at multiple perspectives on the
same subject, the researcher can identify and understand the essence of experts’
experience about overdesign and therefore is able to build a better conceptual
framework for her study. As Creswell (2009) and Leedy and Ormrod (2005) state,
this type of research is a phenomenological study.
3.1.1 Part A: Data Collection Strategy
The primary source of data collection for Part A of the research was interviews. This part
started with asking general questions, followed by collecting an extensive amount of
verbal data from participants, and then organizing the data using verbal descriptions to
portray the overall picture and situation. The following research tools were employed for
data collection.
Literature Review
The literature related to the overdesign concept was reviewed to enrich the researcher’s
basic understanding of overdesign. The literature review allowed the researcher to
explore both the knowns and the unknowns in the area of overdesign. It also lead to the
discovery of new ideas, perspectives and approaches for designing the interview
questions. Books, journal papers, conference proceedings and the Internet were the major
sources of information. Information collected from the literature helped the researcher
design the semi-structured interview questions which are detailed below.
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Interviews
The bulk of data collection for this part was dependent on the researcher’s
personal involvement, including interviews and observations in which the
researcher and interviewees worked together to come to a conclusion and to
achieve a shared understanding of the situation. This is in line with statements by
Cresswell (1998) who mention that:
“Phenomenological study depends almost exclusively on lengthy
interviews (perhaps one or two hours in length) with a carefully selected
sample of participants who have direct experience with the phenomenon
being studied” (as cited in Leedy & Ormrod, 2005, p. 139).
The interviewees were purposefully selected among experienced individuals, from
project managers and project engineering managers to senior project engineers and
engineering discipline leads of reputable owner and EPC companies to obtain reliable
information about the matter. Overall, 37 industry experts from 10 reputable companies
(3 owners and 7 EPC firms) participated in the interviews; each participant was
interviewed at least two times. Participants’ demographic information is shown in
Figure 3-5.
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11
26
Categorization of Research Participants Based on theOwner or EPC companies
Owner
EPC
22%
54%
16%8%
Categorization of Research Participants Based on theExperience
More than 25 yearsexperience
Between20 and 25 yearsexperience
Between15 and 20 yearsexperience
Less than 15 yearsexperience
16
10
4
7
Categorization of Research Participants Based on thePosition
Project manager
Project engineeringmanagers
Senior project engineers
Engineering disciplineleads
Figure 3-5: Research Participants’ Demographic Information
The interviews took the form of informal conversations about the subject, with the
participants sharing their everyday experiences related to the concept of overdesign. Each
of the interviews took about an hour and half, with the interviewees doing most of the
talking and the researcher most of the listening. However, as the research study
proceeded, the researcher started to ask more specific questions in later interviews and
participated much more in conversations to both manage the meeting and avoid
occasional sidetracks.
The questions asked were general and open-ended in nature and focused on the
concept of overdesign. However, as the study proceeded, the researcher’s understanding
of overdesign increased, so she became increasingly able to ask more specific questions
about the subject. Gray (2009) refers to this type of interview as semi-structured, in
which the interviewer prepares a list of pre-planned issues and questions when he/she
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starts the interview; however, the list may not be exactly followed during the course of
the interview. Depending on the direction the interview takes, the interviewer may skip
some questions, change the order of questions, or even ask extra questions not anticipated
before the interview which arose from answers to other questions (Gray 2009).
Appendix A lists some of the questions asked during the interviews.
During the interviews, responses were noted and documented. However, the
researcher did not record all the interviews as some of the interviewees did not feel
comfortable having their voice recorded. Furthermore, as mentioned earlier, the
interviews took the form of informal conversations about the subject to enable the
researcher to collect as much information as possible.
3.1.2 Part A – Data Analysis Strategy
After the interviews, the researcher studied the interview notes and documents and tried
to identify common themes in the descriptions of experiences by taking the following
steps.
a) The researcher created an MS Word document from the interview notes. Then,
she reviewed and studied each document and highlighted the important and
relevant information, which she then moved to a new MS Word document. This
separated the important and relevant information from unimportant details and
irrelevant information in each interview document.
b) The researcher divided the relevant information into small segments using
keywords, each reflecting a single, specific thought. She then grouped the
segments into categories that reflect various aspects of the concept of overdesign
as experienced by the professionals.
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c) The above mentioned process was repeated for all interview documents. The
researcher reviewed them again and sought divergent perspectives by considering
the various ways in which different people experienced overdesign.
d) Finally, the researcher tried to develop an overall integrated framework of the
concept of overdesign as it is typically experienced.
3.2 Part B - Formulating the Research Problem
The results of Part A feed Part B of the research, which deals with formulating the
research problem. Data collection involved both the literature review and interviews as
well as data analysis which helped portray the overall picture of the concept of
overdesign. Following this, focus groups were formed comprising three individuals – the
researcher and two highly experienced industry professionals (with more than 20 years
industry experience and in project management and senior project engineering positions
of EPC firms). These focus groups were formed for the purpose of brainstorming
sessions. In these sessions, the researcher and participants worked together to review,
discuss and analyze the results of the data collection and data analysis described in
sections 3.1.1 and 3.1.2 respectively.
Overall, 15 brainstorming sessions were held, each lasting approximately one
hour. Each session started with a presentation prepared by the researcher covering the
discussion topics as well as potential questions. Then, the researcher and participants
worked together to address these topics and questions.
The outcome of these brainstorming sessions was a formulation of the overdesign
time-cost trade-off problem shown in Figure 3-6. This is a high level presentation of the
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research problem and will be elaborated in Chapters 6 and 7, in which the time-cost
trade-off problem is customized for modular steel pipe racks. It is important to note that
the industry participants helped a great deal by presenting the problem in such a way that
the practicality of the research was addressed. Likewise, the researcher’s prior literature
review helped ensure the research’s contribution to the literature.
Figure 3-6: Overview of the Research Problem
After formulating the overdesign time-cost trade-off problem and customizing it for
modular steel pipe racks, the next step was to search for an appropriate optimization
technique for solving the problem. As discussed in detail in Chapter 2, existing
optimization techniques for time-cost trade-off problems have been reviewed and the
Stochastic Decision Tree was chosen. Justification for this decision has been provided in
Chapter 2. Then, the overdesign time-cost trade-off problem developed in the previous
step was modeled under the stochastic decision tree, which will be further used in
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designing the decision support system. Chapter 7 elaborates on the decision support
system.
3.3 Part C – Quantitative Research
When investigating rework as one of the potential consequences of the application of
overdesign in modular steel pipe racks, the researcher tried to use historical project
information, including pipe load changes and their effects on steel design to establish, a
relationship between the overdesign factor and the probability of rework. This
information will be further fed into the decision support system.
In this part of the research, the researcher tried to examine the existing situation
while remaining detached from the research participants to be able to draw unbiased
conclusions. Therefore, she sought to collect real data to allow for deductive reasoning.
The researcher believes this method allowed her to draw valid, unbiased and more
defendable conclusions. As Leedy and Ormrod (2005) state, in these situations, taking the
quantitative approach is appropriate in conducting the research.
3.3.1 Part C- Data Collection Strategy
In this part of the research, the intent was to track pipe load changes along the pipe rack
and their effects on steel design. Since it is impossible to study every single pipe load, the
researcher intended to gather pipe load data from a sample of the population to help her
understanding of the large population. Figure 3-7 provides an overview of the sampling
process.
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Figure 3-7: Overview of the Sampling Process for the Quantitative Part
The researcher gathered real project data from drawings and documents of six
modular steel pipe racks that were made available to the researcher. Therefore, the
selection of the projects was based on the concept of convenient sampling. Table 3-1
provides information about these pipe racks. As an example, Project A is a pipe rack
project consisting of 36 modules with a weight of 1577 metric tons and 17758 lineal
meter pipe length. Names of the projects have been removed to maintain confidentiality;
however, all six pipe racks were from recent SAGD projects (within the last 5 years) in
the oil and gas industry in Alberta, Canada.
87
Table 3-1: Sample Pipe Rack Projects Project Module Structural Steel Pipe Length
Name Counts Module (MT) (LM)
A 36 1,577 17,758
B 74 1,506 30,694
C 66 2,807 40,227
D 181 4,338 68,965
E 81 1,930 33,823
F 63 4,398 29,184
For the next step, within the six projects named in Table 3-1, 774 individual pipe
lines located on 130 beams on the pipe racks were quite randomly selected for the study.
For the sample beams, pipe load data were collected twice: 1) when design was in the
preliminary stage and 2) when design was at the final stage. The hypothesis is that the
preliminary sample data and final sample data are different. Or in other words:
H1= μ Preliminary samples μ Final samples
To test the validity of this hypothesis, a null hypothesis is formed, which states
that the true mean difference of two samples (preliminary and final) is zero. Therefore:
H0= μ Preliminary samples = μ Final samples
In Chapter 5, the validity of the null hypothesis is tested using the Pair Wise T-Test. The
Pair Wise T-Test is used to compare two population means with two samples in which
observations in one sample can be paired with observations in the other sample. An
example of where this might occur is before-and-after observations on the same subjects.
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As mentioned earlier, the details of the Pair Wise T-Test as well as the details of
the process of data gathering are discussed in Chapter 5. Therefore, in order to prevent
duplication, this section is an introduction to the research tools employed to assist data
collection – readers are encouraged to read Chapter 5 for the detailed process.
Besides actual document investigation, which is the main tool for data collection,
a literature review and interviews were used in support of the main research tool.
Relevant literature was reviewed with regards to pipe rack design, piping engineering and
structural engineering to give the researcher an overview of pipe rack design. It is
important to note that the intention was not to master detailed technical considerations of
pipe rack design, but rather to identify the determinant parameters. As well, several semi-
structured interviews were held with industry practitioners to discover the availability and
location of the required data. Details are presented in Chapter 5.
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CHAPTER FOUR: OVERDESIGN
As stated earlier, the concept of overdesign is immature in the current literature due to a
lack of previous research. Most overdesign-related research studies discuss overdesign in
a purely technical context, which is applied only to ensure feasible operation over a range
of operating conditions. Therefore, those research studies serve different purposes. The
current research considers overdesign as a specific strategy to reduce information
dependency between activities and consequently to expedite the project. In other words,
this study investigates overdesign only from a managerial standpoint.
The objective of this chapter is to introduce the overdesign concept and to provide
in-depth information about the overdesign theory, drivers and consequences of
overdesign. A major part of the information presented in this chapter is the result of the
interviews through which the researcher tried to develop an overall integrated framework
of the concept of overdesign as it is typically experienced, as well as its drivers and
consequences.
4.1 Overdesign Definition
As stated in Chapter Two and presented in Figures 2-10 and 2-11, most engineering
activities require input from other activities and therefore have information dependency.
The required information might be provided by internal sources – for example, other
disciplines in the project organization – and/or by external sources such as vendors
outside the project organization who are very difficult to manage. As well, information
required to complete an engineering activity sometimes cannot be produced until the very
end of the project. A good example is drawings required for building foundations, which
are usually one of the early activities on the construction site. As Figure 2-11 shows,
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process flow diagrams and equipment lists are provided first. At the next stage, process
and mechanical datasheets, which are part of the bid documents, are produced. After
going through the procurement cycle, either in the form of bid or sole source
negotiations, a vendor is selected who will provide equipment information such as
equipment weight. Only then is this information passed on to the civil discipline to design
the required foundation for that equipment. Like this example, many other engineering
activities are heavily dependent on vendor information.
Reducing waiting times for receiving information, either from internal or external
sources, provides potential opportunities for schedule reduction and can satisfy the ever-
increasing demand for shorter engineering durations. One way to achieve this shorter
duration is to reduce or remove the information needed between dependent activities.
When there is insufficient design information, designers usually make more generous
allowances and adopt conservative assumptions in their designs than would normally be
the case, especially where the structural integrity of the asset is concerned. This process,
called overdesign, is basically an effective technique for design under uncertainties. With
overdesign, dependent activities can proceed well ahead and long before accurate details
can be determined; this helps to expedite the project. For example, there may be little cost
difference for the project as a whole if piling is 30%-50% overdesigned, but site work can
proceed well in advance (Eastham, 2000).
The level of overdesign for an activity depends on the extent of the uncertainties,
activity characteristics and cost of the material or equipment to be overdesigned. In some
cases, such as large foundations, it may be a good idea to design more conservatively.
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However, in other cases, such as the support of a small pump, less conservatism is
necessary as the consequences of under-design in this case are minimal.
At the beginning of the design process, when no information is available, the
design might be based on the maximum expected values, e.g. the weight of the super
structure, with an appropriate safety factor. However, as the design proceeds, more
information will be available and therefore less overdesign factor is required. The
overdesign factor will be suggested by the technical engineering lead and approved by
the engineering manager and possibly the client, depending on the criticality of the case
to be overdesigned. It should be recorded in the Engineering Deviation Notice (EDN).
When deciding on the overdesign factor, referring to catalogues, consulting with the
vendor even before awarding the contract, similar past experience, engineering judgment
and risk analysis are all useful.
4.2 Main Drivers of Overdesign
As stated earlier, the intention of the overdesign process described in the current research
is to either achieve the schedule or make it more effective. However, it is important to
note that there are other reasons for overdesign, such as safety and operational
necessities. Overall, there are two main drivers for the application of overdesign:
1. Offsetting the effect of uncertainties and ensuring feasible operation over a range
of operating conditions (technical considerations). In this kind of overdesign, the
primary incentive to incur the additional capital costs associated with overdesign
is to offset the very high operating costs resulting from anticipated process
disturbances and possible errors in design model parameters (Fisher et al., 1985).
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2. Reducing or removing information need and therefore compressing the schedule.
Overdesign in this context is more a managerial consideration and can serve as
one of the overlapping strategies. In this kind of overdesign, the primary incentive
to incur the additional costs associated with overdesign is to achieve time savings
and the associated business benefits.
As discussed earlier, the focus of this research is overdesign for managerial
considerations.
Project managers have many drivers to push designers to proceed with overdesign
in the absence of hard information. Results of the interviews show that the following are
among the most important factors:
a) Wait times for receiving accurate information and/or vendors’ certified
information
Sometimes activities have a slow evolution cycle, meaning they take more time to
provide the required information. Reasons for slow evolution of information in an
activity include:
• The necessity of solving complex technical problems before the information
can be determined;
• Dependency on other activities that are prone to changes;
• Requiring information from external sources such as vendors that is not
available until late in the process (Bogus et al., 2006).
The longer the duration of receiving accurate information, the more motivation
for project managers to push for proceeding with insufficient design information and
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consequently for overlapping. For example, information from vendors required for
many design activities will not be available until very late in the process. This
information, called vendors certified information in the EPC business, is required to
issue drawings for construction (IFC drawings). Usually because of schedule
reduction purposes, the logical links between vendors certified information and
issuance of IFC drawings are removed to accelerate the project. This requires
designers to make educated guesses in order to proceed with their designs.
b) Opportunities for more standardization
Standardization is the extensive use of components, methods or processes in which
there is regularity, repetition and a background of successful practice and
predictability. Standardization allows activities to have faster evolutions (Bogus et al.,
2006). Therefore, it presents great time savings for engineering activities. It also
expedites the procurement and, potentially, the construction duration, resulting in a
shorter project duration. Standardization also creates cost saving opportunities by
reducing or eliminating engineering costs and increasing constructability. Because of
repetition and regularity involved in the standardization process, there may be fewer
risks and lower levels of rework.
Overdesign sometimes provides opportunities for standardization and
consequently time and cost savings, which motivates project managers to encourage
designers to implement overdesign. For example, pipe racks are usually standardized
to keep the member size standard and create opportunities for cost savings.
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c) Risk minimization
In two activities which have information dependencies, a change in upstream activity
information is one of the major risks adversely affecting downstream activity.
Overdesign can be considered as a mitigation technique to alleviate this risk: if the
upstream activity is overdesigned, there will be a lower probability of change in
upstream activity information. On the other hand, if the downstream activity is
overdesigned, it will be less impacted by change in the upstream activity. This is also
confirmed by De la Garza and Hidrobo (2006) and Bogus et al. (2006), who name
overdesign as a strategy that reduces the sensitivity of the downstream activity to
changes in the upstream activity information.
d) Resource consideration and market conditions
Project managers sometimes encourage proceeding with the job with incomplete
information and overdesign to take advantage of the availability of resources. For
example, some construction equipment is very expensive and may not always be
available. For timely utilization, it is critical to properly plan in advance and expedite
the design work to achieve that plan. If the plan cannot be achieved on time, there
will be significant cost consequences for the project, as keeping that equipment idle is
very costly.
With regards to material, the market condition is really a determinant factor in
planning. Design activities should be done by a certain time to be able to order and
procure the material on time. Availability and price are important considerations
which dictate the right time for procurement of material; this drives the related design
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activities. For example, when steel is cheap and available, project managers may
prefer to buy and stock it even without completing the design work.
The same consideration might be true for human resources. Market conditions
may dictate project managers to staff for the project at an earlier time. At this time,
there may be many uncertainties around the design work, meaning making more
assumptions in the design.
e) Location
Location related factors are some of the determinant factors in planning for
construction activities. One such factor is weather conditions, which dictate the right
time for many activities. For example, concrete pouring is too expensive in locations
with harsh weather conditions (e.g. Fort McMurray, Alberta in winter). Therefore, it
is preferable to avoid wait times for receiving accurate information for designing the
foundations so the construction schedule can be met.
Likewise, labour costs are dependent on location. In Alberta, for example,
construction labour costs are very expensive, so it is cheaper to do rework in a home
office rather than at the construction site. Therefore, overdesign seems to be
appropriate in locations like Alberta to reduce the probability of rework at site.
f) Progress issues
If the project progress is not satisfactory, project managers may decide to accelerate
the design activities to provide enough work fronts for procurement and construction.
In this case, overdesign may be used to finish the design activities sooner.
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g) Simplicity
Less complexity involved in the design of some activities may lead to a preference to
design with assumed values rather than waiting for precise information. For example,
making a generous allowance may be preferred to lay a large foundation for small
pumps rather than waiting to receive vendor information for each of the pumps.
h) Owner related factors
Owner requirements are also determinant factors in planning design activities. The
owner may ask for early issuance of the drawings because of commitments to
fabricators, which leads to designing without accurate information.
It is important to note that the contract’s pricing arrangement between owner and
contractor plays an important role. Unit price contracts provide favorable conditions
for overdesign, while overdesign is avoided in lump sum contracts.
4.3 Consequences of Overdesign
Although the application of overdesign is related to the engineering phase, the effect will
be extended to other engineering deliverables as well as activities in subsequent phases,
i.e. procurement and construction. Consequences of overdesign mainly involve time
saving, extra cost and probable rework. These consequences are discussed in the
following.
a) Time Impact
In overdesign, design parameters are calculated by making educated guesses and
conservative assumptions rather than waiting to receive precise values either from other
engineering disciplines or vendors. Therefore, it can be concluded that by removing the
logical link between activities by overdesign, the duration of the design phase is
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decreased, leading to an earlier start of the succeeding activities, e.g. procurement and
construction. This helps expedite the project and is the main potential for time savings.
The duration of the successor activities in the procurement phase, however, is
somewhat controversial. It may either slightly increase or remain unchanged. In some
cases, there may be opportunities for reduction, especially if overdesign results in
standardization or supply of off-the-shelf items. The duration is therefore very case
specific and has no general rule. In the construction phase, the most probable scenario is
an increase in the duration of the construction activity.
b) Cost Impact
Overdesign is associated with extra costs and increased materials wastage, as by
definition, overdesign produces designs that are overly conservative for the actual
conditions. This translates to lack of design optimization, which is one of the major
concerns in overdesign. In a chain of dependent activities, if each activity is designed
conservatively, then the final asset will be far removed from the optimum design. This
may create problems in the operational stages (snowball effect). Lack of design
optimization may also mean less available physical space and congestion in the
construction site, which adversely affects operability, maintenance and access. So it is
important to consider the physical changes that happen in the overdesign process.
In the engineering phase, there will be neither extra costs nor cost savings. This is
because no extra resources are required to perform the job and all the design processes
are performed exactly like a normal execution (without overdesign), except with assumed
values. Instead, the major cost increases occur in the procurement and construction
phases. The extent of the extra costs is directly related to the level of overdesign as well
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as the cost of the material or equipment to be overdesigned. This latter factor is especially
important. For example, making generous allowances in the design of a steel structure
will be much more costly in the procurement and construction phases than would making
the same level of allowances in the design of a concrete structure.
The quality of the upstream information used for overdesign is also a determinant
factor in extra overdesign costs. The worse the quality of upstream information, the more
conservative the downstream design needs to be. Likewise, activities with high sensitivity
to changes in upstream activity information ask for more conservative design and
consequently result in more cost (Bogus, 2004).
c) Rework
Rework is another risk involved in overdesign. When real information is received from
vendors or other disciplines, it is compared against the assumptions to make sure that
design meets the requirements. It is possible that the assumptions made were not
conservative enough. This is called underdesign and results in rework, which is translated
into extra costs and delays. The consequences of rework can be more devastating if it
extends to procurement and especially to construction – this depends on the degree of fast
tracking. In extreme fast track projects, there is a high probability of starting procurement
and construction activities even before validating assumptions against real, precise
information. In this case, the effects of underdesign are detrimental. Furthermore, other
problems, such as congestion and working under too many constraints, are added to this
when rework happens in the construction site.
Rework will, in fact, likely take place. The probability of rework depends on the
quality of information used for overdesign in the downstream activity. Upstream
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activities with a fast evolution are, by their nature, able to provide better information for
the downstream activities to base their overdesign assumptions on. Therefore, there is a
lower probability of rework when upstream activity is fast evolving. Upstream activities
with a slow evolution may also provide adequate information for overdesign to
downstream activities with a low sensitivity; however, as a general rule, slow evolving
upstream activities provide a higher probability of rework, as by definition, these
activities have less information available to make correct overdesign assumptions (Bogus
et al., 2006).
The most successful overdesign happens when upstream activity is fast evolution
and downstream activity is low sensitive to the changes in the upstream information. This
is the best situation for making correct assumptions and results in a low probability of
rework. In contrast, when the upstream activity is slow evolution and the downstream
activity is highly sensitive, the probability of rework is high; this is the most risky
situation for overdesign.
Dehghan (2011) argues that the probability of rework also depends on the amount
of overlapping that occurs between two activities. This is true because as the level of
overlap increases, it becomes more difficult to always make correct overdesign
assumptions. Therefore, the probability of rework is a non-decreasing function of the
overlap between upstream and downstream activities.
The amount of rework itself depends on the sensitivity of the downstream activity
to the information provided by the upstream activity. Highly sensitive downstream
activities generate more rework. Dehghan and Ruwanpura (2011) argue that the amount
of rework is also a function of the overlapping duration between upstream and
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downstream activities as well as the intensity of nonconformity between final and
preliminary information. They note that maximum rework may happen when the final
information is significantly contradictory to the preliminary information. In such a
situation, the worst case scenario is that the successor activity must disregard all its
progress and start over. Therefore, the rework duration cannot logically be more than the
overlapping duration. Dehghan and Ruwanpura (2011) surveyed industry practitioners
and found that the rework duration can be up to 30% of the overlapping duration. This is
a rough estimate based on the general knowledge and experience of risk specialists.
4.4 Overdesign Theory
Concept-wise, activities may have four types of dependencies to each other (Bogus,
2004):
- Information dependency
- Resource dependency
- Permission dependency
- Physical dependency
When activities have information dependency, it means one activity should provide
information for the dependent activity. In other words, the dependent activity cannot start
and proceed without the information provided by its predecessor. The majority of
information dependencies happen in the design phase of projects.
In the case of resource dependency, the same resource is shared between two
activities. This kind of dependency is not an actual dependence; it relies more on resource
constraint. Permission dependency happens when some kind of permit is required to start
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and proceed with the activity. And finally, physical dependency is actually the result of
physical constraints that prevent the dependent activity from starting. The majority of
physical dependencies occur in the construction phase of projects. Figure 4-1 shows an
example of different kinds of dependencies.
Figure 4-1: Different Types of Activity Dependencies (Bogus, 2004)
The focus of this research is the subject of overdesign, which concerns information
dependency between activities.There are four possible scenarios when investigating
information dependencies between design activities (Bogus et al., 2006; Krishnan et al.,
1997; Prasad, 1996).
1. Dependent activities: one activity requires the final information from another
activity to start.
2. Semi-independent activities: one activity requires only partial information from
other activities to start.
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3. Independent activities: No information dependency exists between two activities.
4. Interdependent activities: mutual information exchange between the activities
occurs until final completion of both of the activities.
Obviously, there is no point of discussion for overdesign for independent
activities since they have no information dependency. Dependent activities have the most
room for an overdesign discussion, as one activity needs final information from the other
activity to start. Therefore, if final information is not available, there may be a high
potential of rework for part or all of the dependent activity. The researcher believes that
in the case of opportunities for overdesign, other types of dependencies are convertible to
the dependent activities case. Therefore, this research will discuss the subject of
overdesign around dependent activities, but it can also be extended to semi-independent
or interdependent activities.
Figure 4-2 shows two dependent design activities with information dependency.
Dehghan and Ruwanpura (2011) developed the upper part of Figure 4-2 for overlapping,
and the researcher modified it to show the overdesign concept. In Figure 4-2, activity B is
waiting for final information from activity A. Provision of this final information may take
some time because of the need for detailed calculations and the unavailability of the
vendor’s information or other engineering disciplines’ inputs. Because of schedule
constraints or to meet the schedule, designers may release preliminary information plus
an overdesign factor. This allows the two activities to proceed in parallel for a while until
the final information becomes available; at that time, activity A will release the final
information to its successor, activity B. At this point, if preliminary information does not
comply with final information, changes and adjustments should be made to activity B to
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make it compatible with the final information. The changes and adjustments will take
some additional work (rework) that require extra man-hours and time, which means an
increase in the duration of the successor activity compared to its normal duration
(Dehghan & Ruwanpura, 2011). As stated earlier, rework is not certain to happen, it is
probabilistic.
However, if preliminary information complies with final information, time
savings will be achieved. These savings are the result of overlapping activities A and B
through overdesign. In contrast, an extra overdesign cost will be manifested in the
procurement and construction phases. Therefore, the time savings should be traded off
with the extra costs.
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Figure 4-2: Overdesign Theory
(Adapted from Dehghan & Ruwanpura, 2011 and modified for overdesign)
All parameters mentioned in Figure 4-2 (i.e. time savings, extra cost and rework)
depend on the level of overdesign, or, in other words, the overdesign factor. At the
beginning of the design process, when no information is available, the design may be
based on maximum expected values, resulting in a larger overdesign factor. However, as
the design progresses, more information will be available and therefore less overdesign
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factor is required. Therefore, it can be said that the overdesign factor is a non-increasing
function of the progress of the upstream activity (Equation [4.1]).
OD= f (PC) [4.1]
Where
OD Overdesign factor
PC Percent complete of the upstream activity
As stated earlier, by definition, overdesign produces designs that are overly
conservative for the actual conditions, resulting in extra materials and cost. A larger
overdesign factor results in further extra costs (Equation [4.2]).
Cod = g (OD) [4.2]
Where
Cod Cost of overdesign
OD Overdesign factor
When making an overdesign decision at the beginning of the design process, a
larger overdesign factor is required to mitigate the risk of rework. However, the sooner
the decision is made, the greater the achieved time savings. In contrast, if the overdesign
decision is postponed to receive more accurate information, a smaller overdesign factor is
required, which of course results in achieving fewer extra costs but also less time savings
(Figure 4-3).
