Planning a Dynamic and Financially Sustainable
Municipal Water Management System
by
Ramona Mirtorabi
rmirtora@uwaterloo.ca
Supervisors:
Dr. Andre Unger
and
Dr. Mark Knight
A Thesis Proposal
Presented to the Department of Civil and Environmental Engineering
at the University of Waterloo
for the Doctor of Philosophy Comprehensive Examination
Waterloo, Ontario, Canada, 2015
©Ramona Mirtorabi 2015
ii
Table Of Contents
List of Figures .......................................................................................................................... v
List of Tables .......................................................................................................................... vi
ABSTRACT .......................................................................................................................... viii
1.
Introduction ................................................................................................................... 10
1.1 Background ................................................................................................................... 10
1.2 Problem Statement ........................................................................................................ 12
1.3 Organization of the Proposal ......................................................................................... 14
2.
Literature Review .......................................................................................................... 15
2.1 Research Gaps ............................................................................................................... 19
2.2 Research Objectives ...................................................................................................... 20
3.
Proposed Methodology .................................................................................................. 22
3.1 Multi-perspective Modeling Framework ...................................................................... 22
3.2 Prioritizing Action Number (PAN) Model.................................................................... 24
3.2.1 Assigning Weights to the Selected Parameters for Linear Assets .......................... 24
3.2.2 Condition Measurement Model .............................................................................. 26
3.2.3 Performance Measurement Model.......................................................................... 30
3.2.4 Criticality Model..................................................................................................... 33
3.2.5 Total PAN Number ................................................................................................. 35
3.3 Fuzzy Logic Analysis .................................................................................................... 36
3.3.1 First Layer Fuzzy Expert System for Watermain Sections with High PAN Scores38
3.3.2 Risk Analysis at Second Layer Fuzzy System ....................................................... 47
3.4 Cost Model and Budget Forecasting ............................................................................. 50
iii
3.5 Summary ....................................................................................................................... 51
4.
Preliminary Results ....................................................................................................... 52
4.1 Site Selection and Data Characteristics ......................................................................... 52
4.2 PAN Calculation ........................................................................................................... 54
4.3 First and Second Layer Fuzzy Analysis Result ............................................................. 55
5.
Future Plans and Contributions................................................................................... 57
5.1 Research Tasks and Schedule ....................................................................................... 57
5.2 Potential Contributions.................................................................................................. 59
6.
References....................................................................................................................... 61
iv
List of Figures
Figure 2-1 Levels of Asset Management System ................................................................... 16
Figure 3-1 Proposed Method Overview .................................................................................. 23
Figure 3-2 PAN Number Flow Chart...................................................................................... 25
Figure 3-3 Total Condition Score ........................................................................................... 30
Figure 3-4 Total Performance Score ....................................................................................... 33
Figure 3-5 Total Criticality Score ........................................................................................... 35
Figure 3-6 Priority Action Number (PAN) ............................................................................. 36
Figure 3-7 Two Level Fuzzy Logic Analysis Chart ............................................................... 38
Figure 3-8 Remaining Service Life vs. Degree of Membership ............................................. 41
Figure 3-9 Total Number of Breaks vs. Degree of Membership ............................................ 41
Figure 3-10 Total Condition Score vs. Degree of Membership.............................................. 43
Figure 3-11 Fuzzy Logic Model for Condition Score ........................................................ 45
Figure 3-12 Fuzzy Logic Model for Performance Score ........................................................ 46
Figure 3-13 Fuzzy Logic Model for Criticality Score ............................................................ 46
Figure 3-14 Second Layer Fuzzy Logic Analysis................................................................... 47
Figure 4-1 Selected Sample Area ........................................................................................... 53
Figure 4-2 Different Pipe Diameters in Sample Dataset ........................................................ 53
Figure 4-3 PAN Score Layer .................................................................................................. 54
v
List of Tables
Table 2-1 Related Literature in Strategic Asset Management Category ................................ 16
Table 2-2 Related Literature in Tactical Asset Management Category .................................. 17
Table 2-3 Related Literature in Operational Asset Management Category............................ 18
Table 3-1 Weighting Factors .................................................................................................. 25
Table 3-2 Remaining Asset Service Life Score ...................................................................... 27
Table 3-3 Corrected Expected Service Life of Pipe based on Corrosion Factor .................... 28
Table 3-4 Scores for Total Number of Breaks ........................................................................ 29
Table 3-5 Scores for Total Number of Breaks within Last Five Years .................................. 29
Table 3-6 Maintenance Index (MI) and its Scores.................................................................. 30
Table 3-7 Head-Loss Scores ................................................................................................... 31
Table 3-8 Water Quality Score ............................................................................................... 32
Table 3-9 Standard Conformance Scores ................................................................................ 32
Table 3-10 Diameter Scores.................................................................................................... 33
Table 3-11 Environmentally Sensitive Area Score ................................................................. 34
Table 3-12 Accessibility Score ............................................................................................... 35
Table 3-13 Fuzzy Boundaries for Remaining Service Life .................................................... 39
Table 3-14 Fuzzy Boundaries for Total Number of Breaks ................................................... 40
Table 3-15 List of Fuzzy Logic Grades for Condition Model ................................................ 42
Table 3-16 List of All Possible Fuzzy Rules for Condition Model ........................................ 43
Table 3-17 Second Layer Fuzzy Logic Grades....................................................................... 48
Table 3-18 Possible Fuzzy Rules for Risk Model .................................................................. 49
vi
Table 4-1 Different Pipe Materials within the Sample Dataset .............................................. 53
Table 4-2 PAN Score Thresholds ........................................................................................... 54
Table 4-3 PAN Scores Calculated for Sample Area ............................................................... 55
Table 4-4 Fuzzy Analysis Results........................................................................................... 55
Table 5-1: Task Schedule........................................................................................................ 58
vii
ABSTRACT
A number of past efforts have been devoted to the development of a sustainable water system
that captures the performance of each segment within the system, analyzes the risk of failure,
and optimizes the replacement program. Sustainability and self-financing regulations are
dependent on having a sustainable water system in each municipality. A sustainable water
system is commonly defined as a balanced structure with stable revenue and proper
forecasting techniques to maintain a reliable level of service to its users.
Motivated by the need to access safe drinking water while manage the aging watermain
infrastructure, past research has mostly focused on various decision support modeling
approaches such as:
a) Cost-benefit analysis on individual pipe (Hong et al., 2006; Loganathan et al., 2002;
Kanakoudis & Talikas, 2001; Kleiner et al., 2001; Walski, 1987; Shamir & Howard
1979);
b) System prioritization analysis (Moglia et al., 2006; Saegrov, 2005; Burn et al., 2003;
Deb et al., 1998);
c) Optimization modeling approach (Kleiner et al., 2010; Saldarriaga et al., 2010;
Berardi et al., 2008);
d) Casual loop diagram analysis (Rehan et al., 2013; Guest et al., 2010; Sianchi et al.,
2010; Adeniran & Barniro, 2010; Bagheri & Hjorth, 2007; Barton, 1994); and
e) Failure risk and complex analysis systems (Fares & Zayed, 2010; St. Clair & Sinha,
2014).
These past efforts have several critical limitations.
a) First, they do not provide a comprehensive perspective of the water system regarding its
condition, performance, criticality, and costs to help make informed decisions at an
operational level;
b) Second, they have poor transferability; most models are developed for specific locations
and certain pipe materials and cannot be used in other regions or with different pipe
materials. This precludes using a uniform model as a benchmarking tool;
viii
c) Third, they have limited use at an strategic level and are often criticized by expert opinion
at operational (Hong, Allouche, & Trivedi, 2006; Bagheri & Hjorth, 2007; Bianchi, et al.,
2010; Sægrov, et al., 2003; Loganathan, Park, & Sherali, 2002; Romney & Winsor, 2009;
Shivalingappa, 2014; Walski, 1987, Shamir & Howard, 1979;
d) Fourth, they do not forecast the risk of failure and its consequences for every segment of
the water system as well as the associated costs.
This paper introduces a new approach that entails the performance, condition, and criticality
