EPA/600/R-02/029
Decision-Support Tools for Predicting the
Performance of Water Distribution and
Wastewater Collection Systems
by
Steve Stone
Emil J. Dzuray
Deborah Meisegeier
AnnaSara Dahlborg
Manuela Erickson
Logistics Management Institute
McLean, VA 22102-7805
Contract GS-23F-9737H
Project Officer
Anthony N. Tafuri
Water Supply and Water Resources Division
National Risk Management Research Laboratory
U.S. Environmental Protection Agency
Edison, NJ 08837-3679
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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Notice
The U.S. Environmental Protection Agency, through its Office of Research and Development,
funded the research described here under Contract No. GS-23F-9737H to Logistics Management
Institute. It has been subjected to the Agency's peer and administrative review, and has been
approved for publication as an EPA document. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
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Foreword
The U.S. Environmental Protection Agency is charged by Congress with protecting the
Nation's land, air, and water resources. Under a mandate of national environmental laws, the
Agency strives to formulate and implement actions leading to a compatible balance between human
activities and the ability of natural systems to support and nurture life. To meet this mandate, EPA's
research program is providing data and technical support for solving environmental problems today
and building a science knowledge base necessary to manage our ecological resources wisely,
understand how pollutants affect our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory is the Agency's center for investigation
of technological and management approaches for reducing risks from threats to human health and
the environment. The focus of the Laboratory's research program is on methods for the prevention
and control of pollution to air, land, water and subsurface resources; protection of water quality in
public water systems; remediation of contaminated sites and ground water; and prevention and
control of indoor air pollution. The goal of this research effort is to catalyze development and
implementation of innovative, cost-effective environmental technologies; develop scientific and
engineering information needed by EPA to support regulatory and policy decisions; and provide
technical support and information transfer to ensure effective implementation of environmental
regulations and strategies.
This publication has been produced as part of the Laboratory's strategic long-term research
plan. It is published and made available by EPA's Office of Research and Development to assist the
user community and to link researchers with their clients.
Hugh W. McKinnon, M.D., Acting Director
National Risk Management Research Laboratory
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Abstract
Water and wastewater infrastructure systems represent a major capital investment; utilities must
ensure they are getting the highest yield possible on their investment, both in terms of dollars and
water quality. Accurate information related to equipment, pipe characteristics, location, site
conditions, age, hydraulic rates, and water quality is critical to industry and municipalities to enable
the most cost-efficient operation, maintenance, and rehabilitation of existing systems. This report
summarizes information on European efforts to optimize operation, maintenance, and rehabilitation
activities related to water distribution and wastewater collection systems. The report includes a
description of:
* the capabilities and the data required to run eight pipe assessment software applications or
models,
* the infrastructure performance indicators used by three European water authorities, and
+ an approach to collect the necessary performance indicator (PI) data, based on our assessment
of the European experience.
Based on the review and analysis of European research and product literature related to the use of
models for rehabilitation management, there does not appear to be a widespread use of modeling
applications in Europe. Each model presented in this report has been applied in selected urban or
rural water services but not on a large national scale. UtilNets is the most comprehensive model. It
contains capabilities to model pipe failures, water quality, and rehabilitation scenarios. However, it
is only in the prototype development stage. The concept of modeling the impact of pipe failures on
water quality and using that information for rehabilitation planning has not yet been implemented
in practice. Only the EPAREL/EPANET and UtilNets models have integrated a water quality
module.
Data collection costs associated with using models are high; accordingly, water services must avoid
the collection of unnecessary data. The minimum data elements required by the models to develop
a prioritized list of pipes based on risk of failure include: pipe material, pipe age, section length,
number of breaks or bursts, and diameter. Additional information such as location, date and nature
of last break, type and cost of rehabilitation options, and type of customers that would be affected
by a service interruption, is necessary if managers are to assess the impact of different rehabilitation
scenarios.
Spatial analysis plays an important role in rehabilitation planning since the research shows that a
significant number of failures appear in geographic clusters. However, only four of the models (i.e.,
AssetMap, Gemini VA, KureCad and UtilNets) integrated a geographic information system (GIS)
user interface.
IV
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Based on a review of three case studies and European research papers related to the use of
performance indicators, it was found that the practice of using performance indicators as a
management tool is not widespread or standardized across European countries. Only the UK is using
a well-defined and nationally standardized approach. However, even in the UK, there has been no
study of the costs of additional data collection versus the benefits of additional system serviceability.
The Pis used in the case studies varied considerably, but could be grouped into indicators of: network
type and size; customer service; water distribution system effectiveness and reliability; wastewater
collection system effectiveness and reliability; environmental impact; and infrastructure construction
and rehabilitation cost-effectiveness. The performance measurement system in the UK was found
to be the most developed and could serve as a model for the US. Although all of the case studies
provided examples of how Pis could be used for intra-system and inter-system comparisons, only
the UK's OFWAT uses Pis to approve rehabilitation plans and price rate changes. A private water
authority must demonstrate via Pis how its rehabilitation plans will improve the distribution or
collection systems' serviceability to customers.
Based on the finding of this study, it is recommended that a web-based survey of industry, state and
local government officials, and academic and professional groups be developed. The purpose of the
survey would be to select the most important performance indicators, create uniform definitions, and
verify the core data elements necessary to support the selected indicators. The results from the web
survey could serve as a basis to convene an expert steering committee to provide direction to the
development, fielding and use of the database. Participation should include representatives of
industry, local government and water authorities. Once uniform definitions are developed, volunteer
water authorities should be solicited to collect the data necessary to develop a statistically significant
database of infrastructure performance indicators.
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Acknowledgments
For its review of operation and maintenance practices related to the use of decision-support tools,
the LMI study team obtained information from the European Cooperation in the field of Scientific
and Technical Research (COST) C3 group that addresses the diagnosis of urban infrastructure. In
addition, the research was based on review of current European technical literature, case studies, and
interviews with European and United States practitioners. The LMI study team consisted of Mr.
Steve Stone, P.E., Mr. Emil J. Dzuray, Ms. Deborah Meisegeier, Ms. AnnaSara Dahlborg, and Ms.
ManuelaErickson. We would like to acknowledge and express appreciation to Dr. Peter Stahre, Dr.
Sveinung Saegrov, Dr. Paul Conroy, Dr. Gerald Jones, Prof. Dr.-Ing. Raimund Herz, and Mr. Keith
Edwards who helped refine the scope of this study, provided research materials and provided an
overview of key issues in the European infrastructure management community. In addition to these
European experts, we would like to express our appreciation to Mr. Michael R. Caprara, Project
Manager, American Water Works Association Research Foundation for his assistance in identifying
appropriate research documentation.