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Figure 4-3: Relationship of Overdesign Factor, Time Savings and Extra Cost
Finally, a larger overdesign factor results in a lower probability of rework (Equation 4.3).
Prrw [4.3]
Where
Prrw Probability of rework
OD Overdesign factor
As discussed earlier, the amount of rework itself depends on the sensitivity of the
downstream activity to changes made in the upstream activity.
Chapter Summary
In this chapter, the concept of overdesign was investigated from a managerial standpoint
in which overdesign is used as a schedule compression technique. By using overdesign,
information dependency between activities is reduced or removed, which helps reduce
the overall schedule. Also, drivers and consequences of overdesign in terms of time, cost
and rework were discussed. The major portion of the information presented in this
chapter was derived from the interviews which helped portray the overdesign concept.
In Chapter 5, the application of overdesign on modular steel pipe racks is discussed in
detail.
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Contributions: Chapter 4 provided a comprehensive study of overdesign as a schedule
compression technique. The subjects discussed in this chapter are theoretical and
literature contribution of this study to the overdesign concept.
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CHAPTER FIVE: OVERDESIGN ON MODULAR PIPE RACKS
In Chapter 4, the general concept of overdesign was discussed. However, as mentioned
earlier, the scope of the research in this study is limited to the application of overdesign
on modular steel pipe racks, which are vital areas used extensively in oil and gas projects.
The main objective of this chapter is to determine overdesign opportunities in the design
of modular steel pipe racks. To achieve this objective, the researcher’s intent was to
review the design process of pipe racks at a very high level. It is important to note that
the researcher does not intend to discuss and master detailed technical considerations in
the development of the pipe rack. Instead, the purpose was to identify the main
overdesign opportunities as well as major design inputs that relate to the study’s purpose.
This chapter’s findings provide the foundation for Chapters 6 and 7.
Generally, most inline plant arrangements are furnished by a central pipe rack
system that acts as the main artery of the unit supporting process and utility pipelines,
instrument and electrical cables, cable trays and sometimes equipment mounted over all
of these. A large number of these lines cannot run through adjacent areas and so run
through pipe racks from one piece of equipment to another, or from one unit to another.
The pipe rack is usually made of structural steel, either single- or multi-level to suit the
width and capacity of the unit it serves. Pipe rack configurations are dictated by the
equipment location, site conditions, client requirements and plant economy (Bausbacher
& Hunt, 1993). Figure 5-1 shows a typical steel pipe rack.
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Figure 5-1: Typical Steel Pipe Rack
Many clients prefer modular pipe racks for their plants because of the benefits of
modularization. In fact, pipe racks are almost exclusively modular in design. A pipe rack
module is comprised of structural frames completely fitted with pipes and cable trays
(Figure 5-2).
Figure 5-2: Modular Pipe Rack
Modularization is used vs. stick-built construction and involves structuring the
overall asset as a series of modules or components and then assembling these components
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into a system that is delivered to the construction site as a unit. In modularization,
identifying all interdependencies between the separate modules is of vital importance;
these dependencies should be taken into account as the design of the asset progresses.
The decision to use modularization should be made early on, typically during the
conceptual phase (Khoramshahi & Ruwanpura, 2011).
With modularization, it may be possible to progress separate modules in parallel
using separate teams. This helps to expedite the overall project. Even if it is not possible
to progress the modules in parallel, the experience gained in early modules can be
utilized to improve the efficiency and shorten the duration of constructing later modules.
In addition, within each module, we may have integrated design and construction which
provide more opportunities for schedule reduction. Modularization also has the potential
to introduce a number of other schedule reduction techniques. For example, it allows for
pre-fabrication/pre-assembly and also standardization, in which standard products or
systems can be procured and installed more quickly. Choosing modularization vs. stick-
built construction will have an enormous economic impact on the project, as vastly
different technical considerations and types of investment will be required for each
approach (Meissner, 2003). Overall, modularization has potential cost savings because of
lower labour cost. Also, it helps overcome some of the obstacles and risks in a
conventional approach such as weather, schedule and resource conflicts. A downside to
modularity (and this depends on the extent of modularity) is that modular systems are not
optimized for performance. This is usually due to the cost of putting up interfaces
between modules (Khoramshahi & Ruwanpura, 2011).
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As discussed in Chapter 1, the scope of the research in this study is limited to the
application of overdesign on modular steel pipe racks, as the researcher believes they are
very good candidates for overdesign due to the following reasons:
a) Construction and installation of pipe racks is a complex and resource-consuming
operation and requires considerable planning and coordination with other groups. On
the other hand, a pipe rack is one of the first structures erected in the plant, and since
it is used to support pipes, cables and various types of equipment, it is the
predecessor for many activities such as piping, mechanical installations, electrical
and instrumentation works. Consequently, the pipe rack is one of the most important
structures in oil and gas projects; improving design and construction of this time-
consuming and resource-intensive structure will lead to a smooth and timely start of
subsequent works and results in a shorter project duration. Therefore, pipe racks
have the potential to significantly impact time spent on the project.
b) Pipe racks are located in the middle of most plants; therefore, the pipe rack must be
erected first before it becomes obstructed by rows of equipment. The corresponding
piping drawings are also required early on for the same reason (Bausbacher & Hunt,
1993). However, because little information usually exists at that time, the structural
designer should coordinate with many disciplines (piping, electrical, control systems
and mechanical) to obtain as much preliminary information as possible in order to
make educated guesses and thereby proceed with the work.
c) The scope is changing around pipe racks. Changes in process or in Piping and
Instrumentation Diagrams (P&IDs) may lead to changes in stress analysis, which
cascades into structural calculations of the pipe racks. On the other hand, in the
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modularization approach, late design changes have huge rework implications and
adversely affect the cost and timing of the building and assembly of the pipe rack
modules and, consequently, the overall project. Therefore, studying overdesigning
pipe racks provides a great opportunity to investigate the consequences of under-
design and consequent rework.
d) The modular approach for pipe racks reduces the complexity of the study of time and
cost impacts of overdesign, as the modules are similar in nature. Also, this approach
requires fewer assumptions, which is desirable because increased assumptions limit
the validity of the research.
e) The researcher has previous experience studying pipe racks and has developed, with
two other researchers, a simulation model for optimization of pipe rack construction
(Dehghan et al. 2009). Therefore, the current research can be viewed as
complementary to her previous efforts.
5.1 Introduction to Modular Steel Pipe Rack Design
As discussed earlier, pipe racks represent the spine or main artery of the plant and consist
of an overhead structure supporting the process pipes entering and leaving a unit. Utility
lines supplying steam, water, air and gas to process equipment are also on the rack, as are
instrument lines and electrical cables. This entire structure is supported by a foundation.
Therefore, there are two basic components of modular pipe racks (Figure 5-3):
- A structural frame fitted with pipes and cable trays
- A foundation
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Figure 5-3: Different Components of the Pipe Rack Module
These components are discussed in the next section.
5.1.1 Structural Frame
The primary data required for the detailed development of the structural frame of the pipe
rack includes the followings:
- Process Flow Diagrams (PFDs) and Utility Flow Diagrams (UFDs)
- Piping and Instrumentation Diagrams (P&IDs) and Line Designation Tables
(LDTs)
- Plot plan
- Plant layout specification
- Client specification
- Construction materials
- Fireproofing requirements
Process flow diagrams show main process lines and lines interconnecting process
equipment; utility flow diagrams show the required services, e.g. steam, condensate,
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water, air, gas, etc. Using these two documents, the designer can prepare a pipe routing
diagram, which is the first step in the development of any pipe rack. The pipe routing
diagram is a schematic representation of all process piping systems drawn on a copy of
the plot plan. Although it disregards exact locations, elevations or interferences, it locates
the most congested piping bent in the pipe rack (Figure 5-4).
Figure 5-4: Pipe Routing Diagram
Information available on early piping and instrumentation diagrams generally
only covers commodity, line number and preliminary sizes. Process flow diagrams
provide insight to operating temperatures and identify the need for insulation. Once the
routing diagram is complete, the development of the rack dimension may proceed
(Bausbacher & Hunt, 1993). These dimensions, including rack width, bent spacing,
numbers of levels and elevations, are discussed in the following.
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Pipe Rack Dimensions
The first dimension is pipe bent spacing, which is shown in Figure 5-5. A pipe bent
consists of a vertical column or columns and a horizontal structural member or members
that carry piping systems, usually above headroom.
.
Figure 5-5: Pipe Rack Bent Spacing (Bausbacher & Hunt, 1993)
The line sizes that are installed in the rack establish the bent spacing. The pipe
span on the rack depends on pipe size, thickness, commodity and insulated or bare pipe
(Bausbacher & Hunt, 1993).
The next step is setting the width of the pipe rack, which is influenced by the
number of lines, electrical/instrument cable trays and space for future lines. The pipe rack
width is the basis for determining the number of levels required for the rack. Pipe racks
usually carry process lines on the lower level(s) and utility lines on the top level.
Instrument and electrical trays are integrated on the utility level, if space permits, or on a
separate level above all pipe levels (Bausbacher & Hunt, 1993).
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Finally, the elevation of the pipe rack must be set. The area beneath the rack is
often used by operations for vehicle traffic, so it must be unobstructed. Plant roads,
headroom for access to equipment under the pipe rack and headroom under lines
interconnecting the pipe rack and equipment located outside all influence pipe rack
elevations.
Structural Design Input
A structure should be designed for all gravity and internal forces as well as natural
hazards such as winds, earthquakes and temperature variation. Therefore, a structural
designer should consider all loads acting on a structure, which are broadly categorized as
follows:
1- Dead loads
2- Live loads
3- Environmental loads
The above items are the required design inputs for structural calculation of the pipe rack.
The following provides a brief description of each structural design input.
1. Dead load
Dead loads are those that are constant in magnitude and fixed in location
throughout the lifetime of the structure, such as:
1.1. The weight of materials forming the structural unit, including:
a) The weight of the structural frame itself
b) The weight of the equipment with all attachments such as platforms,
ladders and walkways, as applicable
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c) The weight of all piping, valves, fittings and insulation
1.2. Operating load, consisting of the total weight of the equipment and piping
full of operating fluids and/or contents
1.3. Hydrotest load, consisting of the total weight of the equipment and piping
full of test fluids required to pressure test the equipment and piping.
1.4. Thermal loads resulting primarily from the movement or restraint of the
movement associated with hot/cold pipes during startup and shutdown
2. Live load
Live load comprises the superimposed loads on platforms or floors as a result of
operation and maintenance. Therefore, they may be fully or partially in place or
not present at all. Likewise, they are not fixed in location.
3. Environmental loads
The environmental loads mainly consist of wind load, snow load and earthquake
load that are site specific.
As discussed in section 1.4 of Chapter 1, the scope of this research is limited to pipe
operating loads, which are the inputs from the piping stress analysis group to the
structural group for pipe rack design.
Before providing the definition of stress analysis, it is useful to define the stress
itself. Stress is the internal resistance, or counterforce, of a material to the distorting
effects of an external force or load. The impact of high piping stress on operating piping
systems can be dramatic and costly. There are many causes of pipe stress; two of the
most common are weight and thermal causes. These factors may create problems such as
overstressed piping components, overstressed nozzles and impacts on mechanical
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equipment (Bausbacher & Hunt, 1993). To avoid these problems, stress analysis is
performed to ensure safety of piping, piping components, connected equipment and the
supporting structure as well as to ensure piping deflections are within the limits (Azeem,
2001). Piping stress analysis is a term applied to calculations, which address the static
and dynamic loading resulting from the effects of gravity, temperature changes, internal
and external pressures, changes in fluid flow rate and seismic activity. Codes and
standards establish the minimum requirements of stress analysis (Azeem, 2001). The
results of the stress analysis may cause a change in the pipe routing and revised piping
layout. To perform the stress analysis, the following information is required from
different engineering disciplines.
- P&IDs and Line Designation Tables from the process discipline to determine the
scope of the system; load cases to analyze; operating, design, upset temperatures
& pressures; expansion temperatures, pipe material specification and insulation
type & thickness.
- Pipe routing from the piping discipline to determine the system layout
- Mechanical equipment drawings from vendors to determine equipment size,
support locations and nozzle placement and details
The outputs of the stress analysis activity determine the pipe operating loads which
are required for structural calculations.
5.1.2 Pipe Rack Foundation
Another important aspect of pipe rack design is the foundation to support the
structural frame of the pipe rack. Required inputs for designing the foundations are
obtained from the structural calculations.
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In many areas of the world, including Alberta, where poor soil conditions prevail,
pile foundations are used. A pile foundation is a special type of foundation that enables a
structure to be supported by a layer of soil found at any depth below the ground surface.
Poor soil conditions may be difficult to excavate through, and are incapable of supporting
structural loads. However, by supporting a structure on piles, any adverse soil condition
may be virtually bypassed, and adequate foundation support can be obtained at any depth.
A pile foundation comprises two basic structural elements: the pile and the pile
cap. A pile cap is a structural base that supports a structural column, except that it bears
on a single pile or group of piles. A pile can be described as a structural stilt hammered
into the ground. Each pile carries a portion of the pile cap load and transfers it to the soil
in the vicinity of the pile tip, located at the bottom of the pile (Figure 5-6).
Figure 5-6: Pile Foundation
Based on the concepts explained so far, the required inputs, providers of those inputs
and the high level process of pipe rack design are determined (as shown in Figure 5-7).
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Figure 5-7: High Level Design Process of Pipe Rack Modules
5.2 Application of Overdesign on Modular Steel Pipe Racks
As discussed earlier, the location of the pipe racks is established early in the plant design
lifecycle and is usually in the center of the plant. Since the pipe rack must be erected first,
before it becomes obstructed by rows of equipment, the corresponding piping drawings
are also required early for the same reason. Therefore, pipe rack-related drawings
typically represent the first aboveground deliverables issued on a project.
At the estimate stage, when most plot plans are developed, the pipe rack width is
specified on the basis of limited information. Process Flow Diagrams, P&IDs and LDT
and consequently line sizes, cable tray and insulation requirements are not usually
available in their final revisions to accurately work out the exact requirements. Therefore,
designers have to make allowances for the pipe rack width and then proceed with the
work. The pipe rack width can be adequately sized on the basis of approximate line
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sizing, utility piping and insulation requirements by the process system engineer, cable
tray requirements by the electrical and instrument engineer and a future piping allowance.
In the detailed design phase, which is the focus of this research, pipe loads
required for the design of steel frames are not available until the completion of the stress
analysis. Stress analysis is a critical activity in this process and it may take a relatively
long time before accurate values can be determined. Therefore, it can be said that stress
analysis is a slow evolution activity due to the following reasons:
- Dependency on other activities, e.g. P&IDs, LDTs, pipe routing, etc. that are
prone to changes
- Information requirements from external sources, e.g. vendors
- The necessity of solving technical and time-consuming calculations
Because of these factors pipe loads are not available until late in the process.
Therefore, designers usually make conservative assumptions regarding these loads and
then proceed with the structural calculations. When actual information is available, it is
compared against the assumptions. If the assumptions still meet the requirements,
everything is fine. Otherwise, structural calculations have to be reworked. Depending on
the degree of the project’s fast tracking, if procurement and especially construction have
already been started, the effects of rework can be devastating.
Likewise, the information required for pile foundations is only available when
structural calculations are completed. As was just explained, because of dependency on
stress analysis, the results will not be available when they are required. So there is
another opportunity for overdesign in the piling calculations. If there is an overdesign on
both piling and structural calculations, there will be compound effects on time and cost.
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Based on the description above, Figure 5-8 presents the opportunities for the application
of overdesign on the pipe rack in the detailed design phase.
Opportunity for overdesign on pipe loads
Opportunity for overdesign on structural loads
Figure 5-8: Application of Overdesign on Pipe Rack Modules
As demonstrated in Figure 5-8, there are two potentials for overdesign:
overdesign on pipe loads required for structural calculations and overdesign on structure
loads required for piling design. The latter is directly affected by pipe loads. Therefore,
the same overdesign can be carried over to piling design. In other words, it is assumed
that the piling group will not perform a separate overdesign for the structural loads. As a
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result, the focus of this research is on the pipe loads and on tracking how pipe load
changes affect the steel structural design.
5.2.1 Tracking the Pipe Load Changes along the Pipe Rack
To track the pipe load changes, the researcher decided to use information from past
projects. To do this, six modular steel pipe rack projects were chosen to form the sample
space. These projects were introduced in Section 3.3.1 of Chapter 3.
After choosing the sample projects, the researcher tried to learn the extent and
frequency of pipe load changes along the rack as well as their effects on steel structure
design. This information, derived from past similar projects, helped the researcher
determine the probability of non-compliance of preliminary loads with final loads that
may result in reworking the steel structure. Finally, the whole process allowed the
researcher to discover the appropriate overdesign option.
Gathering historical project information, however, was a very challenging and
time consuming process. In order to collect historical pipe loads, piping stress engineers
were asked for records of preliminary and final pipe loads passed to structural designers.
Their responses indicated there was no specific, straightforward record to use to compare
preliminary and final pipe loads. When investigating the work process used in performing
stress calculations, it became evident that pipe load changes can be tracked by reviewing
the revisions in calculations. However, tracking revisions in stress calculations is not as
easy and straightforward as most engineering deliverables. For these, revisions can be
easily found in the Document Management System that is responsible for keeping full and
accurate records of all engineering deliverables revisions. After any changes in a
deliverable, it will be revised and formally re-issued with a more recent revision that is
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recorded in the Document Management System, with the advantage of having access to
all previous revisions. For stress analysis, however, there is no formal issuance after each
change. The final issue of the stress analysis results follows implementing and finalizing
all changes. Therefore, there were no records of historical stress calculations. To solve
this problem, a focus group was formed comprising three piping stress engineers to work
out the best way to extract this historical information. The goal was to find an alternative
way to obtain this information, e.g. to find out the possibility of the existence of another
engineering deliverable that may capture this information. These investigations revealed
that the required information may be obtained from piping General Arrangement
drawings (GAs). General arrangement drawings are issued by the piping discipline for
construction purposes and are used for pipe erection. General arrangement drawings are
very detailed and contain all information required for the erection of piping, including all
dimensions, elevations and pipe routing.
An investigation of the piping stress analysis work process revealed that at some
points during their work process, piping stress engineers mark up the pipe loads on piping
General Arrangement drawings. Therefore, preliminary and final pipe loads can be
extracted from different revisions of these drawings. During the focus group meeting,
several piping General Arrangement drawings were reviewed with piping stress engineers
to verify this matter, which, fortunately, was successful. Appendix B shows samples of
piping General Arrangement drawings along with their historical revisions. As seen in
Appendix B, apart from all other information, the following information required for the
purpose of this research can be obtained from the General Arrangement drawings:
- Number and size of the lines on the rack
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- Pipe routing
- Pipe operating loads
The next step involves collecting piping General Arrangement drawings of the six
modular pipe racks introduced in Chapter 3 (Table 3-1), along with their historical
revisions. Overall, from all pipe racks mentioned in Table 3-1, 774 individual lines
located on 130 beams were quite randomly selected for the study. Then, the first revision
of the General Arrangement drawing was studied to find out the pipe routing along the
pipe rack as well as the number and size of the lines and their loads on the rack. Then, the
same information was extracted from the last revision and changes were determined for
comparison. These changes include either one or all of these:
- Change in pipe load
- Change in pipe routing, including:
- Change in piping layouts and anchor locations
- Deletion of line(s)
- Addition of line(s)
- Change in line size
As stated earlier, the entire process entailed collecting routing, preliminary loads,
final loads and line spaces of 774 individual lines on 130 beams on the pipe racks.
Preliminary load means loads obtained from the first revision of the General
Arrangement drawings, when the design was at the preliminary stage; final load means
loads obtained from the last revision of the General Arrangement drawings, when the
design was at the final stage.
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Table 5-1 provides the raw data collected from the general arrangement drawings.
In Table 5-1, line sizes are the diameter of the pipes in inch, loads are in Kilo Newton
(kN), and line spaces are in millimetres and indicate the space between pipes. As an
example, according to Table 5-1, the first sample beam in the preliminary design stage
consists of 7 lines (four 3” lines, one 2” line, one 4” line, and one 6” line) with the
arrangement and loads shown in Figure 5-9.
Figure 5-9: Pipe Loads and Spaces in the First Sample Beam at the Preliminary
Design Stage
The same beam at the final design stage contains the same 7 lines but with different loads
and arrangements, as seen in Figure 5-10.