that every single segment within the entire water system is analyzed. Using the fuzzy logic
method and water system data from a municipality located in southern Ontario, the risk and
consequence of failure associated with each pipe is determined. The fuzzy logic rules and
hierarchy are designed based on risk results and cost.
It has been shown that the proposed method is capable of prioritizing a replacement and
repair program, maintaining balance in revenue and expenses, and providing an acceptable
service level. Due to its disaggregated construct, this proposed method can potentially be
used to benchmark the repair and replacement program between different jurisdictions, areas,
and pipe materials.
The proposed disaggregated prioritizing model is tested for preliminary results based on
limited historical data from southern Ontario. This limitation prevents fully utilizing the
information about linear assets to make informed decisions. The implementation of a
dynamic asset management system could maximize the current asset management exercise
and leverage the existing corporate technologies and tools to support an effective decisionmaking process. In summary, there is a desperate need to manage the existing linear
infrastructure asset to ensure effective action prioritizing techniques to uphold the aging
linear pipes at an acceptable level of service and guarantee the availability of safe drinkable
water to Ontario residents.
ix
1. Introduction
1.1 Background
To supply safe potable water is one of the essential responsibilities of municipal government.
This critical service requires the design, building, operation, and maintenance of a valuable
infrastructure which includes municipal wells, water treatment plants, storage and reservoirs,
pumping stations, transmission lines, and a water distribution network. The global water market
estimated that Ontario’s linear water system cost roughly $424 billion in 2010 (Water Asset
Map, 2011). The water asset is divided into 49.3% utilities and pipes; 22.9% pumps and valves;
10.5% rehabilitation and other professional services; 11.7% equipment; 3.2% industrial water
services and chemicals; and 2.4% irrigation equipment. With the onset of aging infrastructure,
strict regulations, and limited budgets, the tasks of managing municipal infrastructure and
maintaining the expected quality of service are difficult. This precious asset must be managed
throughout its life cycle to ensure enhanced service and to maintain the expected level of service
through its lifecycle.
An approximate estimate of one trillion dollars will be required to keep the current level of
service in Ontario over the next 25 years (AWWA, 2012). The cost of upgrading municipal
water distribution systems is estimated at approximately 11.5 billion Canadian dollars over the
next 15 years (CWWA, 1997). Managing this massive water asset requires a strong asset
management system.
According to ISO 5500, ISO 5501 and ISO 5502 assets management system is an organizational
tool which outlines how to establish, implement, maintain, and improve the system in general
and specifically water system. An important factor in successful asset management process is the
ability to prioritize the importance of asset data throughout the organizational hierarchy.
Currently in Ontario, linear infrastructure asset management is investigated at the three
organization levels of strategically or long term planning; tactical or medium range (five- to tenyear programs); and finally operational level or annual projects.
Although all levels of the organization are equally responsible for the principles of optimizing
the life cycle of the assets to enhance the end users' satisfaction by improving the performance
10
and satisfaction of all required standards, the majority of past efforts have covered the high
strategic level without including or clearly understanding operational and management
requirements. In addition, since the municipalities’ financial resources are limited, there is a
desperate need to prioritize regular inspections, maintenance, and renewal actions to efficiently
and optimally utilize funds in areas that require it most.
A comprehensive asset management plan optimizes the priorities of asset utilization in shortterm performance, in long-term sustainability, and between capital investments. An asset
management plan is simply more than the capital and operating costs of the linear assets over a
predetermined life span (PASS 55-1 and 2 – 2008). Optimizing the asset life depends upon risk
exposures, performance attributes, criticality, as well as the specific physical condition of the
specific linear assets - in this case watermain system - while maintaining adequate service levels.
Understanding the needs of an asset and knowing when to take action are prime considerations
that must be determined; prioritizing the selection of assets requiring attention is the key decision
of this process.
This research outlines the methodology, selection parameters, and criteria for linear asset
maintenance prioritization while providing a financially sustainable system. Assessing the asset
condition, performance, and criticality as well as the associated risk of pipe failure will provide a
priority system that is subject to change over time due to changing conditions and performance.
Allocating the expected value of funds needed to achieve this work and retaining a contingency
fund to hedge uncertainty in costs is a prerequisite for financial sustainability. Despite the
numerous asset management methodologies at strategic levels that are available and employed
by different levels of government organizations and municipalities such as life cycle cost
analysis, maintenance reliability, risk based inspection, total productive maintenance, and
cost/risk optimization, there is still a huge asset management gap at the operational and
maintenance levels which needs to be fulfilled.
Several asset management models are used in various fields such as pipe infrastructure,
transportation, electrical grids, manufacturing, mining, and oil applications (Rehan et al., 2013;
Ispass, 2007; Pet Armacost et al., 1999; Ayyub, 2003; Aven et al., 2007; Association of Local
Government Engineering New Zealand, Inc., 2006). These models generally quantify the risk of
each individual component such as each pipe by multiplying the likelihood of an event with the
11
consequence of the event. The performance score represents the probability of failure over a
known time horizon while the condition score of the asset represents the likelihood of failure
(Opila & Attoh-Okine, 2011). Consequences of failure include economic, social, and
environmental factors. The significance of all these factors is measured by dollar value at net
present value. Without a clear understanding of the actual costs associated with linear
infrastructure, maintenance and operation planning is impossible.
Ontario's Regulation 453/07 requires municipalities to prepare a detailed six-year financial asset
management plan including projected total revenues, expenses, annual surplus, and deficits
(Ministry of the Environment, 2007a). In addition, the prepared financial plan for water and
wastewater service should ultimately be financed from the user fee-based revenues of these
services (Ministry of the Environment, 2007b). According to Bill 13 (2010), Ontario
municipalities are responsible for ensuring the full recovery and full cost accounting of the water
and wastewater system to sustain their services. The Ministry of the Environment is authorized
to monitor their finances, maintenance, and operational performance (Ministry of the
Environment, 2011). Currently, as Ontario’s population expands, development charges carry
more than 40% of the maintenance, repair, and replacement costs of aging linear assets
maintenance. Therefore, to satisfy all regulations, standards, and by laws, a sustainable financial
management plan combined with an asset management plan is crucial to cover the remainder of
the costs and hence maintain the same level of service. This proposal introduces a pipe repair and
replacement prioritizing process plan at an annual operational level that could potentially provide
a dynamic financially sustainable water system.
1.2 Problem Statement
Maintaining the water distribution and transmission system is critical, not only for abiding by the
Safe Drinking Water Act (MOE, 2002a), but also to avoid certain negative consequences. For
instance, in 2010 three people were killed in Guatemala by sinkholes caused by a watermain
breakage (CNN, 2010). Watermain breaks routinely cause structural damage to the foundations
of buildings and infrastructure across North America. The objective of an infrastructure assetmanagement plan is to provide a feasible solution to manage these linear assets (Hudson et al.,
1997). Due to the limited budget of all Ontario municipalities, funding the condition assessment
of existing infrastructure requires an optimal least-cost allocation of expenditures (Opila et al.,
12
2011). The objective of an asset management process (Eskaf et al., 2014) is to deal with issues
such as changing population demographics, consumer demands, and aging infrastructure. Asset
management combines engineering and financial analysis methods (Rehan et al., 2013); dataset
integration models (Younis, 2010; Halfawy & Figueroa, 2006); tools to rank the existing
condition of the assets (Opila, 2011; Rehan, Knight, Unger, & Haas, 2013; Rizzo, 2010);
forecasting techniques to predict the expected service life of the existing assets (Younis &
Knight, 2010; Ana & Bauwens, 2010); prioritizing strategies to properly manage the asset
service life (Opila & Attoh-Okine, 2006; Saegrov, 2006; Mogila et al., 2011; Younis, 2015); as
well as financial planning models to cover the life cycle costs of the asset (Kleiner et al., 2001;
Kleiner & Ranjani, 2004; Kleiner et al., 2006; Rehan, Knight, Unger, & Haas, 2013; Shadpour,
Unger, Knight, & Haas, 2014). Interconnected technical engineering techniques, financial
models, and social methods are required in managing the existing linear assets. Additionally,
financial self-sustainability implies that increasing user fees payable to local municipalities are
used to generate revenue to meet expenses. The affordability and expected change in yearly
increases of user fees must be justified and approved by publicly elected officials such as
regional council members. Statistically, 80% of water system expenditure comes from
maintenance and performance to keep the accepted level of Service (Kleiner & Rajani, 2001).
While considerable past efforts have mostly covered the strategic and tactical level as
implemented by different government agencies in Canada and around the world, current
operation-asset management practices in Ontario are still limited to heuristic approaches. The
current practice is generally a reactive approach to cover issues and is not reproducible from one
location to another; hence, this current practice is suboptimal in its accomplishments.
Information about linear assets is not fully utilized in making informed decisions. Moreover, an
operation-asset priority system that offers a proactive approach toward managing infrastructure
and identifying critical areas has yet to be proposed. Therefore, there is a desperate need for an
automated dynamic asset management system achieving pre-defined levels of service in the most
cost-effective manner through the acquisition of rehabilitation and renewal programs.
The implementation of a dynamic asset management system at operation level will maximize the
current asset management exercise and leverage the existing corporate strategic priorities and
tactical programs to support an effective decision-making process. In summary, there is a huge