VI
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Contents
Notice ii
Foreword iii
Abstract iv
Acknowledgments vi
Tables viii
Figures xi
Abbreviations xii
Chapter 1 Introduction 1
Chapter 2 Summary of the European Experience Using Non-Hydraulic
Models for Infrastructure Rehabilitation 7
Chapter 3 Summary of European Performance Indicators for Water
Distribution and Wastewater Collection Infrastructure 47
Chapter 4 Recommendations for National Database of Performance
Indicators for Drinking Water and Wastewater Infrastructure 77
Appendix A References 89
Appendix B List of European Experts 97
VII
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Tables
2-1. European Pipeline Rehabilitation Software Applications/Models
Reviewed 9
2-2. Relative Incident Rate Ratios for Sample Pipe Category Attributes 12
2-3. Sample Break Rates for Critical Pipe Section Attributes
from Lyon, France 13
2-4. Data Requirements for European Pipe Rehabilitation Models 24
2-5. Data Collection Efforts by European Water Services 26
2-6. Types of Data Collected by Swedish Water Services 27
2-7. Factors that Influence Pipe Failure Rates 28
2-8. Sample Relative Failure Rates for Different Pipe Materials Used
in Water Distribution Systems 32
2-9. Sample Relative Failure Rates Based on Diameter of Pipes Used
in Water Distribution Systems 33
2-10. Sample Relative Failure Rates for Pipes in Water Distribution
Systems Based on Surrounding Soil Conditions 36
2-11. Sample Relative Failure Rates for Pipes in Water Distribution
Systems Based on Traffic Loads from Failnet 37
2-12. Sample Relative Failure Rates for Pipes in Water Distribution
Systems Based on Traffic Loads from AssetMap Model 37
2-13. Sample Relative Failure Rates Based on Traffic Loads
from AssetMap Model 38
2-14. Summary of Capabilities of European Water and Wastewater
Infrastructure Rehabilitation Software Applications/Models 42
3-1. Summary of Performance Indicators and Required Data for Reggio
Water System, Italy 1994 Leakage Study 49
VIM
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3-2. Leakage Data for Reggio E. Water System, Italy (1989 vs. 1994) 50
3-3. Description of Pis Related to Water and Sewer Pipes for Swedish
Six-Cities Group 52
3-4. Description of the UK OFWAT's Performance Indicators Related
to Water Distribution and Wastewater Collection Systems 55
3-5. Water Infrastructure Standard Costs in the UK - Mains Laying 59
3-6. Water Infrastructure Standard Costs in the UK - Mains Rehabilitation 60
3-7. Sewer Infrastructure Standard Costs in the UK - Sewer Laying 61
3-8. Sewer Infrastructure Standard Costs in the UK - Sewer Rehabilitation 62
3-9. The UK's OF WAT Scoring Criteria for Assessing Company
Performance 63
3-10. Inventory of UK Water Main Pipes by Diameter (March 1998) 64
3-11. Condition Assessment of Water Mains in the UK (March 1998) 64
3-12. Kilometers of Critical Sewers by Size in the UK (March 1998) 66
3-13. Summary of Critical Sewer Condition by Grade in the UK (March 1998) 67
3-14. Kilometers of Non-critical Sewer Pipes in the UK by Diameter
(March 1998) 67
3-15. Condition of Non-critical Sewer Pipes in the UK (March 1998) 67
3-16. Kilometers of Critical Sewers Recapitalized in the UK (by year) 68
3-17. Summary List of Pis Related to Plant Size and Type 69
3-18. Summary List of Pis for Customer Service 69
3-19. Summary List of Pis for Water Distribution System Effectiveness
and Reliability 70
3-20. Summary List of Pis for Wastewater Collection System Effectiveness
and Reliability 71
IX
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3-21. Summary List of Pis for Environmental Impact 72
3-22. Summary List of Pis for Construction, Maintenance, and Rehabilitation
Costs and Effectiveness 72
4-1. Recommended List of Pis for Customer Service 79
4-2. Recommended List of Pis for Water Distribution System Effectiveness
and Reliability 80
4-3. Recommended List of Pis for Wastewater Collection System
Effectiveness and Reliability 81
4-4. Recommended List of Pis for Environmental Impact 82
4-5. Recommended List of Pis for Construction, Maintenance,
and Rehabilitation Costs and Effectiveness 82
4-6. Recommended Data for Pipe Failure Modeling, Rehabilitation
Planning and for Performance Analysis 84
B-l. List of European Potable and Wastewater Infrastructure Experts 97
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Figures
1-1. Sample Forecast of Repair and Replacement Costs (Present Value)
to Determine Optimal Pipe Replacement Year 5
2-1. Spatial Analysis of Break Rate for Water Mains in Lyons, France 13
2-2. Evaluation of Different Rehabilitation Policies on Pipe Break Rates
in Lyons, France 14
2-3. KANEW Approach to Rehabilitation Planning 18
2-4. Leak Frequency Versus Pipe Age for GCI Water Distribution Pipes
Measured over a Five-year Period for Malmo, Sweden 29
2-5. Change in Observed Damage Over 7-Year Period for Sewer Pipes
Installed before 1950 (Class O) and Sewer Pipes Installed
after 1950 (Class N) for Malmo, Sweden 30
2-6. Accumulated Failure Rate Versus Pipe Installation Year for Concrete
Sewer Pipes in Trondheim, Norway 31
2-7. Leak Frequency for Different Diameter Pipes in Malmo, Sweden
(Average Leak Frequency 1989 to 1994) 33
2-8. Collapses of Concrete Sewer Pipes Versus Classes of Pipe Diameter
in Trondheim, Norway 34
2-9. Concrete Sewer Blockages Versus Pipe Diameter in Trondheim, Norway. ... 35
2-10. PVC Sewer Blockages for Different Diameters in Trondheim, Norway 35
2-11. Relationship between Rate of Structural Decay and Initial Condition
of Pipe, Malmo, Sweden 39
2-12. Spatial Analysis of Leak Frequency for the Subdivisions of Malmo, Sweden . 40
3-1. Summary Analysis of Trends of Pis for UK Water Mains for 1979-2000 65
3-2. Summary Analysis of Trends of Pis for UK Sewer Mains for 1975-1998 66
4-1. Framework for Collecting and Communicating PI Data 87
XI
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Abbreviations
ABS
AWWARF
COST
CPVC
DWI
EA
USEPA
ESW
EU
GIS
IRR
MOU
O&M
OFWAT
OM&R
PB
PE
PI
PVC
R&D
SDWA
VAV
acrylonitrile-butadiene-styrene
American Water Works Association Research Foundation
Cooperation in the field of Scientific and Technical (Research)
chlorinated PVC
Drinking Water Inspectorate
Environment Agency
U.S. Environmental Protection Agency
East Scotland Water
European Union
geographic information system
incident ratio rate
memorandum of understanding
operations and maintenance
Directorate General of Water Service
operations, maintenance, and rehabilitation
polybutylene
polyethylene
performance indicator
polyvinyl chloride
research and development
Safe Drinking Water Act
Swedish Association of Water and Sewage Works
XII
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Chapter 1
Introduction
Background
The United States' water and wastewater infrastructure systems represent a major capital investment.
Accordingly, utilities strive to get the highest return possible on that investment, both in terms of
dollars and water quality. Of the approximately 200,000 public water systems in the US, about 30%
are community water systems that serve primarily residential areas and 90% of the population.
Potable water conveyance within these 60,000 community systems represents an estimated 850,000
miles of pipe. Much of this pipe has been installed since World War II. Approximately 26% of that
pipe is unlined cast iron and steel that has been judged to be in fair or poor condition. From a
structural and hydraulic viewpoint, that pipe will require accelerated repair and replacement.
Similarly, wastewater collection systems are an extensive part of the nation's infrastructure. In the
US, approximately 147 million people are served by about 19,000 municipal wastewater collection
systems representing some 500,000 miles of sewer pipe.