Figure 5-10: Pipe Loads and Spaces in the First Sample Beam at the
Final Design Stage
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Table 5-1: Raw Data Collected from the General Arrangement Drawings Showing
Preliminary Loads, Final Loads and Line Spaces
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
1 4 2 3000 3000 3 2 2800 2800 1 3 2 210 3210 4 2 255 3055 1 3 2 215 3425 3 2 425 3480 1 2 1 265 3690 3 2 365 3845 1 3 2 215 3905 3 2 310 4155 1 3 1 385 4290 2 2 261 4416 1 6 2 320 4610 6 2 520 4936 2 16 13.5 625 625 16 13.5 625 625 2 10 10 800 1425 10 10 625 1250 2 10 10 494 1919 10 10 484 1734 2 12 12 540 2459 6 11 540 2274 2 12 12 525 2984 6 8.5 525 2799 2 6 5 445 3429 8 5.5 445 3244 2 2 1 410 3839 6 5 350 3594 2 2 2 266 4105 2 1 261 3855 2 3 1 180 4285 2 1 235 4090 2 3 1.1 515 4800 3 2 200 4290 2 3 1 300 5100 4 3.5 385 4675 2 6 4 260 5360 3 1 300 4975 2 6 4 280 5255 2 3 1.1 295 5550 3 8 5.5 650 650 8 6.5 650 650 3 16 16.5 560 1210 24 32 615 1265 3 16 16.5 655 1865 24 32 800 2065 3 12 11 615 2480 12 24 583 2648 3 6 3.5 470 2950 6 3.5 445 3093 3 12 11 385 3335 12 24 427 3520 3 14 12 535 3870 14 12 385 3905 3 10 10 575 4445 10 7.5 575 4480 3 16 7 616 5061 16 7 616 5096 4 20 25 2390 2390 20 25 2390 2390 4 8 6 615 3005 8 6 615 3005 4 10 8.5 465 3470 10 8.5 465 3470 4 16 17 1080 4550 24 30 1080 4550 4 6 4 1000 5550 2 1.5 566 5116 4 450 6000 6 4 434 5550 5 16 27 500 500 16 27 500 500 5 16 27 630 1130 16 27 630 1130 5 16 27 630 1760 16 27 630 1760 5 20 25 880 2640 20 25 880 2640 5 8 6 615 3255 8 6 615 3255 5 10 8.5 485 3740 10 8.5 465 3720 5 6 6 415 4155 6 6 415 4135 5 6 4 1665 5820 2 1.5 1231 5366 5 6 4 434 5800 6 16 12 1168 1168 16 38 1166 1166 6 16 14 1000 2168 16 18 1002 2168 6 18 20 1055 3223 18 20 1055 3223 6 18 20 1005 4228 18 20 1005 4228 7 16 13 1000 1000 16 7 1000 1000 7 16 13 4000 5000 10 7.5 700 1700 7 10 7.5 2600 4300 7 16 7 700 5000
128
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
8 3 2 698 698 3 2 280 280 8 4 2 200 898 4 2 200 480 8 16 32 460 1358 12 60.5 655 1135 8 16 32 595 1953 12 60.5 1005 2140 8 3 2 485 2438 3 2 740 2880 8 2 1 215 2653 12 22 715 3595 8 6 2 620 3273 12 22 800 4395 8 3 1 385 3658 16 15 775 5170 8 16 15 320 3978 3 2 515 5685 8 16 15 590 4568 8 16 15 602 5170 8 3 2 515 5685 9 16 17 500 500 16 17 500 500 9 16 17 630 1130 16 17 630 1130 9 16 17 630 1760 16 17 630 1760 9 20 25 880 2640 20 25 880 2640 9 8 6 615 3255 8 6 615 3255 9 10 8.5 485 3740 10 8.5 465 3720 9 6 6 415 4155 6 6 415 4135 9 16 25 665 4820 24 37 665 4800 9 6 4 1000 5820 2 1.5 566 5366 9 6 4 434 5800 10 20 40 1165 1165 20 56 605 605 10 14 11 730 1895 14 12 530 1135 10 6 10 525 2420 6 10 200 1335 10 8 6 400 2820 8 5.5 1485 2820 10 8 15 1035 3855 10 9 290 3110 10 10 15 495 4350 8 15 745 3855 10 10 5 525 4875 10 15 495 4350 10 6 2 665 5540 10 7.5 525 4875 10 6 3.5 665 5540 11 48 50 1329 1329 24 30 999 999 11 30 75 1430 2759 30 75 1259 2258 11 12 6.5 835 3594 12 6.5 835 3093 11 16 16 646 4240 16 16 646 3739 11 16 57 840 5080 16 57 829 4568 12 48 50 1400 1400 24 30 1500 1500 12 30 52 1430 2830 30 52 1259 2759 12 16 18 2321 5151 16 18 2310 5069 13 6 10 1000 1000 10 10 1229 1229 13 2 2 410 1410 10 10 494 1723 13 2 4 266 1676 6 11 540 2263 13 3 2 180 1856 6 8.5 525 2788 13 3 3 515 2371 8 5.5 446 3234 13 3 2 300 2671 6 5 350 3584 13 6 8 260 2931 2 1 261 3845 13 2 1 235 4080 13 3 2 200 4280 13 4 3.5 385 4665 13 3 1 300 4965 13 6 4 280 5245 13 3 1.1 295 5540 14 6 4.5 1110 1110 6 4.5 795 795 14 4 3 465 1575 4 3 255 1050 14 6 6 290 1865 4 4 255 1305 14 8 7.5 350 2215 3 4 255 1560 14 12 37.5 676 2891 2 2 405 1965 14 12 37.5 525 3416 2 2 290 2255 14 10 30 540 3956 6 9.5 160 2415
129
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
14 10 25 494 4450 8 7.5 261 2676 14 16 45 500 4950 12 10.5 410 3086 14 4 3 550 5500 12 10.5 445 3531 14 10 30 525 4056 14 10 30 494 4550 14 16 45 600 5150 15 48 50 3000 3000 2 1.2 913 913 15 30 25 1755 2668 16 8 9 650 650 8 6.5 675 675 16 16 23 560 1210 24 32 615 1290 16 16 23 685 1895 24 32 850 2140 16 12 15 615 2510 12 24 583 2723 16 6 4.5 470 2980 6 3.5 445 3168 16 12 13 385 3365 12 24 427 3595 16 14 20 535 3900 14 12 385 3980 16 10 10 575 4475 10 7.5 600 4580 16 16 12 691 5166 16 7 616 5196 17 6 2.5 1115 1115 6 8 1115 1115 17 8 6 385 1500 8 14 385 1500 17 16 30 560 2060 18 2 2 2550 2550 2 2 3300 3300 18 4 4 300 2850 4 4 300 3600 18 6 8 400 3250 6 8 400 4000 18 6 7 550 4550 18 6 7 370 4920 19 14 24 1535 1535 14 24 1535 1535 19 14 20 650 2185 14 20 650 2185 19 14 18 650 2835 14 18 650 2835 19 14 16 650 3485 14 16 650 3485 19 12 11 595 4080 12 11 595 4080 19 6 3 485 4565 6 3 485 4565 19 10 13 435 5000 10 13 435 5000 20 10 8.5 3775 3775 16 15 876.5 876.5 20 10 8.5 500 4275 4 3 2042 2918.5 20 10 5 1225 5500 10 8.5 480 3398.5 20 10 8.5 500 3898.5 20 10 5 1225 5123.5 21 24 29 4207 4207 24 29 4207 4207 21 30 80 1000 5207 30 80 1000 5207 22 14 40 500 500 14 48 500 500 22 14 40 625 1125 14 48 760 1260 22 10 18 600 1725 10 12 1840 3100 22 10 18 365 2090 14 55 465 3565 22 8 10.5 480 2570 10 24 624 4189 22 10 24 566 3136 14 55 611 4800 22 10 12 484 3620 12 33 653 5453 22 14 45 494 4114 22 14 45 450 4564 22 12 30 475 5039 23 3 1.5 315 315 3 1.5 315 315 23 16 15 515 830 16 15 515 830 23 12 12 640 1470 18 25 775 1605 23 12 12 580 2050 18 25 800 2405 23 3 1.5 620 2670 8 10 715 3120 23 6 4 385 3055 3 2 250 3370 23 6 1.5 620 3675 24 45 490 3860 23 3 1.5 215 3890 24 40 775 4635 23 16 17 485 4375 4 2 655 5290 23 16 17 695 5070 3 1.5 200 5490 23 4 2 450 5520 23 3 1.5 200 5720
130
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
24 16 20 1030 1030 16 20 1030 1030 24 16 20 760 1790 16 20 760 1790 24 16 20 760 2550 16 20 760 2550 24 6 5 2990 5540 6 5 2990 5540 25 24 120 747 747 24 120 747 747 25 24 120 911 1658 24 120 911 1658 25 30 150 975 2633 30 150 975 2633 25 42 200 1205 3838 42 200 1205 3838 25 24 130 1140 4978 24 130 1140 4978 26 20 25 2265 2265 6 3 1740 1740 26 6 3 545 2810 18 9 1610 3350 26 18 9 520 3330 16 18 685 4035 26 16 18 685 4015 16 18 655 4690 26 16 18 655 4670 16 18 655 5345 26 16 18 665 5335 27 16 50 2145 2145 12 65 1900 1900 27 12 30 630 2775 12 55 900 2800 27 12 25 550 3325 24 85 800 3600 27 24 36 900 4500 28 12 22 476 476 12 22 476 476 28 14 15 624 1100 14 15 624 1100 28 14 15 702 1802 14 15 702 1802 28 6 8 550 2352 6 8 550 2352 28 10 15 459 2811 10 15 459 2811 28 8 10 486 3297 8 10 486 3297 28 3 5 1020 4317 14 20 1550 4847 28 14 20 560 4877 14 20 625 5472 28 14 20 625 5502 29 48 50 1400 1400 24 30 1500 1500 29 30 52 1430 2830 30 52 1259 2759 29 16 35 1481 4311 16 35 1481 4240 29 16 18 840 5151 16 18 829 5069 30 24 30 1500 1500 24 30 1500 1500 30 30 100 1259 2759 30 100 1259 2759 30 12 11 835 3594 12 12 835 3594 30 16 16 646 4240 16 16 646 4240 30 16 17 840 5080 16 17 840 5080 31 48 50 1500 1500 30 25 1500 1500 32 14 13 1088 1088 14 13 1088 1088 32 14 13.5 700 1788 14 13.5 700 1788 32 14 16 700 2488 14 16 700 2488 32 14 21 700 3188 14 21 700 3188 32 6 3.5 22 3210 6 3.5 22 3210 32 6 6.5 578 3788 6 6.5 578 3788 32 14 12 521 4309 14 12 521 4309 32 16 7 1191 5500 16 7 1191 5500 33 10 10 2265 2265 6 7 1740 1740 34 16 15 3940 3940 8 10 3000 3000 34 8 6 560 4500 6 4 385 3385 34 6 2.5 385 4885 35 4 4 1014 1014 3 4 3815 3815 35 3 2 210 1224 4 4 255 4070 35 3 4 85 1309 3 4 425 4495 35 3 2 130 1439 3 4 365 4860 35 3 4 265 1704 3 4 310 5170 35 3 4 215 1919 3 4 261 5431 35 2 2 385 2304 6 4 320 5751 35 6 4 320 2624
131
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
36 14 18 1700 1700 14 18 1700 1700 36 14 20 650 2350 14 20 650 2350 36 1 25 650 3000 14 25 650 3000 36 1 25 650 3650 14 25 650 3650 36 12 15 595 4245 12 15 790 4440 36 6 10 395 4640 6 10 485 4925 36 1 15 435 5075 10 15 458 5383 37 12 18 700 700 12 18 958 958 37 12 18 1200 1900 10 14 942 1900 37 10 14 1000 2900 12 18 544 2444 37 8 9 1100 4000 8 9 1556 4000 37 8 9 1300 5300 8 9 950 4950 38 14 13 1535 1535 14 13 1535 1535 38 14 13.5 650 2185 14 13.5 650 2185 38 14 16 650 2835 14 16 650 2835 38 14 16 650 3485 14 16 650 3485 38 12 11 595 4080 12 11 595 4080 38 6 4 485 4565 6 4 485 4565 38 10 9 435 5000 10 9 435 5000 39 16 35 450 450 16 17 450 450 39 16 35 630 1080 16 17 630 1080 39 16 35 630 1710 16 17 630 1710 39 20 25 680 2390 20 25 680 2390 39 8 6 615 3005 8 6 615 3005 39 10 8.5 465 3470 10 8.5 465 3470 39 16 17 1080 4550 24 30 1080 4550 39 6 4 1000 5550 2 1.5 566 5116 39 6 4 434 5550 40 30 102 793 793 30 102 793 793 40 24 66 1000 1793 24 66 1000 1793 41 10 8.5 750 750 10 8.5 750 750 41 10 8.5 500 1250 10 8.5 500 1250 41 8 3.5 500 1750 8 3.5 500 1750 41 10 8.5 2500 4250 10 8.5 2500 4250 41 10 8.5 475 4725 10 8.5 475 4725 41 8 3.5 500 5225 8 3.5 500 5225 42 48 50 1327 1327 2 1 1000 1000 42 3 2 1148 2475 30 50 1000 2000 42 6 4.5 480 2955 2 3 1155 3155 42 6 3 300 3255 3 2 245 3400 42 3 1 210 3465 6 9 245 3645 42 2 1.2 215 3680 2 1 300 3945 42 3 2 265 3945 3 2 215 4160 42 3 1 215 4160 3 1 385 4545 42 2 1.2 200 4360 6 2 265 4810 42 2 1 185 4545 6 1.5 370 5180 42 6 4.5 265 4810 6 1 320 5500 42 6 4.5 370 5180 42 6 3 320 5500 43 6 5 610 610 6 5 610 610 43 6 5 770 1380 6 5 770 1380 43 6 7 615 1995 6 7 615 1995 43 30 28 3122 5117 3 1 470 2465 43 30 28 2652 5117 44 5 6 450 450 5 3 450 450 44 6 6 400 850 6 5 400 850 44 6 6 400 1250 6 6 400 1250
132
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
44 2 2 300 1550 3 3 300 1550 44 2 2 250 1800 4 3 250 1800 44 2 2 250 2050 2 1 750 2550 44 2 2 250 2300 4 2 300 2850 44 6 5 300 2600 4 5 400 3250 44 4 2 400 3000 2 1 300 3550 44 6 5 250 3800 44 5 5 370 4170 45 6 2 625 625 6 2 625 625 45 3 2 380 1005 3 2 380 1005 45 4 2 350 1355 4 2 350 1355 45 3 2 390 1745 3 2 390 1745 45 3 2 405 2150 3 2 405 2150 45 3 2 1872 4022 3 2 1872 4022 45 3 2 405 4427 3 2 405 4427 45 4 2 393 4820 4 2 393 4820 45 3 2 347 5167 3 2 347 5167 45 6 2 308 5475 6 2 308 5475 46 14 13 1700 1700 14 13 1700 1700 46 14 13.5 650 2350 14 13.5 650 2350 46 14 16 650 3000 14 16 650 3000 46 14 16 650 3650 14 16 650 3650 46 12 11 595 4245 12 11 790 4440 46 6 4 395 4640 6 4 485 4925 46 10 9 435 5075 10 9 458 5383 47 14 13 800 800 14 13 850 850 47 14 13 650 1450 14 13 650 1500 47 10 11 600 2050 10 11 600 2100 47 6 6 400 2450 6 6 400 2500 47 6 6 1100 3550 2 1 250 2750 47 10 11 400 3950 2 1 600 3350 47 14 13 600 4550 6 6 250 3600 47 14 13 650 5200 10 11 400 4000 47 14 13 500 4500 47 14 13 650 5150 48 16 26 2600 2600 10 5 1000 1000 48 16 26 700 3300 16 17 1600 2600 48 16 26 700 4000 16 17 700 3300 48 10 5 600 4600 16 17 700 4000 48 10 5 1000 5000 49 18 9 1850 1850 18 9 1850 1850 49 16 18 750 2600 16 18 750 2600 49 16 18 700 3300 16 18 700 3300 49 16 18 698 3998 16 18 698 3998 50 20 45 32 32 20 45 668 668 50 6 4.5 604 636 6 4.5 636 1304 50 16 16 546 1182 6 3 1500 2804 50 16 16 2322 3504 18 15 434 3238 50 18 15 515 4019 18 50 881 4119 50 18 50 680 4699 18 50 881 5000 51 24 30 1500 1500 24 30 1500 1500 51 30 52 1259 2759 30 52 1259 2759 51 12 11 835 3594 12 12 835 3594 51 16 16 646 4240 16 16 646 4240 51 16 42 840 5080 16 42 840 5080 52 8 5.5 650 650 8 6.5 650 650 52 16 16.5 560 1210 24 32 615 1265 52 16 16.5 655 1865 24 50 800 2065 52 12 11 615 2480 12 24 583 2648 52 6 3.5 470 2950 6 3.5 445 3093 52 12 11 385 3335 12 24 427 3520
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Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
52 14 12 535 3870 14 12 385 3905 52 10 10 575 4445 10 7.5 575 4480 52 16 7 616 5061 16 7 616 5096 53 20 60 1300 1300 20 42 1300 1300 53 20 33 3400 4700 54 48 50 3492 3492 48 60 3492 3492 55 30 52 2839 2839 30 52 2839 2839 55 16 16 1481 4320 16 16 1481 4320 55 16 17 840 5160 16 17 840 5160 56 24 30 1500 1500 30 52 2759 2759 56 30 52 1259 2759 16 16 1481 4240 56 16 16 1481 4240 16 17 840 5080 56 16 17 840 5080 57 6 4 3210 3210 2 4 900 900 57 3 2 215 3425 6 10 900 1800 57 2 2 265 3690 2 3 900 2700 57 3 2 215 3905 2 1 300 3000 57 3 8 200 4105 3 2 580 4160 57 2 1 1340 5500 57 58 16 26 2600 2600 16 26 2600 2600 58 16 26 700 3300 16 26 700 3300 58 16 26 700 4000 16 26 700 4000 59 16 17 440 440 16 17 406 406 59 16 17 630 1070 16 17 630 1036 59 16 17 630 1700 16 17 630 1666 59 20 25 680 2380 10 8.5 1033 2699 59 10 8.5 615 2995 10 8.5 465 3164 59 10 8.5 485 3480 8 3.5 440 3604 59 8 3.5 440 3920 20 44 640 4244 59 20 40 640 4560 3 2 556 4800 59 10 5 500 5060 10 5 400 5200 59 6 4 500 5560 6 4 400 5600 60 30 52 2825.5 2825.5 30 52 2828.5 2828.5 60 16 16 1484 4310 16 16 1481 4309.5 60 16 17 840 5150 16 17 840 5149.5 61 2 1 3500 3500 2 1 2145 2145 61 2 1 1050 4550 2 1 1000 3145 62 8 5.5 2300 2300 8 5.5 2300 2300 62 6 3.5 695 2995 6 3.5 595 2895 62 6 3.5 605 3600 6 3.5 505 3400 62 14 12 575 4175 14 12 575 3975 62 10 7.5 545 4720 10 7.5 545 4520 62 16 7 615 5335 16 7 615 5135 63 8 5.5 650 650 8 6.5 650 650 63 16 30 560 1210 24 50 615 1265 63 16 12 655 1865 24 25 800 2065 63 12 14 615 2480 12 20 583 2648 63 6 3.5 470 2950 6 3.5 445 3093 63 12 17 385 3335 12 32 427 3520 63 14 12 535 3870 14 12 385 3905 63 10 10 575 4445 10 7.5 575 4480 63 16 7 616 5061 16 7 616 5096 64 4 2 1014 1014 2 2 2000 2000 64 3 1 210 1224 4 3.5 475 2475 64 3 2 85 1309 4 1.1 365 2840 64 3 1.1 130 1439 3 1 310 3150 64 3 2 265 1704 2 1 261 3411 64 3 2 215 1919 6 2 320 3731 64 2 1 385 2304 64 6 2 320 2624
134
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
65 6 3.5 2300 2300 6 3.5 2300 2300 65 14 12 1100 3400 14 12 1100 3400 65 16 7 1191 4591 16 7 1191 4591 66 6 5 4650 4650 6 5 3150 3150 66 10 10 400 5050 10 10 400 3550 66 14 20 580 5630 14 20 580 4130 67 2 1 2800 2800 3 2 2800 2800 67 6 6 350 3150 6 6 350 3150 67 10 11 400 3550 10 11 400 3550 67 14 13 580 4130 14 13 580 4130 67 14 13 635 4765 14 13 635 4765 67 14 13 635 5400 14 13 635 5400 68 16 17 3450 3450 16 17 3450 3450 68 16 17 760 4210 16 17 760 4210 68 16 17 760 4970 16 17 760 4970 69 20 45 2285 2285 6 3 1645 1645 69 6 3 545 2830 18 9 1610 3255 69 18 9 520 3350 16 18 685 3940 69 16 18 685 4035 16 18 655 4595 69 16 18 655 4690 16 35 655 5250 69 16 35 655 5345 70 48 50 1327 1327 2 3.5 1000 1000 70 3 4 1148 2475 3 50 1000 2000 70 6 11 480 2955 2 3 1155 3155 70 6 9 300 3255 3 3.5 245 3400 70 3 4 210 3465 6 9 245 3645 70 2 2 215 3680 2 1 300 3945 70 3 4 265 3945 3 2 215 4160 70 3 4 215 4160 3 3.5 385 4545 70 2 2 200 4360 6 3 265 4810 70 2 2 185 4545 6 3 370 5180 70 6 13 265 4810 6 3.5 320 5500 70 6 13 370 5180 70 6 6 320 5500 71 16 20 1250 1250 10 10 1250 1250 71 8 8 494 1744 10 10 494 1744 71 6 6 540 2284 6 11 540 2284 71 6 8.5 525 2809 72 8 5.5 2300 2300 8 5.5 2300 2300 72 6 3.5 695 2995 6 3.5 595 2895 72 6 4.5 605 3600 6 3.5 505 3400 72 14 12 575 4175 14 12 575 3975 72 10 10 545 4720 10 10 545 4520 72 16 7 615 5335 16 7 615 5135 72 73 16 12 2168 2168 16 38 2168 2168 73 18 20 1055 3223 18 20 1055 3223 73 18 20 1005 4228 18 20 1005 4228 74 6 3.5 2990 2990 6 5 2990 2990 74 14 12 1110 4100 14 12 1110 4100 74 16 7 1191 5291 16 7 1191 5291 75 16 17 600 600 16 17 600 600 75 16 17 630 1230 16 17 630 1230 75 16 17 630 1860 16 17 630 1860 75 20 25 680 2540 10 8.5 1295 3155 75 10 8.5 615 3155 10 8.5 465 3620 75 10 8.5 410 3565 8 3.5 440 4060 75 8 3.5 640 4205 20 40 640 4700 75 20 40 535 4740 3 2 566 5266 75 10 5 465 5205 10 5 34 5300 75 6 4 434 5639 6 4 400 5700
135
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
76 16 26 2600 2600 16 26 2600 2600 76 16 26 700 3300 16 26 700 3300 76 16 26 700 4000 16 26 700 4000 77 16 35 440 440 16 35 406 406 77 16 35 630 1070 16 35 630 1036 77 16 35 630 1700 16 35 630 1666 77 20 25 680 2380 10 8.5 1033 2699 77 10 8.5 615 2995 10 8.5 465 3164 77 10 8.5 485 3480 8 3.5 440 3604 77 8 3.5 440 3920 3 2 1196 4800 77 10 5 1140 5060 10 5 400 5200 77 6 4 500 5560 6 4 400 5600 79 30 120 2280 2280 30 120 1895 1895 79 30 120 1010 3290 30 120 987 2882 79 30 35 1055 4345 30 35 1050 3932 80 46 50 900 900 3 6 900 900 80 6 6 900 1800 46 50 900 1800 80 3 2 215 2015 3 6 900 2700 80 2 2.4 265 2280 2 2 300 3000 80 3 2 215 2495 30 50 580 3580 80 3 16 200 2695 3 4 580 4160 80 2 2.4 185 2880 6 4 650 4810 80 2 2 125 3005 6 3 370 5180 80 6 9 139 3144 2 2 320 5500 80 6 9 370 3514 81 6 3.5 2955 2955 6 3.5 2898 2898 81 6 2.5 470 3425 6 3.5 427 3325 81 14 12 630 4055 14 12 630 3955 81 10 10 575 4630 10 7.5 575 4530 81 16 7 616 5246 16 7 616 5146 82 20 33 610 610 20 33 610 610 82 20 33 770 1380 20 33 770 1380 82 8 3 615 1995 8 3 615 1995 82 30 28 3122 5117 3 2 470 2465 82 30 28 2652 5117 83 46 50 1329 1329 24 35 1329 1329 83 16 16 2810 4139 16 16 2810 4139 84 6 4 2000 2000 6 4 1000 1000 84 6 4 4000 5000 85 6 5 3210 3210 3 5 900 900 85 3 4 215 3425 14 25 900 1800 85 2 2 265 3690 2 2 900 2700 85 3 2 215 3905 2 2 300 3000 85 3 8 200 4105 30 25 580 3580 85 2 2 185 4290 3 2 580 4160 85 2 2 125 4415 2 1 1340 5500 86 30 35 4345 4345 30 35 4513 4513 87 6 10 1500 1500 6 2 1500 1500 87 6 10 1500 3000 30 50 1500 3000 87 30 50 1700 4700 30 50 1340 4340 87 3 2 215 4555 88 14 13 1535 1535 14 24 1535 1535 88 14 13.5 650 2185 14 20 650 2185 88 14 16 650 2835 14 18 650 2835 88 14 16 650 3485 14 16 650 3485 88 12 11 595 4080 12 11 595 4080 88 6 4 485 4565 6 3 485 4565 88 10 9 435 5000 10 13 435 5000
136
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
89 8 5.5 655 655 8 5.5 656 656 89 6 3.5 2300 2955 6 3.5 1869 2525 89 6 2.5 470 3425 6 3.5 427 2952 89 14 12 535 3960 14 12 630 3582 89 10 10 575 4535 10 10 575 4157 89 16 15 616 5151 16 15 616 4773 89 764 5915 754 5527 90 30 52 2759 2759 30 52 2258 2258 90 16 16 1481 4240 16 16 1481 3739 90 16 18 840 5080 16 18 829 4568 91 30 35 2839 2839 30 20 2839 2839 91 16 16 1481 4320 16 16 1481 4320 92 20 20 1300 1300 20 40 1300 1300 93 6 6.5 1200 1200 10 6 600 600 93 14 21 600 1800 6 6.5 1200 1800 93 14 10 700 2500 14 21 600 2400 93 14 11 700 3200 14 10 700 3100 93 14 18 700 3900 14 11 700 3800 93 14 18 700 4500 94 12 22 476 476 12 22 476 476 94 8 10 2821 3297 8 10 2821 3297 95 16 36 2168 2168 16 36 2168 2168 95 18 43 1055 3223 18 20 1055 3223 95 18 40 1005 4228 18 20 1005 4228 96 16 17 500 500 16 17 500 500 96 16 17 630 1130 16 17 630 1130 96 16 17 630 1760 16 17 630 1760 96 20 25 880 2640 20 25 880 2640 96 8 6 615 3255 8 6 615 3255 96 10 8.5 485 3740 10 8.5 465 3720 96 6 6 415 4155 6 6 415 4135 96 6 4 1665 5820 2 1.5 1231 5366 96 6 4 434 5800 97 20 60 1300 1300 20 42 1300 1300 97 20 35 3550 4850 98 48 50 1400 1400 24 30 1500 1500 98 30 52 1430 2830 30 52 1259 2759 98 16 16 1481 4311 16 16 1481 4240 98 24 30 840 5151 24 30 829 5069 98 849 6000 931 6000 99 8 3.5 2630 2630 24 30 905.5 905.5 99 12 11 520 3150 3 2 1459.5 2365 99 20 32 700 3850 8 3.5 470 2835 99 20 32 770 4620 12 12 470 3305 99 20 32 615 3920 99 20 32 770 4690 100 12 11 3160 3160 24 30 905.5 905.5 100 20 37 700 3860 12 12 3099.5 4005 100 20 37 615 4620 101 12 18 958 958 12 22 958 958 102 6 3 1500 1500 6 2 1500 1500 102 6 4.5 500 2000 30 50 1500 3000 102 6 4.5 600 2600 30 50 1340 4340 102 24 40 1300 3900 3 2 215 4555 103 18 9 1430 1430 24 30 1734.5 1734.5 103 16 18 835 2265 18 9 1259 2993.5 103 16 18 646 2911 16 18 735 3728.5 103 16 18 840 3751 16 18 646 4374.5 103 16 18 829 5203.5
137
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
104 16 25 3060 3060 16 38 2700 2700 104 16 25 600 3660 16 38 800 3500 104 24 40 600 4260 24 46 900 4400 105 6 15 4650 4650 6 15 3150 3150 105 10 10 400 5050 10 10 400 3550 106 10 10 1350 1350 10 10 1229 1229 106 10 10 494 1844 10 10 494 1723 106 12 12 590 2434 6 11 540 2263 106 12 12 525 2959 6 8.5 525 2788 106 6 5 445 3404 8 5.5 446 3234 106 2 1 410 3814 6 5 350 3584 106 2 2 266 4080 2 1 261 3845 106 3 1 180 4260 2 1 235 4080 106 3 1.1 515 4775 3 2 200 4280 106 3 1 300 5075 4 3.5 385 4665 106 6 4 260 5335 3 1 300 4965 106 6 4 280 5245 106 3 1.1 295 5540 107 16 17 500 500 16 17 500 500 107 16 17 630 1130 16 17 630 1130 107 16 17 630 1760 16 17 630 1760 107 20 17 880 2640 20 25 880 2640 107 8 6 615 3255 8 6 615 3255 107 10 8.5 485 3740 10 8.5 465 3720 107 6 6 415 4155 6 6 415 4135 107 16 25 665 4820 24 37 665 4800 107 6 4 1000 5820 2 1.5 566 5366 107 6 4 434 5800 108 46 50 1329 1329 24 30 999 999 108 30 52 1430 2759 30 52 2094 3093 108 16 16 1481 4240 16 16 646 3739 108 24 30 840 5080 24 57 829 4568 109 6 4.5 4655 4655 3 2 1500 1500 109 6 4.5 370 5025 6 4.5 790 2290 109 975 6000 2 1 600 2890 109 6000 3 2 250 3140 109 6000 2 2 440 3580 109 6000 6 1.5 215 3795 109 6000 6 2 385 4180 109 6000 3 2 265 4445 109 6000 2 2 370 4815 109 6000 2 2 320 5135 109 6000 2 2 400 5535 110 3 4 2475 2475 2 1 1000 1000 110 6 10 480 2955 30 50 1000 2000 110 6 3 300 3255 2 3 1155 3155 110 3 2 210 3465 3 2 245 3400 110 2 3 215 3680 6 9 245 3645 110 3 2 265 3945 2 1 300 3945 110 3 2 215 4160 3 2 215 4160 110 2 3 200 4360 3 1 385 4545 110 2 2 185 4545 6 2 265 4810 110 6 8 265 4810 6 1.5 370 5180 110 6 8 370 5180 6 1 320 5500 110 6 3 320 5500 111 16 17 450 450 16 17 450 450 111 16 17 630 1080 16 17 630 1080 111 16 17 630 1710 16 17 630 1710
138
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
111 20 25 680 2390 20 25 680 2390 111 8 6 615 3005 8 6 615 3005 111 10 8.5 465 3470 10 8.5 465 3470 111 16 17 1080 4550 24 30 1080 4550 111 6 4 1000 5550 2 1.5 566 5116 111 450 6000 6 4 434 5550 111 6000 450 6000 112 46 50 1329 1329 24 30 999 999 112 30 52 1430 2759 30 52 1259 2258 112 12 6.5 835 3594 12 6.5 835 3093 112 16 16 646 4240 16 16 646 3739 112 24 57 840 5080 24 57 829 4568 113 20 25 2380 2380 10 8.5 2699 2699 113 10 8.5 615 2995 10 10 465 3164 113 10 10 485 3480 8 3.5 440 3604 113 8 3.5 440 3920 3 2 1196 4800 113 10 12 1140 5060 10 12 400 5200 113 6 4 400 5600 114 24 27 1700 1700 24 27 1700 1700 114 12 9 2195 3895 12 15 2194 3894 114 16 30 646 4541 16 30 646 4540 115 20 25 680 680 10 10 680 680 115 10 10 615 1295 10 10 615 1295 115 10 10 485 1780 8 8 485 1780 115 8 5 440 2220 3 2 440 2220 115 10 5 1580 3800 10 15 1580 3800 115 6 4 500 4300 6 4 500 4300 116 12 10 1000 1000 12 10 1000 1000 116 12 10 1500 2500 12 10 1500 2500 116 24 25 1500 4000 24 30 1500 4000 116 10 5 500 4500 117 16 17 3993 3993 16 17 3993 3993 117 30 52 1000 4993 30 52 1000 4993 118 3 2 2200 2200 2 1 1000 1000 118 6 5 580 2780 2 3 1155 2155 118 6 3 300 3080 3 2 1245 3400 118 3 1 210 3290 6 9 245 3645 118 2 2 220 3510 2 1 300 3945 118 3 2 245 3755 3 2 215 4160 118 3 1 200 3955 3 1 385 4545 118 2 2 200 4155 6 2 265 4810 118 2 1 195 4350 6 2 370 5180 118 6 5 300 4650 6 1 320 5500 118 6 5 400 5050 118 6 3 450 5500 119 3 2 480 480 3 2 450 450 119 2 2 250 730 2 2 250 700 119 30 25 2500 3230 2 2 250 950 119 2 2 300 1250 119 2 2 200 1450 119 30 25 2275 3725 119 120 16 15 570 570 16 15 570 570 120 16 15 670 1240 16 15 650 1220 120 20 25 900 2140 20 36 700 1920 120 8 6 615 2755 8 6 615 2535 120 10 9 565 3320 10 9 565 3100 120 16 17 1080 4400 24 30 1080 4180 120 6 4 1050 5450 2 1.5 1050 5230 120 6 4 550 5780
139
Beam #
Preliminary Final Line size
Load Line space
Distance from one end of the beam
Line Size
Load Line space Distance from one end of the beam
121 46 50 1329 1329 24 30 999 999 121 30 52 1430 2759 30 102 2094 3093 121 16 16 1481 4240 16 16 646 3739 121 24 58 840 5080 24 58 829 4568 122 20 25 2285 2285 6 3 1645 1645 122 6 3 545 2830 18 9 1610 3255 122 18 9 520 3350 16 15 685 3940 122 16 15 685 4035 16 15 655 4595 122 16 15 655 4690 16 15 655 5250 122 16 15 655 5345 123 24 30 1400 1400 24 30 1400 1400 123 30 75 1459 2859 30 75 1459 2859 123 12 11 735 3594 12 15 735 3594 123 16 16 646 4240 16 16 646 4240 123 16 20 840 5080 16 20 840 5080 124 6 5 3000 3000 3 3 900 900 124 6 3 210 3210 3 3 900 1800 124 3 1 215 3425 6 5 900 2700 124 2 2 265 3690 2 1 300 3000 124 3 1 215 3905 30 25 580 3580 124 3 8 200 4105 3 2 580 4160 124 2 1.2 185 4290 6 8 650 4810 124 2 1 125 4415 6 8 370 5180 124 6 5 139 4554 2 1 320 5500 124 6 5 370 4924 125 30 20 2000 2000 30 25 2000 2000 125 30 20 2000 4000 30 25 2000 4000 126 48 45 1390 1390 2 1 1000 1000 126 3 2 1048 2438 30 45 1000 2000 126 6 6 500 2938 2 3 1155 3155 126 6 5 300 3238 3 2 245 3400 126 3 1 210 3448 6 9 245 3645 126 2 2 215 3663 2 1 300 3945 126 3 2 255 3918 3 2 215 4160 126 3 1 220 4138 3 1 385 4545 126 2 2 200 4338 6 10 265 4810 126 2 1 180 4518 6 10 370 5180 126 6 6 265 4783 6 1 320 5500 126 6 6 375 5158 500 6000 126 6 3 320 5478 6000 126 522 6000 6000 127 24 25 1600 1600 24 25 1600 1600 127 30 45 1259 2859 30 45 1259 2859 127 12 11 935 3794 12 12 935 3794 127 16 15 646 4440 16 15 646 4440 127 16 20 740 5180 16 20 740 5180 128 6 3 1000 1000 6 4 1000 1000 128 6 3 4000 5000 6 4 4000 5000 129 16 17 450 450 16 17 437 437 129 16 17 630 1080 16 17 630 1067 129 16 17 630 1710 16 17 630 1697 129 20 25 680 2390 20 28 235 1932 129 10 8.5 235 2625 10 8.5 1060 2992 129 10 8.5 445 3070 10 8.5 285 3277 129 8 3.5 615 3685 8 3.5 180 3457 129 24 30 465 4150 24 30 460 3917 129 10 5 440 4590 10 5 640 4557 129 6 4 640 5230 2 1.5 566 5123 129 6 4 440 5563 130 24 25 4000 4000 24 30 4000 4000
140
The following observations were made based on the raw data presented in Table 5-2:
- % of line deletion = Total # of deleted lines/Total lines under investigation ~ 21%
As an example, in beam # 8, three lines were deleted from the preliminary design
stage to the final design stage as seen in Table 5-2.