identified knowledge gap between managing the existing linear infrastructure asset at an
13
operation level to ensure effective action prioritizing techniques and keeping the aging linear
watermains at an acceptable level of service. An asset management system will guarantee the
availability of safe drinkable water to Ontario residents, maintain the current level of service, and
support strategic and technical programs.
1.3 Organization of the Proposal
This proposal is organized as follows: Chapter 2 provides a review of relevant literature on the
development of risk techniques and asset ranking systems as well as financial planning methods
supported by current asset management models; Chapter 3 covers the proposed methodology to
meet the objectives of the research; Chapter 4 provides a sample data set and preliminary results;
whereas Chapter 5 explains future plans to achieve the goals by following a reasonable schedule
for the planned work.
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2. Literature Review
The majority of infrastructures were built post Word-War II without considering maintenance
cost or upholding a certain level of service to consumers in Ontario (Enouy R. , Rehan, Brisley,
& Unger, 2015; VEATCH, 2015; Moustafa, 2010; Bai, Xinghua Zhi, Zhu, & Meng, 2015;
Alvisia & Franchinia, 2014; Opila & Attoh-Okine, 2011; Jenkins, 2014; Conestoga-Rovers,
2010; ISO-55000, 2014 (E); ISO-55001, 2014). With municipalities’ limited budgets, the
traditional pay-as-you-go approach for operating and maintaining the water system is no longer
possible. There is a need to have a coordinated activity plan through a system of organization
that optimally and sustainably manages condition of parts, performance, criticality, risks, and
expenditures over a water system's lifespan for the purpose of achieving municipal
organizational strategic plan. This is called an asset management plan.
Therefore, having a proper asset management plan is vital for organizing the performance and
physical condition of the asset as well as its critical measurements to deliver the proper service
(PASS 55-1/2008). Performing proper asset management includes different levels such as long
and short term strategic planning to create high-level planning policies and standards. As well,
the performance and operational level includes maintenance and capital planning (PASS 551/2008). According to several international asset management standards such as ISO 55000, ISO
55001 and ISO 55002 (2014) provide the opportunity to identify the individual life cycle
optimization that contributes to overall tactical and strategic asset management policies.
Therefore, an integrated asset management system is an essential step to optimize the asset's life
cycle for priorities in risk profile (PASS 55-1/2008). Figure 2-1 presents the different levels of
the asset management system.
Although all organization levels are responsible for having a proper asset management plan,
most past efforts have covered the high strategic level and policies. Traditionally, the operation
and maintenance levels are reactive projects to resolve immediate watermain and water system
issues. In addition, all standards and regulations toward water sustainability require ranking and
prioritization plans at a high strategic level (Hunter, Donmoyer, Chelius, & Naumick, 2011).
Therefore, having a sophisticated proactive watermain prioritizing system to support the annual
decision-making process is mandatory for each municipality (Opila, 2011). On the other hand,
water transmission and distribution systems are complex as they are influenced by several factors
15
such as performance, location, condition, age, pipe material, and many more. Even though
several different types of asset management plans and several modeling approaches have been
attempted by previous studies, these have mostly covered strategic and tactical programs at a
high level rather than operational and annual watermain maintenance projects and decision
support systems.
Figure 2-1 Levels of Asset Management System
Strategic
Long Term Policies
(25-50 year plans)
Tactical
Short Term Programs
(5-10 year programs)
Operation and Maintenance
Short Term Projects
(annual projects)
Tables 2.1, 2.2, and 2.3 summarize past research within their related categories.
Table 2-1 Related Literature in Strategic Asset Management Category
Reference
Year
Description
Shortcoming
Kleiner et al.
1998
Optimizing rehabilitation
strategies based on physical
property of pipes
Saegrov
2003
Long term planning tool to
predict pipe failure
Not enough physical
characteristics
included
Cost-driven model
with too many
assumptions and
physical
characteristics not
included
Strategic
Burn et al.
2003
Jarret et al.
2003
Long term asset management
system predicting watermain
failure - planning and budget
setting
Long term planning tool to
predict pipe failure and
watermain failure with physical
conditions
16
No conclusion or
mitigation
measurements
No conclusion or
mitigation
measurements
Bagheri and
Hjorth
2007
Casual Loop Diagram (CLD) to
propose a sustainable water
system
System improvements
are too complicated
and are not validated
Bianchi et al.
2010
Casual Loop Diagram model
(CLD) identifies long term poor
performance
System improvements
are too complicated
and are not validated
Xu et al.
2013
Long term repair / replacement
optimization model - minimizing
the annual cost
Not validated incomplete results
2013
Casual Loop Diagram model
(CLD) used to improve network
financial management
Internal relationship
is too complex, there
is no final decision,
and this research is
not validated
Rehan et al.
Table 2-2 Related Literature in Tactical Asset Management Category
Reference
Shamir and
Howard
Geehman
Year
Description
Shortcoming
1979
Individual pipe life cycle
analysis - repair cost vs.
replacement cost analysis
Cost-driven model planning period was
assumed to be the
same as optimal
replacement time
1999
Scoring methodology and
weighting factors to
predict the watermain
break for CI pipes
Only one pipe
material - not
transferable - costdriven model
Tactical
Kleiner et al.
2001
Kleiner and
Ranjini
2004
Kleiner et al.
2004
Individual pipe life cycle
analysis - improved
Shamir and Howard study
by extending the decision
period
Large watermain
replacement program
based on lowest total
replacement cost
Individual pipe life cycle
analysis - improved
Kleiner et al.'s 2001
research by constant
replacement time
17
Cost-driven model limited planning
period
More work required missing required cost
Cost-driven model
Hong et al.
2006
Moglia et al.
2006
Ranjani et al.
2006
Kleiner et al.
2006
Dandy and
Englehart
2006
Individual pipe life cycle
analysis - prioritizing
method by minimum
expected average annual
cost
Integrated cost modeling
approach to prioritize the
watermain replacement
Cost-driven model too many
assumptions such as
fixed planning period
Cost-driven model no risk mitigation
plan
Fuzzy Logic method to
translate visual inspection
to pipe condition
Not completed and no
mitigation plan was
proposed after rating
Fuzzy Logic method used
to evaluate the
consequences of failure
Watermain prioritization
program based on
optimizing number of
breaks and replacement
cost
Limited mitigation
plans - cost-driven
method
Several required costs
were missing from
the equation - not
validated
2008
Pipeline condition rating
method identifying the
most significant factors on
watermain failure
Not enough physical
characteristics - not
validated
2008
Multiple objective
hierarchy optimization
model for replacement
program
More than one
optimal solutions post processing is
needed - not reliable
results
Fares and Zayed
2010
Risk of failure prediction
model based on pipe
classification and
consequence of failure
Not enough physical
characteristics - not
validated
St. Clair and
Sinha
2014
Fuzzy Logic method used
to evaluate the remaining
life of the asset
Cost-driven model too much information
and data requirement
Al Barqawi and
Zayed
Giustolisi and
Berardi
Table 2-3 Related Literature in Operational Asset Management Category
Operational
Reference
Year
Description
Shortcoming
Deb et al.
1998
Annual replacement program for CI
pipes only
Only one pipe material
- not transferable - costdriven model
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2.1 Research Gaps
A comprehensive review of the existing literature related to a financially sustainable water
system is provided in this chapter. A summary of the most relevant past efforts within strategic,
tactical, and operational levels are provided. This review clearly shows that there is not enough
literature about the water operational system. This identifies the major planning gap at the
bottom of the asset management organizational hierarchy. A financially sustainable water system
is a water transmission and distribution system that recovers all of its costs of maintenance,
repair, and replacement. Achieving an inclusive management plan requires a complete physical
condition assessment from the entire water system at the operational and maintenance level.
After collecting data, a comprehensive ranking model is required to prioritize repair and
replacement planning. Finally, it is essential to assess a watermain's risk of failure.
The age of the pipeline is one of the most important factors affecting pipeline deterioration due
to various corrosion mechanisms over time that lead to leaks and breaks (Moglia et al., 2006).
On the other hand, municipalities face different challenges in reaching a sustainable water
system due to high growth rate, 2% of total pipe length requiring replacement per year, and 70
years of pipe service. Therefore, it is crucial to consider the entire system rather than the design
life of individual pipes over a limited planning time or annual cost. Thus, individual pipe cost
analysis is not an effective method for investigating the water system. Instead, a comprehensive
operational asset management plan is required that includes the current condition of each
individual pipe.
Assessing the actual physical condition of every single pipeline is impossible due to limited time
and budget. Consequently, all available datasets are imprecise. Several models of past
researchers have used methods of evaluating, predicting, analyzing, and replacement/repair in
prioritizing a water system. Along with accuracy and reliability issues, past researchers have not
proposed a comprehensive method of evaluating a watermain's condition, performance, and
criticality.
Most past efforts have been developed with datasets that are highly aggregated both spatially and
physically. In addition, the results are non-transferable to other locations or different water
distribution networks due to several limitations. Several shortcomings have been identified in
modeling methods used by past researchers such as imprecise datasets, few risk factors, and
19
limited deterioration factors and condition ratings. Moreover, some previous research models are
too complex for decision-makers at local municipalities. In response to these shortcomings, this
research attempts to address the identified issues with an inclusive model that will potentially be
used to plan and prioritize maintenance as well as facilitate repair and replacement programs.
All past water system failure prediction models have analyzed the risk of failure based on
replacement cost to prioritize the replacement program. There is a need to assess the risk of
failure without considering the cost of the replacement based on the importance of the service.
This has yet to be proposed.
2.2 Research Objectives
The purpose of the proposed research is to formulate a water linear asset management annual
prioritization plan to facilitate an infrastructure's annual capital projects and optimize the
decision-making process by:

Providing a ranking system for water infrastructure’s condition, performance, and
criticality;

Assessing the entire water transmission and distribution system at an operational level
based on current performance, condition, and criticality of each individual pipe within the
system;

Developing a watermain prioritizing method to accurately rank all existing water
transmission and distribution lines with a transferable disaggregated method that is not
limited to certain pipe material, location, or any other specific variable;

Completing a comprehensive performance and condition risk assessment (separate from
cost analysis) based on important scientific, physical, and measurement factors;

Estimating the required annual budget for watermain repair, replacement, and upsizing
based on proposed ranking method;

Providing a strong operational decision-making support system for a watermain's annual
planning, future forecasting, and budgeting; and

Validating the proposed method using actual data.
20
This research proposal identifies an improved method of managing linear assets, thereby
ensuring effective decision-making as it pertains to infrastructure assets at an operational level.
21
3. Proposed Methodology
In this chapter, a novel method is proposed to prioritize a watermain's repair by replacing and
upgrading programs to form a sustainable water system for each municipality based on existing
condition, performance measurements, risk variables, and their impacts on the watermain's linear
system. The goal is to propose a sustainable watermain system and supporting decision-making
process for those who are responsible for operation and maintenance. Furthermore, the results
may provide benchmark value according to the existing and expected level of service as well as
precisely estimate the resources needed for each municipality to cover all risk per annum. This
research should conclude with comprehensive financial planning to provide a sustainable
watermain system.
A critical factor in managing linear assets at an operational level is the ability to prioritize
maintenance, repair, or renewal activities. Traditionally this process is based on expert opinions
and is supported by either the optimal benefit-to-cost ratio or the failure of a critical asset. Each
municipality has a specific need based on a unique existing linear asset and its individual
condition. In typical Ontario municipalities, decisions are made for individual pipes and are paid
for as needed. This traditional method is not connected to long-term planning and does not
follow a proper decision-making support system.
3.1 Multi-perspective Modeling Framework
This research attempts to cover all planning requirements for a sustainable water system at an
operational level. The proposed method will define the approach to condition assessment and
performance measurement and link this approach to financial planning to forecast a unique
required budget. All parameters, criterion, and weighting factors have been established in
separate models to evaluate and prioritize the decision-making process in different categories.
The proposed models define the condition, existing and expected performance, and risk
associated with the water system. Next, the cost analysis is proposed based on existing and
expected condition, level of service, performance, and risk to benchmark the true annual dollar
amount required to cover all risk and maintain the expected level of service. This proposal could
be a considerably valuable operational performance measurement tool to evaluate the current
system, assist with annual budget planning, and possibly assist with budget forecasting at the
22
tactical asset management level. Figure 3-1 provides an overview of the proposed research
method.
Figure 3-1 Proposed Method Overview
X KM Complex Water System
Ranking entire water system
to identify pipes that require
further investigation
X KM pipes
identified
Operation
Mitigation
Investigation
Mitigation Plan (m Replacement, m
Repair, m Upsize)
Total Annual
Operation Budget
23
3.2 Prioritizing Action Number (PAN) Model
Each municipality owns a large number of water pipe segments of various age, condition,
performance, and level of service. The first step in prioritizing maintenance is to identify the
pipes that require additional analysis and further investigation. PAN is a score that rates assets in
relative priority based on their condition and performance. An asset is rated on any given
parameter or number of parameters (X), given a score (Y), and weighted (Z). The product of YZ
is the PAN score. The sum of PANs for all parameters represents the overall PAN for each asset.
PAN can be calculated as follows:
𝑃𝐴𝑁 = ∑𝑥𝑖 𝑌𝑍
Equation 3-1
The total PAN score (or the overall PAN score) for an individual asset or network of assets will
assist in prioritizing the selection of assets. Higher numbers represent a higher priority. The total
PAN number includes three categories that prioritize condition, performance, and criticality to
each individual asset. The evaluating asset parameters are:

Condition – The current physical condition of the existing active linear pipe;

Performance – The measure of a pipe's ability to operate or perform as required to meet
customer needs within an expected level of service; and

Criticality – The impact due to loss of service and the likelihood of water failure and its
consequences.
Figure 3-1 presents the general methodology of computing the PAN score. Each parameter is
evaluated and ranked based on a number of criteria. Each criterion is scored on the basis that the
high number identifies a higher risk or impact of failure. All parameters, criterion, and weighting
factors used to develop priority action numbers for linear water assets are selected based on the
literature available through different watermain scoring systems. This selection is confirmed by
brief interviews with decision-makers, maintenance crews, engineers, and members of planning
and finance departments in one of Ontario's municipalities.
3.2.1 Assigning Weights to the Selected Parameters for Linear Assets
The Z-factors or weights are introduced to assess each linear asset. Higher weights represent the
increasing criticality of each parameter. The weighting factors are selected based on expert
24
judgement provided by the results of interviews with municipal decision-makers and
maintenance crew members concerning where the performance of water assets will have the
greatest impact on the water score. Table 3-1 presents the consigned weighting factors. Each
weight score is assigned based on importance and impact of each parameter on the final asset
score as confirmed by municipal staff via interviews.
Figure 3-2 PAN Number Flow Chart
Table 3-1 Weighting Factors
Parameter
Weight
Condition
8
Performance
10
Criticality
6
Performance holds the highest weighting on water assets. The high weighting score indicates the
significant correlation between performance, level of service, and safety. Watermain
performance is ranked high due to the need to meet required service levels on an ongoing basis.
Condition is ranked slightly less due to the ability to maintain short-term performance through
repairs. In addition, the criteria for performance are found to be less definite and therefore reduce
25
the impact. For example, a history of high breaks does not necessarily mean that the pipe is at
risk of failure.
Risk is considered an intangible prediction based on critical factors. The criticality score is given
the lowest weighting as it is not meant to drive the prioritization. Instead, the criticality score is
intended to function more as a mechanism to identify the pipes that are more important or
difficult to repair (large diameter or in an environmentally sensitive area or easement) and which
should therefore be higher in the ranking.
3.2.2 Condition Measurement Model
Condition, which is considered a physical property of linear assets, directly contributes to
performance measurement. A selected condition assessment protocol (i.e. WRc, NAPPI)
provides condition grading based on structural defects, operational defects, and hydraulic
properties. In a typical system, higher condition grading scores indicate poor condition. If an
asset’s condition is poor, it will not perform well and will be vulnerable to excessive or
catastrophic failure. In this case immediate attention is necessary or it is likely that there will be
extra or excessive costs for emergency repair in the near future. The parameters that define the
condition of the linear assets are service life or age of the asset, number of breaks experienced at
each individual pipe both overall and in the final five years of the asset, as well as maintenance
factors.
3.2.2.1 Remaining Service Life (RL)
Every linear asset is designed for an anticipated service life, and each asset is expected to
provide service at a certain level for the duration of this service life. However, an asset's service
life has been proven to change based on soil condition and pipe material. The remaining life
(RL) of an asset can be defined as:
RL = Expected Life – Age in Service
Equation 3-2
Table 3-2 shows scores for the remaining life ranges of pipes.
26
Table 3-2 Remaining Asset Service Life Score
RL (years)
Score
<15
15
15 - 29
10
50 - 30
5
>50
0
The minimum remaining life of less than 15 years is chosen to coincide with the typical
maximum lifespan of a road surface. Therefore, if a watermain has an RL < 15 years, then
replacement may be given additional weighting as the pipe will likely require consideration
before the next regular road resurfacing is required. Since the design service life is normally 70
years, the RL > 50 years is considered a new pipe with a score of zero due to less likelihood of
needing replacement.
Condition parameters consider the predicted remaining service life of pipes both inside and
outside of corrosive soils. Soil corrosively is identified as soil with high clay content. The
following parameters for the remaining service life are considered:

Material Types: Asbestos cement (AC), cast iron (CI), ductile iron (DI), poly vinyl
chloride (PVC), poly ethylene (PE), steel (ST), copper (CU), concrete pressure pipe (CPP),
and concrete (CONC);

Soil Corrosively: Corrosive and non-corrosive properties; and

Age: Pipe's age based on construction year, time of major repair/rehabilitation, or
time of complete replacement.
27
Table 3-3 Corrected Expected Service Life of Pipe based on Corrosion Factor
Material Type
AC
CI
CU
DI
PV
C
P
E
S
T
CPP/CONC
VC
Expected
Service Life (ESL)
7
0
7
0
8
0
7
0
8
0
4
0
7
0
8
0
8
0
Corrosion Factor
0.1
0.3
0.1
0.5
0.1
0
0.3
0
0.1
Corrected ESL
(rounded)
6
5
5
0
7
0
3
5
7
0
4
0
5
0
8
0
7
0
As shown in Table 3-3, pipes in corrosive soils are given a reduction to their remaining life
expectancy. The corrected estimated service life based on material and corrosion factors is also
presented in this table. The installation date which is available in the dataset represents the years
in service.
Some non-ferrous pipes are assigned a corrosion factor to account for metal fittings (valves,
bends, services, etc.) that will weaken at these locations similarly to ferrous pipes, thereby
causing breaks in the watermain and reducing the usable lifespan of the pipe itself.
3.2.2.2 Total Breaks
Watermain breaks are the primary concern and the single highest factor in maintenance costs and
service disruption. Breaks are also the leading indicator of the watermain's condition. Scores are
provided according to the number of breaks with consideration of the following factors:

Total number of breaks per km (η/L)

