PIPELINE INFRASTRUCTURE

MANAGEMENT BUILDINGS IN CARNEGIE MELLON UNIVERSITY

PITTSBURGH CAMPUS

Ruhi Thakur truhi04@gmail.com

Abstract This document has a report and presentation describing

probability of failure based on different failure causes. May 2014.

2nd May, 2014 1

Pipeline Infrastructure Management of Buildings in Carnegie Mellon University, Pittsburgh Campus

R. M. Thakur2, N. Nalwala2, H. Jampala3 and P. Ravikumar4

Abstract: Like any long lived asset pipe line systems of a building must be managed efficiently. The

ultimate goal is to rectify the problem as soon as possible and reduce the number of pipe failures. The scope of

this paper is restricted to pipe line systems infrastructure management of buildings in Carnegie Mellon

University (CMU), Pittsburgh campus. All the pipe failure records from 2009 to 2014 were studied, personnel

in charge of pipe line assets were interviewed and based on this information it was found that the facility

management services department of CMU campus does not have a well-structured inspection program albeit

incurring heavy costs due to pipe failure, with University Centre and Mellon Institute buildings being the most

prone to pipe failures. Furthermore, two buildings: Bramer house and Mellon Institute were taken up as case

studies and the occurrence of pipe failures were studied in detail. From these case studies all the factors

influencing the deterioration of pipe lines were analyzed and finally a fault tree analysis using analytical

hierarchy process (AHP), and a markov chain was formulated to keep track of the condition of pipelines and

calculate the probability of occurrence of pipe failure.

Introduction:

Any kind of damage in pipe resulting in leakage or

burst can be categorized as pipe failure. Flooding

due to pipe failure can cause a lot of damage to the

buildings and incur a lot of cost in repairs.

Pipelines must be maintained efficiently and

replaced/rehabilitated in order to provide maximum

comfort and quality to building user. The long term

planning and maintenance of pipelines requires

condition assessment and the probability of failure

(Sadiq, et al., 2004)

During the past five years CMU has witnessed a lot

of pipe failures across various buildings, some of

which have caused electricity outage and damage

to expensive equipment. It was observed from the

study that age, weather and human error are the

three main factors for the cause of pipe failures.

Literature review:

Sudies on pipe breaks in USA started in early

1980s with O’Day (1982) proposing from a study

in Philadelphia that pipes of diameter 150 – 200

mm are prone to circumferential breaks and pipes

of diameters greater than 250mm are susceptible to

longitudinal breaks along with his proposal that

analytical decisions must be made instead of rule

of thumb in order efficiently manage pipe lines.

Two alternative approaches exist according to

Samola (2004) for estimating the leak and rupture

frequencies of piping. One is based on probabilistic

fracture mechanics (PFM) and the other one on

statistical estimation from large databases. (Simola,

et al., 2004)

The above two methods have been used for piping

in Swedish Boiling Water Reactor. A study to

apply a piping failure database to estimate the leak

and rupture frequency in reactor coolant pressure

boundary piping was conducted in 1998. The

LOCA frequencies of the Barseback piping were

assessed statistically on a basis of a large data base

consisting of operating experience of set of nuclear

power plants (Lydell, 1999). An approach based on

probability fracture mechanics model was

2nd May, 2014 2

developed, and the method was applied to create

ISI-priorities for piping components at Oskarshamn

(Brickstad, 2000).

In a reactor safety project (Simola, 2002), a

comparative study of the two above-mentioned

methods was accompanied. For the study, 28 welds

were selected from the Barseback 1 piping. The

most important degradation mechanism in these

boiling water reactor piping systems is the

intergranular stress corrosion cracking. The crack

growth is influenced by material properties,

stresses and water chemistry. The rupture

frequencies of the selected welds were estimated

by using the fracture mechanistic codes, and the

results were compared to the earlier results

obtained with the statistical approach (Simola, et

al., 2004).

In a study of pipe leakage occurrence in

commercial nuclear power plants, the following

correlation between piping design and operational

parameters and the frequency of leakage was

proposed (Thomas, 1981)

λF-ToT = λBASEQEFB

where, λF-ToT, plant-specific, total leakage

frequency; λBASE = base-line (or generic) frequency

of leakage; QE, multiplier representing the change

in reliability by piping size and shape differences;

F, plant age factor; as suggested by Thomas, the

frequency of leakage declines with plant age; and

B, design learning curve factor; new piping designs

have higher-than-average failure frequency

(Lydell, 2000).