As these urban infrastructure systems age, more preventive maintenance, repair and replacement of
existing systems will be required. The Congressional Budget Office estimated that the total public
spending on wastewater infrastructure was approximately $22 billion in 1994 alone. This
represented 13% of total infrastructure spending in the US. The cost of building, operating and
maintaining water and wastewater facilities over the next 20 years is projected to be $95 billion per
year.1 In order for municipalities to cost-effectively plan, organize, and implement this maintenance
and renewal effort, they will need more extensive information about pipe system structural
conditions, enhanced decision-making tools, improved operation and maintenance practices, and
state of the art techniques for repair and rehabilitation. Exemplary European water distribution and
wastewater collection systems are potential sources of novel and efficient infrastructure maintenance
and rehabilitation practice improvements, which should be considered by US researchers and
utilities.
Despite the high cost and the key role that wastewater collection systems play in servicing the public
and protecting the environment, a study by the National Research Council2 found that few or no
standards exist for evaluating its performance. Pipe age is clearly a factor; however, it is usually a
combination of several factors that causes failures and influences maintenance decisions. This
complicates the decision-making process. To effectively manage maintenance and rehabilitation
programs, managers are looking more and more for a quantitative picture of the condition and
Water Infrastructure Network, Clean and Safe Water for the 21ST Century: A Renewed National Commitment to Water and Wastewater
Infrastructure, The Water Infrastructure Network, Washington, D.C., 2000. From the RAC website http://www.rebuildamerica.org
2
National Research Council Committee on Measuring and Improving Infrastructure Performance, Measuring and Improving Infrastructure
Performance, National Academy Press, Washington D.C., 1995.
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performance of their systems. Conceptually, this quantitative picture can be generated through the
selection of a suitable set of performance indicators, followed by collection of the required data and
analysis of the information using computer applications.
Objective
The overall objective of this project was to identify and describe European practices that managers
are using to make rehabilitation decisions (performance indicators) and the non-hydraulic models
for predicting failures, and managing and optimizing the operation and maintenance of water
distribution and wastewater collection systems. This report also recommends a conceptual
framework for developing a standardized US national database that could maintain performance
indicators related to pipe failures, their causes, repair costs, and other important factors.
Approach
The LMI study team worked with USEPA staff and European experts to develop a study outline and
identify reference materials. Appendix A provides references and Appendix B a list of experts
contacted for this study. The research was based on a review of current technical literature (English
and select European languages), case studies, and interviews with European practitioners and
researchers. A four-step approach was used in assessing decision-support tools:
1. Identify existing non-hydraulic models.
2. Categorize and describe pipe assessment methodologies and software applications that
assist managers in quantifying and ranking the condition of pipelines.
3. Based on the methodologies and software applications identified, develop a list of
recommended data elements to be collected for use in decision making.
4. Identify existing management approaches to collect, evaluate and utilize performance
indicator data for decision making.
To determine essential information for rehabilitation planning, the decision-making process for both
water and wastewater pipelines was examined.
Overview of the Rehabilitation Planning Process for Water and Wastewater Pipes
A rational decision to replace or to not replace a pipe at a particular time is often made by evaluating
two alternatives:3
o
Shamir, U., and C. Howard, "An Analytic Approach to Scheduling Pipe Replacement." Journal of The American Water Works Association
(AWWA), pp. 248-258. New York, NY: The AWWA, May, 1979.
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+ replace a pipe, incurring the replacement cost and future costs associated with the new line,
or
* retain the existing length of pipe, saving the replacement costs, but incurring increasing costs
of repair, disruption of services, and damages.
The process infrastructure managers use to make this rehabilitation decision can be categorized as
either proactive or reactive.
Reactive rehabilitation approaches are quite simple in that a manager repairs a pipe only after it fails
to meet its performance requirements such as hydraulic carrying capacity (i.e., experiences a break,
blockage, lowpressure, excessive overflows, or excessive leakage) and water quality (e.g., excessive
rust in distributed water). The benefit to this approach is that a pipe section realizes its full economic
life. The disadvantage of this approach is that the cost of fixing a pipe after it fails is unplanned and
may be more than fixing it prior to failure. In addition to the potential for increased and unplanned
direct rehabilitation costs, there may be additional indirect costs due to customer service
interruptions, damages to co-located utilities, damages to property, and traffic interruptions.
In a proactive rehabilitation approach, a manager attempts to rehabilitate a pipe section prior to its
failure. Although there are many variations to proactive rehabilitation management, the approaches
typically involve the following steps:
+ Step 1. Identify performance indicators for the entire network and each pipe section. These
performance indicators may include hydraulic capacity (i.e., flowrate, pressure, and leakage
rates), water quality (i.e., compliance with regulatory standards, bacterial growth, taste, color,
and odor), customer service (i.e., number of interruptions), cost-effectiveness, and
environmental impact (i.e., number of combined sewage overflows).
+ Step 2. Assess the network's characteristics (i.e., pipe lengths, size, material, age, operational
conditions, and environmental conditions), functional capabilities (i.e., hydraulic capacity
and water quality) and structural condition. This step usually culminates with managers
assigning each pipe section4 a condition classification.
+ Step 3. Prioritize the pipes for rehabilitation. Since the proactive approach requires managers
to replace pipes before they fail, managers must estimate the future date on or about which
a pipe will fail. Typically managers use the projection of when the pipe will break as the
primary basis for prioritization, but can also consider a wide variety of other decision
attributes such as economics, risk of customer service interruptions, risk of property damage,
risk of utility damage, water quality impacts and environmental impacts. The prioritization
4
In practice, managers have classified pipe sections using different approaches to include by each discrete section of pipe (e.g. each 10' length)
and by similar pipe characteristics (e.g. all cast iron pipes greater than 50 years old).
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process can also involve predicting future benefits and implications of different rehabilitation
strategies.
+ Step 4. Select potential options for corrective action or rehabilitation. In this step, managers
attempt to select the most cost-effective rehabilitation option. Since this step requires
comparing corrective actions that have different initial costs, operating costs and life
expectancy, managers often use economic models to support the selection process.
+ Step 5. Implement rehabilitation options and reassess system performance. In this step,
managers implement the rehabilitation options and determine the overall effectiveness of the
selected rehabilitation strategy by assessing the network's performance over time and by
comparing its performance to other systems.
Since the goal of proactive infrastructure management is to minimize life-cycle rehabilitation costs
for an infrastructure network while meeting its performance targets, thorough economic analysis is
essential. The desired degree of economic analysis requires the following information:
+ projected number of failures in future years in the existing pipe,
+ projected number of failures in the new or rehabilitated pipe from the time it is installed,
+ cost of repairing one break or failure,
+ cost of replacing the existing pipe with a new one, and
+ discount rate used in converting future expenditures to present value.
If this information is known, managers can calculate the following parameters as illustrated in Figure
1-1:
+ The present value (dollars) of all future repair costs for the existing pipe shown as a function
of the replacement year.
+ The present value (dollars) of replacing the existing pipe with a new pipe shown as a
function of the replacement year.
* The total life-cycle cost shown as a function of replacement year, which is the sum of the
present value of all future repairs and the present value of replacing the pipe.
^ The optimal year for replacement which is equal to the year at which total life-cycle cost is
minimum.