η = Total number of breaks during asset's life cycle

L = 1 km

Number of breaks within last five years (η last 5 years/L)

Type of breaks
Pipes with a node-to-node distance of less than 1 km are prorated to breaks per kilometre.
Scores for the total number of breaks are summarized in Table 3-4.
28
Table 3-4 Scores for Total Number of Breaks
Total Breaks (η/L)
# Breaks
Score
>9
5-9
15
10
1-4
5
0
0
3.2.2.3 Recent Number of Breaks within Last Five Years
Assessing breaks that have occurred within the last five years identifies pipes with a recent
history of problems. This is an excellent indicator that the pipe is reaching the end of its lifespan.
Scores for the total number of breaks in the last five years are summarized in Table 3-5.
Table 3-5 Scores for Total Number of Breaks within Last Five Years
Breaks within last five years (η last 5 years/L)
# Breaks
Score
>5
3-5
1
51
1-2
05
0
0
3.2.2.4 Maintenance Index
Maintenance activities include operational (annual cost $); maintenance (flushing, regular
inspection); and repair and rehabilitation (break repair/leakage) work during the life cycle of an
asset. Operation and maintenance cost is the known amount of money referred to as the
Maintenance Index (MI). MI can be expressed as a percentage value and is quantified by:
MI = (Operational + Maintenance) $ / Replacement $
Equation 3-3
The present value of an asset consists of Total Costs (TC) including initial costs and all future
operational, maintenance, and repair costs for a specific time period (N) and discount rate (i)
brought up to present dollar value. Operation and maintenance cost is a value known by each
municipality to forecast the budget requirements every year. Currently in Ontario, each asset
29
requires assessment at least once every 10 years by its operating agencies. The operation and
maintenance placement costs depend on diameter, length, and depth of the linear pipe.
Scores for MI are summarized in Table 3-6.
Table 3-6 Maintenance Index (MI) and its Scores
Description
Levels
Score
>5%
High
1 – 5%
<1%
Medium
15
10
Low
5
The average overall maintenance cost of maintaining watermains throughout a region located in
southern Ontario is used as the benchmark for this index. When comparing the average total
maintenance cost over the total replacement value of the system, the maintenance index (MI) is
calculated at 1%.
3.2.2.5 Total Condition Score Number
Figure 3-3 Total Condition Score
RSL
(0-15)
Total
Breaks
(0-15)
Breaks in
last five
years (0-15)
MI
Weight
(0-15)
(8)
Condition
Score
(MAX 480)
The total existing condition score for each linear asset is equal to the sum of condition scores
multiplied by the weighting score. The higher the number, the worse the existing condition of the
watermain. For example, with a condition score of 480 as the highest score, the watermain has to
be replaced. However, the timing to replace this watermain is determined later by risk score and
performance score.
3.2.3 Performance Measurement Model
Performance of a watermain system is critical for meeting all guidelines of an operating water
system while delivering an acceptable level of service. The performance of a pipe is indicated by
hydraulic parameters including all hydraulic properties such as head loss (HL/L, flow, pressure,
30
and capacity). Water quality is also a major concern, as performance indicators should follow
water quality standards. Factors for considering conformance to standards are associated with
minimum sizing (diameter) and land use requirements.
3.2.3.1 Hydraulic Capacity
Hydraulic properties as a criterion include capacity, head loss (HL/L), flow velocity, and
pressure. Head loss is considered the main measured criterion for transmission mains,
distribution feedermains, and local watermains (50 mm to 300 mm in diameter). Higher head
loss values indicate that a pipe may be undersized and should be considered for replacement by a
larger diameter main.
Head loss gradient scores are assigned based on diameter, smaller diameter (0 – 600 mm), and
larger diameter (600 mm). Scores for hydraulic capacity are summarized below.
Table 3-7 Head-Loss Scores
Description
Small (0 – 600 mm Φ) HL/L
Score
Large (≥ 600 mm Φ) HL/L
> 5.0 m/km
> 2.5 m/km
30
2.0 – 5.0 m/km
1.5 – 2.5 m/km
15
≤ 2.0 m/km
≤ 1.5 m/km
0
3.2.3.2 Water Quality
Water quality parameters are very important factors in providing quality service. According to
the Clean Water Act, certain water quality standards are currently adopted in Ontario. Data is
available by each municipality regarding customer complaints about poor water quality including
odour, colour, chlorine residuals, etc. In addition, all required tests recording chlorine residuals
for dead-ends and other problem area pipes in the system are available by each agency as well as
all required water quality tests at pumping stations and reservoirs before entering the water
system. The scores presented in Table 3-8 have been considered for water quality.
31
Table 3-8 Water Quality Score
Description
Score
Water quality-related complaint or
poor chlorine residual test
15
Unlined CI (lead joint WM only)
15
Remaining Pipes
0
3.2.3.3 Conformance to Standards
Conformance to minimum sizing standards is an important parameter to sustain the required
level of service. Two important factors for considering conformance to regional standard
specifications are land use criteria and minimum sizing for pipe diameter. According to Ontario's
standard based on types of land use, the minimum watermain diameter for industrial sites, high
density residential areas, commercial properties, schools, hospitals, long term care centers, and
medical offices is 300 mm.
Table 3-9 summarizes scores for pipes conforming to standards.
Table 3-9 Standard Conformance Scores
Description
Score
Pipes with a diameter <100 mm (Copper excluded)
15
Pipes with a diameter <300 mm in the following land use areas:



Industry
High density residential / commercial
15
Schools / hospitals / long term care centres
Pipes with small services (service diameter < 19 mm)
10
Remaining pipes
0
3.2.3.4 Total Performance Score Number
The total performance score for each watermain is equal to the sum of head-loss score, quality,
and standard conformance scores multiplied by the weighting score. A high performance score
either indicates a low level of service or that a pipe is not performing well. For example, a pipe
section with a performance score higher than 500 means that the watermain has to be replaced or
32
upsized to provide acceptable service. However, based on risk score and condition score,
replacement can be planned and scheduled.
Figure 3-4 Total Performance Score
Capacity
Quality
(0-30)
(0-15)
Standard
(0-15)
Performance
Score
Weight
(10)
(MAX 600)
3.2.4 Criticality Model
The importance of loss of service to the customer, the impact of failure to the surrounding
environment, and the ability to effectively repair failed assets are called criticality. Size, location,
land use, and accessibility (depth and easements) are all factors that impact the criticality, cost,
and time associated with emergency watermain repairs. For example, repairing a pipe with a
large diameter that is located in an environmentally sensitive area and is servicing a hospital with
poor accessibility would be more critical than repairing an accessible large diameter pipe located
in a sensitive area.
3.2.4.1 Diameter
The impact of failure has an increasing linear relationship with pipe size. Due to the higher cost
of repair, the potential to cause higher levels of damage and harm, and a higher number of
customer service interruptions, large diameter pipes have higher consequences of failure.
Therefore, lower scores are assigned to smaller assets as shown in Table 3-10.
Table 3-10 Diameter Scores
Pipe Diameter
Score
>750 mm
15
600 mm – 750 mm
10
400 mm – 600 mm
5
<400 mm
0
33
3.2.4.2 Environmentally Sensitive Areas
Environmental factors measure the impacts of performance, condition, and risk factors when
catastrophic failure occurs. When watermain failure occurs, all of its surrounding environment
may be compromised by risk. The high risk environmental areas or Environmentally Significant
Policy Areas (ESPAs) to watermain failures include:

Watercourses such as creeks, rivers, and ponds

Hospitals, airports, and long term care centers

Hydro corridors and high voltage hydro poles

Gas lines and pipelines

Major intersections, highway crossings, and railway crossings
The above information is available in GIS at any municipality. Scores are assigned based on the
presence of an Environmentally Significant Policy Area (ESPA). Table 3-11 summarizes the
assigned scores.
Table 3-11 Environmentally Sensitive Area Score
ESPA/Watercourse
Yes
Score
15
No
0
3.2.4.3 Accessibility
Similar to environmentally sensitive areas, accessibility becomes an issue when there is a need to
take mitigating measures in an emergency situation in which immediate, unfettered access to the
infrastructure is required to undertake repairs and to prevent further damage or interruption of
service. Areas where accessibility to infrastructure may hamper corrective measures include:

Narrow or no easements

Extra deep infrastructure

Impassible access to vehicles
This information is available by legal plans at each municipality; however, much of this
information can be obtained through observations from the Operations and Maintenance staff.
Table 3-12 presents the given accessibility scores.
34
Table 3-12 Accessibility Score
Accessible
Score
No Easement
15
Marginal (<9 m wide easement)
10
Yes (>9 m wide easement)
0
3.2.4.4 Total Criticality Score
Figure 3-5 Total Criticality Score
Diameter
Location
Accessability
Weight
(0-15)
(0-15)
(0-15)
(6)
Criticality
Score
(MAX 540)
The total criticality score for each watermain is equal to the sum of the diameter of the
environmentally sensitive area and the accessibility score multiplied by criticality. A high
criticality score indicates a high consequence of failure or a critical pipe location. For example,
higher criticality scores more often lead to higher cost and consequences of failure. Therefore,
pipes with high criticality scores require more attention to keep the performance score and
condition score low.
3.2.5 Total PAN Number
Due to money and time constraints, it would be impossible to assess all watermain systems every
year. Therefore, a priority action plan is required to identify problematic pipes. One goal of this
research is to provide a priority plan for repair, replacement, and upsize program for water assets
based on existing condition, performance level, and criticality as well as associated failure risk.
This plan is called Priority Action Number (PAN). The process is automated by Microsoft-based
software that is easy to use, easy to access, and can be installed by all government agencies. This
technique will provide all scores and PAN numbers dynamically. The figures below illustrate the
framework for a water and sewer asset’s PAN calculation. The framework shows how to
35
calculate the overall PAN for an asset and to compare PAN scores within the network of assets
for the purpose of prioritization. The higher the overall score, the higher the need for action.
Using this method to assess an entire water system is easier and faster than any other proposed
method thus far. As a result, all PAN scores are transferred to the GIS map into different bins
with different thresholds to identify the pipe section with high PAN scores. Thus, the limited
time and budget would focus on highlighted pipes instead of the entire system.
Figure 3-6 Priority Action Number (PAN)
Condition
Score (0 - 480)
Performance
Score
Criticality
Score
(0 - 600)
(0-540)
PAN
(MAX 1620)
After identifying the PAN thresholds and highlighting the PAN sections that require more
investigation, two layers of fuzzy analysis are proposed to interpret the expert judgments as well
as investigate the failure risk within the system. The second layer mitigates the failure risk with a
proper maintenance program such as replacement, repair, and upsizing of pipes.
3.3 Fuzzy Logic Analysis
Fuzzy models comprise a problem-solving method (Zadeh, 1965) that uses imprecise data as
well as inexact expert opinions. Decisions must be made, even with vague or inaccessible data
and incomplete knowledge (Karray & de Silva, 2004). Therefore the fuzzy logic model is
recognized as a useful method of analyzing the watermain system and its risk of failure for the
following reasons:

experts’ knowledge and experience are very important factors in any watermain repair
and replacement program;

vague watermain issues such as watermain programs must be aligned or combined with
other programs including road resurfacing;

reasonable decisions have to be made even with incomplete experts’ knowledge; and