After the study of prediction of pipe failure pipe

renewal prioritization must be studied for the

formulation of an efficient pipeline management

system. Prioritization approaches can be classified

into the following categories (Peter, et al., 2009):

Deterioration point assignment (DPA)

Economic models

Mechanistic models

Regression and failure probability

In the deterioration point assignment method

factors contributing to the failure of pipes are

identified and gicen points. Based on the the

condition of the pipe weightage is given to each

factor which is finally summed up to give a total

failure rate.

In the economic model the present worth of future

repairs and replacements is calculated based on a

regression analysis of previous pipe failures.

The mechanistic models deal with formulation of

degradation equations based on pressure loads,

frost loads, corrosion induced stresses etc.

Regression models use regression analysis and fit

the previous pipe failure data into a model and

predict the future pipe failures.

Motivation:

Studies by ASCE and the American Water Works

Association estimate the costs for upgrading the

nation’s aging pipe infrastructure from $100 to

$325 billion over the next 20 years (Grablutz and

Hanneken 2000)

Apart from the facts provided by ASCE there were

a total of 11 pipe burst cases in January,2014 and

many more leakage cases.(As per the information

provided to us from Steven Guenther, Director of

Facility Operations, Carnegie Mellon University)

This led to large scale damages and cost in terms of

time and money . This has ill effects which are not

only limited to one facility or operation but is

pervasive in nature. Pipes constitute of a major part

of campus facilities and no recent studies regarding

CMU Pipe Burst have been conducted.

Data Collected and Analysis

In order to understand the pipeline infrastructure

management on campus, we spoke to Steven

Guenther, Director Facilities Operations, CMU.

We assimilated the key points in the discussion -

1. Water Sewer system in Pittsburgh is quite

old(approximately 100 years) and is made up

of clay, bricks which extend 30ft down under

the ground, cast iron etc.

2nd May, 2014 3

2. Carnegie Mellon University: Inspection

Approach

Almost all assets on campus have an

inspection plan. Some are monthly, some

are weekly etc. The inspection plans are

generated automatically through the

software system used by the university.

Weather is an important factor governing

different inspection routines.

Employees through past experiences have

the knowledge about the critical points to be

focused on in case of a pipe burst/overflow

etc.

There is a divide between theoretical

assessment and the practical approach. Due

to the old infrastructure there are a lot

breaks & maintenance issues. Although

there are multiple range of data acquisition

equipment presents the management of that

data is a major issue.

If a pipe leak does take place then within 48

hours of a leak everything must be cleaned

and the affected area must be dried out to

prevent mold from developing. Pipelines

remediation is carried out by accessing the

pipeline and tearing down the structure that

obstructs the access. Time is prime factor as

in determining the maintenance cost in these

aspects.

Detailed preventive maintenance program

incorporated and a manual is adopted to

explain the plan.

Quality of fluid flowing through the pipes is

a very important quality check to make sure

that the fluid flowing through the pipes are

not degrading the pipes in any manner.

(George Papuga, Campus Zone Supervisor,

Craig Street Zone)

3. Categories of incidents that occurred on

campus are divided into the following

categories based on the causes-

A-Human errors,

Building occupants leave the windows

open which in turn leads to freezes.

To solve this issue the maintenance team talks to

the people occupying the place and create

awareness, but since different people respond

differently the problem could persist.

B-Aging of the infrastructure

C-Unexpected occurrences which cannot

be prevented and they lead to large costs

(eg the Bramer house incident)

4. A Condition rating for the assets on campus is

not yet in place, but under process. It is

difficult to agree upon a complete

infrastructure condition rating system. A

common consensus needs to be agreed upon

the description of each condition state.

5. Redundancies that have been built in the

system-

Business continuity is important in

buildings hence it is important to keep the

heating and cooling systems functioning.

Heating is required to protect the building

and cooling is required to protect the assets.

Heating is important than the cooling as

Pittsburgh has a cooler climate.

Water supply network is highly intricate and

buildings are fed in multiple ways – high

level of redundancy. Power system has a lot

of redundancy built into the system.

There are 3 main power sources from

Duquesne. Each building has a set of

parallel electrical lines and plus another

power source that is generator back

up.(UPS)

2nd May, 2014 4

Fire alarms & Exit lights in buildings have a

generator back up so will always be

functioning independent of power

conditions on University campus.

Water for fire extinguishers doesn’t have

any redundancy due to the water pipeline

redundancy.

Human redundancy is also built into the

system.

6. How do separate alarms from those which are

real to those which are not real? Alarms are set

in such a way that it sends out messages to the

individuals in charge on their cell phones. The

alarms are not sent out to everyone so that only

the people who are responsible for the area of

alert respond.

