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PIPELINE INFRASTRUCTURE
MANAGEMENT BUILDINGS IN CARNEGIE MELLON UNIVERSITY
PITTSBURGH CAMPUS
Ruhi Thakur [email protected]
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
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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.
An
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Alt
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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
pro
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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.