most available watermain data within Ontario municipalities do not accurately forecast
future failure.
36
Fuzzy logic model is a logical nonlinear forecasting tool which uses an imperfect dataset (Fayek
& Sun, 2001). The if-then relationship between the different parameters within the analysis as
well as input and output values can be presented by linguistic terms such as high, moderate, and
low. The results of each rule can be combined and defuzzified to a final output (Jang, 1993; Jang
& Sun, 1995). In addition, expert opinion and historical values can be translated into rules that
affect the final output result (Mahabir et al., 2003). Fuzzy logic procedure is comprised of four
parts: fuzzification of all input parameters, if-then rules by expert opinions, aggregation of rules,
and defuzzification of the output into different sections. The membership function of each
parameter is established with expert judgment and translated into if-then rules for input - output
variables. After combining the input parameter with rules and the degree of membership, the
outputs need to be defuzzified into a single value. The number of input parameters and variables
and their linguistic terms such as low, moderate, and high introduces the number of if-then rules.
In general, m input parameters and n linguistic terms result in nm rules. Fuzzy analysis occurs in
the four steps listed below:
1- Inputs Fuzzification
2- Fuzzy Rules Evaluation
3- Consequent Aggregation
4- Defuzzification of Each Rule into Output
37
Figure 3-7 Two Level Fuzzy Logic Analysis Chart
First Layer
Fuzzy Analysis
Second Layer
Fuzzy Analysis
All four steps are completed for each layer of fuzzy analysis. This research proposes two layers
of fuzzy analysis to measure the performance, condition, and criticality for high PAN score pipe
section in layer one and to mitigate the risk of failure in layer two. Figure 3-7 presents a full view
of the two-layer fuzzy analysis. Thus, the result is obtained at the end of the second fuzzy
analysis layer in terms of linear metre of pipe with known diameter and pipe size. Finally, by
considering repair, replacement, and construction unit costs along with second layer fuzzy
results, the required annual operation and maintenance budget for water distribution and
transmission lines in the system can be estimated.
3.3.1 First Layer Fuzzy Expert System for Watermain Sections with High PAN Scores
After identifying pipe sections that are in the threshold of high PAN scores, the risk must be
analyzed within PAN score calculation parameters using MATLAB software and Fuzzy Logic
Modeling. All five steps will be completed for each item based on condition, performance, and
criticality scores as explained in section 3.2. The purpose of this layer is to analyze the risk
38
within each parameter as well as evaluate the expert judgment with a scientific method to
increase the accuracy of the proposed performance measurement technique.
3.3.1.1 Membership Determination for all Different Parameters
Membership functions can be formed by triangle, trapezoid, bell curve, or any other type of
function depending on desired accuracy. This step converts the data input of all parameters
described in section 3.2 such as RSL, total number of breaks, diameter, accessibility, and
standard conformance into fuzzy value within 0 and 1 for membership function.
As explained in section 3.2 and its subsections, all factors are observed and selected based on
information gathered from the literature as well as interview results from different levels of
operational and maintenance workers in a southern Ontario municipality. These factors are
recognized as affecting the risk of failure. All factors are evaluated and scaled between 0 and 15.
In this section all parameters must be fuzzified using trapezoid function by assigning the logical
grade to each score and introducing the fuzzy boundaries for each score. This step is repeated for
all parameters according to condition, performance, and criticality sections. Tables 3-13 and 3-14
demonstrate a sample of the fuzzification steps that are completed for all factors related to
performance, condition, and criticality. After fuzzifying the boundaries for all factors, the degree
of membership graphs are developed for every single factor. Figures 3-7 and 3-8 are presented as
sample graphs for RSL and number of breaks to reveal the degree of membership. This
summarizes step one in the fuzzy logic model.
Table 3-13 Fuzzy Boundaries for Remaining Service Life
RSL (years)
Score
Fuzzy Logic Grade
Boundary of
Trap mf
<15
15
Very High
[0 0 12 20]
15 – 29
10
High
[10 17 28 35]
50 – 30
5
Medium
[25 33 48 55]
>50
0
Low
[45 53 inf inf]
39
Table 3-14 Fuzzy Boundaries for Total Number of Breaks
Total Breaks (η/L)
# Breaks
Score
Fuzzy Logic
Grade
Boundary of Trap mf
>9
15
Very High
[8 9 10 10]
5-9
10
High
[4 5.5 8 10]
1-4
5
Medium
[0 2 4.5 6]
0
0
Low
[0 0 1 2]
40
Figure 3-8 Remaining Service Life vs. Degree of Membership
Figure 3-9 Total Number of Breaks vs. Degree of Membership
41
3.3.1.2 Fuzzy Inference Model in First Layer Analysis
Brief interviews were conducted with several operation and maintenance decision-makers in the
water industry to develop the risk of watermain failure model in fuzzy rules inference. The
Mamdani fuzzy rules system is used to define the if-then rules. The Mamdani method follows a
very simple structure and is easy to understand.
j
j
j
j
𝑅𝑢𝑙𝑒 𝑗 : 𝐼𝐹 𝑋1 is A1 and 𝑋2 is A2 and 𝑋3 is A3 and 𝑋1 is A1 … . then y is B j
Equation 3-4
𝑗
Where 𝑅𝑢𝑙𝑒 𝑗 = 𝑗𝑡ℎ 𝑅𝑢𝑙𝑒 , 𝐴𝑖 (𝑗 = 1,2, 3. . ; 𝑖 = 1, 2, 3 … ), and 𝐵 𝑗 = 𝑓𝑢𝑧𝑧𝑦 𝑜𝑢𝑡𝑝𝑢𝑡
The proposed scientific knowledge-based fuzzy rules cover all possible combinations of
parameters in three separate fuzzy analyses of condition, performance, and criticality with
linguistic membership functions including low, medium, high, and very high. Table 3-15
displays all fuzzy logic grades for condition analysis. Figure 3-16 lists several possible
combinations for fuzzy logic rules.
Table 3-15 List of Fuzzy Logic Grades for Condition Model
Fuzzy Logic
Grade in
Remaining Life
Breaks
Breaks
Occurring in
the Last Five
Years
Maintenance
Index
Very High(1)
Low(1)
Low(1)
Low(1)
High(2)
Medium(2)
Medium(2)
Medium(2)
Medium(3)
High(3)
High(3)
High(3)
Low(4)
Very
High(4)
Very High(4)
-
-
-
-
-
-
-
-
-
-
-
-
-
Condition
Boundary of
Trap mf
Extremely
Low(1)
Very Low(2)
Moderately
Low(3)
[0 1.75 3.25]
Medium(4)
[3.25 5 6.5]
Moderately
High(5)
Very High(6)
Extremely
High(7)
[0 0 1.75]
[1.75 3.25 5]
[5 6.5 8]
[6.5 8 10]
[8 10 10]
The condition variable B is considered in seven linguistic variables: extremely low, very low,
moderately low, medium, moderately high, very high, and extremely high. These are applicable
to all four models (condition, performance, criticality, and risk). Figure 3-10 illustrates the total
condition score related to degree of membership.
42
Figure 3-10 Total Condition Score vs. Degree of Membership
Table 3-16 List of All Possible Fuzzy Rules for Condition Model
Possible Fuzzy Rules for Condition Model
Breaks
Occurring
Rule
RSL
Break
in the last
MI
Condition
Number
Five
Years
1
1
1
1
1
4
2
1
1
1
2
5
3
1
1
1
3
6
4
1
1
2
1
5
5
1
1
2
2
7
6
1
1
2
3
7
7
1
1
3
1
7
8
1
1
3
2
7
.
.
.
190
191
192
.
.
.
4
4
4
.
.
.
4
4
4
.
.
.
4
4
4
43
.
.
.
1
2
3
.
.
.
7
7
7
3.3.1.3 First Layer Aggregation Rules
Each rule that is developed in the last step is evaluated by expert judgement and the membership
value of each consequent membership function is judged once again for output linguistic
variable. After evaluation, the proper linguistic term is assigned to each rule. These terminations
are defuzzified into output in the next step. This process is repeated for each model.
3.3.1.4 First Layer Defuzzification Process
The defuzzification method reconverts the fuzzy consequent value of the linguistic term into a
number. The condition linguistic variable B is considered in seven linguistic variables: extremely
low, very low, moderately low, medium, moderately high, very high, and extremely high. This is
applicable to all four models (condition, performance, criticality, and risk). Since pipes with high
PAN scores have been selected, there are no pipes in extremely low, very low, or moderately low
bins for any category. All pipes in this section are defuzzified into a condition, performance, and
criticality score between 1 and 7.
3.3.1.5 First Layer Fuzzy Logic Model Summary
In first layer fuzzy analysis, the condition, performance, and criticality scores are finally
analysed into seven general scores. Figures 3-11, 3-12, and 3-13 summarize the analysis. The
purpose of this analysis is to minimize the internal risk as well as provide scientific consideration
of the expert opinion regarding the actual scores.
44
Figure 3-11 Fuzzy Logic Model for Condition Score
45
Figure 3-12 Fuzzy Logic Model for Performance Score
Figure 3-13 Fuzzy Logic Model for Criticality Score
46
3.3.2 Risk Analysis at Second Layer Fuzzy System
After identifying the condition, performance, and criticality scores, the second layer of fuzzy
analysis is proposed to investigate the risk of pipe failure as well as the consequence of pipe
failure. The output of this layer is the mitigation technique that is the result of risk evaluation.
The mitigation technique is comprised of four categories of do nothing, replace, repair, and
upsize the pipe section. Figure 3-14 provides the proposed fuzzy analysis to mitigate the pipe
failure risk.
Figure 3-14 Second Layer Fuzzy Logic Analysis
3.3.2.1 Membership Determination for Second Layer
Memberships are determined using triangle functions as a result of the first layer in seven
categories. This section proposes fuzzifying the result of the first layer analysis.
47
3.3.2.2 Second Layer Fuzzy Interference Model
The result of the interview that was completed by decision-makers in the water industry is used
to mitigate the risk of the water main failure model by fuzzy rules inference. Using Mamdani
fuzzy rules system, the if-then rules are defined for the second layer. The proposed scientific
knowledge-based second layer of fuzzy rules covers all of the possible combinations of
condition, performance, and criticality scores with linguistic membership functions of low,
medium, high, and very high. Table 3-17 displays all fuzzy logic grades for risk analysis and
Table 3-18 lists several possible combinations for fuzzy logic rules.
Table 3-17 Second Layer Fuzzy Logic Grades
Condition
Performance
Criticality
Risk Result
Extremely Low(1)
Extremely Low(1)
Very Low(1)
Do Nothing (1)
Very Low(2)
Very Low(2)
Low(2)
Repair (2)
Moderately Low(3)
Moderately Low(3)
Medium(3)
Replace (3)
Medium(4)
Medium(4)
High(4)
Upsize (4)
Moderately High(5)
Moderately High(5)
Very High(5)
Very High(6)
Very High(6)
-
Extremely High(7)
Extremely High(7)
-
48
Table 3-18 Possible Fuzzy Rules for Risk Model
Possible Fuzzy Rules for Risk Model
Rule
Number
1
2
3
4
5
6
7
8
.
.
.
243
244
245
Condition
Performance
Criticality
1
1
1
1
1
1
1
1
.
.
.
7
7
7
1
1
1
1
1
2
2
2
.
.
.
7
7
7
1
2
3
4
5
1
2
3
.
.
.
3
4
5
Risk
Mitigation
1
1
1
1
1
1
1
1
.
.
.
4
4
4
3.3.2.3 Second Layer Aggregation Rules
Like the first layer, every rule developed in this step is evaluated by expert judgement and
membership value. In addition, each consequent membership function is judged again for output
linguistic variable.
3.3.2.4 Second Layer Defuzzification Process
In this step all risk mitigation values are converted into a number that is the length of that
specific pipe section in metres. Thus the conclusion linguistic variable 𝐵2 is considered
according to four linguistic variables: do nothing and conduct further investigation next year,
repair, replace, and up-size. Each of these linguistic mitigation plans are proposed for pipe
sections that have known certain lengths. The length of each section is the result of the
defuzzification process. Therefore, we propose a risk mitigation plan for a known pipe length.
3.3.2.5 Summary of Second Layer Fuzzy Logic Analysis
In second layer fuzzy analysis, the failure risk associated with condition, performance, and
criticality scores is analysed according to four risk mitigation plans. The mitigation plans are
proposed based on expert opinions regarding repair, replacement, and upsizing of the pipes. The
purpose of this fuzzy analysis is to investigate the risk of failure and to propose a plan for
49
mitigating the risk. Therefore, the final result of the second fuzzy layer analysis determines how
many pipe metres need to be replaced, repaired, or upsized in order to maintain the same level of
service ever year.
3.4 Cost Model and Budget Forecasting
Each municipality requires an accurate Capital Asset Evaluator to provide a dynamic
methodology for determining the best risk mitigation plan for linear infrastructure assets. A
proper decision support system is critical for budgeting and planning annual operations and
maintenance costs. This proposed method offers a very strong, accurate, high level, and strategic
decision support system which is appropriate for renewal and rehabilitation plans for water linear
assets. Unlike past efforts, this proposal focuses on asset condition, performance, and criticality
instead of constant parameter or single-sided factor such as failure rate, financial factors, and