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The costs shown are expressed as current dollars (present value) to facilitate the comparison of
alternatives. Future expenses are discounted using a rate based on both inflation and the interest
rate normally involved in the utility's financing. It is important to note that studies5 have shown
$140,000-
$120,000-
« $100,000-
w
T5 $80,000 -
| $60,000 -
0)
^ $40,000 -
$20,000 -
Optimal rep lac em era year
(tot a^ minim urn)
oooooooooooooooooooooooooo
Rep air Costs
Rep lace me rt Costs
•Total
Figure 1 -1. Sample forecast of repair and replacement costs (present value) to determine optimal
pipe replacement year.
that this type of economic analysis is very sensitive to model parameters (forecasted break rates,
discount rates, and costs) that are difficult to accurately determine.
The forecast of pipe break rates can be derived from an analysis of previous failure data collected
from maintenance records using stochastic analysis, such as Poisson or Weibull models. This
analysis can also be based on a correlation between various network attributes (age, length, season,
pipe material, diameter, soil type, traffic conditions, loading, or location) and failure rate. The result
of such analysis is an estimate of failure rates for either groups of like pipes or for each pipe section
Shamir, U., and C. Howard, "An Analytic Approach to Scheduling Pipe Replacement." Journal of The American Water Works Association
(AWWA), pp. 248-258. New York, NY: The AWWA, May, 1979.
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in the network (by nodes). With this information and the cost estimates for repair and replacement,
managers can identify the impacts of different rehabilitation strategies and make better decisions.
Report Organization
The remainder of this report provides a description of the European research on the use of modeling
techniques for proactive rehabilitation management and discusses how they have been used to
improve rehabilitation decisions to optimize the operation and maintenance of water distribution and
wastewater collection systems. It is organized into the following sections:
+ Chapter 2 describes the existing non-hydraulic models and decision-support tools used in
Europe to assess the condition of pipes and recommends the data necessary to predict future
performance and to select rehabilitation options.
+ Chapter 3 provides a summary of the key information and performance indicators used in
Europe for rehabilitation planning.
+ Chapter 4 recommends a framework for a database of key information on performance
indicators.
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Chapter 2
Summary of the European Experience Using Non-Hydraulic Models for
Infrastructure Rehabilitation
In Europe, several universities, research centers, and private companies have developed various
models for assessing rehabilitation and renewal needs for water and wastewater infrastructure. The
objective of this chapter is to describe European non-hydraulic pipe assessment models and
software applications, assess the significance of the factors influencing pipe failures, and
recommend the type of data necessary to ensure valid and reliable decision making.
Recommendations provided at the end of the chapter are based on a review of:
* the capabilities of eight pipe assessment software applications,
* the data required to run each model, and
* the data typically collected by more than 15 European water services.
The assessment of capabilities and data requirements was based solely on a review of European
research and product literature.
Modeling Techniques to Predict Rehabilitation Requirements
The models and methods presented in this chapter were developed to assist managers who
proactively plan infrastructure rehabilitation activities for water distribution and wastewater
collection systems. An effective model should help a decision maker identify cost-effective
rehabilitation strategies that result in an acceptable level of service. In its simplest form, the decision
can be based on a cost comparison of two alternatives at any point in time:
+ replace a pipe, incurring the replacement cost and whatever future costs are associated with
the new line, or
* retain the existing length of pipe, at least for the time being, saving the replacement costs,
but incurring the risk of greater future costs of repair, disruption of services, and damages.
However, managers may consider other factors in addition to costs such as risk of service
interruptions, hydraulic capacity, reliability, water quality (for water distribution systems), risk of
property damage, and environmental impacts. The analysis to support this decision, whether it
involves only cost or other considerations, requires an estimate of the expected rate of breaks in the
two pipes - the existing one and the potential replacement.
While there are many different approaches to predict failure rates and support rehabilitation
planning, they typically incorporate one or all of the following major modeling techniques:
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* Probabilistic or statistical methods that estimate a pipe's condition, defined as a probability
of failure, based on a statistical analysis of the historical performance (break rate or expected
life) of like pipes in similar conditions (operational or environmental). Statistical models can
also be used to predict future system requirements by assuming that past break patterns will
continue into the future.
* Deterministic methods that identify the best solution (i.e., pipe replacement date, least cost
analysis, etc.) based, not on probability, but on a function of the initial pipe conditions and
an understanding of how it modifies given changes in operational conditions, environmental
conditions, or time.
+ Heuristic methods that enable managers to apply expert judgement and weights to different
decision criteria and to prioritize different rehabilitation strategies.
Regardless of the modeling technique, software applications or methodologies should help managers
maintain information about the pipe network, assess current pipe conditions, prioritize repair options,
and predict impact on future costs or system performance.
Summary of Models Available in Europe1
Eight models or methodologies designed to support rehabilitation planning were identified through
a review of European research papers, company product literature, and interviews with European
researchers and practitioners. An overview of each model is presented in the context of the
following attributes:
+ A description of the type of infrastructure assets covered - water and/or wastewater.
+ A description of its capabilities to assess current pipe conditions and prioritize pipes for
detailed evaluation and possible replacement, including
>• a point classification system for scoring observed defects,
>• a failure model to estimate break/burst rates for existing pipes,
>• a hydraulic model to calculate current capacity and vulnerability,
>• an economic model to estimate asset value, O&M costs for current pipes, and
replacement costs,
>• a water quality model (for water distribution systems only) that estimates the
concentrations of contaminants throughout the system and over time, and
>• a prioritization system to select critical pipes for rehabilitation.
The authors would like to acknowledge the invaluable contribution to the information presented in this chapter by Dr. Sveinung Saegrov, from
the SINTEF Civil and Environmental Engineering, Department of Water and Wastewater, Trondheim, Norway. Much of the information presented
in this section is based upon his research.
8
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A description of its capabilities to forecast future conditions, including
>• a failure model for estimating future break or burst rates and replacement dates,
>• an economic model to estimate future replacement and rehabilitation costs,
>• a hydraulic model to estimate future capacity and vulnerability,
>• a water quality model (for water distribution systems only) to estimate changes in
distributed water quality based on network changes,
>• an economic model that estimates future asset values, future O&M costs and
replacement costs, and
>• a tool to enable managers to compare future network rehabilitation scenarios.
A description of its user interface and output options - GIS and graphical report generation.
Table 2-1 provides a summary of the models reviewed. The following sections provide a brief
description of the eight models and, where available, provide references to additional information.
Table 2-1. European pipeline rehabilitation software applications/models reviewed
Model Name
AQUA- WertMin 4.0
AssetMap
EPAREL/EPANET
Failnet
Gemini VA
KANEW
KureCad
UtilNets (prototype)
Assets Covered
Water
Distribution
X
X
X
X
X
X
X
X
Wastewater
Collection
X
X
X
Country of Origin or Primary Use
Germany
France
Norway and USA
France
Norway
Germany and USA
Germany
Various European Countries
AQUA-WertMin 4.0 (Germany)
AQUA-WertMin was developed in Germany to assist infrastructure managers with the planning of
TV-inspection, renovation and new construction strategies for water and wastewater networks.
Based on the conditions observed during inspections, users enter pipe condition scores into the
application. The software assigns one of the following six classifications to each pipe section in
the network:
* Class 6: Excellent condition - no observed defects
9
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+ Class 5: Good condition - few defects observed, repair as needed
* Class 4: Fair condition - minor defects observed that will require repairs in long-term plan
* Class 3 : Poor condition - defects observed that will require major repairs, but no renovation
in the mid-term plan.