Case Study 1: Bramer House (BRAM)

Studied for the purpose of analysis

Location: Bramer House and Garage, Carnegie

Mellon University

Issue: Bramer House Flooding

Figure 1: Bramer House

(http://www.cmu.edu/fms/)

Description: First Floor Flooded, Water line froze

and Broke. We approached Joseph Vanyo, Campus

Zone Supervisor, Housing, Carnegie Mellon

University who guided us on this incident.

On the shorter exterior wall of room 102, a pipe

located inside the wall on the first floor, froze due to

the harsh winter and burst. This happened over a

weekend and was realized by the building workers on

the following Monday as there was an inch of water

in room 102 which had leaked into the basement and

also flowed into the adjoining hallway.

Figure 2: Floor Plan, Bramer House

(http://www.cmu.edu/fms/)

The first work order was placed by Terry Price from

the housing department and the manager in charge

was Ronald Cunningham. From the work orders we

procured from Joseph Vanyo we traced out the

different works carried out to mitigate and repair the

damage and also the cost for each work order.

The assets which were damaged and repaired or

replaced were the plumbing pipelines, insulations &

finishes, heaters and the AHU located in the

basement. Several plumbing systems, desk rug units,

ceiling and walls needed to be repainted. All this

work was outsourced to private firms.

2nd May, 2014 5

Case Study 2: Mellon Institute

Figure 3: Mellon Institute

Building Description: Mellon Institute was built

in 1937 and has been classified as a historic

chemical landmark by American Chemical

Society(2013), majorly noted for its neo classical

architecture. It is a seven story low rise building

with 3 stories below the ground level. It currently

houses the Office of the Dean for Carnegie Mellon

University's Mellon College of Science

administrative offices and research laboratories for

the Department of Biological Sciences and

Department of Chemistry. It is one of the

significant buildings owned by Carnegie Mellon

University as it receives high sums of grants from

various government & private funding

organizations. It covers huge space 315,970 sqft

and has an old infrastructure which requires

detailed level of maintenance.

Our Findings: This case study focuses on the pipe

infrastructure of Mellon Institute. In our research

for Mellon Institute we found that there were many

pipe failure incidences in recent years. In the years

2010 to 2014 thirteen pipe failure cases (pipe

bursts & leakages) were reported. After speaking

with George Papuga, Campus Zone Supervisor,

Craig Street Zone, we realized the major cause of

most of the pipe failure incidences in winter 2013-

2014 are due to human errors and improper

installations. By human error we refer to pipe

failure cases caused due to an open window for

couple of hours leading to a pipe freeze in the

winters. We also learned that approximately thirty

percent pipes are accessible and the rest are either

embedded in the walls or not directly accessible

due to being hidden behind HVAC systems or

finishes. Due to this it is difficult to detect &

predict pipe failures in the building.

A recent incident of pipe failure was reported in the

building which was caused due to incorrect

programming of the HVAC system. The system

ran exactly opposite the way it was suppose to

function causing huge damage to the HVAC

system resulting in the freezing of a pipe leading to

a pipe burst incident. A second major incident

reported in the recent years was regarding the pipe

burst on the ninth floor of the building. Due to the

building structure and pipe networks the bursts

outcomes were seen on the sixth floor rather than

the seventh and eight floors. The complexity in the

pipe networks is such that the flow of water in the

building is not directly vertical as expected in most

buildings. In most cases a pipe burst incident of the

above magnitude in Mellon Institute does not show

immediately on the floor just beneath it therefore

requiring the maintenance team to inspect at least 4

floors below and 1 floor above the affected level

within few hours of the pipe burst.

The experienced maintenance team of Mellon

Institute suggested the time required for the

pipeline system to deteriorate back to the condition

just before replacement/renovation is

approximately 15 to 20 years for the building. Also

the renovations carried out in the building are

classified into two categories first being the best

improvements and second as a band-aid fix. The

former is being carried out 90% of the time a

renovation plan is established. The maintenance

team also follows an inspection plan which

regulates yearly, half yearly, quarterly and monthly

checks depending on the conditions of different

types of pipe networks within the building. A

critical infrastructure requires more number of

inspections being carried out compared to a newly

installed pipe infrastructure.

We were able to obtain few condition ratings for

2nd May, 2014 6

the building on a scale of 1 to 10. 1 was assumed to

be the best condition suitable to cater future needs

and 10 being the immediate failure condition. The

current condition of the building was rated as 4 for

the exposed pipe infrastructure and 5 for the

unexposed parts. These ratings are a function of the

condition of the pipes and the renovations made in

the system. Due to an earlier incident in 2011

major renovations including remodeling of second

floor unit, were carried out in the building which

led to drastic improvements in the condition of the

pipe network in the building.

Model Aim: We propose to develop a deterioration

model for Mellon Institute which will enable the

stakeholders to realize the appropriate juncture for

pipe replacement, repair or complete renovation.