pipe's age or type of material. After developing PAN scores, identifying the pipe sections that
require more investigation, and assessing the risk of failure among the identified watermain
sections, a proper maintenance program is proposed. Thus, the length of proposed pipe for the
repair, replacement, and upsizing programs is found using the proposed fuzzy model. Therefore,
by knowing the total linear length required for repair, replacement, and upsizing as well as the
unit cost of each activity, the total annual budget can be estimated using the simple equation
below:
𝑻𝑨𝑩 = ((∑𝒏𝒊 𝒂𝒊 𝑼𝒂𝒊 ) + (∑𝒏𝒊 𝒃𝒊 𝑼𝒃𝒊 ) + (∑𝒏𝒊 𝒄𝒊 𝑼𝒄𝒊 )
Where:
TAB = Total Annual Budget
a = linear repair required (m)
Ua = Unit cost of repair ($)
b = linear replacement required (m)
Ub = Unit cost of replacement ($)
c = linear upsizing construction required (m)
Uc = Unit cost of upsizing ($)
50
Equation 3-5
i to n represent different pipe sizes or pipe diameters.
To plan a sustainable water system and to know the annual cost of operation and maintenance,
the user cost per 𝑚3 could be easily calculated to recover all annual capital costs and any risk
associated with failure of the water system.
3.5 Summary
In summary, this proposal describes an approach of addressing the challenge of planning and
prioritizing asset management. This chapter first describes models which quantify the existing
condition of a watermain system and measure the performance of each asset. The risk associated
with the failure of each pipe is also described and measured using fuzzy logic analysis within the
condition performance and criticality model. A comprehensive multi-criterion PAN is introduced
and described to identify the critical pipes within an entire system. The risk mitigation program
is projected using the fuzzy logic model to eliminate the risk of failure by a repair, replacement,
and upsizing program to maintain a certain level of service. Using fuzzy logic model and expert
judgement, the total pipe lengths for annual repair, replacement, and upsizing of pipes are
identified. The total budget required for all maintenance programs is proposed. Therefore, the
total annual budget could be forecasted and monitored to investigate the proper user fee charge
in planning a sustainable water system.
51
4. Preliminary Results
Following introductions of the methodology employed in prioritizing the watermain maintenance
activity and ranking of water distribution and transmission lines, this chapter provides a sample
dataset and case study as preliminary results. The sample study is conducted with actual data
from a municipality located in southern Ontario. This case study presents the PAN score
calculations and identifies the PAN score thresholds to highlight problematic pipe sections. All
PAN scores are shown on the GIS layer to visualize the result.
Fuzzy analysis is also performed to illustrate all input variables by their membership function.
All membership functions, weighting scores, and expert opinions are combined to determine the
rules for the first defuzzification step. This step analyzes the risk within each parameter to result
in more accurate performance, condition, and criticality scores. The second fuzzy layer is
performed by combining all results from layer one with its membership function and expert
judgments. Second layer defuzzification results in risk mitigation programs such as repair,
replacement, as well as upsizing the pipe sections based on scientific hypothesis and expert
opinion.
4.1 Site Selection and Data Characteristics
Sample data was selected from a diverse section that includes all critical parameters such as
highway crossing, creek crossing, different pipe materials, various remaining service life, and
different diameters. Figure 4-1 shows the selected area that includes 283 pipe sections between
2.5 m to 1600 m in length. The age of these pipes ranges from 7 years to 81 years. The selected
pipe sections are between 300 mm in diameter and 2100 mm in diameter with different pipe
materials such as PVC, CI, DI, and CPP. Table 4-1 presents the percentage of different pipe
materials in the dataset.
52
Table 4-1 Different Pipe Materials within the Sample Dataset
Material
CPP
CI
DI
PVC
Total
% of
Data
49
6
23
22
100
Figure 4-1 Selected Sample Area
Figure 4-2 presents the percentage of different pipe diameters within the dataset's sample area.
Figure 4-2 Different Pipe Diameters in Sample Dataset
% of Data
60
40
20
% of Data
0
300
400
450
600
750
900 1050 2100
mm Diameter of the Pipe
53
4.2 PAN Calculation
All weight scores are assigned according to the explanation presented in Chapter 3. All
condition, performance, and criticality scores are calculated using formulas presented in Figures
3-2, 3-3, and 3-4. Finally, PAN scores are calculated for all pipe sections using the formula in
Figure 3-5. These are divided into different bins to identify and highlight the high scores in the
GIS layer. Table 4-2 presents the different PAN scores and Figure 4-3 shows the highlighted
PAN scores belonging to the selected sample site.
Table 4-2 PAN Score Thresholds
PAN Score
Severity
Colour
0 - 499
500 - 799
Low
Moderate
Blue
Green
800 - 1099
High
Yellow
1100 - 1620
Very High
Red
Figure 4-3 PAN Score Layer
54
As shown clearly in Figure 4-3, the majority of pipe sections have low PAN scores which are
shown in blue. There is no very high PAN score in the sample dataset. There are a few moderate
PAN scores and only five high PAN scores identified in the selected sample area.
Table 4-3 PAN Scores Calculated for Sample Area
Material
Diameter
(mm)
Installation
Year
Age
PAN
Score
(MAX 1620)
CPP
CPP
CPP
CPP
CPP
CPP
600
600
600
600
600
900
1980
1980
1980
1980
1980
1980
35
35
35
35
35
35
810
890
810
850
850
840
Table 4-3 presents all high PAN scores calculated for the sample area. This interesting result
shows all critical pipes which require more investigation, which were constructed in 1980, and
which are 35 years of age. In addition, all high PAN scores are CPP pipes. They are at a lower
limit of PAN threshold and require more investigation regarding condition, performance, and
criticality scores as well as risk associated to failure of these pipe sections using Fuzzy Analysis.
4.3 First and Second Layer Fuzzy Analysis Result
The results of fuzzy analysis are very similar for all identified high PAN score pipes. Due to their
similar physical characteristics and locations, the selected pipes have similar risks and results.
Table 4-4 summarizes the fuzzy analysis results for two pipe sections as an example.
Table 4-4 Fuzzy Analysis Results
Diameter
mm
Condition
Score
Performance
Score
Criticality
Score
Final Result
Fuzzy Result
600
900
Medium 4
Medium 4
Medium 4
Medium 4
Medium 3
Medium 3
Repair 2
Repair 2
1650m Repair
1175m Repair
55
Since the risk mitigation plan is the repair of sample pipes, the required budget can be calculated
using Equation 3-5 below.
𝑻𝑨𝑩𝒔𝒂𝒎𝒑𝒍𝒆 𝟐𝟎𝟏𝟓 = (𝟏𝟔𝟓𝟎𝒎 ∗ (𝒖𝒏𝒊𝒕 𝒄𝒐𝒔𝒕 𝒐𝒇 𝟔𝟎𝟎 𝒎𝒎 𝑪𝑷𝑷 𝑹𝒆𝒑𝒂𝒊𝒓)) + (𝟏𝟏𝟕𝟓 𝒎 ∗
(𝒖𝒏𝒊𝒕 𝒄𝒐𝒔𝒕 𝒐𝒇 𝟗𝟎𝟎 𝒎𝒎 𝑪𝑷𝑷 𝑹𝒆𝒑𝒂𝒊𝒓))
Equation 4-1
This research attempts to conduct a highly strategic disaggregated level approach to predict
annual budget required to maintain the watermain system at its current level of service. In
addition, the proposed method provides decision-making support for a safer and more reliable
watermain system.
56
5. Future Plans and Contributions
The main objective of this research is to prioritize annual operational and maintenance projects
based on an accurate scientific technique that includes watermain risk of failure using existing
pipe condition, performance, and criticality. A three-dimensional cross-sectional dataset is
prepared by integrating data from different data analyses and planning departments in a
municipality located in southern Ontario. This data includes the watermain's physical
characteristics as well as breakage history and pipe location. An initial step is taken to develop
quantitative knowledge on various types of prioritization models and maintenance decision
support processes. Sets of different proposed models are developed and calibrated using a sample
cross-sectional dataset that includes physical conditions, performance measurement, and location
information. The predicted annual budget that is required to maintain a certain level of service
and maintenance cost is also proposed using a water unit rate. Preliminary results are
encouraging in terms of quantifying the effects of watermain condition, performance, and
criticality on watermain failure. This chapter outlines various tasks associated with the proposed
research followed by a proposed timeline. Potential contributions of this research are also
discussed.
5.1 Research Tasks and Schedule
The proposed research includes the following six major steps:
Step 1: Expand the database to include all existing water transmission and distribution systems.
This will result in a very large dataset and require a large data integration process.
Step 2: Develop PAN scores as well as condition, performance, and criticality scores for every
pipe in the entire water system.
Step 3: Calibrate PAN scores and all assigned weights for condition, performance, and criticality
scores.
Step 4: Identify pipes that require more investigation using PAN scores.
Step 5: Develop Fuzzy Logic model using expert opinion rules to analyze performance,
condition, and criticality models.
57
Step 6: Conduct a Fuzzy Risk Model to investigate the risk of failure and its associated
consequences.
Step 7: Calibrate the fuzzy model using past scenarios.
Step 8: Identify the required maintenance action such as repair, replacement, and upsize based
on risk investigation using fuzzy model.
Step 9: Validate the final output maintenance result using historic data points.
Step 10: Estimate the annual watermain maintenance budget required to maintain a certain level
of service.
Step 11: Transfer the forecasted data into a visual GIS layer.
Step 12: Finish writing the thesis.
A schedule for the completion of these tasks is provided in Table 5-1.
Table 5-1: Task Schedule
Timing
W 2016
Activities
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Step 8
Step 9
Step 10
Step 11
Step 12
58
S 2016
F 2016
W 2017
5.2 Potential Contributions
The goal of this effort is to provide a comprehensive ranking system to support a decisionmaking process based on existing condition, performance, and criticality of the pipe using expert
judgment complemented by a scientific method at operation and maintenance level. The
suggested comprehensive disaggregated technique is used to estimate the required annual budget
for maintaining the current water system. The proposed method could potentially be used for
future budget forecasting. This method could possibly provide a link between tactical program
level and operational project to benchmark the required budget for maintaining a certain level of
service.
The results of this study may provide a baseline that could potentially be used to benchmark the
watermain performance measurement as well as budget planning at different levels of municipal
organizations. A scientific prioritization model that is based on expert opinion is proposed. This
research attempts to develop a novel approach that would be a valuable link between strategic,
tactical, and operational levels to evaluate the watermain system. This proposed method could
potentially be used in a small spatial area such as a small city as well as in a large geographical
area such as the province of Ontario. This research forecasts an annual budget that could be used
as a managing tool for a more reliable watermain system.
The following academic and practical contributions are expected to result from this research:

This research will result in a large comprehensive cross-sectional database that could be
used for other research activities.

This research will be the first of its kind to investigate the feasibility of developing a
multiple criteria scoring system and measure the weighting factors among different parameters to
quantify the risk and consequences of failure associated with the condition, performance, and
criticality of the watermain section.

This research will conduct a disaggregated investigation at an operational and
maintenance level. This approach will result in models which are capable of predicting the
annual budget required for maintaining the watermain system to continue providing a certain
level of service.
59

This knowledge could be sufficient in ranking each pipe section within transmission and
distribution lines. The proposed model will mitigate the risk of failure with proper programs such
as repair, replacement, and upsizing.

The method developed in this attempt could be a very valuable operation and
maintenance measurement tool to evaluate the current watermain system as well as budget
forecasting that is disaggregated from a certain type of pipe material, location, and any other
limitation.

The predicted budget could potentially be used to benchmark the performance
measurement; in addition, the predicted budget could be applied as a decision-making support
tool for a safer and more reliable watermain system.
60
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