* Class 2: Very poor condition - defects observed that require major renovations, but not
replacement in the near-term plan
* Class 1 : Pipe failed - needs immediate replacement
The software then calculates the probability of a pipe section (or group of like pipe sections)
transitioning from one condition class to the next lower (worse) class in the form of a Herz
distribution.2 To determine the transitional function, the program applies a survival model for groups
of similar sewer sections, and a Weibull probability distribution function developed at the University
of Karlsruhe for water distribution pipes.3
For sewer pipes, the survival function is based on the following equation:
S(t,a,b) = (a+l)/(a+exp[b(t)])
Where:
t = years since the pipe was installed and is > 0
a = aging coefficient for the pipes grouped by age, material and condition at last
inspection
b = failure coefficient for the pipes grouped by age, material and condition at last
inspection
This equation is modified slightly for water distribution mains to:
S(t,a,b) = (a+1) / (a+exp[b(t-c)])
Where:
c = years since the pipe was installed until its first rehabilitation.
The software calibrates the survival functions using data collected from the network inspection
records to include year of pipe installation, year of inspection, pipe diameter, and pipe condition.
AQU A- WertMin provides a forecast for the deterioration of pipe condition and future rehabilitation
2
Herz, Raimund K., Aging Processes And Rehabilitation Needs Of Drinking Water Distribution Networks, Journal of Water, SRT-Aqua
Volume 45, 1996, pp 221-231.
Eisenbeis, P., P. Le Gauffre, and S. Saegrov, Water Infrastructure Management: An Overview of European Models and Databases, AWWARF
Infrastructure Conference, Baltimore MD, 2000.
10
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needs. It enables users to compare the costs of different rehabilitation strategies based on an
economic analysis of costs and time of repair.
AssetMap: Asset Maintenance Procedure (France)
AssetMap is not a software application, but an experimental modeling approach involving the use
of probability and spatial analysis to determine break rates in existing pipes in the urban community
of Lyon, France. Research4 is carried out within the framework of a doctoral thesis jointly financed
by the General e des Eaux - Service Lyon Agglomeration and the Association Nationale de la
Recherche Technique.
The modeling approach utilizes a commercially available GIS system and statistical analysis
software to accomplish the following five steps:
+ Step 1: Forecast break rates for the network of pipes assuming that no rehabilitation will
occur.
+ Step 2: Conduct a statistical analysis of break rates using Poisson Regression for each group
of pipe categories.
+ Step 3: Present a spatial analysis of the break rates via a GIS using the statistical results of
step 2, in order to identify other location factors for consideration (i.e., a visual display of
break rates superimposed on a map displaying other geographical information such as
buildings, roads, soil types, vegetation, other utilities, etc.).
+ Step 4: Support decision making through the use of a multi-attribute utility analysis to rank
critical pipes and evaluate different rehabilitation rates and criteria.
+ Step 5: Forecast break rates by simulating various rehabilitation policies (rehabilitation
spending and impact on prices).
In the first step, researchers develop a forecast of break rates without rehabilitation based on a model
calibrated with historical data from the actual network. In the second step, researchers conduct a
statistical analysis of break rates by Poisson Regression grouped by definition of pipe categories.
Categories are defined for each length of pipe section with the same attributes (i.e., material,
diameter, and location) as shown in Table 2-2.
Malandain J., Le GauffreP., Miramond M. Organizing A Decision Support System For Infrastructure Maintenance: Application To Water Supply
Systems, Proceedings. First International Conference on New Information Technologies for Decision-making in Civil Engineering. Montreal
(Canada) 11-13 Oct 1998, pp. 1013-1024. ISBN 2-921145-14-6.
11
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Table 2-2. Relative incident rate ratios for sample pipe category attributes
Modeling Attribute
Pipe diameter (mm)
Pipe material
Pipe location
Attribute Value
60-80
100-135
150-175
200-350
>=400
Ductile iron
Grey cast iron
Under sidewalk
Under roadway with light
traffic
Under roadway with heavy
traffic
Under roadway with very
heavy traffic
Code
DO
Dl
D2
D3
D4
MO
Ml
EO
El
E2
E3
Relative
Incident Rate
Ratio for
Attribute Value
1 (reference)
0.77
0.36
0.16
0.06
1
(reference)
9.99
1
(reference)
1.13
1.33
1.78
95% Confidence Interval
0.68
0.28
0.12
0.03
7.91
0.98
1.16
1.36
0.86
0.44
0.21
0.12
12.6
1.30
1.53
2.32
For each pipe attribute, researchers are able to determine a relative incident rate ratio (IRR) that
compares the break rate for a standard reference value for a particular attribute (i.e., diameter
between 60 mm and 80 mm, material is ductile cast iron, and location is under a sidewalk) to the
failure rate for each other attribute (e.g., Dl, Ml, El). The model uses as a reference the pipe
category whose coded variables are DO, MO and EO to calculate a combined IRR for the pipe
category based on the IRR for each individual attribute. This calculation can be shown by:
Pipe category 0 (DO, MO, EO) has a combined IRR =1*1*1 = 1,
Pipe category i (D3, MO, EO) has a combined IRR = 0.16*1*1 = .016,
Pipe category] (D3, Ml, EO) has a combined IRR = 0.16*9.99*1 = 1.60.
The researchers found the main advantage of the Poisson Regression model is that it provides
processed data that can easily be used for decision-support since the results provide a prioritized list
of critical pipes. This means that if the only basis for renewal is structural reliability, then annual
rehabilitation and leak detection programs can focus on just a few of the 40 categories in the model
that have the highest IRR. Table 2-3 shows sample break rates modeled for 'critical' categories based
on the experience in Lyons:
12
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Table 2-3. Sample break rates for critical pipe section
attributes from Lyon, France
Category of Pipe Section
C(DO, Matl,E3)
C(Dl,Matl,E3)
C (DO, Mail, El)
Modeled break rate/km year
0.343
0.263
0.219
In step 3, researchers input break rate information into a GIS system to conduct a spatial analysis of
break rates in order to identify other geographic factors to be considered in rehabilitation planning.
An example spatial analysis of break rates is shown in Figure 2-1.
_..U^jP?f^
!Sfi^
IHl •OO.ll h/km.yesir
IFFRS
\[\l ~ > D.07tAtti,year
. j. _
;i).04fc.fltm.year
Figure 2-1. Spatial analysis of break rates for water mains in Lyons, France.
In step 4, researchers use a multi-attribute utility analysis to identify the most critical pipes, not only
on the risk of failure as determined in step 2, but also on the potential impact that particular pipe
failure will have on the system. For example, researchers combine the pipe failure rate with the
number of customers served by the pipe to determine an overall vulnerability risk criterion to
13
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prioritize renewal planning. A specific approach related to risk mitigation is currently under
development.
In step 5, researchers use a statistical analysis to develop forecasts of break rates as a function of time
for various rehabilitation policies (rates and rehabilitation scenarios). On one part of the Lyons
network studied, researchers projected the number of future breaks from 1999 onwards for three
different rehabilitation scenarios, as shown in Figure 2-2.
l>
I50"
i- A t\ -f-
^N Li 1 1
^30 -
u
|20 -
llfl -
Z /.
n C 0 0 0 0 0 0 0 0 C
(-. o Q ^^AA
o o ° °
o HO
D 111
^ 112
1985 1990
1995 2000 2005
date (year)
2010 2015
HO = Projected pipe break rate without any pipe replacements or rehabilitation.