For this we are using two techniques:

1. Fault Tree Analysis

2. Markov Deterioration Model

Fault Tree Analysis: For fault tree analysis firstly

we require to identify all the possible causes which

have been responsible for pipe failures in Mellon

Institute. We referred with the US Department of

Transportation, Pipeline & Hazardous materials,

Safety Administration website for its validity to

relate our causes with nationwide statistics. A

summary for all the pipe failure incidences in pipe

networks all over United States can be seen as

follows

We identified the following causes from the above

for Mellon Institute pipe failure incidents:

1. Age,

2. Corrosion, Over Pressure,

3. Incorrect Installation,

4. Construction/ Pre-fabrication,

5. Pump/ Valve,

6. Freeze,

7. Fire,

8. Human Error,

9. HVAC

To determine the probabilities of each of the causes

occurring individually Analytical Hierarchy

Process (AHP) was used. In this process each

cause was compared to other causes and a value

was assigned to the first cause with respect to the

second based on the number of pipe failure

occurred specifically due to these considered

causes. In this way we were able to judge relatively

which cause had caused higher pipe failures.

Finally for each cause the values assigned to them

are summed up and then normalized with the total

sum of all the values assigned to all the causes.

Through this way we were able to determine a

probability for each cause. For more accurate

results we did four cases of AHP comparison and

found four probabilities for each cause. To reduce

judgment error an average of all four values was

considered as the final probability value.

Figure 4: Summary of Pipe Failure Incidences (US)

2nd May, 2014 7

Table 1: AHP

2nd May, 2014 8

Table 2: Probabilities for causes based on occurrence of Pipe Burst

Causes PROBABILITY

1 AGE 0.178

2 Corrosion 0.064

3 Incorrect operation Over Pressure 0.037

Incorrect installation 0.053

4 Material/Weld/ Equipment

Failure

Construction/prefabrication 0.041

Pump/Valve related 0.042

5 Weather- Freeze 0.144

6 Other causes Fire 0.019

Human Error 0.332

HVAC 0.088

SUM 1

Pipe Failure due to all the above causes:

P{failure} = 1-Π(1-P{subnode failure})

P{failure} = 1- (1-0.178)(1-0.064)(1-0.037)(1-

0.053)(1-0.041)(1-0.42)(1-0.144)(1-0.019)(1-

0.332)(1-.088)

= 1- 0.32911 = 0.67089

ie, 67.09% chances of failure due to all causes

acting simultaneously

The next step is to determine probabilities of pipe

failure if a combination of causes are considered

Figure 5 : Fault Tree

2nd May, 2014 9

simultaneously. For this there are 2 feasible

approaches:

1. Establish an algorithm to obtain all

possible combinations of the 10 causes

occurring and not occurring. This process

will require coding in Matlab or similar

coding software. If we want to proceed

without the help of a programmer the

alternate approach can be used which is

the second method.

2. This method establishes user defined

weights to each cause’s probability and

sums up the product of probability and

weights to get the final probability for the

system (DPA method)

Illustration for DPA process: The weights for this

example are defined from 0% to 100 % at an

interval of 10%. And only age (50%), corrosion

(10%), pump/valve related issues (10%), freeze,

human Error (60%) & HVAC(20%) are considered

for this case:

Table 3: DPA Method

From the above consideration: Failure due to age(50%), corrosion(10%), pump/valve related issues(10%),

freeze, human Error(60%) & HVAC(20%) is 35.96%

Similarly by assigning different weights possible probabilities for failure could be deduced.

2nd May, 2014 10

Though this process helps derive failure

probabilities without much difficulty, it does

endure the following limitations:

• Model will give best results only if the

user has high level of judgment regarding

pipe failure issues

• The above practice is still in theory phase

and needs to be assessed practically

• The intervals between the percentages are

upto the user’s discretion hence

standardization of this technique is

difficult to achieve.

With the increase of percentage intervals the

resultant probabilities tend to be less accurate.

Markov Model:

Condition assessment helps in assessing the

depreciation of an asset over a useful life. For

example the structural deterioration of storm water

pipes is assessed using condition ratings where the

ratings take the form of separate states in order to

reduce the computational complexity so as

associated with continuous condition rating system

(Madant, et al., 1995). Five states were used to

describe the structural condition of the storm water

pipes where 1 indicates near new condition and 5

indicates unserviceable. (Micevski, et al., 2002)

To determine the overall condition of pipelines in

this building the facilities management person in

charge of pipeline assets of this particular building

was interviewed. The scale of rating used was

from 1 to 10 where 1 being the best and only

achievable when the complete pipeline system was

newly installed; and 10 being in the

worst/unserviceable condition. Based on the

interview of the person the current condition,

condition five years ago, probable condition after 5

years and the time for the system to reach the

current condition if the whole system had been

replaced was found.