- % of line addition = Total # of added lines/Total lines under investigation ~ 22%
As an example, in beam # 2, two lines were added from the preliminary design
stage to the final design stage as seen in Table 5-2.
- Total line change = Total # lines added or deleted/ Total lines under investigation
~ 43%
- % of line size change = Total # lines with size change/ Total lines under
investigation ~ 4%
As an example, in beam # 3, three 16” lines were changed to 24” lines from the
preliminary design stage to the final design stage as seen in Table 5-2.
- % of change in small bore pipes (<10”) = Total # lines <10” added or deleted/
Total lines under investigation ~ 29%
- % of change in large bore pipes (>10”) = Total # lines >10” added or deleted/
Total lines under investigation ~ 14%
5.2.2 Effect of Pipe Load Change on the Steel Design
So far, data have been collected with regards to individual pipe load changes. However, it
is important to note that individual pipe load changes do not necessarily change the beam
design. In order to determine how pipe load changes may affect beam design, it is
necessary to determine the governing parameters in the beam design. These parameters
141
include shear force and bending moment, which are internal loadings developed in the
beam to prevent translation and rotation, respectively. Therefore, it is necessary to
determine them in order to be sure the beam can resist these loadings. As Hibbeler (2012)
states, the actual design of a beam requires a detailed knowledge of the internal shear
force and bending moments resulting from the load’s application. “In most cases, the
loads applied to a beam act perpendicular to the beam’s axis and hence produce only an
internal shear force and bending moment. And for design purposes, the beam’s resistance
to shear and particularly to bending, is more important than its ability to resist a normal
force” (Hibbeler, 2012, p. 347).
Therefore, the next step is to determine the moments and shear force for all 130
beams twice: 1) with preliminary loads and 2) with final loads. Comparing these two
factors determines whether individual load changes impact the beam design.
However, it is important to note that only individual pipe loads are recorded on
the General Arrangement drawings – no information about the bending moments and
shear force are included in these drawings. In fact, calculating the bending moment and
shear force is a function of the Structural group. On the other hand, the structural group
performs all required calculations for the design of the structural members, using
commercial software packages available in the market, as the actual design is much more
complicated in practice. Unfortunately, historical information was not available in any of
the selected projects. In all cases, the new information resulting from the change in stress
loads overwrote the previous loads. Therefore, to track the changes, the researcher had to
calculate the bending moments and shear forces in all 130 beams mentioned earlier. The
142
following describes the basis on which the bending moments and shear forces have been
calculated.
Figure 5-11 shows one of the typical beams under study which is fixed at both
ends. This is a reasonable assumption, as most of the beams on the pipe rack are fixed at
both ends.
l P
a b
Figure 5-11: A Typical Fixed-fixed Beam
In Figure 5-11, P is a point load (acts at a point) applied on the beam with fixed
ends. This point load is the same as one of the vertical loads obtained from the General
Arrangement drawings explained earlier. Likewise, a and b are obtained from pipe spaces
recorded on the same drawings. Therefore, P, a and b are known parameters for the
researcher as they can be extracted from the General Arrangement drawings. Applying
the point load P on the beam creates reactive forces and moments at the end of the beam:
R1, R2, M1 and M2 in Figure 5-12. As discussed earlier, these are the same parameters
required for the beam design because the beam has to be designed in a way to be capable
of resisting all forces, moments and shears produced by loads. These four parameters are
unknown for the researcher.
143
L P
Figure 5-12: Free Body Diagram of a Typical Fixed Beam Under the Study
M1 M2
a b
R1 R2
As it can be seen from Figure 5.12, there are four reactions, which are unknown. On the
other hand, two equations of static equilibrium exist in this case (∑Fy = 0 and ∑ M=0).
Therefore, it is not possible to determine all reaction forces and moments using
equilibrium equations. Before trying to find the reactive forces and moments at the end of
the beam, the following are some definitions of common terminology in statistics and
mechanics of materials, quoted from Hibbeler (2012), Timoshenko and Gere (1972).
• The beams that have a larger number of reactions than the number of equations of
static equilibrium are said to be statically indeterminate
• The number of reactions in excess of the number of equilibrium equations is
called degree of statical indeterminacy
• Any reaction in excess of the number needed to support the structure in a
statically determinate manner are called statical redundant
Therefore, the beams on the pipe rack studied in this research are statically
indeterminate to second degree, so equilibrium equations are not enough to determine the
reactive forces and moments at the end of these beams. Timoshenko and Gere (1972)
suggest the following way to solve the problem.
144
I
Considering M1 and M2 as redundant gives a released structure in the form of a simply
supported beam (Figure 5-13):
L P
Θa Θb
a b
Figure 5-13: Released Structure in the Form of a Simple Beam
In Figure 5-13, the angles at the end produced by the load P are obtained from Equation
[5.1], which is based on the slope of the deflection curve in simple beams (Timoshenko
and Gere, 1972).
Θa = Pab (L+b)/6LEI and Θb = Pab (L+a)/6LEI Equation [5.1]
Where
E Young's modulus of elasticity
Area moment of inertia
E and I are constant values. E, Young's modulus of elasticity, is a measure of the stiffness
of an elastic material and is a quantity used to characterize materials.
I is the area moment of inertia and the quantity of EI is known as the flexural rigidity of
the beam (Timoshenko and Gere, 1972).
Now, if redundant moments M1 and M2 are applied as if they were loads on the
released structure (Figures 5-14 and 5-15), the angles at the end due to M1 and M2 are
obtained from Equations [5.2] and [5.3], which are again based on the slope of the
145
deflection curve in simple beams.
L
Θ˝a Θ˝b
M1
Figure 5-14: Applying the Redundant Moment M1 as Load on the Released Structure
L
Θ’’’ a Θ’’’ b
M2
Figure 5-15: Applying the Redundant Moment M2 as Load on the Released Structure
Θ˝a= M1L/3EI and Θ˝b= M1L/6EI Equation [5.2]
Θ”’a = M2L/6EI and Θ’’’b = M2L/3EI Equation [5.3]
Because the angles of rotation at both ends in the original beam are zero, we have:
Θa= Θa- Θ˝a- Θ’’’a=0 Equation [5.4]
Θb= Θb- Θ˝b- Θ’’’b=0 Equation [5.5]
When the various expressions for the angles are substituted into Equations [5.4] and
[5.5], we arrive at two simultaneous equations containing M1 and M2 as unknowns:
M1L/3EI + M2L/6EI= Pab (L+b)/6LEI
M1L/6EI+ M2L/3EI= Pab (L+a)/6LEI
The solutions are:
146
M1= Pab2/L2 and M2= Pa2b/L2 Equation [5.6]
Using these results and also equilibrium equations (∑Fy = 0 and ∑ M=0), we obtain the
following formulas for vertical reactions R1, R2 as well as the moment at point of load
(Ma).
R1=Pb2 (3a+b)/L3 and R2=Pa2 (a+3b)/L3 Equation [5.7]
Ma = 2Pa2b2/L3 Equation [5.8]
Equations [5.6], [5.7] and [5.8] are used to determine the reactive forces and moments at
the end of the beams under study. From the standpoint of a design based upon bending
only, the maximum of M1, M2 and Ma is the governing parameter. Therefore, the next step
applies these formulas to the research’s 130 sample beams to come up with their bending
moments; this determines whether pipe load changes impact the beam design. As
mentioned earlier, the pipe loads and pipe spaces are obtained from the General
Arrangement drawings. Therefore, Equations [5.6], [5.7] and [5.8] are applied twice: first
on preliminary loads and then on final loads. To be efficient, the researcher used one of
the commercially available software packages for structural calculations. This software is
called RISA-2D version 10.1.0 and was easy to learn and powerful to use. These features
were especially important for the researcher as she had no previous experience working
with structural software. The researcher was able to obtain a trial version of the software
with limited features that was appropriate for this study. Figure 5-16 shows a screenshot
of the RISA software in data entry mode for one of the beams under study.
147
Figure 5-16: Screenshot of the RISA Software used for Structural Calculations
The results of all calculations with preliminary and final loads are presented in
Table 5-3 and compared against each other to track the changes. In Table 5-3, M1, M2,
and Ma are moments at both ends of the beam and at points of load obtained respectively
from Equations [5.6] and [5.8]. R1 and R2 are reactive forces at both ends of the beam
obtained from Equation [5.7].
148
Table 5-2: Moment and Support Reactions Calculated from Preliminary and Final
Pipe Loads
Beam Prelim inary Final
# M1 M2 Ma Maximum Moment
R1 R2 M1 M2 Ma Maximum Moment
R1 R2
1 9.939 6.492 -6.081 9.939 4.046 -7.954 6.986 11.312 -6.037 11.312 4.385 -9.615 2 48.750 34.791 -24.838 48.750 46.250 -26.350 49.349 36.719 -23.366 49.349 47.891 -29.209 3 59.183 52.249 -28.782 59.183 51.669 -41.331 102.480 82.486 -53.143 102.480 87.537 -60.963 4 36.009 41.736 -23.965 41.736 25.075 -35.425 39.630 53.540 -26.341 53.540 27.074 -47.926 5 86.848 50.105 -38.231 86.848 93.780 -36.720 87.038 50.928 -38.342 87.038 93.895 -38.105 6 42.648 42.810 -26.372 42.810 33.750 -32.250 65.864 49.555 -30.179 65.864 59.999 -36.001 7 10.833 10.833 -2.167 10.833 13.000 -13.000 14.971 14.971 -4.779 14.971 14.500 -14.500 8 73.481 61.618 -30.955 73.481 65.646 -55.354 122.751 89.523 -55.437 122.751 115.185 -72.815 9 71.074 63.385 -35.131 71.074 69.505 -55.995 73.707 73.586 -36.343 73.707 70.947 -68.053 10 66.633 52.623 -27.316 66.633 61.819 -42.181 68.991 51.556 -24.972 68.991 89.176 -44.324 11 117.051 120.121 -73.055 120.121 94.966 -109.534 114.737 110.288 114.737 114.737 95.304 -89.196 12 43.112 62.781 -27.641 62.781 29.551 -61.449 23.103 49.411 -21.189 49.411 14.555 -51.445 13 24.229 12.917 -11.545 24.229 23.029 -7.971 42.601 35.797 -22.729 42.601 34.956 -28.644 14 98.815 147.802 -75.694 147.802 66.966 -132.034 68.652 112.274 -45.250 112.274 50.338 -112.112 15 37.500 37.500 -37.500 37.500 25.000 -25.000 41.927 33.083 -36.287 41.927 30.258 -20.942 16 80.008 65.216 -38.158 80.008 71.107 -48.393 101.053 83.894 -53.609 101.053 85.468 -63.032 17 33.559 16.042 -20.754 33.559 29.155 -9.345 17.725 5.287 -7.824 17.725 19.086 -2.914 18 10.290 10.544 -9.052 10.544 6.874 7.126 10.172 23.121 -10.684 23.121 5.960 -22.040 19 69.250 64.713 -39.243 69.250 53.939 -51.061 69.250 64.713 -39.243 69.250 53.939 -51.061 20 14.999 16.814 -11.995 16.814 10.006 -11.994 21.936 21.657 -12.754 21.936 21.791 -18.209 21 18.172 73.342 -23.889 73.342 10.044 -98.956 18.172 73.342 -23.889 73.342 10.044 -98.956 22 144.988 156.689 -69.448 156.689 143.710 -138.790 120.111 146.772 -68.721 146.772 126.850 -148.150 23 43.896 47.592 -17.367 47.592 42.694 -57.306 89.648 103.465 -49.016 103.465 78.776 -88.244 24 48.785 24.847 -23.952 48.785 46.473 -18.527 48.785 24.847 -23.952 48.785 46.473 -18.527 25 415.717 415.198 -215.108 415.717 370.372 -349.628 415.717 415.198 -215.108 415.717 370.372 -349.628 26 43.480 62.773 -27.825 62.773 29.887 -61.113 21.474 48.270 -19.612 48.270 13.429 -52.571 27 84.847 65.867 -56.940 84.847 62.559 -42.441 160.557. 168.868 -103.907 168.868 115.365 -125.635 28 64.545 59.632 -30.655 64.545 67.966 -62.034 63.098 55.893 -29.794 63.098 67.145 -57.855 29 96.035 90.975 -55.667 96.035 79.050 -75.950 82.139 86.796 -53.259 86.796 62.872 -72.128 30 120.039 111.737 -88.892 120.039 89.611 -84.389 120.617 112.601 -89.286 120.617 89.965 -85.035 31 42.187 14.063 -21.094 42.187 42.187 -7.813 21.094 7.031 -10.547 21.094 21.094 -3.906 32 59.869 55.415 -35.939 59.869 48.058 -44.442 59.869 55.415 -35.939 59.869 48.058 -44.442 33 8.777 5.323 -6.525 8.777 6.801 -3.199 6.140 2.508 -3.547 6.140 5.575 -1.425 34 9.076 20.235 -11.615 20.235 5.255 -18.245 10.072 10.829 -9.779 10.829 6.617 -7.383 35 21.333 9.114 -9.304 21.333 20.667 -5.333 6.172 18.701 -7.237 18.701 3.517 -24.483 36 75.499 89.141 -50.255 89.141 54.051 -73.949 72.795 85.410 -48.275 85.410 52.490 -75.510 37 41.288 31.753 -20.095 41.288 41.072 -26.928 45.414 33.122 -22.767 45.414 41.984 -26.016 38 52.285 54.849 -32.332 54.849 38.954 -43.546 52.285 54.849 -32.332 54.849 38.954 -43.546 39 96.513 51.589 -37.375 96.513 113.627 -39.873 64.397 53.689 -28.698 64.397 67.006 -46.994 40 119.097 34.073 -45.629 119.097 148.967 -19.033 119.097 34.073 -45.629 119.097 148.967 -19.033 41 19.805 19.956 -6.967 19.956 21.358 -19.642 19.805 19.956 -6.967 19.956 21.358 -19.642 42 52.079 32.256 -17.428 52.079 51.283 -27.617 55.672 40.572 -32.105 55.672 44.388 -29.112 43 15.878 22.582 -6.072 22.582 16.017 -28.983 16.733 23.179 -6.481 23.179 16.650 -29.350 44 23.438 10.096 -9.672 23.438 26.940 -6.060 25.104 19.641 -11.936 25.104 24.736 -14.264 45 9.702 9.685 -3.492 9.702 9.733 -10.267 9.702 9.685 -3.492 9.702 9.733 -10.267 46 50.084 56.834 -32.789 56.834 36.183 -46.317 48.496 54.725 -31.609 54.725 35.260 -47.240 47 46.851 46.851 -19.649 46.851 43.000 -43.000 48.449 49.339 -21.366 49.339 43.660 -44.340 48 51.889 65.061 -42.122 65.061 34.071 -48.929 37.276 44.016 -27.700 44.016 26.827 -34.173 49 43.034 45.744 -30.424 45.744 30.079 -32.921 43.034 45.744 -30.424 45.744 30.079 -32.921 50 43.227 70.122 -29.332 70.122 72.599 66.901 67.199 97.179 -40.038 97.179 71.207 -96.473 51 84.384 95.330 -54.964 95.330 64.309 -86.691 84.962 96.194 -55.359 96.194 64.662 -87.338 52 59.183 52.249 -28.782 59.183 51.669 -41.331 118.468 90.876 -61.270 118.468 100.608 -65.892 53 47.862 13.238 -20.650 47.862 52.771 -7.229 40.784 35.591 -12.392 40.784 40.916 -34.084 54 30.507 42.476 -35.261 42.476 18.905 -31.095 36.608 50.971 -42.313 50.971 22.686 -37.314 55 48.113 61.297 -42.182 61.297 32.058 -52.942 48.113 61.297 -42.182 61.297 32.058 -52.942 56 75.041 69.347 -49.262 75.041 58.838 -56.162 49.729 60.909 -42.466 60.909 33.525 51.475 57 9.360 15.491 -9.270 15.491 5.692 -12.308 13.462 12.481 -9.252 13.462 10.963 -10.037 58 50.637 60.946 -41.090 60.946 33.382 -44.618 50.637 60.946 -41.090 60.946 33.382 -44.618 59 79.705 78.083 -37.250 79.705 76.976 -68.524 71.691 71.303 -32.642 71.691 72.340 -62.160 60 48.351 61.213 -42.434 61.213 32.279 -52.721 48.351 61.213 -42.434 61.213 32.279 -52.721
149
Beam Preliminary Final
# M1 M2 Ma Maximum Moment
R1 R2 M1 M2 Ma Maximum Moment
R1 R2
61 0.873 1.685 -0.958 1.685 0.523 1.477 1.598 1.277 -1.084 1.598 1.172 -0.828 62 16.161 28.860 -12.155 28.860 10.454 -28.546 18.002 29.842 -13.777 29.842 11.655 -27.345 63 72.116 59.825 -34.174 72.116 64.663 -46.337 111.949 87.229 -53.420 111.949 99.010 -64.490 64 10.749 4.583 -4.679 10.749 10.419 -2.681 8.047 7.144 2.813 8.047 5.782 -4.818 65 12.495 17.696 -11.199 17.696 8.135 -14.365 12.495 17.696 -11.199 17.696 8.135 -14.365 66 2.871 17.300 -4.237 17.300 1.537 -33.463 17.496 30.225 -17.973 30.225 10.570 -24.430 67 20.113 42.806 -19.690 42.806 12.120 -44.880 20.910 43.503 -20.090 43.503 12.670 -45.330 68 19.453 41.329 -20.315 41.329 11.569 -39.431 19.453 41.329 -20.315 41.329 11.569 -39.431 69 61.715 82.393 -37.923 82.393 43.622 -84.378 24.498 59.172 -22.670 59.172 15.286 -67.714 70 70.290 65.252 -31.799 70.290 62.778 -61.222 54.995 41.146 -31.701 54.995 45.098 -32.902 71 27.945 10.231 -11.404 27.945 28.176 -5.824 32.999 17.526 -17.260 32.999 28.922 -10.578 72 17.274 31.704 -13.354 31.704 11.099 -31.401 18.689 31.942 -14.400 31.942 12.037 -29.463 73 31.402 39.518 -24.217 39.518 21.275 -30.725 54.394 52.526 -32.911 54.394 39.544 -38.456 74 8.085 17.122 -7.974 17.122 4.877 -17.623 9.213 18.243 -8.298 18.243 5.631 -18.369 75 78.886 79.392 -38.907 79.392 72.708 -72.792 58.289 65.101 -25.323 65.101 57.638 -64.862 76 50.637 60.946 -41.090 60.946 33.382 -44.618 50.637 60.946 -41.090 60.946 33.382 -44.618 77 104.719 54.387 -42.933 104.719 119.864 -39.636 83.505 39.112 -27.140 83.505 105.694 -30.806 79 197.283 192.615 -138.172 197.283 139.032 -135.968 216.186 166.548 -140.003 216.186 164.796 -110.204 80 39.844 30.152 -26.764 39.844 31.435 -19.365 86.210 77.446 86.210 86.210 68.624 -58.376 81 12.348 27.536 -12.040 27.536 7.371 -27.629 13.070 26.753 -12.619 26.753 8.053 -25.447 82 49.015 29.214 -14.467 49.015 64.478 -32.522 50.727 30.408 -14.574 50.727 65.743 -33.257 83 46.644 25.628 -16.218 46.644 47.390 -18.610 34.569 22.190 -10.952 34.569 34.272 -16.728 84 3.556 1.778 -2.370 3.556 2.963 -1.037 3.333 3.333 -0.667 3.333 4.000 -4.000 85 12.629 21.432 -12.542 21.432 7.668 -17.332 43.815 36.588 -23.620 43.815 35.834 -26.166 86 11.570 30.377 -16.546 30.377 6.520 -28.480 11.570 30.377 -16.546 30.377 6.520 -28.480 87 26.969 50.197 -16.417 50.197 19.462 -50.538 52.326 83.155 -49.705 83.155 36.344 -67.656 88 52.285 54.849 -32.332 54.849 38.954 -43.546 69.250 64.713 -39.243 69.250 53.939 -51.061 89 13.625 33.452 -14.031 33.452 13.625 -34.875 22.390 35.923 -17.927 35.923 17.423 -32.077 90 49.848 61.569 -42.520 61.569 33.589 -52.411 58.849 56.547 -38.215 58.849 43.140 -42.860 91 32.998 38.704 -28.900 38.704 21.968 -29.032 21.178 28.089 -17.963 28.089 13.865 -22.135 92 15.954 4.413 -6.883 15.954 17.590 -2.410 31.908 8.826 -13.766 31.908 35.180 -4.820 93 48.286 39.994 -25.657 48.286 38.459 -27.951 44.717 47.513 -26.957 47.513 35.451 -37.049 94 15.568 8.927 -7.557 15.568 25.867 -6.133 15.568 8.927 -7.557 15.568 25.867 -6.133 95 71.298 76.950 -51.187 76.950 49.941 -57.059 52.626 51.525 -32.239 52.626 38.139 -37.861 96 66.413 44.384 -33.175 66.413 66.984 -33.516 66.603 45.170 -33.287 66.603 67.099 -34.901 97 47.862 13.238 -20.650 47.862 52.771 -7.229 39.739 35.566 -12.635 39.739 40.304 -36.696 98 90.782 81.917 -52.436 90.782 76.033 -71.967 76.672 78.070 -50.014 78.070 59.704 -68.296 99 34.362 65.417 -33.671 65.417 20.842 -57.658 54.312 70.920 -33.878 70.920 49.232 -62.268
100 25.956 41.436 -27.193 41.436 15.823 -32.177 33.940 44.421 -18.127 44.421 36.226 -42.774 101 12.177 2.314 -3.837 12.177 16.770 -1.230 14.883 2.828 -4.690 14.883 20.497 -1.503 102 29.398 41.207 -26.150 41.207 19.832 -32.168 56.326 83.155 -49.705 83.155 36.344 -67.656 103 46.640 40.827 -30.149 46.640 35.043 -27.957 50.112 59.554 -26.897 59.554 38.344 -54.656 104 46.615 75.971 -43.324 75.971 28.707 -61.293 68.520 97.300 -55.792 97.300 44.203 -77.797 105 4.797 18.892 -7.270 18.892 2.609 -22.391 16.580 20.360 -16.576 20.360 10.578 -14.422 106 41.882 33.529 -23.856 41.882 33.563 -25.537 42.601 35.797 -22.729 42.601 34.956 -28.644 107 64.451 58.181 -29.428 64.451 64.788 -52.712 73.707 73.576 -36.343 73.707 70.947 -68.053 108 91.554 80.941 -52.104 91.554 78.076 -69.924 81.902 105.690 -58.491 105.690 65.868 -89.132 109 1.650 6.721 -2.489 6.721 0.895 -8.105 11.786 15.387 -7.456 15.387 8.475 -14.525 110 21.581 36.599 -17.367 36.599 13.833 -36.167 55.672 40.572 -32.105 55.672 44.388 -29.112 111 69.671 50.900 -32.864 69.671 70.993 -40.506 73.382 62.704 -34.115 73.382 72.991 -53.009 112 98.536 104.359 -56.142 104.359 82.083 -99.417 94.537 98.099 98.099 98.099 79.625 -81.857 113 37.322 40.211 -25.592 40.211 26.152 -32.848 17.617 26.730 -14.851 26.730 11.549 -28.451 114 35.945 42.375 -16.662 42.375 28.731 -37.269 38.821 47.698 -20.363 47.698 30.428 -41.572 115 38.474 18.106 -12.673 38.474 46.553 -12.447 31.160 23.611 -12.845 31.160 31.487 -17.513 116 26.562 29.687 -16.354 29.687 21.979 -23.021 30.191 38.351 -21.036 38.351 24.057 -30.943 117 14.909 51.373 -18.318 51.373 8.336 -60.664 14.909 51.373 -18.318 51.373 8.336 -60.664 118 14.387 23.269 -11.144 23.269 9.357 -22.643 11.803 17.783 -10.111 17.783 8.110 -15.890 119 19.150 20.296 -18.349 20.296 14.947 -14.053 19.831 23.335 -16.676 23.335 17.297 -17.703 120 57.608 48.531 -26.212 57.608 56.153 -44.847 73.867 57.612 -32.107 73.867 69.968 -46.352 121 94.899 99.407 -53.636 99.407 79.849 -96.151 118.465 145.145 -95.270 145.145 89.849 -116.151 122 40.952 56.154 -25.758 56.154 28.330 -53.670 20.708 42.551 -18.215 42.551 13.192 -43.808 123 98.033 97.750 -70.649 98.033 74.481 -77.519 100.345 101.203 -72.402 101.203 75.895 -80.105 124 14.149 26.411 -12.648 26.411 8.643 -23.566 27.098 40.305 -23.005 40.305 19.159 -36.841 125 26.667 26.667 -13.333 26.667 20.000 -20.000 33.333 33.333 -16.667 33.333 25.000 -25.000 126 52.599 38.240 -20.258 52.599 48.849 -33.151 53.563 49.663 -29.310 53.563 41.937 -43.063 127 68.848 74.655 -46.909 74.655 51.601 -64.399 68.848 74.655 -46.909 74.655 51.601 -64.399 128 2.500 2.500 -0.500 2.500 3.000 -3.000 3.333 3.333 -0.667 3.333 4.000 -4.000 129 84.033 71.569 -41.710 84.033 80.160 -55.340 88.418 70.289 -40.027 88.418 85.666 -54.334 130 11.111 22.222 -14.815 22.222 6.481 -18.519 13.333 26.667 -17.778 26.667 7.778 -22.222
150
As can be seen in Table 5-3, the maximum bending moment has decreased in
32% of the beams, remained unchanged in 15% of the beams and increased in 53% of the
beams.