H1 = Projected pipe break rate with a random renewal of 1% of mains for 15 years.
H2 = Annual renewal of 1% of small diameter pipes bebw roadways for 15 years.
Source: Malandain J., Le Gauffre P., Miramorid M., Organizing a Decision Support System forlnfrastnjctuie
Maintenance: Application to Water Supply Systems. Journal of Decision Systems, Volume. & - N'^'ISSg, pp.
2Q3r222, 1999.
Figure 2-2. Evaluation of different rehabilitation policies on pipe break rates in Lyons, France.
Researchers compare projected break rates between a strategy of non-intervention, apian of random
renewal of 1% across all pipe mains for 15 years, and an annual renewal of 1% of small diameter
pipes below roadways for 15 years. At the time of this paper, researchers were still refining this
model.
14
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EPAREL and EPANET (Norway and US)
The Norwegian research institute, SINTEF, has developed a water quality modeling tool, EPAREL,
that builds on a USEPA tool, EPANET, to assess the risk of a network failure that would interrupt
the supply of quality water to any customer. EPANET is a computer program that performs
extended-period simulation of hydraulic and water quality behavior within pressurized pipe
networks. EPANET tracks the flow of water in each pipe, the pressure at each node, the height of
water in each tank, and the concentration of a chemical species throughout the network during a
simulation period consisting of multiple time steps. In addition to calculating water age, the model
can also simulate source tracing within the network. EPANET runs on Microsoft Windows® and
provides an integrated environment for entering network data, running hydraulic and water quality
simulations, and viewing the results in a variety of formats. These include color-coded network
maps, data tables, time series graphs, and contour plots.
EPAREL builds on the EPANET model by applying statistical models, based on a modified "Non-
Homogeneous Poisson Process" and a Weibull function, to calculate the failure probability for each
pipe section in a network. The probability of failure is modeled for groups of pipes characterized by
material, construction year, water quality, surrounding soil and diameter. For those pipes with a
record of very few failures (i.e., large diameter pipes), the failure probability is estimated from
professional judgement.5 The models are being tested with data from Norwegian municipalities.
Failnet: Analysis and Forecasting of Water Network Failures (France)
This methodology of analyzing and forecasting water pipe failures has been applied at several urban
and rural water services in France, including Bordeaux, Alsace, and Charente-Maritime.
It consists of three steps:
+ Analysis of historical failure records using a proportional hazard model. The system
analyzes historical data, valuates factors that influence failures, and identifies factors that
maximize the likelihood of failures.
+ Definition of survival functions based on a Weibull Model. The system integrates the failure
relationships determined in the previous steps with information on the pipes' current
conditions to calculate the probability of a group of pipes (grouped by material and current
condition) to survive at a given condition level during a given time period.
+ Forecasting the number of failures for a defined period using a Monte-Carlo method. The
system then forecasts the number of failures from the survival functions for each group of
pipes (materials and current condition). This forecast can be used in combination with a
5 R0stum I and Schilling W., Predictive Service-Life Models Used for Water Network Management, 14th EJSW, Dresden, 8-12 September 1999.
15
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hydraulic reliability model, in an economic model, or alone as one of the rehabilitation
criteria.
Gemini VA (Norway)
In Norway, almost all the major municipalities use the Gemini VA information system to manage
water and sewer network data.6 This makes the exchange of key numbers from the same data
platform possible. The system enables the integration of pipe failure information with a GIS
interface. The user interface is a computer-generated network map, from which a user can select a
single pipe, and the system will retrieve the required data from the database.
The system can store network, pipe, failure, repair, and maintenance data. The property tables store
position, depth of pipe nodes, diameter, pipe material, joint system, and construction year
information. The system also stores information about pipes that were replaced or taken out of
service.
Gemini contains a module to record operation and maintenance information. The module enables
users to record a chronological operations history of the water and sewer network, including:
+ Incidents (bursts, leaks, operational interruptions),
+ Conducted work (repairs, TV inspections, high pressure flushing, etc.),
+ Secondary failures,
+ Network condition and reasons of failures, and
+ Quality considerations.
Gemini VA also includes a report generator that can be used for statistical analyses and graphical
presentations.
KANEW: Exploring Rehabilitation Strategies (Germany and US)
KANEW is a cohort survival model for infrastructure that has been developed at Karlsruhe
University to predict future rehabilitation needs for water infrastructure. Based on this approach,
Dresden University of Technology developed a software application in a research project sponsored
by the American Water Works Association Research Foundation (AWWARF). It is available in
Microsoft Access® format to AWWARF subscribers. KANEW predicts when select pipe sections
will reach the end of their service lives, differentiated by date of installation and by type of pipe
sections with distinctive life-spans.
Saegrov, S., Selseth, I., and Schilling W., Management of Sewer System Data in Norway. EWPCA Symposium, "Sewerage Systems - Costs and
Sustainable Solutions." 4-6 May, 1999 in conjunction with 12th FAT 1999 Exhibition, Munich, Germany.
16
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The system assumes service-life to be a random variable, starting after some time of resistance and
being characterized by a median age and a standard deviation, or age that would be reached by a
certain percentage of the most durable pipe section. The user can choose the parameters of the Herz-
distribution. Predictions are based on optimistic assumptions of service-lives that are derived from
failure and rehabilitation statistics for different types of pipes. The cohort survival model of
KANEW is a tool for exploring network rehabilitation strategies.7 KANEW contains a network
inventory module, a failure and break forecasting module, an economic data module and a strategy
comparison module. Figure 2-3 shows relationships between the different KANEW modules.
KureCAD (Finland)
The Viatek Group in Finland developed a tool, KureCAD, which uses GIS to manage sewer pipe
rehabilitation. The system can store information on all infrastructure assets, prioritize the
rehabilitation of pipes, and provide the necessary documents to implement the rehabilitation. The
KureCAD user interface is based on a map of the network. If maps are not digitally available from
the outset, hard copies can be scanned into the system or manually digitized. Using the GIS
interface, a user can recall a wide variety of information, including location, size, and type data for
manholes, valves, and fire hydrants.
To ensure consistency for data collection during field inspections and maintenance, the
KureCAD system provides instruction. Once the KureCAD system contains all the necessary
data, it enables managers to assess and prioritize system conditions. For each pipe section, the
system enables users to record three basic types of data:
+ Structural condition (strength and shape),
+ Functional condition (its ability to transport water), and
+ Leakage rates (estimated leakage from the pipe).
For each type, users can employ data from internal inspections or maintenance records to
summarize the pipe's condition by assigning a score from 1 (good, no repairs required) to 4 (very
bad, needs to be repaired immediately). Users can also rate each pipe using other factors entered
by the user. The system records whether the entered data is based on estimates or actual
inspections. The KureCAD system then combines all of the condition scores into one condition
index and converts it to a GIS display.
Herz, R. Aging Processes And Rehabilitation Needs Of Drinking Water Distribution Networks, Journal of Water, SRT-Aqua Volume 45, 1996,
pp 221-231 and HERZ, R. Exploring Rehabilitation Needs and Strategies for Water Distribution Networks. Journal of Water, SRT-Aqua Volume
47, 1998, pp 1-9.