It was observed that renovation/replacement was

done based on the condition and availability of

funds. From this two possible markov models were

formulated. One being the case when funds were at

disposal and the other when funds were.

In the case when funds were readily available and

the condition of the piping system was 6 then it

would be renovated and the condition would be

improved to state 3. And the case when funds were

not available; the condition would be let to fall

down till 9 and then improved to reach state 6.

The following are the two markov chains:

Case 1: Sufficient funds available to perform renovation at early stage

2nd May, 2014 11

[ 0.875 0.125 0 0 0 0 0 0 0 0

0 0.875 0.125 0 0 0 0 0 0 0

0 0 0.857 0.143 0 0 0 0 0 0

0 0 0 0.833 0.167 0 0 0 0 0

0 0 0 0 0.75 0.25 0 0 0 0

0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0]

Case 2: Insufficient funds

[ 0.875 0.125 0 0 0 0 0 0 0 0

0 0.875 0.125 0 0 0 0 0 0 0

0 0 0.857 0.143 0 0 0 0 0 0

0 0 0 0.833 0.167 0 0 0 0 0

0 0 0 0 0.75 0.25 0 0 0 0

0 0 0 0 0 0.75 0.25 0 0 0

0 0 0 0 0 0 0.833 0.167 0 0

0 0 0 0 0 0 0 0.75 0.25 0

0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0]

2nd May, 2014 12

Results and Conclusion:

This study aimed to analyze two structures in

Carnegie Mellon University: Bramer House &

Mellon Institute. Study of the solo pipe burst

incident in Bramer House was for the purpose of

understanding the causes behind such incidences

in campus. Due to its small size and easily

available information this building was considered.

Based on this analysis a more complex building

was then considered for further analysis. Mellon

Institute is a well established building & has rich

infrastructure data available with the Facility

Management Services, Carnegie Mellon

University. Two kinds of analysis were carried out

on Mellon Institute: Fault Tree & Markov Model.

According to fault tree analysis the current

probability of pipe system failure due to the age,

corrosion, over pressure, incorrect installation,

construction/ pre-fabrication, pump/ valve, freeze,

fire, human error and HVAC depends on the user

defined weights for each pipe system.

If all the above pre defined causes act

simultaneously the probability of failure of the

pipe system for Mellon Institute will be 67.09%

Depending on the availability of funds and the

current condition of the pipe system in Mellon

Institute renovations are carried out. When funds

were readily available and the condition of the

piping system was 6 it was renovated and the

condition would be improved to state 3. And the

case when funds were not available; the condition

would be let to fall down till 9 and then improved

to reach state 6.

With the help of this study inspection plans can be

revised based on cause probabilities developed I

the fault tree analysis. This study is able to

establish a base for a condition rating system for

Mellon Institute and can be used for fund

disbursement proposals for the pipe infrastructure

maintenance of the building.

References: • Brickstad B The use of risk based methods for

establishing ISI - priorities for piping

components at Oskarshamn 1 Nuclear Power

Station [Journal]. - Sweden : SKI report, 2000. -

83p.

• Lydell B Failure rates in Barseback - 1 reactor

collant pressure boudary piping. An application

of piping failure database. [Journal]. - [s.l.] :

SKI Report, 1999. - 98:30.

• Lydell B.O.Y Pipe failure probability

[Journal]. - California : Reliability Engg. and

Safety Systems, 2000. - 68, 207 - 217.

• Madant S, Mishalani R and Wan Ibrahim W.

H Estimation of infrstructure transition

probabilities from condition rating data

[Journal]. - [s.l.] : Journal of Infrastructure

systems, 1995. - 121(3): 267 - 272.

• Micevski Tom, Kuczera George and Coombes

Peter Markov model for storm water pipe

deterioration [Journal]. - [s.l.] : ASCE -

Journal of infrastructure systems, 2002. - 8: 49 -

56.

• Peter D, Rogers P.E and Grigg Neil S. Failure

assesment modeling to prioritize water pipe

renewal: two case studies [Journal]. - [s.l.] :

ASCE, Journal of Infrastructure Stystems,

2009. - 15: 162-171.

• Sadiq R, Rajani B and Keiner K Probablistic

risk analysis of corrosion associated failures in

cast iron water mains [Journal] // Reliable

engineering systems. - 2004. - 86. - pp. 1 - 10.

• Simola K Advances in operational safety and

severe accident research [Book]. - [s.l.] : SOS

2, 2002. - Vols. ISBN 87-7893-116-9.