Figure 5.17 presents a comparison of preliminary and final maximum bending
moments in all sample beams. Likewise, Figure 5.18 shows a scatter plot of the range of
the differences between preliminary and final maximum bending moments.
151
0
50
100
150
200
250
300
350
400
450
1 11 21 31 41 51 61 71 82 92 102 112 122
Maxim
umBe
ndingMom
ent(KNm)
Beam #
Preliminary
Final
Figure 5-17: Preliminary and Final Maximum Bending Moments in all Sample Beams
152
0
20
40
60
80
100
120
140
50 40 30 20 10 0 10 20 30 40 50 60 70 80 90
Difference Between FinalMoment and Preliminary Moment (KNm) inall Beams
‐ ‐ ‐ ‐ ‐
Figure 5-18: Range of the Differences between Preliminary and Final Maximum
Bending Moments
The preliminary and final maximum bending moments presented in Table 5-3
were statistically analyzed using the SPSS software (version 20.0.0) to perform
correlations and Pair Wise T-Test. As discussed in Chapter 3, the Pair Wise T-Test is
used to compare two population means where there are two samples in which
observations in one sample can be paired with observations in the other sample.
Therefore, the Pair Wise T-Test was chosen to compare the means of preliminary and
final moments. As discussed in Chapter 3, the hypothesis is that preliminary and final
samples are different and the true mean difference of two samples is not zero:
H1= μ Preliminary samples μ Final samples
Table 5-4 presents the result.
153
Table 5-3: Pair Wise T-Test Results
N Correlation
Preliminary maximum bending moment & Final maximum bending moment 129 0.93
Paired Samples Correlations
Paired Samples Test
Paired Differences
t df P-Value Mean Std. Deviation Std. Error
Mean
95% Confidence Interval of the
Difference
Lower Upper
Preliminary maximum bending moment & Final maximum bending moment -3.96 17.34 1.53 -6.98 -0.94 -2.60 128.00 0.011
As can be seen in Table 5-3, the correlation coefficient between preliminary and
final moments is 0.93, which indicates a strong correlation between preliminary and final
moments.
To perform the Pair Wise T-Test, a null hypothesis is formed, which states that
the true mean difference of two samples is zero. In our case, a null hypothesis is as
follows:
H0= μ Preliminary maximum bending moment = μ Final maximum bending moment
Then, the probability of having a true null hypothesis is calculated using t distribution
and compared against a cut-off criterion (usually 0.05). If the calculated probability is
greater than 0.05, the null hypothesis is accepted; otherwise, it is rejected. The results
obtained from the SPSS software, which were based on the data shown in Table 5-3,
show that the probability for the null hypothesis to be true is 0.011(P value). This value is
smaller than 0.05 and therefore we reject the null hypothesis and conclude that
preliminary and final moments are statistically significantly different. As can be seen in
Table 5-4, we can be 95% sure that the true mean difference lies somewhere between
6.98 and -0.94.
154
Chapter Summary
This chapter contains a high-level discussion of the process of designing pipe racks.
Following this, the main opportunities for the application of overdesign on modular steel
pipe racks were identified: 1) overdesign on pipe loads required for structural calculations
and 2) overdesign on structure loads required for piling design. The latter is directly
affected by pipe loads. Therefore, the researcher focused on overdesign on pipe loads
required for structural calculations. To investigate this, the researcher decided to use
information from historical projects. Six modular steel pipe rack projects were chosen for
the study, and from these, 774 loads located on 130 beams were randomly selected to
form the sample space. Preliminary and final pipe operating loads were collected from
the drawings of the pipe racks mentioned above. This information was used to calculate
bending moments, which is the governing parameter in beam design and shows how
changes in pipe loads affect steel design. Changes in preliminary and final bending
moments were statistically analyzed using SPSS software.
In the next chapter, we see how the results presented in this chapter are used to
determine the probability of non-compliance between preliminary and final information
and consequently rework. This is one of the variables used for formulating the time-cost
trade-off problem, which is also discussed in Chapter 6.
Contributions: The study of historical pipe load changes on pipe racks and their effects
on steel design is considered as literature and industry contribution of this research.
Likewise, investigating the application of overdesign specifically on modular pipe racks
is a unique endeavour.
155
CHAPTER SIX: FORMULATING TIME-COST TRADE-OFF FOR OVERDESIGNING MODULAR STEEL PIPE RACKS
Based on the overdesign concept discussed in Chapter 4, benefits of overdesign include
potential time savings, while losses include the cost of overdesign and possible rework.
Therefore, when investigating an overdesign option, extra costs and possible rework
should be traded off with the benefits of time savings. The purpose of this chapter is to
investigate the consequences of overdesigning modular steel pipe racks in terms of time,
cost and rework. The findings will help introduce variables for formulating the time-cost
trade-off problem. In the following sections, each of these consequences will be
individually discussed.
In the rework section, the findings from the previous chapter with regards to the
preliminary and final maximum bending moment are used to establish a relationship
between the degree of overdesign and probability of rework.
6.1 Time Impact Study in Overdesigning Modular Pipe Racks
As discussed in Chapter 4, overdesign involves calculating design parameters using
conservative assumptions. This means eliminating the wait times to obtain precise values,
either after detailed calculations and/or after receiving required inputs from other
engineering disciplines or vendors. This reduces the design phase duration and leads to an
earlier start of the succeeding activities and consequently helps expedite the overall
project.
As explained in Chapter 5, the focus of the design phase in overdesigning
modular pipe racks will be on three distinct activities: stress analysis; structural
calculations and drawings; and piling design and drawings (Figure 6-1).
156
Figure 6-1: Focus Activities in Overdesigning Modular Steel Pipe Racks
The results of stress analysis provide the pipe loads on the rack. Steel calculations
will be done based on the pipe loads received from the stress group. The final information
from the structural calculations will be passed on for piling design.
In the absence of hard information for the pipe loads, structural designers may
make educated guesses based on preliminary information obtained from the stress group
and then proceed with structural calculations. In this case, the main potential for saving
time results from overlapping the stress analysis activity with structural calculation,
which leads to an earlier start of succeeding activities, i.e. structural calculations and
piling design. In the case of successor activities in the design phase, it can be said that the
duration of steel structure calculations and piling design will remain unchanged, as all
calculations should be done, although with preliminary values rather than final values
(Figure 6-2 and Equation 6.1).
157
Figure 6-2: Engineering Time Saving (in Focus Activities) in Overdesigning
Modular Pipe Racks
TSeg= (tb –ta)-(tb’-ta) = tb- tb [6.1]
Where
TSeg Engineering time savings
tb-ta Duration of the stress analysis activity to provide final loads
tb’-ta Duration of the stress analysis activity to provide preliminary loads
According to Figure 6-1, successor activities in the procurement phase include
steel purchase and fabrication as well as piling material purchase. To procure steel and
pile, a purchase order (PO) is placed which consists of the following activities:
- Bid period activities including:
- Preparing for bid
158
- Invitation to bid
- Bid evaluation
- Purchase Order (PO) award
- Fabrication time
When undertaking overdesign, the duration of the bid period activities does not
normally change. Fabrication time, however, may increase depending on the level of
overdesign and extra material, i.e. steel members and piles that need to be fabricated.
In the construction phase, the most probable scenario is that the duration may either
increase or remain unchanged, again depending on the level of overdesign. With a high
level of overdesign, more piles may need to be installed. Likewise, more and possibly
heavier steel members need to be erected as well. Consequently, the piling and steel
erection duration may increase.
From the above discussion, it can be concluded that there are usually no time
savings in the procurement and construction phases. Equation 6.2 summarizes the
overdesign time impact.
TIod = TSeg- Dsf- Dpp-Dse-Dpi [6.2]
Where
TIod Overdesign time impact
TSeg Engineering time savings
Dsf Increase in duration of steel fabrication after overdesign
Dpp Increase in duration of piling purchase after overdesign
Dse Increase in duration of steel erection after overdesign
159
Dpi Increase in duration of piling installation after overdesign
In Equation 6.2, the process for estimation of procurement duration for steel and
pile after overdesign as well as the installation duration is exactly the same as the process
before overdesign. Fabricators or construction contractors can easily estimate those
values just as they do in the case of normal execution and without overdesign.
If TIod in Equation 6.2 is a positive value, overdesign results in overall time
savings, which will be translated to the benefits of overdesign. Otherwise, it is not
beneficial and results in loss. Since, pipe racks are usually on the critical path of the
project, based on the discussion about the time impact of overdesign, the overall project
duration after overdesign will be obtained from Equation [6.3].
Where
Tod Project duration after overdesign
TN Project duration in normal execution or before overdesign
TIod Overdesign time impact
If the project duration after overdesign is earlier than the target date agreed upon
in the project contract, then the project will benefit from the early completion; however,
if it is later than the target date, there will be additional costs due to missing the project
schedule, but this cost might be less than the cost of missing the schedule in a normal
execution. When the project duration after overdesign is equal to the project target
duration in the contract, there will be no cost/benefit (Equation 6.4). In Equation [6.4], if
160
Bod is positive, this means there will be a benefit; otherwise, that cost will be added to
other overdesign extra costs.
Where
Bod Benefit of overdesign
Tt Target duration
Tod Project duration after overdesign
Bec Daily benefits of project early completion, including revenue and daily
incentives for early completion
Clc Daily costs of project late completion, including loss and daily penalties
for late completion
Dehgahn (2011) surveyed industry practitioners and found that daily benefits of early
completion include, but are not limited to, the following:
• Daily benefits resulting from saving indirect costs
• Daily incentive for early completion according to the project contract
• Benefits of new opportunities because of early completion
• Benefits of increased reputation for timely completion
Also based on his interviews, he identified the following as contributing factors to
the daily costs of project late completion:
• Daily indirect costs
161
• Daily liquidated damages for late completion according to the project contract
• Cost of missing opportunities because of late completion
• Losses related to losing reputation because of schedule overrun
6.2 Cost Impact Study in Overdesigning Modular Pipe Racks
To study the cost impact of overdesigning modular pipe racks, the cost impact in each
phase (i.e. engineering, procurement and construction) will be investigated separately. In
the engineering phase, there will be almost neither extra costs nor cost savings, as the
entire design process (i.e. stress analysis, structural calculations and drawings and piling
design and drawings) is performed like normal execution (without overdesign) but with
assumed values. Hence, neither extra resources are required to perform the job nor are
any costs saved.
The major cost of overdesign is in the procurement and construction phases,
during which steel members may increase in both number and weight, which in turn
increases the steel erection cost. Likewise, more piles may need to be procured and
installed. The magnitude of cost increase in procurement is even more than in
construction, as steel prices are sometimes subject to wild changes that may result in cost
overruns.
Therefore, in the procurement phase, the extra cost results from the cost of
purchasing more material, i.e. steel and piles. Material Take Off, provided by
engineering, determines the amount of steel and pile required. Similar to the duration
discussion, the cost of the extra piles and steel can be obtained from fabricators’
estimation using the Material Requisitions for Quotation (MRQ) – this process is no
different than normal execution. Therefore:
162
Cod = Cpp+ Csf+ Cpi + Cse [6.5]
Where
Cod Cost of overdesign
Cpp All possible costs of purchasing additional piles required as a result of
overdesign
Csf All possible costs of fabricating additional steel required as a result of
overdesign
Cpi All possible costs of installation of additional piles required as a result of
overdesign, including wages, equipment cost, overhead, etc.
Cse All possible costs of erection of additional steel required as a result of
overdesign, including wages, equipment cost, overhead, etc.
6.3 Rework Study in Overdesigning Modular Pipe Racks
When formulating the time-cost trade-off for overdesigning modular pipe racks, rework
plays an important role alongside time savings and cost. As discussed in Chapter 5, two
main dependent activities which are the focus of this research are Stress Analysis and
Structural Calculations and Drawings. Figure 6-3 shows the information exchange
between these two activities. Dehghan and Ruwanpura (2011) developed this figure to
clarify the overlapping concept and the researcher modified it to show the overdesign
concept.
163
Figure 6-3: Information Exchange between Stress Analysis and Structural
Calculation and Design (Adapted and Modified from Dehghan & Ruwanpura, 2011)
As discussed in Chapter 5 and shown in Figure 6-3, Stress Analysis provides
preliminary pipe loads for structural calculations. To meet tight schedule demands,
designers usually consider an overdesign factor for preliminary pipe loads and then
proceed with structural calculations. This overdesign factor will be suggested based
mainly on similar past experience and engineering judgment. When final loads become
available, they will be checked against the assumed loads; if assumed loads do not
comply with real loads, then structural calculations should be reworked. Depending on
the degree of project fast tracking, there may be rework in procurement and even in
construction phases, which are much more costly. In that case, modifications should be
made to the pipe rack structure. Modifications could entail replacing and/or adding
members, adding horizontal bracing to the transverse beams to resist significant loads
164
from the anchor(s), strengthening members (i.e. cover plating, etc.), and/or relocating the
anchor and guide load(s).
The rework explained in the above section results in both delays and extra costs
that should be taken into account in calculating the expected value of overdesign.
However, it is important to note that the rework is not certain to happen and is in fact
probabilistic. Therefore, in a study of rework, both the probability of rework and the
impact of rework should all be studied.
6.3.1 Probability of Rework
In order to determine the probability of rework when picking an overdesign factor for
pipe loads, the researcher decided to use historical projects’ information. However, in
practice, it is impossible to gather direct information about overdesign cases and their
success or failure, as no mechanism exists in industry to capture and record this
information. Therefore, the researcher tried an alternative way, which involved
comparing the preliminary design with the final design in the past projects. The intention
was to find out, in theory, the amount of overdesign factors which should have been
added to the preliminary information to obtain the exact final information (Figure 6-4).
The researcher called this factor the Ideal Overdesign Factor.
Figure 6-4: Concept of Ideal Overdesign Factor
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In Chapter 5, the process of collecting preliminary and final loads data from six
modular steel pipe racks was explained in detail. Also discussed was how the collected
data were used to calculate the maximum bending moment in both preliminary and final
designs. This was needed to track the effect of pipe load changes on the steel calculation,
as maximum bending moment is the governing parameter in beam design.
Table 5-3 presented the calculated bending moments from both preliminary loads
and final loads. From these data, the researcher calculated the Ideal Overdesign Factor
for all beams under the study from Equations [6.6] and [6.7].
Where
PMx+(PMx IODx)= FMx [6.6]
IODx
PMx
FMx
Ideal overdesign factor for beam x
Preliminary moment for beam x
Final moment for beam x
And from there
IODx = (FMx/PMx)-1 [6.7]
According to Equations [6.6] and [6.7], if the preliminary bending moment for a
specific beam would have been overdesigned by IODx, then the resulting moment would
exactly match the final moment. Picking an overdesign factor greater than IODx meets
and exceeds the design requirements. Therefore, this may not necessarily be desirable,
depending on its effect on steel structure design and consequently additional cost.
However, choosing any overdesign factor less than IODx may not meet the design
requirements and may be a case of underdesign. In other words:
166
AOD =IODx Then PMX +AOD = FMX Ideal Overdesign
AOD >IODx Then PMX +AOD > FMX Overdesign (not necessarily desirable)
AOD <IODx Then PMX +AOD < FMX Underdesign
Where
AOD Assumed overdesign factor
Table 6-1 presents the results related to the calculation of Ideal Overdesigned Factors for
all beams under the study.
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1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465
Table 6-1: Ideal Overdesign Factors for All Beams under the Study Beam
#
Preliminary Bending Moment
Final Bending Moment
Ideal Overdesign
Factor
9.939 11.312 14% 48.75 49.349 1% 59.183 102.48 73% 41.736 53.54 28% 86.848 87.038 0% 42.81 65.864 54% 10.833 14.971 38% 73.481 122.751 67% 71.074 73.707 4% 66.633 68.991 4%
120.121 114.737 -4% 62.781 49.411 -21% 24.229 42.601 76%
147.802 112.274 -24% 37.5 41.927 12%
80.008 101.053 26% 33.559 17.725 -47% 10.544 23.121 119% 69.25 69.25 0% 16.814 21.936 30% 73.342 73.342 0%
156.689 146.772 -6% 47.592 103.465 117% 48.785 48.785 0%
415.717 415.717 0% 62.773 48.27 -23% 84.847 168.868 99% 64.545 63.098 -2% 96.035 86.796 -10%
120.039 120.617 0% 42.187 21.094 -50% 59.869 59.869 0% 8.777 6.14 -30% 20.235 10.829 -46% 21.333 18.701 -12% 89.141 85.41 -4% 41.288 45.414 10% 54.849 54.849 0% 96.513 64.397 -33%
119.097 119.097 0% 19.956 19.956 0% 52.079 55.672 7% 22.582 23.179 3% 23.438 25.104 7% 9.702 9.702 0% 56.834 54.725 -4% 46.851 49.339 5% 65.061 44.016 -32% 45.744 45.744 0% 70.122 97.179 39% 95.33 96.194 1% 59.183 118.468 100% 47.862 40.784 -15% 42.476 50.971 20% 61.297 61.297 0% 75.041 60.909 -19% 15.491 13.462 -13% 60.946 60.946 0% 79.705 71.691 -10% 61.213 61.213 0% 1.685 1.598 -5% 28.86 29.842 3% 72.116 111.949 55% 10.749 8.047 -25% 17.696 17.696 0%
Beam #
Preliminary Bending Moment
Final Bending Moment
Ideal Overdesign
Factor
66 17.3 30.225 75% 67 42.806 43.503 2% 68 41.329 41.329 0% 69 82.393 59.172 -28% 70 70.29 54.995 -22% 71 27.945 32.999 18% 72 31.704 31.942 1% 73 39.518 54.394 38% 74 17.122 18.243 7% 75 79.392 65.101 -18% 76 60.946 60.946 0% 77 104.719 83.505 -20% 79 197.283 216.186 10% 80 39.844 86.21 116% 81 27.536 26.753 -3% 82 49.015 50.727 3% 83 46.644 34.569 -26% 84 3.556 3.333 -6% 85 21.432 43.815 104% 86 30.377 30.377 0% 87 50.197 83.155 66% 88 54.849 69.25 26% 89 33.452 35.923 7% 90 61.569 58.849 -4% 91 38.704 28.089 -27% 92 15.954 31.908 100% 93 48.286 47.513 -2% 94 15.568 15.568 0% 95 76.95 52.626 -32% 96 66.413 66.603 0% 97 47.862 39.739 -17% 98 90.782 78.07 -14% 99 65.417 70.92 8%
100 41.436 44.421 7% 101 12.177 14.883 22% 102 41.207 83.155 102% 103 46.64 59.554 28% 104 75.971 97.3 28% 105 18.892 20.36 8% 106 41.882 42.601 2% 107 64.451 73.707 14% 108 91.554 105.69 15% 109 6.721 15.387 129% 110 36.599 55.672 52% 111 69.671 73.382 5% 112 104.359 98.099 -6% 113 40.211 26.73 -34% 114 42.375 47.698 13% 115 38.474 31.16 -19% 116 29.687 38.351 29% 117 51373 51373 0% 118 23.269 17.783 -24% 119 20.296 23.335 15% 120 57.608 73.867 28% 121 99.407 145.145 46% 122 56.154 42.551 -24% 123 98.033 101.203 3% 124 26.411 40.305 53% 125 26.667 33.333 25% 126 52.599 53.563 2% 127 74.655 74.655 0% 128 2.5 3.333 33% 129 84.033 88.418 5% 130 22.222 26.667 20%
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Then, the cumulative density function graph for all ideal overdesign factors was drawn by
taking the following steps:
1- The ideal overdesign factors were sorted in ascending order
2- The frequency of each ideal overdesign factor was determined
3- The cumulative frequency was calculated
4- The probability of cumulative frequencies was determined by dividing each
cumulative frequency over the total number of samples
Table 6-2 shows the final results and different steps of creating the cumulative density
function for ideal overdesign factors.
169
Cumulative
Table 6-2: Steps for Calculating the Cumulative Density Function for Ideal Overdesign Factors
Step 1: Step 2: Step 3: Step 4: IOD: Ideal Overdesign Factor Sorting the IODs Determining the Calculating Determining
frequency of each the cumulative the probability IOD frequency of cumulative
Step 4 Ste 1 frequencies
pStep 1 Step 2 Step 3 Step 2 Step 3 Step 4
IOD Frequency Cumulative Frequency
Probability
-50.00% 1 1 1% -47.18% 1 2 2% -46.48% 1 3 2% -33.53% 1 4 3% -33.28% 1 5 4% -32.35% 1 6 5% -31.61% 1 7 5% -30.04% 1 8 6% -28.18% 1 9 7% -27.43% 1 10 8% -25.89% 1 11 9% -25.14% 1 12 9% -24.22% 1 13 10% -24.04% 1 14 11% -23.58% 1 15 12% -23.10% 1 16 12% -21.76% 1 17 13% -21.30% 1 18 14% -20.26% 1 19 15% -19.01% 1 20 16% -18.83% 1 21 16% -18.00% 1 22 17% -16.97% 1 23 18% -14.79% 1 24 19% -14.00% 1 25 19% -13.10% 1 26 20% -12.34% 1 27 21% -10.05% 1 28 22% -9.62% 1 29 22% -6.33% 1 30 23% -6.27% 1 31 24% -6.00% 1 32 25% -5.16% 1 33 26% -4.48% 1 34 26% -4.42% 1 35 27% -4.19% 1 36 28% -3.71% 1 37 29% -2.84% 1 38 29% -2.24% 1 39 30% -1.60% 1 40 31% 0.00% 20 60 47% 0.22% 1 61 47% 0.29% 1 62 48% 0.48% 1 63 49% 0.75% 1 64 50% 0.91% 1 65 50% 1.23% 1 66 51% 1.63% 1 67 52% 1.72% 1 68 53% 1.83% 1 69 53% 2.64% 1 70 54% 3.23% 1 71 55% 3.40% 1 72 56% 3.49% 1 73 57%
IOD Frequency Frequency
Probability
3.54% 1 74 57% 3.70% 1 75 58% 5.22% 1 76 59% 5.31% 1 77 60% 5.33% 1 78 60% 6.55% 1 79 61% 6.90% 1 80 62% 7.11% 1 81 63% 7.20% 1 82 64% 7.39% 1 83 64% 7.77% 1 84 65% 8.41% 1 85 66% 9.58% 1 86 67% 9.99% 1 87 67% 11.81% 1 88 68% 12.56% 1 89 69% 13.81% 1 90 70% 14.36% 1 91 71% 14.97% 1 92 71% 15.44% 1 93 72% 18.09% 1 94 73% 20.00% 1 95 74% 20.00% 1 96 74% 22.22% 1 97 75% 25.00% 1 98 76% 26.26% 1 99 77% 26.30% 1 100 78% 27.69% 1 101 78% 28.08% 1 102 79% 28.22% 1 103 80% 28.28% 1 104 81% 29.18% 1 105 81% 30.46% 1 106 82% 33.32% 1 107 83% 37.64% 1 108 84% 38.20% 1 109 84% 38.59% 1 110 85% 46.01% 1 111 86% 52.11% 1 112 87% 52.61% 1 113 88% 53.85% 1 114 88% 55.23% 1 115 89% 65.66% 1 116 90% 67.05% 1 117 91% 73.16% 1 118 91% 74.71% 1 119 92% 75.83% 1 120 93% 99.03% 1 121 94%
100.00% 1 122 95% 100.17% 1 123 95% 101.80% 1 124 96% 104.44% 1 125 97% 116.37% 1 126 98% 117.40% 1 127 98% 119.28% 1 128 99% 128.94% 1 129 100%
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
60% 40% 20% 0% 20% 40% 60% 80% 100% 120% 140%
Prob
ability
Overdesign Factor
Figure 6-5 provides the cumulative density function graph for ideal overdesign factors
drawn from data presented in Table 6-2.
‐ ‐ ‐
Figure 6-5: Cumulative Density Function for Ideal Overdesign Factors
Figure 6-5 helps provide some information for comparing the preliminary and
final design in the selected beams in past projects. The following observations can be
made from Figure 6-5:
• 47% of the beams did not require an additional overdesign factor, as the
preliminary moment was either equal to or higher than the final moment. This
means only 53% of the beams required being overdesigned.
• 80% of the beams should have been overdesigned by 28% or less
• About 20% of all beams should have been overdesigned by 10% or less (but
greater than zero)
• About 7% should have been overdesigned by a factor between 10% and 20%
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• About 8% should have been overdesigned by a factor between 20% and 30%
• About 4% should have been overdesigned by a factor between 30% and 45%
• About 4% should have been overdesigned by a factor between 46% and 55%
• About 5% should have been overdesigned by a factor between 55% and 100%
• About 5% should have been overdesigned by a factor greater than 100%
Figure 6-6 provides a graphical representation of the above mentioned interpretations.
Figure 6-6: Graphical Presentation of the Required Overdesign Factor in
Sample Beams
The curve presented in Figure 6-5 can be used to determine the probability for an
optional overdesign value to be a successful overdesign, based on information from past
projects. For example, according to Figure 6-5, there is a 74% chance of success for a
20% overdesign factor based on past project information.
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6.3.2 Rework
After discussing the probability of rework, the next step is to determine the impact of
rework, which is translated to both delay and cost. In the current research, proceeding
with structural calculations without finalizing the pipe loads may result in rework in this
activity; however, depending on the degree of project fast tracking, if procurement and
construction have already been started before finalizing the pipe loads, there may be
reworks in procurement and construction as well. Therefore, the total rework duration
will be obtained from Equation [6.8]:
RW = RWe + RWp + RWc [6.8]
Where
RW Total rework duration
RWe Engineering rework
RWp The equivalent rework duration for procurement, as a result of change in
pipe loads
RWc The equivalent rework duration for construction, as a result of change in
pipe loads
In the engineering phase, according to Figure 6-1 and as discussed in section 6.3,
if the preliminary pipe loads do not comply with final loads, there is a potential change in
the steel design, which results in rework in steel structure calculations and drawings and
in some cases in piling design and drawings as well. Hence, in the engineering phase, the
rework will be obtained from Equation [6.9].