17
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Inuentory of water mains
C Lrrent stock of network
mileage by types of pipes and
year of construction
Cohort survival model
for stock forecasting
Annual mileage of network
rehabilitation in total and by
types of pipes
Statistics
of failures, leakage and rehabilitation
Definition of
water mail categories
Decision criteria for
rehabiitation strategies
- Rehabilitation needs in
total and by types of pipes
- Age of netvwrk and pipes
- Residual Efedmes of
network end pipes
- Failure rates
-Water losses
- Rehabilitation costs
- Cost savings of
reduced repair and v\ater I osses
- Balance of costs
-Internal rate of return
Lifetime estimates
of water mains
Calibration of
ageing Junctions
Options of rehabilitation
-fully/partly
-in time/delayed
-replace /rencvate
-chace of material
Economic input data
- Past investments
in network extensions
and rehabilitation
- Unit costs of rehab
techndogies
- Price iidces
- Inflation rate
- Discount rate
- Fixed and variable costs
- Water price
- Budget restrictions
Choice of best rehabilitation strategy
Figure 2-3. KANEW approach to rehabilitation planning.
Once the KureCAD system is populated with all the required data, managers are equipped
with the information needed to assess the condition of pipes in the network, prioritize pipes
for rehabilitation, identify alternative rehabilitation options, and determine associated costs.
Finally, KureCAD can store and generate the planning and design documentation required
by the water service to begin rehabilitation. This includes detailed site maps, detailed
construction specifications, and contract conditions.
18
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UtilNets (Various European Countries)
UtilNets is a prototype of a reliability-based, decision-support system for the maintenance
management of underground utilities. It performs current-condition assessments and reliability-based
life predictions of pipes, and determines the consequences of maintenance decisions. At the time of
this study, the software was in a prototype phase and only assessed gray and ductile cast-iron water
mains.
In contrast to models that attempt to predict pipeline failure based on service failure statistics and
using data from specific systems, the UtilNets model is based on physical models of the degradation
process. It calculates the remaining service life of single pipe sections and water networks. The
model considers internal and external corrosion of cast iron and ductile iron pipes. Since the rate of
corrosion varies to a large extent along the length of a pipeline, the model utilizes a probabilistic
distribution function of corrosion. UtilNets also considers external load conditions to include soil
weight, traffic load, uneven foundation (pipe acting as a "beam"), pipe wall temperature, and frost
action. The load variation along a pipeline is modeled using a probabilistic function, and the
extreme values are determined from "worst case" estimates.
UtilNets classifies pipe components as links and segments. A link is the length of pipe between two
nodes. A node may be a connection of pipes, a network structure such as a reservoir, or just a change
of basic network characteristics (e.g., pipe material or age). A segment is a part of a link and links
may be divided into segments for various reasons (e.g., if a main road crosses the link, the part under
the road is considered a separate segment). A long pipe can be divided into segments of equal length.
The system consists of a GIS-based user interface and results are presented as thematic maps and
tables. It also utilizes a decision-support system to support rehabilitation planning by ranking each
pipe segment in the whole network on a basis of need for rehabilitation. It provides a forecast on
the aggregate structural, hydraulic, water quality, and a service reliability profile of the network,
together with an assessment of the required rehabilitation expenditures. UtilNets attempts to answer
questions such as:
+ What is the structural life expectancy of a specific water main segment?
+ What is the probability that the pressure at the end of a specific water main segment will be
adequate in 3 years?
+ Which pipe segments will cause dirty water problems?
+ What is the optimal rehabilitation scheme for a specific water main segment?
+ What should be the current rehabilitation budget for the utility?
+ What should the future rehabilitation budget for the utility be in 5 or 10 years time?
19
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UtilNets consists of eight modules divided into three groups:
+ Analysis Modules:
>- Structural Reliability Module (Ml)
^ Hydraulic Reliability Module (M2)
V Water Quality Reliability Module (M3)
^ Service Reliability Module (M4)
+ Optimization Modules:
^ Options and capital costs for water main rehabilitation (M5)
^ Non-quantifiable consequences of failure ("Risk Module", M6)
^ Prioritization of water main rehabilitation (M7)
+ Background Information Module:
^ Network reliability (M8)
STRUCTURAL RELIABILITY MODULE (Ml)8
This module assesses the structural performance in service over time for each selected pipe segment.
First, it determines the deterioration caused by corrosion. The resulting decrease of resistance is
compared to the internal water pressure and external loads (soil, temperature, traffic etc.). The
system develops an estimate (probability distribution) of structural reliability by monitoring, as a
function of time, the magnitude of the difference between the operating characteristics of the water
main (pit depth, stress and stress intensity factors) and the operating limit (wall thickness, strength
and fracture toughness). When the operating characteristics reach a prescribed limit, the pipe, link,
segment or whole network begins to operate unsatisfactorily and this qualifies as failure.
Structural analysis is performed in two steps: deterministic and then stochastic. The deterministic
approach calculates all the constant or frequently applied loads, sums them according to the direction
in which they operate, and compares them with remaining stress and strain carrying capacity of the
pipe. This can be compared to the conventional process of designing a pipe from a list of known and
given loading conditions. The deterministic sub-modules are:
The module capabilities are taken from the following research paper and not from an actual review of the software. Hadzilacos Th., Kalles D.,
Preston N., Melbourne P., Eimermacher M., Kallidromitis V., Frondistou-Yannas S., Saegrov S., "UTILNETS: A Water Mains Rehabilitation
Decision Support System", Computers, Environment and Urban Systems, special issue on Urban Knowledge Engineering, accepted.
20
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+ Loads: computes present values for loads and stresses, and
+ Safety Factors: based on the loads and stresses, a first estimate is computed for each selected
segment.
For several features, such as internal pressure, a ratio between the strength of the pipe segment and
the loads is given as a safety factor. The user might focus, for instance, on the "worst" segments for
the next steps.
Due to the degradation of the pipe, there is a point in time where the probability of different loads
being applied together at the same moment will cause a failure. From this point on, the system
considers the probability and randomness of these loads, their type and frequency, and the future
degradation of the pipe. The pipe degradation previously computed is compared with a stochastic
process of load events (e.g., heavy truck traffic above a pipe segment or cold weather).
The module also contains two stochastic sub-modules:
+ Structural Reliability: computes the structural life expectancy for each selected segment by
estimating the probability of coincidences of external loads, and
+ Structural Reliability Fast: Here, only the first year where risk for the pipe rises above zero
is computed. (In contrast, the full version computes a curve showing the increasing risk from
zero to one over time.) If only aggregated data is needed for the priority of rehabilitation, this
fast version is recommended.
HYDRAULIC RELIABILITY MODULE (M2)9
This module assesses the hydraulic performance of a water main in service by comparing its state
of behavior, as a function of time, to each one of two limit states. These limit states are defined as
the maximum demand requested, and a specified minimum operating pressure. The method is
similar to the one adopted for the structural sub-module and is essentially based on the analysis of
interference data. An estimate of hydraulic reliability is obtained by monitoring, as a function of
time, the magnitude of the interference between the operating characteristics on the water main
(friction factor, head loss, etc.) and the operating limits (maximum flow and minimum pressure).