• Simola Kaisa [et al.] Comparison of approaches

for estimating pipe rupture frequencies for risk

informed in-service inspection [Journal]. -

Finland : [s.n.], 2004. - 84.

• Thomas HM Pipe and vesse failure probability,

Relaibility Engineering [Book]. - 83-124 :

[s.n.], 1981.

Infrastructure Management Presentation

Pipe Failures on CMU Campus

Nafisa Nalwala, Ruhi Thakur, Himanshu Jampala, Prathik Ravikumar

Contents

M o t i v a t i o n

R e s e a r c h

L i t e r a t u r e R e v i e w

C a s e S t u d y : B r a m e r H o u s e

C a s e S t u d y : M e l l o n I n s t i t u t e

A l t e r n a t e A p p r o a c h

R e s u l t s & C o n c l u s i o n

100 year old water sewer system in Pittsburgh

It is made up of Clay, bricks 30ft down under, cast iron etc.

11 pipe burst cases in CMU Campus in January,2014 and many more leakage cases

High amount damage caused: Time & Money

Affects running of the other facilities on campus

No recent studies regarding about CMU Pipe Burst

Pipes constitute of a major part of campus facilities & same goes for the city infrastructure

Mo

tiva

tio

n

Pittsburgh & CMU

Aff

ecte

d B

uild

ings

Th

is W

inte

r

CMU: Inspect ion Approach

Almost all assets on campus have an inspection plan. Some are monthly, some are weekly etc. generated automatically.

Pipelines have no predefined standard inspection plan

The different routines and procedures for inspection are carried out based on the weather conditions

Employees know the critical points to focus on in case of a pipe burst/overflow etc.

Pipelines remediation is done by accessing the pipeline and tearing down anything that obstructs the access.

Res

earc

h

Categor izat ion of p ipe burst Inc idences

A-Human errors

Windows left open

To resolve these issues the maintenance team talks to the people occupying the place and creates awareness,

Problem still persist because different people respond differently

B-Aging of Infrastructure

Old infrastructure, needed repair

C-Unexpected Occurrences

Instances which cannot be controlled

Extreme Weather condition like the past winter

Res

earc

h

What is Pipe fai lure?

Leaking or bursting of pipes due to mechanical failure or human errors or extreme weather conditions.

Few common cases of pipe failure: Other reasons are due to age, human errors, equipment failure or incorrect operation.

Lite

ratu

re R

evie

w

http://www.sevacall.com/blog/2014/01/s/plumbers/frozen-pipes-burst/

Anomaly: Intent iona l P ipe Burst

Trenchless method of replacing buried pipelines, without a need for a trench.

Pulling the new pipe through the old while fragmenting the old one

Eg: Sewer, water or natural gas pipes

http://www.psivail.com/pipe-bursting/

I n s p e c t i o n M e t h o d s

Periodic inspection Pipeline Pigs

In-line inspection (ILI) tools, aka intelligent pigs

Ultrsonic inline inspection

Infrared Thermography (IR) inspection

Magnetic flux leakage

Checking the quality of fluid flowing through the pipes

Lite

ratu

re R

evie

w

http://www.hj3.com/products/carbonseal/steel-pipes/

Bramer HouseC

ase

Stu

dy

1

Bramer HouseC

ase

Stu

dy

1

Location: BRAM, Bramer House and Garage, CMU

Case: Bramer House Flooding First Floor Flooded, Water line froze and Broke

Mel lon Inst i tute

Cas

e St

ud

y 2

Built in 1937

Noted for its neo-classical architecture style

7-storey low-rise building

Covers an area of 315,970 sq. feet

Major impact pipe bursts are due to human errors and improper installation

13 cases reported for pipe bursts and pipe leakages in last 5 years.

Only 30% of the pipes are accessible

Drastic improvement in the condition of pipes after major renovation in 2011.

Life span and effective service life of pipes are 20-25 years.

Analytical Hierarchy Process

Fix and compare all causes of pipe failures in Mellon Institute

Calculate probabilities for each cause based on occurrence of pipe failures

CausesAge, Corrosion, Over Pressure, Incorrect Installation,

Construction/ Pre-fabrication, Pump/ Valve, Freeze, Fire, Human Error, HVAC

(All Reported Pipeline Incidents By Cause,

http://primis.phmsa.dot.gov/comm/reports/safety/AllPSIDet_1994_2013_US.html?nocache=7203)