RWe = RWsc + RWpd [6.9]
Where
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RWe Engineering rework
RWsc The equivalent rework duration for structural calculations and drawings,
as a result of change in pipe loads
RWpd The equivalent rework duration for piling design and drawings, as a result
of change in pipe loads
In Equations [6.8] and [6.9], the reader’s attention is drawn to the term equivalent
rework duration, which is not necessarily the whole duration of the activity that is being
reworked (Dehghan, 2011). In two dependent activities, the change in the upstream
activity may cause rework on the whole duration or just a part of the downstream activity.
The sensitivity of the downstream activity to changes in upstream activity information
determines the amount of rework required if upstream information changes (Krishnan et
al., 1997). Dehghan (2011) argues that this rework is a function of overlap between the
two dependent activities and that the rework duration does not supersede the overlapping
period.
If rework occurs, based on the above discussion, the rework period should be
deducted from the overdesign time impact (Equation [6.10]). Therefore, assuming the
criticality of the pipe rack, Equation [6.3] is replaced by Equation [6.11].
NTIod =TIod – RW [6.10]
Where
NTIod Net time impact of overdesign
TIod Overdesign time impact
RW Total rework duration
Tod-r = (TN-NTIod) [6.11]
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Where
Tod-r Project duration after overdesign (in case of rework)
TN Project duration in normal execution
NTIod Net time impact of overdesign
Likewise, in case of rework, the benefits of overdesign will be obtained from Equation
[6.12].
Where
Bod-r Benefits of overdesign in case of rework
Tod-r Project duration after overdesign (in case of rework)
Bec Daily benefits of project early completion, including revenue and daily
incentives for early completion
Clc Daily costs of project late completion, including loss and daily penalties
for late completion
Tt Target duration
So far, we have discussed the rework duration. However, rework also results in
extra cost, which is obtained from Equation 6.13.
CRW= Crwe + Crwp+ Crwc [6.13]
Where
CRW Total cost of rework
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Crwe All possible costs of rework in the engineering phase including daily
wages and overhead
Crwp All possible costs of rework in the procurement phase, including daily
salaries, overhead, change in material, etc.
Crwc All possible costs of rework in the construction phase, including daily
salaries, overhead, demolition, reconstruction/reinstallation, etc.
In case of rework, the cost of rework obtained from Equation [6.13] will be added to
other extra costs of overdesign (Equation [6.14]).
Cod-r= CRW + Cod [6.14]
Where
Cod-r Total cost of overdesign in case of rework
CRW Total cost of rework
Cod Cost of overdesign
6.4 Overdesign Time-Cost Trade-Off
Based on the discussion about time, cost and rework impacts of overdesign, the
overdesign time-cost trade-off problem is formulated as shown in Figure 6-7. This figure
shows that there are two possible consequences when choosing any assumed overdesign
factor: 1) no rework 2) rework. In case of no rework, the benefits of overdesign are traded
off for its extra cost, while in case of rework, rework duration and cost should be taken
into account in calculating the benefits and cost of overdesign, and then the trade-off
should be performed.
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Figure 6-7: Graphical Representation of the Overdesign Time-Cost Trade-Off
Problem for the Modular Steel Pipe Racks
Chapter Summary
In this chapter, the consequences of overdesigning modular steel pipe racks in terms of
time, cost and rework were discussed in different project phases, i.e. engineering,
procurement and construction. In the rework section, the information collected from past
projects was used to establish a relationship between the overdesign factor and the
probability of rework.
The subjects discussed in this chapter led to identification of different variables
required for formulating the overdesign time-cost trade-off problem. This time-cost trade-
off problem is applied to the decision tree model, which is explained in Chapter 7.
Contributions: Addressing a method to relate the degree of conservativeness of the
assumptions in overdesign to the probability of rework associated with any overdesign
decision is methodological, theoretical, literature and industry contributions of this
177
research. Likewise, formulating the time-cost trade-off problem for overdesigning
modular steel pipe racks is another contribution of this research to the overdesign
literature and concept.
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CHAPTER SEVEN: DECISION SUPPORT SYSTEM FOR OVERDESIGNING MODULAR STEEL PIPE RACKS
This chapter discusses the steps required to develop a decision support system. Taking
these steps, the researcher modeled the overdesign problem using the stochastic decision
tree principles and developed a decision support system to assist in solving the problem.
Chapters 5 and 6 provided the foundation for constructing the decision tree model and
decision support system, as discussed next.
Chapter 2 discussed the use of decision support systems for semi-structured
decisions in which not every relevant parameter is known. However, in these situations,
gathering as much relevant information as possible and identifying important parameters
help model the decision problem. Then, using the model, different scenarios involving
the decision problem are analyzed to arrive at a reasonable decision based on the
analysis. Therefore, rather than actually making the decision for the user, decision
support systems help the decision maker make his or her own reasonable decision.
Making decisions about an overdesign problem requires evaluating different
overdesign options using a trade-off between time savings, on one hand, and extra costs
and the possibility of rework on the other hand. However, decision parameters vary from
one option to another. Therefore, the researcher believes it is important to properly model
the decision problem in a way which allows for sensitivity analysis, which helps in
making effective decisions about different overdesign options. In other words, a decision
support system is required to address the overdesign problem.
To develop a decision support system, the following steps should be taken
(Power, 2002):
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1- Modeling the research problem
2- Identifying input variables
3- Building the processing module
4- Defining required outputs
5- Designing the user interface
These steps are explained in the following sections to show the development process of a
decision support system for overdesigning modular steel pipe racks, called hereinafter
pipe rack overdesign DSS (Figure 7-1).
180
Figure 7-1: General Overview of Pipe Rack Overdesign DSS
181
7.1 Modeling the Research Problem
A model is defined as a representation of a system for the purpose of studying the system
(Ruwanpura & Ariaratnam, 2007). Since construction problems are complex in nature
and therefore difficult to model, it is not possible and necessary to consider all details.
The model should be sufficiently detailed to permit valid conclusions to be drawn for the
real system (Mihram & Mihram, 1974). Hence, a model is a substitute for and a
simplification of a system.
As discussed earlier in Chapter 2, the overdesign problem is modeled under the
Decision Tree principles. Chapter 6 identified and discussed relevant information and
time/cost parameters required to model the research problem. These were also used to
formulate the time-cost trade-off. Besides them, the following components should be
determined to allow construction of the decision tree.
• Available decision alternatives
• Possible outcomes of each decision
• The probability of the outcomes
• Pay-off of the decisions
Based on the overdesign concept, the available decision alternatives are as follows:
Decision alternative 1 (d1): Making no overdesign and waiting to receive complete
information
In this case, the benefits of overdesign will be ignored and the losses of overdesign will
also be avoided. Hence, it is certain that no overdesign time savings will be obtained and
no extra costs will be imposed because of overdesign. This translates to a 100%
probability of occurrence for these outcomes, if this decision is made.
182
However, waiting to receive accurate information has other subsequent outcomes
for the project. According to interviews, the following, all of which are related to the
business benefit of early project completion, are among the most important consequences
of ignoring the overdesign option:
• Potential of missing the schedule and therefore missing potential incentives
• Cost of an extended schedule, including extra costs related to project support
elements, project financing, tax considerations, time value of money, etc.
• Missing earlier production benefits, including market share and competitive
positioning in the market
Some of the above items are very difficult to quantify. However, since they are related to
strategic considerations of a project, they should be determined by senior management.
For the purpose of this research, one value defined as daily benefits of project early
completion and another value defined as daily costs of project late completion including
loss and daily penalties for late completion, both of which are determined by senior
management, represent the above consequences. It is assumed that senior management
has considered all of these factors when determining these values. As discussed in section
1.5 of Chapter one, determining these values while considering all strategic and
contextual factors is beyond the scope of this research.
If the project duration in normal execution and without overdesign is greater than
the target date agreed upon in the project contract, then there will be additional costs due
to missing the project schedule. However, if the duration is less than the target date, then
the project will benefit from early completion. However, overdesign may result in more
183
time savings and therefore more benefit. Hence, project managers may be interested in
comparing these two.
Decision alternative n (dn): Proceed with incomplete information with an assumed
overdesign factor
As discussed in detail in Chapter 6, in case of overdesign with any assumed value, the
consequences will be a potential for time savings, extra costs and probable rework. These
parameters have been used to model this decision and calculate the expected value of this
decision afterwards.
Probabilities associated with the outcomes of each decision alternative as well as
the decision pay-offs are discussed in section 7.3.1. Figure 7-2 presents a graphical
representation of the decision tree model. This decision tree can be expanded by adding
more branches as more overdesign options are compared.
Figure 7-2: Decision Tree Model of the Overdesign Problem
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7.2 Identifying Input Variables
Based on the discussion in Chapter 6, the items presented in Table 7-1 are considered
input variables for the decision support system. These variables are categorized as
follows.
1- Contract parameters
2- Time parameters
3- Cost parameters
4- Rework parameters
Table 7-1: Input Variables
Input Definition
Variable
Contract Parameters
Tt Project target duration according to contract
Daily benefits of early completion, including revenue and Bec
daily incentives for early completion
Daily costs of project late completion, including loss and Clc
daily penalties for late completion
TN Project duration in normal execution
Time Parameters
TSeg Engineering time savings
Dsf Increase in duration of steel fabrication after overdesign
Dpp Increase in duration of piling purchase after overdesign
185
Dpi Increase in duration of piling installation after overdesign
Cost Parameters
All possible costs of purchasing additional piles required as Cpp
a result of overdesign
All possible costs of fabricating additional steel required as Csf
a result of overdesign
All possible costs of installation of additional piles
Cpi required as a result of overdesign including wages,
equipment cost, overhead, etc.
All possible costs of erection of additional steel required as
Cse a result of overdesign including wages, equipment cost,
overhead, etc.
Rework Parameters
RWsc
The equivalent rework duration for structural calculations
& drawings, as a result of change in pipe loads
RWpd
The equivalent rework duration for piling design &
drawings, as a result of change in pipe loads
RWp
The equivalent rework duration for Procurement, as a
result of change in pipe loads
RWc
The equivalent rework duration for Construction, as a
result of change in pipe loads
Crwe All possible costs of rework in the engineering phase
186
including daily wages and overhead
Crwp
All possible costs of rework in the procurement phase
including daily salaries, overhead, change in material, etc.
All possible costs of rework in the construction phase
Crwc including daily salaries, overhead, demolition,
reconstruction/reinstallation, etc.
Prrw Probability of rework
All of the above input variables and the process for obtaining them have been
discussed in detail in Chapter 6. In Table 7-1, contract parameters are defined once for
every project but all other parameters are a function of the degree of overdesign and
therefore change from one overdesign option to another.
In order to address real life situations and uncertainties associated with project
activities and the consequent risk involved in decisions, the decision support system was
designed to allow for stochastic values (ranges and probabilities) for time, cost and
rework parameters. “Working with ranges and probabilities, instead of single point
estimates, for time and money is not only more realistic, but also needs to better decisions
and ownership by senior management” (Hartman, 2000, p. 28). For contract parameters,
however, only deterministic values are assumed because, to the best knowledge of the
researcher, these values are expressed deterministically in real life projects.
Among all possible statistical distributions, Triangular distribution has been
chosen for time, cost and rework variables presented in Table 7-1. Triangular distribution
has three different parameters: Lower Value (L), Upper (Higher) Value (H) and Mode
187
Value. In our case, these values represent optimistic, likely and pessimistic estimates, and
according to Hartman (2000), they are used to capture perfect, likely and outrageous
guesses of what might happen. Hartman (2000) called this process as PLO estimating. He
replaced optimistic and pessimistic with perfect and outrageous.
Furthermore, the results of the previously discussed interviews show that industry
practitioners strongly intend to state all cost and duration parameters in the form of
triangular distribution. They usually talk about best case scenario, worst case scenario
and most likely, which exactly match the parameters required for triangular distribution.
7.3 Building Processing Modules
Two processing modules have been built for the overdesign decision support system, as
follows:
1- Decision Tree Calculation Module
2- Sensitivity Analysis Module
a. One-way sensitivity analysis
b. Two-way sensitivity analysis
The Decision Tree Calculation Module takes the input variables defined in section 7.2
and processes them based on the stochastic decision tree principles. The output of this
module will be transferred to the Sensitivity Analysis Module for one-way and two-way
sensitivity analysis. The outputs are presented in the form of different reports that assist
managers in effective decision making. These two modules are discussed in more detail
in sections 7.3.1 and 7.3.2.
188
To build both the Decision Tree Calculation Module and the Sensitivity Analysis
Module, a database management software (such as a spreadsheet) that has strong data
manipulation capabilities was required. As listed in Table 7-1, many input variables
require further processing, i.e. calculation, analysis and retrieval. Besides, these variables
can assume stochastic values which add to the complexity of the calculations, as
stochastic calculations contain a large volume of data which require calculation, analysis,
storage and retrieval. Therefore, the researcher decided to build the calculation module in
MS Excel spreadsheets. MS Excel is a powerful and widely used tool that helps users
analyze information more efficiently. It is quite simple and fast to perform data
manipulations like calculations due to the presence of easy automated commands and
built-in functions to perform such tasks. Creating graphs/charts is fast and easy. Retrieval
of a huge collection of data is also easy. Further, working with MS Excel does not require
the application of programming languages.
7.3.1 Decision Tree Calculation Module
The overall function of this module is to use input variables defined in Table 7-1 to
calculate the expected value of each overdesign decision alternative (to a maximum of 10
different decision alternatives) and to store them for further comparisons. To perform this
function, the following four distinct sub-modules were defined for the decision tree
calculation module.
1. Time impact module
2. Cost impact module
3. Rework module
4. Pay-off and expected value module
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1. Time impact module
This module calculates the overdesign time impact using Equation 6.2 from
Chapter 6.
TIod = TSeg- Dsf- Dpp-Dse-Dpi [6.2]
2. Cost impact module
This module calculates the overdesign cost impact for the desired overdesign option
using Equation 6.5 from Chapter 6.
Cod = Cpp+ Cps+ Cpi + Cse [6.5]
3. Rework module
This module calculates the rework associated with each overdesign option. To do this,
two rework parameters were defined: 1) probability of rework associated with each
overdesign option and 2) impact of rework.
The process for obtaining the probability of rework has been described in detail in
Chapter 6, section 6.3.1. To avoid duplication, readers are encouraged to review that
chapter. Suffice to say that the database created from the preliminary and final design
information from past projects is used in this section to estimate the probability of
rework associated with each overdesign option. In fact, this database is part of the
rework module of the pipe rack overdesign DSS.
For the impact of rework, Equations [6.8], [6.9] and [6.13] from Chapter 6 were
used in the rework calculations.
RWe = RWsc + RWpd [6.8]
RW = RWe + RWp + RWc [6.9]
CRW= Crwe + Crwp+ Crwc [6.13]
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4. Pay-off and expected value module
As stated in Chapter 2, the expected value of each decision in the decision tree is
calculated from the product of the value of each decision, the so-called pay-off, by its
probability. The following explains the process for calculating the expected value of each
overdesign decision alternative.
Expected Value of Decision Alternative 1 (d1): Making no overdesign and waiting to
receive complete information.
Decision makers are usually interested in the expected value of making no overdesign
decision as the first point of comparison when evaluating different overdesign options. In
the decision tree calculation module of the pipe rack overdesign DSS, calculating the
pay-off of this decision is based on Equation 7.1
Where
Pay-offd1 Pay-off of making no overdesign decision
Bd1 Benefits of making no overdesign decision
Cd1 Cost of making no overdesign decision
If normal project duration is equal to project target duration in the contract, there will be
no cost/benefit. However, if normal project duration is less than project target duration, it
means there will still be some benefits for the project; otherwise, there will be cost. In
other words:
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Where
Bd1 Benefits of making no overdesign decision
Cd1 Cost of making no overdesign decision
TN Project duration in normal execution
Tt Target duration
Bec Daily benefits of project early completion, including revenue and daily
incentives for early completion
Clc Daily costs of project late completion, including loss and daily penalties
for late completion
The probability associated with making no overdesign decision is 100%, as this is certain
to happen when waiting to receive complete information. According to decision tree
principles, the expected value of this decision is obtained from Equation 7.3.
EV (NOD) = 100% × Pay-offd1 [7.3]
Where
Pay-offd1 Pay-off decision 1
EV (NOD) Expected value in normal execution and with no overdesign
Expected Value of Decision Alternative n (dn): Proceed with incomplete information
with an assumed overdesign factor
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In this case, for each single decision, the pay-off will be calculated based on Equation
[7.4].
Where
Pay-offdn Pay-off decision n
Bod Benefit of overdesign
Cod Cost of overdesign
Tt Target duration
Tod Project duration after overdesign
Bec Daily benefits of early completion, including revenue and daily incentives
for early completion
Clc Daily costs of project late completion, including loss and daily penalties
for late completion
Bod-r Benefits of overdesign in case of rework
Cod-r Total cost of overdesign in case of rework
TN Project duration in normal execution
NTIod Net time impact of overdesign
CRW Cost of rework
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According to decision tree principles, the pay-off from Equation 7.4 is used in Equation
7.5 to calculate the total expected value of this decision.
EV (AOD) = [(1- Prrw) × (Bod - Cod)] + [Prrw (Bod-r - Cod-r] [7.5]
Where
EV (AOD) Expected value of overdesign with any assumed value
Prrw Probability of rework
Bod Benefit of overdesign
Cod Cost of overdesign
Bod-r Benefit of overdesign in case of rework
Cod-r Cost of overdesign in case of rework
As mentioned earlier in the decision tree calculation module of the DSS, all the
variables can take stochastic values. According to Ruwanpura (2008), the following steps
should be taken to fully perform stochastic analysis (Figure 7-3).
1- Generating a uniform random number on the interval (0 – 1)
2- Transforming the random number into triangular statistical distribution, referred
to as a random variate
3- Substituting the random variates into the appropriate variables in the model
4- Calculating the desired output parameters within the model
5- Storing the resulting output for analysis
6- Repeating previous steps a large number of times with different random numbers
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Figure 7-3: Steps for Performing Stochastic Analysis
As mentioned earlier, Triangular distribution has been considered for the input
variables of the pipe rack overdesign DSS. Triangular distribution has three different
parameters: Lower Value (L), Upper (Higher) Value (H) and Mode Value. The decision
tree calculation module takes these values as user inputs. It then generates different
uniform random numbers on the interval (0 – 1) and, using Equation 7.6, transforms the
random number to triangular stochastic variates.
Then, stochastic variates are applied to calculate time and cost impact, rework, pay-off
and finally the expected value of each decision using all the equations mentioned in
section 7.3.1. This process is repeated for 1000 runs and the results will be stored for
further analysis. Using these values, a cumulative density function graph for expected
values is drawn and some statistics are presented to allow the user to perform further
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analysis. The statistics include average expected value, standard deviation and minimum
and maximum expected value obtained in 1000 runs. This information is provided for
different overdesign decisions to enable the decision maker to compare different
overdesign options. Furthermore, the pipe rack DSS provides the decision maker with the
optimality index for each overdesign decision. The decision optimality index identifies
the probability that a decision falls on the optimum decision path (Moussa et al., 2006).
Optimality index is determined by comparison of the maximum expected values in all
1000 run. In each run, the decision, which yields the maximum expected value, is
determined. This process is repeated for all 1000 runs. Comparison of the results
identifies the probability that a decision yields maximum expected value. By examining
the decisions’ variability (range of outcomes) and the optimality index, the decision
maker can analyze the decisions. However, the final decision is dependent on his/her risk
attitude and the amount of risk s/he is willing to accept.
Figure 7-4 shows an example of the outputs of the pipe rack DSS provided for
two different hypothetical overdesign decisions, followed by the analysis. The outputs
show probability density function graphs for the expected value of the decisions along
with their associated statistics as well as optimality indices.
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0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
150,000,000.00 100,000,000.00 50,000,000.00 0.00 50,000,000.00 100,000,000.00 150,000,000.00 200,000,000.00
Prob
ability
Expected Value
1000 Runs
Cumulative Density Functio
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
100 000,000.00 50 000 000 00 0 00 50,000,000 00 100 000,000 00 150 000 000.00 200,000 000.00 250,000 000 00
Prob
ability
Expected Value
1000 Runs
Cumulative Density Functio
Decision 1 Decision 2
‐ ‐ ‐‐ , ‐ , , . . . , . , , , , .
Statistics
Decision 1: 10% Overdesign
Average Expected Value Standard Deviation
33,117,109 54,448,424
Optimality Index
21%
Min
-126,741,336
Max
174,577,877
Statistics
Decision 2: 40% Overdesign
Average Expected Value Standard Deviation Min
92,167,970 45,156,396 -56,990,295
Optimality Index
79%
Max
207,940,818
Figure 7-4: Expected Value Graphs of Two Hypothetical Overdesign Decisions and Associated Statistics
197
According to Figure 7-4, there is about a 27% chance of having a negative expected value
for the hypothetical decision 1, while decision 2 only has 3% chance of having a negative
expected value. In 79% of the runs, decision 2 yielded the maximum expected value
(optimality index), while this chance for decision 1 is only 21%. Furthermore, the
minimum loss in decision 2 is significantly lower than that of decision 1. Likewise, the
maximum gain in decision 2 is significantly higher. So comparison between these two
options results in choosing the decision 2. Section 7.7 presents the cumulative density
function graph and its statistics for a real overdesign decisions.
7.3.2 Sensitivity Analysis Module
Sensitivity analysis is a systematic study of how the solution to a decision model changes
as the assumptions are varied. In other words, sensitivity analysis examines the impact of
input data on the output results. In sensitivity analysis, the user can vary one, two or all
the parameters simultaneously to discover which parameter significantly impacts the
results. This sort of examination is crucial as it helps to obtain a more comprehensive
understanding of the dynamics of the decision problem. It also helps to identify the
important elements in the decision problem. There are two types of sensitivity analysis:
• One-way sensitivity analysis
• Two-way sensitivity analysis
One-way sensitivity analysis examines whether a variable really makes a difference
in the decision by varying its value while keeping other variables at their base values
(most likely values). Two-way sensitivity analysis is the study of the joint impact of
changes in two variables.
198
In the sensitivity analysis module of the pipe rack overdesign DSS, both one-way and
two-way sensitivity analyses were designed to help the decision maker see the range of
effects of the input variable on the expected value of the decision alternatives. The
researcher believes sensitivity analysis is of vital importance for this research as many
input variables of the decision model of this research are inherently uncertain, such as the
cost of steel, labour costs, etc. Therefore, sensitivity analysis helps the decision maker
predict the outcome of his/her decision if a situation turns out to be different compared to
the original predictions.
One-way sensitivity analysis module
In this module, sensitivity of the expected value of the selected decision alternative to
individual input variables defined in Table 7-1 is analyzed. To build the one-way
sensitivity analysis module, the decision tree calculation module explained in section
7.3.1 was changed to generate different scenarios based on the range of the changes in
each input variable. Each input variable has a base value, also called a most likely value;
a lower bound/pessimistic value/worst case scenario; and finally an upper
bound/optimistic value/best case scenario. Base, pessimistic and optimistic values are
defined by the user and originate from cost evaluations, facts and figures from past
projects, experts’ knowledge, etc.
To generate different scenarios for one-way sensitivity analysis, one input
variable changes in its range of worst case and best case scenarios, while other variables
are kept at their base value or most likely value. For each scenario, the expected value
calculations are done using the decision tree calculation module and the results are stored
for the purpose of comparison. At the end of the process, a graph is drawn to consolidate
199
the results of all changes made on one variable and the impact of those changes on the
total expected value of the decision. In other words, the graph shows the range of changes
in expected value vs. changes in the selected input variable. These graphs assist the
decision maker in evaluating the importance of each input variable in the decision
problem. This process is performed for every single input variable defined in Table 7-1,
with a total of 20 graphs provided. Figure 7-5 presents an example in which the
sensitivity of the expected value to time savings is analyzed for a hypothetical overdesign
case.
Figure 7-5: One-Way Sensitivity Analysis for Time Saving Vs. Expected Value for a
Hypothetical Overdesign Case
According to Figure 7-5, if time savings is varied between 100 days (worst case
scenario) and 130 days (best case scenario), the expected value will change from
$24,550,000 to $114,550,000. In other words, a $90,000,000 increase in expected value
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0
1
2
3
4
5
6
7
8
9
1 0
1 1
1 2
1 3
1 4
1 5
1 6
1 7
1 8
1 9
2 0
2 1
2 2
2 3
2 4
150,000,000 100,000,000 50,000,000 0 50,000,000 100,000,000 150,000,000
Expected value
TornadoDiagram
Time savings
Steel Fabrication Duration
Steel Erection Duration
Steel Fabrication Extra Cost
Steel Erection Extra Cost
Steel Structure Rework
Cost of Engineering Rework
Piling Purchase Duration
Piling Installation Duration
Pile Purchase Extra Cost
Piling Installation Extra Cost
PilingDesign Rework
ProcurementRework
Construction Rework
Cost of ProcurementRework
Cost of Construction Rework
vs. 30 days more time savings. This demonstrates that this specific hypothetical decision
is highly sensitive to time savings. Section 7.7 presents the one-way sensitivity analysis
reports generated for a real overdesign case.
Availability of this information for all input variables of the decision helps the
decision maker find the most sensitive input variables and act on them accordingly
because they have the potential to make huge changes on the outcome. In order to make
this easier for the decision maker, after completing the one-way sensitivity analyses, a
Tornado Diagram is drawn to allow the decision maker to compare one-way sensitive
analyses for many input variables at once. In a Tornado Diagram, a bar (or line) is used
to represent the range of expected values due to the variation of input variables. Figure 7
6 presents an example of the tornado diagram for a hypothetical overdesign case. Section
7.7 presents the tornado diagram generated for a real overdesign case.
‐ ‐ ‐
Figure 7-6: Tornado Diagram for a Hypothetical Overdesign Case
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Two-way sensitivity analysis module
In this module, the joint impact of changes in two variables on the expected value of a
selected decision will be studied. Since time savings and overdesign extra costs are the
two most critical variables in the overdesign problem, a two-way sensitivity analysis
module has been designed to enable the decision maker to better understand the joint
impact of changes of these two variables on the expected value of the decision. To
achieve this purpose, for each overdesign decision, all the input variables in Table 7-1
(other than time savings and overdesign extra cost) are set at their base values, which are
known values. Therefore, the only unknown parameters are time savings and overdesign
extra cost. The following shows the step-by-step process.
According to Equation [7.5]:
EV (AOD) = [(1- Prrw ) (Bod - Cod)] + [Prrw (Bod-r - Cod-r] [7.5]
With replacement of Equations [6.2], [6.3], [6.4], [6.10], [6.11], [6.12], [6.14] in
Equation [7.5], we have:
EV (AOD) = [(1- Prrw) × Bod - (1- Prrw) × Cod] + [(Prrw (Bod-r) – (Prrw (Cod-r)] =
= [((1- Prrw) × (Tt ‐Tod) Bec ‐ ((1- Prrw) × Cod)] + [(Prrw Tt ‐Tod-r) Bec ‐
(Prrw (CRW + Cod)]=
= [((1- Prrw) × (Tt ‐ TN+TIod) Bec ‐ ((1- Prrw) × Cod)] + [(Prrw Tt ‐TN+NTIod )
Bec ‐(Prrw CRW + Cod)]=
= [((1- Prrw) × Bec × (Tt ‐ TN+TSeg-Dpp-Dsf-Dse-Dpi)) ‐ ((1- Prrw) × Cod)] + [(Prrw
Bec Tt ‐TN+TIod -RW))‐(Prrw CRW) – (Prrw Cod)]=
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= [((1- Prrw) × Bec × (Tt - TN -Dpp-Dsf-Dse-Dpi)) +( (1- Prrw) × Bec × TSeg )- ((1-
Prrw) × Cod)] + [(Prrw× Bec × (Tt -TN –RW+ TSeg-Dpp-Dsf-Dse-Dpi))-(Prrw× CRW) –
(Prrw× Cod)]=
= [((1- Prrw) × Bec × (Tt - TN -Dpp-Dsf-Dse-Dpi)) + ((1- Prrw) × Bec × TSeg )- (1- Prrw)
× Cod)] + (Prrw× Bec) × (Tt -TN –RW-Dpp-Dsf-Dse-Dpi)) + (Prrw× Bec) TSeg -(Prrw×
CRW) – (Prrw× Cod)]=
= Bec × TSeg - Cod + [(1- Prrw) × Bec × (Tt - TN -Dpp-Dsf-Dse-Dpi) + (Prrw× Bec) × (Tt
-TN –RW-Dpp-Dsf-Dse-Dpi)]
The latter part written in brackets in the last line of the above equation represents a fixed
value, as all variables in that equation have been set at their base values, which are known
values for us. Therefore, if we consider:
[(1- Prrw) × Bec × (Tt ‐ TN -Dpp-Dsf-Dse-Dpi) + (Prrw Bec Tt ‐TN –RW-Dpp-Dsf-Dse-Dpi)]
= Z
Then, the expected value in Equation [7.5] is summarized in Equation [7.6]
EV (AOD) = Bec × TSeg - Cod + Z [7.6]
If the expected value obtained from Equation [7.6] is greater than the expected value of
no overdesign option, then it is desirable to overdesign; otherwise it is not desirable to
overdesign. Therefore, Equation [7.7] determines the indifference line equation.
Bec × TSeg - Cod + Z= EV (NOD) [7.7]
Using Equations [7.6] and [7.7], another section was added to the decision tree
calculation module to calculate the indifference line equations and expected value
equations for each individual decision. These equations allow for two-way sensitivity
analysis.
203
The output of this section will be a report showing the graphical representation of the
indifference line equation, which allows the decision maker to change the time savings
and costs in their range of worst case and best case scenarios while providing the decision
maker with the joint impact of changes of time savings and overdesign extra cost on the
expected value. Figure 7-7 presents an example of the output of a two-way sensitivity
analysis module on a hypothetical overdesign decision. Section 7.7 presents the two-way
sensitivity analysis report generated for a real overdesign case.
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192,000,000
242,000,000
292,000,000
342,000,000
392,000,000
100 110 120 130 140 150 160 170 180
Extracost$
Time Savings (Day)
Two‐WaySensitivity AnalysisTime Savings vs. Extra Cost of Overdesign
Indifference Line Equation
3,000,000 Tseg - Cod = 108,000,000
Tseg = 100 Cod = 192,000,000 Tseg = 180 Cod = 432,000,000
Expected Value Equation 3,000,000 Tseg - Cod + -123,000,000
Enter your options here Tseg
Cod
Expected Value No overdesign -15,000,000
Data Entry just in pink cells
110 195,000,000
EV your option 12,000,000
Figure 7-7: Output of the Two-Way Sensitivity Analysis Module for a Hypothetical Overdesign Decision
205
An analysis of Figure 7-7 is provided in Figure 7-8. In Figure 7-8, all points on
the indifference line result in an expected value equal to no overdesign. Any
combinations of time savings and overdesign extra costs chosen from the area below the
indifference line result in an expected value greater than the no overdesign option.
However, combinations of time savings and overdesign extra costs chosen from the area
above the indifference line result in an expected value lower than the no overdesign
option; therefore, they are not desirable options. For example, a combination of 110 days
time saving and $195,000,000 extra costs (the green point in Figure 7-8) results in an
expected value greater than the no overdesign option. So, the overdesign is preferable.
Therefore, moving in the purple area in Figure 7-8 always results in a higher expected
value than no overdesign. The red point represents an option with 110 days time saving
vs. $295,000,000 extra costs, which is not a desirable option. So in this case, it is
preferred not to overdesign. Finally, a combination of 110 days time savings and
$222,000,000 extra costs results in an expected value equal to the no overdesign option.
So technically it is better not to do overdesign.
206
Figure 7-8: Analysis on the Output of the Two-Way Sensitivity Analysis Module for a Hypothetical Overdesign Decision
207
The main objective of the two-way sensitivity analysis is to provide the decision
maker with a tool to help him/her find the areas in which a higher expected value can be
obtained, considering the existing constraints for time savings and extra costs of
overdesign.
7.4 Defining Required Outputs
In this research, the pipe rack overdesign DSS has been designed in a way to address the
following requirements:
1- Evaluating an overdesign option from the expected value perspective and also
analyzing the sensitivity of that decision to its different input variables
2- Comparison between an overdesign option (with any assumed overdesign factor)
with normal execution and no overdesign option
3- Comparison between two overdesign options with different degrees of overdesign
or different timings or both
The first requirement is addressed by the outputs of the decision tree calculation
module as well as the sensitivity analysis module. For each decision, a cumulative
density function graph is provided for expected values along with statistics, i.e. average,
standard deviation, minimum and maximum expected values in 1000 runs. Then, the
results are exported to the sensitivity analysis module to be further analyzed for the
purpose of identifying the important elements in the decision problem. For this purpose,
one-way sensitivity analysis reports, a tornado diagram and two-way sensitivity analysis
outputs are provided.
The second and third requirements are addressed by the outputs of the decision tree
calculation module. The expected value of the decision of making no overdesign is
208
calculated and will be further compared with the decision(s) to overdesign with any
assumed overdesign factor(s). Figure 7-9 summarizes all the reports provided in the pipe
rack overdesign DSS.
209
Figure 7-9: Different Kinds of Pipe Rack Overdesign DSS Reports
210
7.5 Designing the User Interface
As discussed earlier, the entire structure of the pipe rack overdesign DSS, including the
user interface, was designed in MS Excel 2007. The researcher is familiar with this
software and found it to be robust, especially for the features that were important to her,
such as being user friendly both for the researcher for the purpose of developing and for
the user.
The user provides the inputs (Table 7-1) through an input sheet designed in MS
Excel and the pipe rack overdesign DSS provides the user with the outputs in the same
software. Appendix C provides a screenshot of the input sheet. The outputs were
explained in section 7.4.
7.6 Verification
As discussed earlier, the overdesign problem was modeled under the decision tree
principles. This model provided the foundation for the decision support system. In this
section, the researcher attempted to verify that the model and the pipe rack overdesign
DSS are logically sound, comprehensive enough and suitably simplified. To achieve this
purpose, the researcher took one step back to review the process of developing the
decision tree model and the pipe rack overdesign DSS. As elaborated in Chapter 3, this
part took a qualitative approach in which interviews, focus group discussions and
brainstorming sessions helped a great deal in formulating the time-cost trade-off problem
and developing the decision tree model. During the data collection for this part of the
research, to support the validity of the findings, the researcher used Triangulation, which
is a method whereby data from at least three different perspectives or alternatively three
or more different kinds of data are collected on the same issue/event so that they can be
211
cross-validated (Somekh and Lewin, 2005). Collected data from interviews with project
managers, engineering managers, senior project engineers, and discipline of owners and
EPC firms were compared against each other and with related literature to ensure
information collected from different sources was aligned.
After data collection and analysis for the qualitative part of the research, the next step
was to make sure that the conclusions which led to formulating the time-cost trade-off
problem as well as developing the decision tree model are warranted, based on the
opinions and information collected in the interviews, focus groups and brainstorming
sessions. As Leedy and Ormrod (2005) state, this process addresses the internal validity
of the research. In other words, the internal validity of the research ensures that the
conclusions drawn are truly warranted by the data. To enhance the internal validity of the
research, the researcher used the following two methods to address the internal validity of
the qualitative research methods (Leedy & Ormrod, 2005).
• Respondent Validation
• Feedback from Others
In Respondent Validation, the researcher takes the conclusions back to the
respondents and asks if they agree with the conclusions. In the current study, the
researcher took the results and decision tree model back to the research participants and
asked them if they agree with the structure of the decision tree model as well as the pipe
rack overdesign DSS.
Taking another action to increase the internal validity of the model, the researcher
also used another method, called Feedback from Others. The process used in this method
212
is similar to the process used in Respondent Validation; the main difference is that in
Feedback from Others, the results are taken to professionals who were not part of the
study.
In both of the Respondent Validation and Feedback from Others methods, the
purposes were: 1) to confirm that the DSS is comprehensive enough without considering
all details, which means it is suitably simplified, and 2) to ensure that the researcher has
made appropriate interpretations and drawn valid conclusions from the information
collected. To achieve this purpose, the researcher defined subjective criteria against
which judgement about the validity of the model is made. These criteria are:
• Realistic
• Comprehensive
• Logically sound
• Suitably simplified
Then, the following questions were designed (according to the Likert scale (Likert,
1932)) to address the aforementioned criteria.
1. To what extent do you agree that the available decision alternatives were
comprehensively defined?
2. To what extent do you agree that possible outcomes and consequences of each
decision were comprehensively defined?
3. To what extent do you agree that the time impact parameters were
comprehensively defined?
213
4. To what extent do you agree that the cost impact parameters were
comprehensively defined?
5. To what extent do you agree that the rework parameters were comprehensively
defined?
6. To what extent do you agree that the pipe rack overdesign DSS is logically
sound?
7. To what extent do you agree that the pipe rack overdesign DSS, including the
decision tree model and time-cost trade-off problem, reflects real world practice?
8. To what extent do you agree that more details should have been included in the
pipe rack overdesign DSS?
9. To what extent do you agree that less detail should have been included in the pipe
rack overdesign DSS?
10. To what extent do you agree that input variables of the pipe rack overdesign DSS
are obtainable from real projects?
After developing the questions, the pipe rack overdesign DSS was presented to 10
individuals (5 experts who had participated in the research and 5 who had not participated
in the research). Figure 7-10 provides the demographic information of the participants in
the verification process.
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46
Categorization of Respondents to VrificationQuestions Based on the Owner or EPC companies
Owner
EPC
30%
50%
20%
Categorization of Respondents to Vrification QuestionsBased on the Experience
More than 25 yearsexperience
Between20 and 25 yearsexperience
Between15 and 20 yearsexperience
55
Categorization of Respondents to Vrification Questions Based on the Position
Project manager
Project engineeringmanagers
Figure 7-10: Demographic Information of the Respondents to Veification Questions
After presenting the pipe rack DSS, the above questions were asked of
participants. Appendix D provides the respondents’ answers. Analyzing those responses
proved that the respondents unanimously verified and endorsed the internal validity of the
pipe rack overdesign DSS (Appendix D).
7.7 Validation
The validity of a research study is the extent to which the results of the study can be
generalized to other situations beyond the study itself (Leedy & Ormrod, 2005). In the
context of the current research, the validation question is to which extent the decision
support system developed in the current research can be used when making decisions
about overdesign in modular steel pipe racks other than those selected for the study. To
answer this question, the following different aspects are discussed.
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The structure of the decision support system is not specific to particular pipe
racks. To be able to make generalizations, the scope of the study has been limited to
modular pipe racks, as they are standard in nature and share the same characteristics.
Likewise, as discussed in Chapter 5, design, procurement and construction activities in
modular steel pipe racks are the same for all modular steel pipe racks. Therefore, from a
structure standpoint, the decision support system can be used when making decisions
about overdesigning other modular steel pipe racks.
As discussed in Chapter 5 and 6, the researcher randomly collected 774 individual
pipe loads located on 130 beams for the study. These data were used to help the decision
maker determine the probability of rework when picking an overdesign factor. As
discussed earlier, these data were collected from six modular pipe racks. In other words, a
sample was studied to learn about the population of pipe racks. For this part of the
research, the researcher strived to increase the validity of the findings so that the samples
can be representative of the population about which inferences are to be drawn. The first
action concerns sampling: as discussed in Chapter 3, sampling was based on a random
selection. Overall, 774 individual lines located on 130 beams were randomly selected for
the study. This is a large sample and the population is fairly homogeneous.
The second action to increase the validity of the findings was to use the decision
support system to make decisions about a real life setting in which the results are already
known. Therefore, the researcher was able to compare the results obtained from the
decision support system with actual results. Leedy and Ormrod (2005) state that applying
research results in a real life setting is one of the tools for enhancing the research validity.
216
The validation scenario involves choosing an already completed pipe rack project
for which all the design history information is available, including the very first design in
the Front End Engineering Design Phase (FEED) and subsequent modifications based on
preliminary and final loads calculated by the stress analysis group in the detailed design
phase. This pipe rack is part of a mega project; no samples were selected from this
project in the sampling process.
After investigating the design history documents and extracting the required
information, the researcher assumed the project was at the preliminary stage and
increased the preliminary loads by 100%. Then, consequences in terms of time savings,
extra cost and rework were evaluated using already known final information.
At the next step, the same decision was modeled in the pipe rack DSS to compare
outcomes of the decisions recommended by the DSS with the real, known outcomes.
Figure 7-11 presents an overview of the validation scenario.
217
Figure 7-11: Overview of the Validation Scenario
In practice, it was neither possible nor practical for the researcher to work on the
entire pipe rack for the validation part. Therefore, she decided to choose a part of the pipe
rack which includes two modules and 24 beams in four elevations (Figure 7-12). The
researcher believes this is a reasonable choice for the validation stage because in real
world practice, modules are usually progressed in parallel, and a delay in one module sets
back the whole pipe rack. To facilitate comprehension, the part selected for validation is
simply called the project hereinafter.
218
Figure 7-12: Screenshot of the Selected Project for Validation in STADD Software
The validation process started with collecting and investigating the project drawings and
documents, including:
• Pipe rack design procedure
In real world practice, the original pipe rack design is based on the companies’
specific procedures, which are more or less similar across the industry as they
have to follow certain codes and standards. Overall, there will be an initial design
based on the procedure in the FEED Phase and then modifications based on real
available information at each stage.
• Pipe rack general arrangement drawings showing preliminary and final loads, as
well as pipe routing
219
• Structural calculation sheets created by the designers, including design basis,
assumptions and the calculation process
• Structural steel location plans
Likewise, the 3D model of the pipe rack project, which was built in STADD
software, was requested and investigated. STADD software is a 3D structural analysis
and design engineering software used by the owner of the project which participated in
the research. Based on the information extracted after investigating the above documents,
the initial design of the project was based on the project owner’s specific procedure. This
was based on the assumption that the beam is loaded with a uniformly distributed load
across all of its length with a load equal to an 8" diameter pipe load, plus the adjustments
for point loads both at the preliminary and final loads received from the stress group.
Table 7-2 summarizes the results of the investigation of the drawings, documents and 3D
model of the project after receiving the preliminary information.
Table 7-2: Project Information at the Preliminary Stage EL +105.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length (m)
Weight (MT)
237-242 11.4
2 0 5.250
34.2 W310X67 67 6 0.402
1 0 4.500 2 0 3.750 2 0 3.000 2 0 2.250 1 0 1.500 2 0 0.750
Total 2.412
EL +107.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237-242 11.4 3.50 0.00 3.120
39.837 W310X67 67 6 0.402 12.00 6.05 1.925 7.00 0.47 0.730
Total 2.412
220
EL +109.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237 11.4
17.0 10.5 5.445
86.7 W310X74 74 6 0.444
17.0 10.5 4.815 17.0 10.5 4.185 25.0 17.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.940
44.0 36.3 1.400 5.0 0.0 0.900 4.0 0.2 0.400
238 11.4
35.0 28.5 5.445
106.053 W310X79 79 6 0.474
35.0 28.5 4.815 17.0 10.5 4.185 35.0 27.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.940 0.0 0.0 1.400 5.0 0.0 0.900 4.0 0.2 0.400
239 11.4
0.0 0.0 5.445
53.64 W310X74 74 6 0.444
0.0 0.0 4.815 0.0 0.0 4.185
25.0 17.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.940 0.0 0.0 1.400 5.0 0.0 0.900 0.0 0.0 0.400
240 11.4
35.0 28.5 5.445
113.437 W310X79 79 6 0.474
35.0 28.5 4.815 35.0 28.5 4.185 25.0 17.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.940 0.0 0.0 1.400 5.0 0.0 0.900 4.0 0.2 0.400
241 11.4
17.0 10.5 5.445
75.657 W310X74 74 6 0.444
17.0 10.5 4.815 17.0 10.5 4.185 25.0 17.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.400 0.0 0.0 1.400 5.0 0.0 0.900 4.0 0.2 0.400
242 11.4
17.0 10.5 5.445
75.657 W310X74 86 6 0.516
17.0 10.5 4.815 17.0 10.5 4.185 25.0 17.3 3.505 8.5 3.5 2.945 8.5 3.5 2.480 3.5 0.0 1.400 0.0 0.0 1.400 5.0 0.0 0.900 4.0 0.2 0.400
Total 2.796
221
EL +112.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237 11.4
26 18.32 4.040
75.717 W310X74 74 6 0.444
18 11.47 3.320 18 11.47 2.660 13 7.05 2.030 13 7.05 1.480 13 7.05 0.840
238 11.4
26 18.32 4.040
115.665 W310X79 79 6 0.474
18 11.47 3.320 18 11.47 2.660 30 24.05 2.030 30 24.05 1.480 30 24.05 0.840
239 11.4 26 18.32 4.040
67.421 W310X74 74 6 0.444 18 11.47 3.320 18 11.47 2.660
240 11.4
50 42.32 4.040
161.692 W310X79 79 6 0.474
42 35.47 3.320 42 35.47 2.660 30 24.05 2.030 30 24.05 1.480 30 24.05 0.840
241 11.4 13 7.05 2.030
50.676 W310X74 74 6 0.444 13 7.05 1.480 13 7.05 0.840
242 11.4
50 42.32 4.040
130.052 W310X79 79 6 0.474
42 35.47 3.320 42 35.47 2.660 13 7.05 2.030 13 7.05 1.480 13 7.05 0.840
Total 2.754
Total Weight 10.374
After investigating the project’s information, the researcher modified the 3D
model in the STADD software by increasing the preliminary loads by 100%. This is an
exaggerated case, as it rarely occurs in normal practice that loads are increased by 100%
from the preliminary stage to the final stage. The researcher intentionally developed this
scenario to evaluate an extreme case.
After increasing the loads, the 3D model was investigated again to evaluate the
effect of load changes on the steel design in terms of the beam size. It is worth
mentioning that when investigating the 3D model, a value called Beam Capacity Ratio
was obtained, which represents how much of the beam capacity is loaded. This value is
important as it should not exceed a certain limit to allow for possible load additions
222
during either the construction or future expansion. For this project, when increasing the
loads, consideration was given to selecting a beam size to maintain almost the same
Beam Capacity Ratio.
Based on the changes in the beam size, the new weight of the beams was
calculated as shown in Table 7-3. It is important to note that after evaluating the change
in the weight of the beams, the piles and columns were also investigated again to
determine potential changes in the piling design as well as column design. Fortunately,
no change was required in those activities.
Table 7-3: Change in Project Information after Increasing the Loads by 100% EL +105.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237-242 11.4
4 0.19 5.250
36.675 W310X67 67 6 0.402
2 0.00 4.500 4 1.09 3.750 4 1.09 3.000 4 1.09 2.250 2 0.00 1.500 4 0.19 0.750
Total 2.412
EL +107.000
Beam # Distributed
Load Point Loads Load Take-off per Pipe Moment Arm
Maximum bending moment
Beam size Nominal
mass (kg/m)
Beam Length
(m)
Weight (MT)
237 11.4 7.00 3.19 3.120
56.727 W310X74 74 6 0.444 24.00 18.05 1.925 14.00 7.47 0.730
Total 2.66
223
EL +109.000
Beam # Distributed
Load Point Loads Load Take-off per Pipe Moment Arm
Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237 11.4
34.0 27.5 5.445
169.161 W310X79 79 6 0.474
34.0 27.5 4.815 34.0 27.5 4.185 50.0 42.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.940
88.0 80.3 1.400 10.0 5.0 0.900 8.0 4.2 0.400
238 11.4
70.0 63.5 5.445
206.564 W310X86 86 6 0.516
70.0 63.5 4.815 34.0 27.5 4.185 70.0 62.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.940 0.0 0.0 1.400
10.0 5.0 0.900 8.0 4.2 0.400
239 11.4
0.0 0.0 5.445
87.978 W310X74 74 6 0.444
0.0 0.0 4.815 0.0 0.0 4.185
50.0 42.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.940 0.0 0.0 1.400
10.0 5.0 0.900 0.0 0.0 0.400
240 11.4
70.0 63.5 5.445
220.85 W310X86 86 6 0.516
70.0 63.5 4.815 70.0 63.5 4.185 50.0 42.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.940 0.0 0.0 1.400
10.0 5.0 0.900 8.0 4.2 0.400
241 11.4
34.0 27.5 5.445
145.26 W310X79 79 6 0.474
34.0 27.5 4.815 34.0 27.5 4.185 50.0 42.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.400 0.0 0.0 1.400
10.0 5.0 0.900 8.0 4.2 0.400
242 11.4
34.0 27.5 5.445
145.26 W310X79 79 6 0.474
34.0 27.5 4.815 34.0 27.5 4.185 50.0 42.3 3.505 17.0 12.0 2.945 17.0 12.0 2.480 7.0 2.6 1.400 0.0 0.0 1.400
10.0 5.0 0.900 8.0 4.2 0.400
Total 2.898
224
EL +112.000
Beam # Distributed Load Point Loads Load Take-
off per Pipe Moment Arm Maximum bending moment
Beam size Nominal mass (kg/m)
Beam Length
(m)
Weight (MT)
237 11.4
52 44.32 4.040
144.234 W310X79 79 6 0.474
36 29.47 3.320 36 29.47 2.660 26 20.05 2.030 26 20.05 1.480 26 20.05 0.840
238 11.4
52 44.32 4.040
224.132 W310X86 86 6 0.516
36 29.47 3.320 36 29.47 2.660 60 54.05 2.030 60 54.05 1.480 60 54.05 0.840
239 11.4 52 44.32 4.040
117.111 W310X79 79 6 0.474 36 29.47 3.320 36 29.47 2.660
240 11.4
100 92.32 4.040
316.184 W310X107 107 6 0.642
84 77.47 3.320 84 77.47 2.660 60 54.05 2.030 60 54.05 1.480 60 54.05 0.840
241 11.4 26 20.05 2.030
81.316 W310X74 74 6 0.444 26 20.05 1.480 26 20.05 0.840
242 11.4
100 92.32 4.040
247.315 W310X86 86 6 0.516
84 77.47 3.320 84 77.47 2.660 26 20.05 2.030 26 20.05 1.480 26 20.05 0.840
Total 3.066
Total Weight 11.040
Based on Tables 7-2 and 7-3, the change in weight of the beams in the original project
and after increasing the loads is as follows.
Total weight of the beams in original project (MT) 10.37
Total weight of the beams after increasing the loads (MT) 11.04
Difference (MT) 0.67
In real world practice, the cost of steel fabrication and erection is determined
based on the steel weight. According to an industry survey, the estimated cost for steel
fabrication in Alberta varies from $3200 to $4000 per metric ton (MT), with the most
likely value being $3500 per MT. Likewise, the cost of steel erection varies from $1200
to $1700 per MT, with the most likely value being $1500 per MT. Based on these
225
estimations, the difference in the cost of steel fabrication and erection in both scenarios is
shown in Table 7-4.
Table 7-4: Increase in Cost of Steel Fabrication and Erection for One Module after
Increasing the Loads by 100%
Low Most Likely High
Total increase in cost of steel fabrication $ 2,144 $ 2,345 $ 2,680
Total increase in cost of steel erection $ 804 $ 1,005 $ 1,139
Grand Total $ 2,948 $ 3,350 $ 3,819
According to Table 7-4, if the individual loads on the beams in the selected
module of the pipe rack are increased by 100%, the cost of the steel fabrication and
erection is most probably increased by $3,350. Since this pipe rack has 420 modules,
based on a rough estimate, the extra cost of increasing the loads by 100% in the entire
pipe rack will be as shown in Table 7-5.
Table 7-5: Rough Estimate of Increase in Cost of Steel Fabrication and Erection for
the Entire Pipe Rack after Increasing the Loads by 100%
Low Most Likely High
Total increase in cost of steel fabrication $ 900,480 $ 984,900 $ 1,125,600
Total increase in cost of steel erection $ 337,680 $ 422,100 $ 478,380
Grand Total $ 1,238,160 $ 1,407,000 $ 1,603,980
226
The extra cost shown in Table 7-5 should be traded-off for the benefits of time savings,
which is explained in the following.
In this project, the pipe rack was on the critical path and final loads were issued
120 days after preliminary loads (on average). This means there would have been 120
days of time savings if the structural calculations and drawings were progressed with
overdesigning preliminary loads by 100%. On the other hand, a comparison between
preliminary and final loads in this project shows that a 100% increase in pipe loads would
have definitely resulted in no rework.
Now, assuming $9.6 M for daily benefits of early completion that is an estimate obtained
from the owner of the selected project, the monetary value of time savings would be:
Time savings value = Time savings (days) Daily benefits of early completion ($) Equation [7.8]
Therefore,
Time savings value: 120 $ 9,600,000 = $ 1,152,000,000
Comparing the extra cost of overdesign and time savings proves that it would have been
much more worthwhile for the project to add an extra 100% to pipe loads rather than
waiting for the final information and saving $1,407,000.
Net benefit= Time savings value – Extra Cost Equation [7.9]
Therefore,
Net benefit = $ 1,152,000,000 - $1,407,000= $ 1,150,593,000
It is important to note that this time-cost trade-off is from the owner’s point of view. So
far, the consequences of increasing the loads by 100% have been analyzed. However, it is
important to note that the above analysis was possible because the selected project was
already completed and the results were already known. In practice, when making
227
overdesign decisions, the results are unknown and it is not possible to perform the same
analysis. Therefore, a decision support system, like the pipe rack DSS designed in this
research project, is needed to assist the decision maker.
To examine the validity of the pipe rack DSS, the same decision (increasing the
preliminary loads by 100%) was modeled in the pipe rack DSS. The intention was to
compare the closeness of the outcome of the recommended decision by the DSS to real
outcomes. Required inputs for modeling this decision are explained in the next section.
7.7.1 Pipe Rack DSS Input Variables for the Validation Project
The input variables required for the pipe rack DSS were determined as follows, based on
the inputs from the owner of the selected project.
• Project normal duration/target duration = 72 weeks
• Project daily benefits of early completion = $9.6 M
• Estimate of duration between providing preliminary loads and final loads = (21,
25, 30) weeks
• Overdesign factor: 100% on individual loads. Comparison of maximum bending
moments shown in Tables 7-2 and 7-3 gives the overdesign factor for beams as
seen in Table 7-6. As seen in this table, the average overdesign on beams is
about 56%.