An

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AGE Corrosion

Incorrect operation Material/Weld/ Equipment Failure

Weather-Freeze

Other caues

Over PressureIncorrect

installationConstruction/ prefabrication

Pump/ Valve related

Fire Human Error HVAC SUM Probabilities

AGE 1.00 5.00 5.00 5.00 4.00 4.00 1.00 6.00 0.10 2.00 33.10 0.072639

Corrosion 0.20 1.00 1.00 1.00 0.80 0.80 0.20 1.20 0.02 0.40 6.62 0.014528

Incorrect operation

Over Pressure 0.20 1.00 1.00 1.00 0.80 0.80 0.20 1.20 0.02 0.40 6.62 0.014528

Incorrect installation 0.20 1.00 1.00 1.00 0.80 0.80 0.20 1.20 0.02 0.40 6.62 0.014528

Material/Weld/ Equipment Failure

Construction/ prefabrication

0.25 1.25 1.25 1.25 1.00 1.00 0.25 1.50 0.03 0.50 8.28 0.01816

Pump/Valve related 0.25 1.25 1.25 1.25 1.00 1.00 0.25 1.50 0.03 0.50 8.28 0.01816

Weather Freeze 1.00 5.00 5.00 5.00 4.00 4.00 1.00 6.00 0.10 2.00 33.10 0.072639

Other caues

Fire 0.17 0.83 0.83 0.83 0.67 0.67 0.17 1.00 0.02 0.33 5.52 0.012107

Human Error 10.00 50.00 50.00 50.00 40.00 40.00 10.00 60.00 1.00 20.00 331.00 0.726392

HVAC 0.50 2.50 2.50 2.50 2.00 2.00 0.50 3.00 0.05 1.00 16.55 0.03632

455.68 1

P a r t i c i p a n t 1

Age CorrosionIncorrect operation Material/Weld/ Equipment Failure Weather Other caues

Over PressureIncorrect

installationConstruction/ prefabrication

Pump/ Valve related

Freeze Fire Human Error HVAC SUM Probabilities

Age1 2 5 2 4 2 0.5 10 1 3 30.50 0.156033

Corrosion0.5 1 2.5 1 2 2 0.25 5 0.5 1.5 16.25 0.083133

Incorrect operation

Over Pressure 0.2 0.4 1 0.4 0.8 0.4 0.1 2 0.2 0.60 6.10 0.031207

Incorrect installation 0.5 1 2.5 1 2 1 0.25 5 0.5 1.5 15.25 0.078017

Material/Weld/ Equipment

Failure

Construction/ prefabrication

0.25 0.5 1.25 0.5 1 0.5 0.125 2.5 0.25 0.75 7.63 0.039008

Pump/ Valve related 0.5 1 2.5 1 2 1 0.25 5 0.5 1.5 15.25 0.078017

Weather Freeze 2 4 10 4 8 4 1 20 2 6 61.00 0.312067

Other caues

Fire 0.01 0.2 0.5 0.2 0.4 0.2 0.05 1 0.01 0.3 2.87 0.014682

Human Error 1 2 5 2 4 2 0.5 10 1 3 30.50 0.156033

HVAC 0.33 0.66 1.66 0.66 1.33 0.66 0.166 3.33 0.33 1 10.13 0.051803

195.47 1

Pro

ced

ure

P a r t i c i p a n t 2

AGECorrosion

Incorrect operationMaterial/Weld/ Equipment

FailureWeather Freeze

Other caues

Over Pressure Incorrect installationConstruction/prefabrication

Pump/ Valve related

FireHumman

ErrorHVAC SUM Probabilities

AGE 1.00 5.00 7.00 7.00 8.00 12.00 6.00 15.00 2.00 2.00 65.00 0.350191

Corrosion 0.20 1.00 1.40 1.40 1.60 2.40 1.20 3.00 0.40 0.40 13.00 0.070038

Incorrect operationOver Pressure 0.14 0.71 1.00 1.00 1.14 1.71 0.86 2.14 0.29 0.29 9.29 0.050027