228
Table 7-6: Overdesign Factor for Beams of the Validation Project
Elevation Beam # Maximum bending moment (Preliminary )
Maximum bending moment after 100% increase in individual
loads
Overdesign Factor
EL +105.000 237-242 34.2 36.675 7% EL +107.000 237-242 39.837 56.727 42%
EL +109.000
237 86.7 169.161 95% 238 106.053 206.564 95% 239 53.64 87.978 64% 240 113.437 220.85 95% 241 75.657 145.26 92% 242 75.657 145.26 92%
EL +112.000
237 75.717 144.234 90% 238 115.665 224.132 94% 239 67.421 117.111 74% 240 161.692 316.184 96% 241 50.676 81.316 60% 242 130.052 247.315 90%
Average of Overdesign Factors 56%
• Probability of rework: Based on Table 7-6, the average beam overdesign factor is
56%. Referring to Figures 6-5 and 6-6 shows that this overdesign factor covers
90% of cases. In other words, the probability of rework is almost 10% for 56%
overdesign (Figure 7-13).
Figure 7-13: Cumulative Density Function for Ideal Overdesign Factors showing the
Probability of Rework for 56% Overdesign Factor (Refer to Figures 6-5 and 6-6)
229
• No impact on duration of steel fabrication and erection by 100% overdesigning
preliminary loads
• No impact on duration of piling purchase and installation, as piles were already
overdesigned and can still tolerate 100% increase on individual loads
• Cost of fabrication and erection of additional steel = per calculations shown in
Table 7-5
• Equivalent rework duration for structural calculations & drawings, if preliminary
loads plus overdesign factor do not comply with final loads= (30, 40, 50) days
• Equivalent rework duration for piling design & drawings, if preliminary loads
plus overdesign factor does not comply with final loads=No rework on piling as
piles were largely overdesigned in this project
• Equivalent rework duration for procurement of steel and pile, if preliminary loads
plus overdesign factor do not comply with final loads= No impact as final loads
are available when steel fabrication is in progress (per project schedule)
• Equivalent rework duration for steel erection and pile installation, as a result of
change in pipe loads= No impact as final loads are available before starting the
steel erection (per project schedule)
• Costs of rework in the engineering phase = duration 2200
• Costs of procurement rework
• Steel fabrication = (7,000, 8,500, 10,000) per MT
Since the probability of rework is 10%, it is assumed that in the case of
rework, 10% of the beams should be replaced. Therefore:
230
Weight of the beams in one module after increasing the loads 11.040
(MT)
Module count 420
Estimate of the weight of the beams in the entire pipe rack (MT) 4636.8
10% of the weight of the beams in the entire pipe rack (MT) 463.68
And from there, the cost of procurement rework (cost of replacing the
beams) is calculated as follows:
Low Most probably High
Cost of rework per MT $ 7,000 $ 8,500 $ 10,000
Cost of procurement rework
(replacing the beams) $ 3,245,760 $ 3,941,280 $ 4,636,800
• Piling: No impact
• Costs of rework construction: No impact as final loads are available before
starting the steel erection (per project schedule)
7.7.2 Pipe Rack DSS Outputs for the Validation Project
Based on the inputs defined in section 7.7.1, the pipe rack DSS output for the decision to
increase the preliminary loads by 100% was generated. Figure 7-14 shows the probability
density function graph for the expected value of this decision along with its statistics.
231
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1,024,398,051 1,074,398,051 1,124,398,051 1,174,398,051 1,224,398,051 1,274,398,051
Prob
ability
Expected Value ($)
1000 Runs
Statistics Decision 1: 56% Overdesign
Average Expected Value Standard Deviation Min Max
1,173,122,759 61,947,147 1,024,398,051 1,298,030,934
Figure 7-14: Cumulative Density Function for the Real Overdesign Case (Decision
to Increase the Preliminary Loads by 100%)
As seen earlier in Equation [7.9], the net benefit of the project with real, known
outcomes was estimated as $ 1,150,593,000. As seen in Figure 7-14, this value is
reasonably between the estimated minimum and maximum expected value obtained from
the pipe rack DSS in which the real outcomes were not known. This means that if the
decision maker had used the pipe rack DSS for decision making, the expected value of
his/her decision estimated by the pipe rack DSS was reasonably close to that of real,
known outcomes. Figures 7-15 to 7-20 provide the outputs of the one-way sensitivity
analysis module for this decision (increasing the load by 100%). As mentioned earlier,
these outputs examine whether a particular decision variable really makes a difference in
232
1,015,790,072
1,065,790,072
1,115,790,072
1,165,790,072
1,215,790,072
1,265,790,072
110 115 120 125 130 135 140
Expe
cted
Value
$
Time Savings (day)
One way Sensitivity AnalysisVarying time savings other variables fixed at their base value
the expected value of the decision. This process is performed by varying the value of a
particular decision variable between its high and low value while keeping other decision
variables at their most likely (base) values. For example, in Figure 7-15, the engineering
time saving was varied between its low value (110 days) and high value (140 days), while
other decision variables were fixed at their most likely values. All of these values were
defined in section 7.7.1. Figure 7-15 shows the effect of the change in the time saving
value on the final expected value. It shows if time savings is varied between 110 days
and 140 days, the expected value will change from $1,015,790,072 to $1,303,790,072. In
other words, increasing the time saving by 30 days generates $288,000,000 more money.
‐‐
Figure 7-15: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Time Savings, Other Decision Variables Fixed at
their Base Value)
233
1,207,649,492
1,207,699,492
1,207,749,492
1,207,799,492
1,207,849,492
900,480 950,480 1,000,480 1,050,480 1,100,480
Expe
cted
Value
$
Increase in cost of steel fabrication $
One way Sensitivity AnalysisVarying Steel FabricationExtraCost other variables fixedat their base value
1,207,734,492
1,207,754,492
1,207,774,492
1,207,794,492
1,207,814,492
1,207,834,492
1,207,854,492
1,207,874,492
337,680 357,680 377,680 397,680 417,680 437,680 457,680 477,680
Expe
cted
Value
$
Increase in cost of steel erection $
One way Sensitivity AnalysisVarying Steel Erection ExtraCost other variables fixedat their base value
‐‐
Figure 7-16: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Extra Cost of Steel Fabrication, Other Decision
Variables Fixed at their Base Value)
‐‐
Figure 7-17: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Extra Cost of Steel Erection, Other Decision
Variables Fixed at their Base Value)
234
1,198,190,072
1,200,190,072
1,202,190,072
1,204,190,072
1,206,190,072
1,208,190,072
1,210,190,072
1,212,190,072
1,214,190,072
1,216,190,072
30 32 34 36 38 40 42 44 46 48 50
Expe
cted
Value
$
Increase in duration of steel structure design rework (day)
One way Sensitivity AnalysisVarying Rework of Steel Structure Design other variables fixed at their base value
1,207,787,872
1,207,788,372
1,207,788,872
1,207,789,372
1,207,789,872
1,207,790,372
1,207,790,872
1,207,791,372
72,000 75,000 78,000 81,000 84,000 87,000 90,000 93,000 96,000 99,000 102,000 105,000 108,000
Expe
cted
Value
$
Increase in cost of engineering rework $
One way Sensitivity AnalysisVarying Cost of Engineering Rework other variables fixedat their base value
‐‐
Figure 7-18: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Steel Structure Calculation Rework, Other Decision
Variables Fixed at their Base Value)
‐‐
Figure 7-19: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Cost of Engineering Rework, Other Decision
Variables Fixed at their Base Value)
235
One‐way Sensitivity AnalysisVarying Cost of Procurement Rework ‐other variables fixed at their base value
Expe
cted
Value
$
1,207,840,574
1,207,820,574
1,207,800,574
1,207,780,574
1,207,760,574
1,207,740,574
1,207,720,574
3,245,760 3,445,760 3,645,760 3,845,760 4,045,760 4,245,760 4,445,760
Increase in cost of procurement rework $
Figure 7-20: Output of the One-Way Sensitivity Analysis Module for the Real Overdesign Decision (Varying Cost of Procurement Rework, Other Decision
Variables Fixed at their Base Value)
Comparison of Figures 7-15 to 7-20 helps the decision maker identify the
important elements in this overdesign decision. As explained in section 7.3.2, besides the
above outputs, the one-way sensitivity analysis module of the pipe rack DSS provides the
Tornado Diagram, which allows the decision maker to compare one-way sensitive
analyses for many input variables at once. Figure 7-21 provides the Tornado diagram for
the overdesign decision in the real validation project.
236
Figure 7-21: Tornado Diagram for the Validation Project
As seen in Figure 7-21, the decision to increase the preliminary loads by 100% in
the validation project is highly sensitive to engineering time saving following by steel
structure calculations and drawings rework. Overall, this particular decision is not sensitive to
cost parameters.
As discussed earlier in section 7.3.2, to study the joint impact of changes in
engineering time saving and the overdesign extra cost, another report is provided by the
pipe rack DSS, which is called two-way sensitivity analysis report. Figure 7-22 presents
this report for the overdesign decision in the real validation project.
As seen in Figure 7-22, any combinations of time savings and overdesign extra
costs chosen from the area below the indifference line result in an expected value greater
than the no overdesign option. However, combinations of time savings and overdesign
extra costs chosen from the area above the indifference line result in an expected value
237
lower than the no overdesign option; therefore, they are not desirable options. For
example, a combination of 125 days time saving and $900,000,000 extra costs results in
an expected value greater than the no overdesign option (Green cell in Figure 7-22). So,
the overdesign is preferable. Likewise, an option with 125 days time saving vs.
$1,200,000,000 extra costs results in expected value lower than that of no overdesign
option. Therefore, it is preferred not to overdesign. Finally, a combination of 125 days
time savings and $1,161,197,000 extra costs results in an expected value equal to the no
overdesign option. So technically it is better not to do overdesign.
238
Indifference Line Equation Preferred overdesign option 56%
9,600,000 Tseg - Cod = 38,802,928
9,600,000 Tseg - Cod + -38,802,928
Enter your options here
Tseg = 110 Cod = 1,017,197,072 Tseg = 140 Cod = 1,305,197,072
Expected Value Equation
Tseg
Cod
Enter your options here Tseg
Cod
Enter your options here Tseg
Cod
125 1,161,197,072
EV your option 0 Expected Value No overdesign 0
125 1,200,000,000
EV your option -38,802,928 Expected Value No overdesign 0
Expected Value No overdesign 0
Data Entry just in pink cells
125 900,000,000
EV your option 261,197,072 1,017,197,072
1,067,197,072
1,117,197,072
1,167,197,072
1,267,197,072
1,217,197,072
110 115 120 125 130 135
Extra cost
$
Time Savings (day)
Two‐Way Sensitivity Analysis Time Savings vs. Extra Cost of Overdesign
Indifference Line
Figure 7-22: Two-Way Sensitivity Analysis Report for the Real Validation Project
239
140
Chapter Summary
This chapter explains the different steps involved in building a decision support system
for overdesigning modular steel pipe racks. These steps include:
1- Modeling the research problem
2- Identifying input variables
3- Building the processing module
4- Defining required outputs
5- Designing the user interface
The overdesign problem was modeled under the stochastic decision tree principles.
Available decision alternatives in the overdesign problem and their outcomes were
identified based on the overdesign concept. The next step involved defining required
inputs based on the discussion in Chapter 6. These inputs were categorized as time, cost
and rework parameters. In order to address the real life situations and uncertainties
associated with project activities and consequently the risk involved in the decisions, a
decision support system was designed to allow for stochastic values for the input
parameters.
Step three explained building the processing modules for the pipe rack overdesign
DSS. The processing module contains two main modules: 1) the Decision Tree
Calculation Module and 2) the Sensitivity Analysis Module. The decision tree calculation
module is further broken down into the time impact module, cost impact module, rework
module and pay-off and expected value module. The rework module includes the
database of the preliminary and final design information from past projects, which was
explained in detail in Chapter 6. This database is used to help the decision maker
240
determine the probability of rework. The sensitivity analysis module consists of one-way
and two-way sensitivity analysis modules, which help the decision maker obtain a more
comprehensive understanding of the dynamics of the decision problem and also help
identify the important elements in the decision problem.
In step four, required outputs were defined and the process for creating them
using the pipe rack overdesign DSS was explained. The entire structure of the pipe rack
overdesign DSS, including the user interface, was designed in MS Excel 2007.
After developing the pipe rack overdesign decision support system, the next step
was to verify and validate it. To address the verification and internal validity, two
methods, called Respondent Validation and Feedback from Others, were used. Likewise,
to enhance the external validity of the research, the application of the pipe rack
overdesign DSS on a real life setting was investigated. The validation scenario involved
choosing an already completed pipe rack project for which all the design history
information is available. After investigating the design history documents and extracting
the required information, the researcher assumed the project was at the preliminary stage
and increased the preliminary loads by 100%. Then, consequences in terms of time
savings, extra cost and rework were evaluated using already known final information.
At the next step, the same decision was modeled in the pipe rack DSS to compare
outcomes of the decisions recommended by the DSS with the real, known outcomes. The
results proved that the expected value of the decision estimated by the pipe rack DSS was
reasonably close to that of real, known outcomes. This addresses the effectiveness of the
pipe rack DSS for decision making when real outcomes are unknown.
241
Contributions: modeling the overdesign problem using the stochastic decision tree
principles and developing a decision support system based on that is another
methodological contribution of this research. The decision support system developed in
this part of the research can assist in discovering the best overdesign options that provide
the maximum schedule benefit versus minimum extra costs. This DSS also considered as
the theoretical, literature and industry contributions of this research.
242
CHAPTER EIGHT: CONCLUSION
This study investigated the application of overdesign on modular pipe racks, which
represent the spine or main artery of oil and gas plants. Although the upfront cost of
overdesign and the probability of rework associated with any overdesign decision may
not look justifiable, overdesigning pipe racks can address aggressive and ever-increasing
schedule demands, a tactic which is rewarding for owners and in some cases for
contractors in any competitive industry. On the other hand, both the extent and timing of
overdesign have cost implications.
A review of the overdesign literature pointed to several areas where explicit
research is lacking; specifically: 1) a comprehensive study of overdesign as a specific
strategy to reduce information dependency between activities and consequently to
expedite the project; 2) a study addressing a method to create balance between increasing
the conservativeness of the assumptions in overdesign and maintaining a reasonable cost
for the project; and 3) a study of the probability of rework associated with any overdesign
decision. Therefore, this study was designed to fill these gaps by developing a decision
support system that can assist in discovering the best overdesign options in modular pipe
racks that provide the maximum schedule benefit versus minimum extra costs.
A mixed methods approach was taken to conduct this research. For the qualitative
part, the primary research tools were semi-structured interviews followed by focus group
sessions. Overall, 37 industry experts from 10 reputable owners and EPC firms
participated in the interviews, and each participant was interviewed at least two times. As
well, 15 focus group sessions were held for data analysis and to design the research
direction. The outcome of this qualitative part of the research was the overdesign
243
conceptual framework. This framework formed the basis for formulating the overdesign
time-cost trade-off problem, which was further modeled using stochastic decision tree
principles. The entire process constituted the foundation for developing the decision
support system, which was discussed in Chapter 7. The decision support system has the
ability to compare different overdesign options and provide one-way and two-way
sensitivity analyses reports that help the decision maker arrive at a reasonable decision
based on the analysis.
A critical component of the decision support system is a rework module, in which
the information collected from past projects was used to establish a relationship between
the overdesign factor and the probability of rework. This part of the research involved a
quantitative approach in which research data included information from real projects,
gathered from drawings of six modular steel pipe racks. Overall, 774 individual lines
located on 130 beams on the pipe racks were randomly selected for the study. Details of
this process and key findings were discussed in detail in Chapters 5 and 6.
As a summary, comparison between preliminary and final design information in all
beams helped determine their overdesign requirements and this served as a guide to
determine the probability of rework when picking an overdesign option. According to
this research, 47% of the beams of the study did not require an additional overdesign
factor, as the preliminary moment was either equal to or higher than the final moment.
This means only 53% of the beams required being overdesigned. Likewise, 80% of the
beams should have been overdesigned by 28% or less.
The validity of this decision support system was addressed when it was used in a
real life setting. The validation scenario involved choosing an already completed pipe
244
rack project for which all the design history information was available and real outcome
was known. The outcome of the decision recommended by the decision support system
was reasonably close to real outcomes. The entire validation process revealed that the
extra cost of overdesign was lower than the expected benefits of early completion.
8.1 Research Contributions
The contributions of this research are categorized under the following four distinct
categories:
• Theoretical contributions
• Literature contributions
• Methodological contributions
• Industry contributions
8.1.1 Theoretical contributions
The research problem stated in section 1.1 of Chapter 1 is, as a whole, a unique topic. To
the best of the researcher’s knowledge, there is no particular similar study that addresses
all of its aspects. Most overdesign-related research studies discuss overdesign in a purely
technical context, which is very specific to special equipment. This kind of overdesign is
only applied to ensure feasible operation over a range of operating conditions. Therefore,
those research studies serve different purposes. The current research is a comprehensive
study of overdesign as a specific strategy to reduce information dependency between
activities and consequently to expedite the project. In other words, this study investigates
overdesign only from a managerial standpoint, in which overdesign is considered as a
schedule compression technique which imposes extra costs and risks that need to be
245
actively managed in order to benefit from the overdesign. Although some research
studies look at overdesign from this perspective, they do not provide in-depth knowledge
about the overdesign concept in general and its application on modular steel pipe racks in
oil and gas projects in particular.
Furthermore, a review of the overdesign literature points to other gaps in the
existing overdesign theory. In particular, there is no study which addresses the probability
of rework associated with any overdesign decision, nor is there a method for choosing an
appropriate level of overdesign while maintaining a reasonable cost for the project. This
study contributes to overdesign theory by addressing these issues and filling these gaps.
Also, based on the findings of this research, a decision support system was
developed in this study that can assist in discovering the best overdesign options that
provide the maximum schedule benefit versus minimum extra costs. This DSS is also
unique – to the best of the researcher’s knowledge, no similar decision support system
exists so far.
8.1.2 Literature contributions
- In Chapter 2, several areas where explicit research is lacking in the overdesign related
literature were discussed in detail. Also, the summary of those gaps were presented in
the Theoretical Contribution section discussed earlier in this chapter. To avoid
duplication, the researcher skipped repeating those gaps in this section but she
believes this study contributes to the overdesign literature by filling those gaps and
contributing to overdesign theory.
‐ When studying the engineering phase of oil and gas projects, the researcher
uncovered a gap in the literature involving a lack of information about the
246
relationship between different engineering disciplines and their main deliverables.
The researcher found Baron’s book, The Oil and Gas Engineering Guide (2010), a
major and valuable source of information that gave the researcher an overview of
how oil and gas facilities are engineered. In his book, Baron illustrated the
relationship between different engineering deliverables using European terminology.
However, he showed these relationships as a whole rather than within specific
engineering disciplines. Also, the process for providing those deliverables was not
demonstrated. The researcher tried to build on Baron’s work by distinguishing the
deliverables of each specific engineering discipline and adding the intermediate
process which results in the production of each deliverable. Also, the researcher
modified some of the relationships in Baron’s work and added to that based on the
information obtained from both literature and an investigation of current industry
practice in North America. As well, the terminology was changed to North American
terminology. The findings were shared and reviewed with industry practitioners and
engineering discipline leads for the purpose of verification. More detail can be found
in Chapter 3. The researcher believes this is the contribution of this research to
literature, as it fills an existing gap and provides useful information for those looking
for engineering multidisciplinary and interdisciplinary information.
8.1.3 Methodological contributions
This study makes methodological contributions by addressing a method to relate the
degree of conservativeness of the assumptions in overdesign to the probability of rework
associated with any overdesign decision. Gathering historical information about pipe load
247
changes along the pipe rack and investigating their effects on steel design is a unique
approach. The researcher found no similar data collection or process either in the
literature or in industry. As mentioned earlier, 774 individual pipe loads and pipe spaces
located on 130 beams were randomly gathered from the drawings of six modular pipe
racks twice: 1) when design was at the preliminary stage and 2) when design was at the
final stage. These two instances were compared against each other to track the changes.
To investigate their impact on the steel design, preliminary and final loads were fed into a
structural analysis software (RISA) to investigate the changes in bending moments,
which is the governing parameter in the beam design. This process helped determine the
ideal overdesign factors that should have been picked in sample past projects and served
as a guide for determining the probability of rework when picking an overdesign option.
The researcher believes this provides a valuable database for future studies and can be
enriched by enlarging the sample size.
Likewise, modeling the overdesign problem using the stochastic decision tree
principles and developing a decision support system based on that is another
methodological contribution of this research.
8.1.4 Industry contributions
- The decision support system developed in this research can also be considered the
contribution of this research to industry. As discussed earlier, the decision support
system has the ability to compare different overdesign options and to provide one-
way and two-way sensitivity analyses reports that help the decision maker arrive at a
reasonable decision based on the analysis. The assistance provided by the decision
248
support system that leads to recommendations to industry practitioners can benefit
industry in different ways:
• It helps managers by providing them with information about the long-term
implications of their overdesign decisions in terms of schedule benefits, extra
cost and rework. It provides them with enough information to make an
effective trade-off between the time savings and the extra costs of overdesign.
Also, it helps them decide on the best time to overdesign as it has cost and
schedule implications. In case of contractor companies, it helps them justify
the extra cost of overdesign for the owners.
• Sensitivity analysis reports provided by the decision support system help the
cost manager and cost engineers evaluate the cost impacts of different
overdesign options. This information will help them in cost planning and cost
management, which are two of the fundamental and yet most challenging
tasks for a project manager.
• Although the primary function of the decision support system is to assist
managers in making effective overdesign decisions by looking at the big
picture, it also benefits designers by helping them look at the design from a
different perspective. Some designers can contribute to a missed schedule in
their search for the most optimized design solutions. This decision support
system can both portray the big picture for them and convince them of a near
optimum or even fit-for-purpose design solution.
‐ According to the industry practitioners, the study of historical pipe load changes
on pipe racks and their effects on steel design is a valuable and unique source of
249
information. The researcher witnessed and felt their curiosity in evaluating the
collected data. The researcher found no similar information either in the industry
or in the literature.
‐ As elaborated in Chapter 5, stress analysis activity is a key activity in pipe rack
design. When studying the stress analysis work process, the researcher identified
an unanticipated finding: she found a gap in industry in recording the revision
rates and progress of the stress analysis activity. The researcher came up with an
innovative approach for calculating the progress and revision rates of this activity.
This approach was fully verified and validated by the industry practitioners, and
three of the companies that were involved in the research are currently using the
same approach for calculating revision rates and progress of their stress analysis
activity.
8.2 Research Limitations and Areas for Future Research
- As discussed in Chapter 2, there are gaps in the current literature about overdesign in
general and overdesigning pipe racks in particular. This study was designed to fill
those gaps. As discussed in section 8.1, the researcher’s methodological approach for
establishing a relationship between the overdesign factor and the probability of
rework and the findings were unique, and to the best of the researcher’s knowledge,
no similar study exists in the literature. Therefore, this research is considered
exploratory in nature. For the quantitative part of the research, the researcher selected
her sample pipe rack projects from six modular pipe racks that were made available to
her. In other words, selection of the pipe racks was based on this convenient
250
sampling. Within each pipe rack, however, the beams and pipe loads were randomly
selected to be included in the study. All six pipe racks were from SAGD projects in
the oil and gas industry in Alberta, Canada. Therefore, the study is limited to SAGD
projects in the oil and gas industry in Alberta, Canada and the findings of this
research should be interpreted in the context of these projects. Future similar research
can help in generalizing the findings to other projects in other industries and
locations.
- As discussed in Chapter 5, 130 beams were included in the study. This sample space
is based on a 95% confidence level with 0.08 margin of error. To reduce the margin
of error to 0.03, which is a reasonable amount and therefore can achieve more reliable
results, the sample size should be increased to at least 350 beams. However, as
mentioned earlier, 130 beams entailed collecting 774 individual pipe loads and pipe
spaces twice: 1) when design was at the preliminary stage and 2) when design was at
the final stage. The next step involved tracking the effects of the pipe load changes on
the steel design, and then all preliminary and final loads were entered in the RISA
software for structural analysis. Collecting more samples was not practical for the
researcher due to time and resource constraints. Future research can build on the
current research by providing and studying a larger sample size. Likewise, as
mentioned earlier, the scope of this study was limited to tracking the changes in pipe
operating loads and investigating their effects on the beam design. Similar research
can be designed and conducted to include other loads and columns.
- The performance of the decision support system developed in this study can be
enhanced by adding more user-friendly features or even re-programming it to allow
251
for commercialization. Also, the current DSS is able to handle 10 different overdesign
options. Future research can re-design the system to allow the addition of more
overdesign decision alternatives.
8.3 Final Words
As mentioned earlier, this research was exploratory in nature and aimed to provide
insight into the overdesign problem. The researcher tried to address significant issues as
much as was practical and feasible within the time and scope of the study. She believes
this study made an important contribution by presenting a sound, feasible and practical
research methodology and data collection method for conducting a similar study. Future
studies can build on this research by addressing the limitation mentioned in the previous
section.
252
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APPENDIX A: INTERVIEW QUESTIONS
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Dear Sir/Madam,
The purpose of this interview is to investigate the application of overdesign as a fast tracking technique and the information provided in this interview will be used to inform my Doctoral project. Please see herein below the abstract of this research for your kind attention.
Since I found your profession and experience relevant and valuable to this research, I kindly invite you to participate by accepting to be interviewed. Your participation in this research is voluntary and you may refuse to participate altogether, may refuse to participate in parts of the study, or may withdraw from the study at any time. Your participation will be publicly cited in relation to your contributions.
Regards, Fereshteh Khoramshahi
Abstract of the Research
To effectively address today’s aggressive schedule demands in the oil and gas industry, engineering and construction activities are usually overlapped to attain more schedule compression. To achieve this overlapping, engineers usually make more generous allowances and adopt conservative assumptions in their designs, than would normally be the case. With this overdesign, dependent activities can start and progress well ahead of the other and long before accurate details can be determined. Although this overdesign may help to expedite the project, it incurs additional costs in subsequent project phases due to lack of design optimization and increased materials wastage. The main goal of this research is to analyze the sensitivity of project schedule and cost for different types and degrees of overdesign to determine the best options which provide the maximum schedule benefit versus the minimum extra costs.
Position:
Project Manager Engineering Manager Senior Proj. Engineer Discipline Lead
Work Experience:
> 25 Years Between 20 & 25 Years Between 15 & 20 Years <15 Years
Type of the Company:
Owner Consultant EPC Contractor Other (please specify):
1. In your own engineering discipline, do you ever overdesign to achieve your schedule
(or make it more effective)?
2. If yes, can you provide some examples of the items that you overdesigned?
3. How do you decide to overdesign (What are the main Influencing factors)?
Internal Factors (Project related)
a) Waiting time for receiving vendors certified information
I. Custom design vs. off-the-shelf items
b) Complexity
c) Saving man-hour
d) Opportunities for more standardization
e) Minimizing risks
External Factors
a) Location
I. Weather
II. Labor cost
b) Market conditions
4. How do you decide on the percentage of overdesign? What is the limitation?
5. What are the main risks and uncertainties involved when you overdesign? Examples
include:
• Extra cost
• Risk of Rework (consequences of underdesign)
• Lack of design optimization
6. What are the impacts on other disciplines?
7. What would be the consequences of overdesign? (Impact areas, effect on time and
cost, etc.)
8. Can you provide some examples of success and failure stories of overdesign?
APPENDIX B: SAMPLE OF THE PIPE RACK GENERAL ARRANGEMENT DRAWING
Preliminary General Arrangement Drawings showing Preliminary Loads
Final General Arrangement Drawings showing Preliminary Loads
APPENDIX C: PIPE RACK DSS INPUT SHEETS
APPENDIX D: VERIFICATION RESPONSES