Incorrect installation

0.14 0.71 1.00 1.00 1.14 1.71 0.86 2.14 0.29 0.29 9.29 0.050027

Material/Weld/ Equipment Failure

Construction/ prefabrication

0.13 0.63 0.88 0.88 1.00 1.50 0.75 1.88 0.25 0.25 8.13 0.043774

Pump/Valve related

0.08 0.05 0.07 0.07 0.08 0.13 0.06 0.16 0.02 0.02 0.75 0.004041

Weather Freeze 0.17 0.83 1.17 1.17 1.33 2.00 1.00 2.50 0.33 0.33 10.83 0.058365

Other caues

Fire 0.07 0.33 0.47 0.47 0.53 0.80 0.40 1.00 0.13 0.13 4.33 0.023346

Humman Error 0.50 2.50 3.50 3.50 4.00 6.00 3.00 7.50 1.00 1.00 32.50 0.175095

HVAC 0.50 2.50 3.50 3.50 4.00 6.00 3.00 7.50 1.00 1.00 32.50 0.175095

185.61 1

AgeCorrosion

Incorrect operation Material/Weld/ Equipment Failure

Weather Freeze

Other caues

Over PressureIncorrect

installationConstruction/ prefabrication

Pump/ Valve related

Fire Human Error HVAC SUM Probabilities

Age1 1.5 2.5 2.5 2 2 1 5 0.5 1.5 19.50 0.13439

Corrosion0.6667 1 1.667 1.667 1.333 1.333 0.667 3.333 0.333 1 13.00 0.089593

Incorrect operation

Over Pressure 0.4 0.6 1 1 0.8 0.8 0.4 2 0.2 0.6 7.80 0.053756

Incorrect installation

2.5 0.6 1 1 1.25 0.80 0.4 2 0.2 0.6 10.35 0.07133

Material/Weld/ Equipment Failure

Construction/ prefabrication

0.5 0.75 1.25 0.8 1 1 0.5 2.5 0.25 0.75 9.30 0.064094

Pump/ Valve related

0.5 0.75 1.25 1.25 1 1 0.5 2.5 0.25 0.75 9.75 0.067195

Weather Freeze 1 1.5 2.5 2.5 2 2 1 5 0.5 1.5 19.50 0.13439

Other caues

Fire 0.2 0.3 0.5 0.5 0.4 0.4 0.2 1 0.1 0.3 3.90 0.026878

Human Error 2 3 5 5 4 4 2 10 1 3 39.00 0.26878

HVAC 0.66667 1 1.667 1.667 1.333 1.333 0.667 3.333 0.333 1 13.00 0.089593

145.10 1

Pro

ced

ure

P a r t i c i p a n t 3

P a r t i c i p a n t 4

Occurrence of pipe burst Causes PROBABILITY

1 A G E 0.178

2 C o r r o s i o n 0.064

3I n c o r r e c t o p e r a t i o n

O v e r P r e s s u r e 0.037

I n c o r r e c t i n s t a l l a t i o n 0.053

4

M a t e r i a l /W e l d /

E q u i p m e n t F a i l u r e

C o n s t r u c t i o n / p r e f a b r i ca t i o n

0.041

P u m p / V a l v e r e l a t e d 0.042

5 W e a t h e r - F r e e z e 0.144

6 O t h e r c a u s e s

F i r e 0.019

H u m a n E r r o r 0.332

H V A C 0.088

SUM 1

Pro

bab

iliti

es

Fault Tree

Pipe Failure

Age0.178

Corrosion

0.064

Over Pressure

0.037

Incorrect installation

0.053

Construction/ pre-fabrication

0.041

Pump/ Valve related

0.042

Weather –Freeze

0.144

Fire

0.019

Human Error

0.332

HVAC

0.088

Mo

del

Fault Tree analysis

Pipe Failure due to all the above causes:P{failure} = 1-Π(1-P{subnode failure})P{failure} = 1- (1-0.178)(1-0.064)(1-0.037)(1-0.053)(1-0.041)(1-0.42)(1-0.144)(1-0.019)(1-0.332)(1-0.088)

= 1- 0.32911 = 0.67089

ie, 67.09% chances of failure due to all causes acting simultaneously

BUT now if we need to find failure probability for a combination of causes,

There are 2 feasible approaches:

Establish an algorithm to obtain all possible combinations of the 10 causes occurring and not occurring. EXTREMELY TIDEOUS!!

OR

Establish user defined weights to each cause’s probability and sum up the product of probability and weights to get the final probability for the system.

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USER BASED PROBABILITY 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Probability x Weights

AGE0.178 • 0.089157

Corrosion0.064 • 0.006432

OverPressure

0.0373 • 0

Incorrect installation

0.053 • 0

Construction/prefabrication

0.041 • 0

Pump/Valve related

0.041 • 0.004185

Freeze0.144 • 0.04331

Fire0.019 • 0

HumanError

0.331 • 0.198945

HVAC0.088 • 0.017641

SUM OF WEIGHTED PROBABILITIES SHOWING THE FINAL FAILURE PROBABILITY 0.35967

From the above consideration

Alt

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Ap

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• Failure due to age(50%), corrosion(10%), pump/valve related issues(10%), freeze,

human Error(60%) & HVAC(20%) is 35.96%

• With this method all combinations of the causes can be worked out easily!

Limitations

• Model will give best results only if the user has high level of judgment regarding pipefailure issues

• The above practice is still in theory phase and needs to be assessed practically

• The intervals between the percentages are upto the user’s discretion hence standardization of this technique is difficult to achieve.

• With the increase of percentage intervals the resultant probabilities tend to be less accurate.