9 ibid
21
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10
WATER QUALITY RELIABILITY MODULE (M3)
This module takes into account effects that pipe condition can exert on water quality, i.e., the inside
surface of the pipe can corrode and the corrosion products can pass into the water. The module is
built around pre-existing research available in the literature on the interaction between pipe materials
and water quality parameters.
SERVICE RELIABILITY MODULE (M4)11
This module combines all the reliability results given above: structural, hydraulic, and water quality.
It is defined by the combined probability of a segment suffering none of these failures in a given
year. This is then calculated into the future until a failure or probability above zero occurs.
OPTIONS AND CAPITAL COSTS FOR WATER MAINS REHABILITATION (M5)12
This module generates a list of technically feasible rehabilitation solutions for the water mains with
a structural, hydraulic, or water quality failure predicted by one of the above modules. Technical
rules, flow carrying comparisons of the different rehabilitation techniques, and scheme details that
might preclude some remedial measures are taken into account. For these solutions, the net present
value of cost is derived in order to select the technically feasible option with minimal cost.
NON-QUANTIFIABLE CONSEQUENCES OF FAILURE ("RlSK MODULE," M6)13
This module assesses the non-quantifiable consequences of a pipe failure. A water pipeline failure
can deprive sensitive customers of supply, cause the collapse of other utilities, produce damage to
streets and other structures, or any combination of these. These outcomes must be taken into account
when assessing priorities for water main rehabilitation. The module ranks each consequence both
individually and in combination with the others. The ranking is shown using an arbitrary scoring
scheme, where a large number indicates a grave consequence. The system generates an overall
hazard score for each failing link based on the risk score for each of the identified risk parameters.
The system is able to select only those consequences that derive from the related cause of failure;
e.g., hydraulic failure does not have consequences for damage to other utilities, whereas structural
failure will have consequences for both sensitive customers and also damage to streets.
10 TU'J
Ibid
11 Ibid
12 Ibid
22
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PRIORITIZATION OF WATER MAIN REHABILITATION (M7)14
This module develops a prioritized list of all failed links for rehabilitation based on a function of
hazard potential and rehabilitation cost.
NETWORK RELIABILITY (M8)15
This module assists users in understanding the reliability of a supply system without doing numerous
iterations on a complete hydraulic model with a complex network solver. Water distribution mains
in a network often have large amounts of redundancy, and although there is rarely true hydraulic
redundancy, there may be a measure of inter-connectivity that rehabilitation planners can exploit.
To assist with this, the system assesses two reliability measures: demand point connectivity, which
is the probability of complete isolation of each demand point from a water source point, and
adequacy of flows at each demand point. Since complete hydraulic calculations and conventional
24-hour simulations are not undertaken within UtilNets, a true adequacy of flow cannot be provided.
An estimated "adequacy of flow" is determined based on rules from which the user may select a
short list for subsequent analysis in a proprietary hydraulic model.
GIS CAPABILITIES
The UtilNets system uses a GIS to store actual location for all parts of the underground network
(e.g., pipes, valves, etc.). Thus, it can correlate the pipe location with:
+ soil types and temperature data to better estimate corrosion rate;
+ existing ground structures, such as roads, to estimate loads on pipes; and
+ consumers information, such as hospitals, to estimate consequences of failure.
The GIS also allows the user to selectively examine parts of the network based on their location with
reference to streets, cities, or other landmarks.
Researchers do stress that at the time of this report, the current prototype of UtilNets is too rigid, too
complex, and may require amounts of data that may be unaffordable to collect and enter into the
system. For this reason, researchers are involving more utilities from across Europe to help design
a commercially available version of UtilNets.
23
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Data Requirements
Based on a review of product literature and a 1999 study16 by Eisenbeis, Le Gauffre, and Saegrov,
required data was classified by each of the European models as "required data," "highly significant
data," and "useful data" (Table 2-4). All the software applications require, at a minimum, the
following five variables for each pipe segment: age, length, material, number of recorded breaks, and
pipe diameter. Other variables used by the models include pipe condition, soil condition, traffic
loading, or the location of the pipe.
Table 2-4. Data requirements for European pipe rehabilitation models
Data Description
Pipe material
Pipe age
Pipe length
Number breaks/bursts
Pipe diameter
Soil data (type varies)
Traffic data (type varies)
Pipe location (type varies)
Water pressure
Failure/defect type
Pipe condition
Type of corrective action
Type of joints
Leakage rates
Date pipe repaired
Date pipe video inspected
Economic data
Rehabilitation cost
Utility locations
Tree locations
Elevation contours
AQUA-
WertMin
X
X
X
X
X
X
X
X
AssetMap
X
X
X
X
Failnet
X
X
X
X
Gemini
X
X
X
X
X
X
X
X
LU
z
X
X
X
X
KureCAD
X
X
X
X
X
X
EPANET/
EPAREL
X
X
X
X
X
X
UtilNets
X
X
X
X
X
X
Summary
X
X
X
X
X
Notes:
required data (according to previous studies or product literature)
highly significant data (according to previous studies or product literature)
useful data (according to previous studies or product literature)
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models and Databases,
AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
24
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Data Typically Recorded by European Water and Wastewater Services
As can be seen from the models described in this report, many parameters must be recorded and
analyzed to assess the condition of water distribution and wastewater collection systems to plan the
most effective maintenance and rehabilitation measures. Although most water service companies
collect much of this information as part of their standard operating procedures, European researchers
and practitioners point to the high cost of collecting additional data as a major barrier to extensive
use of modeling and computer applications.17 However, no specific study was found to quantify this
cost.
Table 2-5 presents the results of a study18 that summarized the data European practitioners collected
in nine cities in Europe (Lyon and Bordeaux in France, Lausanne in Switzerland, Reggio Emilia in
Italy, Bristol in the United Kingdom, Oslo and Trondheim in Norway, Dresden and Stuttgart in
Germany). These cities were chosen as sample sites and may not be completely representative of
the data collected by an "average" service.
Table 2-6 summarizes a study19 on the data collected by five Swedish water services. As can be seen
from the table, none of the Swedish water services collected information on water pressure, whereas
most of the water services in the nine European cities mentioned above did collect that information.
Analysis of the Factors Contributing to Pipe Failure Rates
In Europe, as well as in the US, there has been much research on the factors that contribute to pipe
failures, with the goal of developing or improving predictive planning models. However, this
research has shown that developing models which accurately predict pipe failures is a complex
process because there are many factors that affect failure and influence maintenance decisions. Since
data collection can be very expensive, it is important to assess the significance of the factors that
affect failure rates, so as to identify the important data to collect.
The factors affecting pipe failure rate can be either time-dependent or static. Pipe diameter or pipe
material are examples of static (i.e., will not change over time) factors that affect pipe deterioration.
Stahre, Peter., and Gerald Jones, Diagnosis of Urban Water Supply and Sewerage Systems: Presentation of the Work of COST C3. COST Action
C3 Workshop Proceedings, pp. 112-121. Brussels, Belgium: European Commission, 1996.
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models and Databases,
AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
19
Sundahl, Ann Christin. Diagnosis of Water Pipe Conditions, Lund University, Department of Water Resources Engineering, Lund, Sweden,
1996, ISSN 1101-9824.
25
-------
Table 2-5. Data collection efforts by European water services
Description of
the Data
Collected
Years that
recorded break
date collected
% pipes with age
recorded
% pipes with
length of pipe
segments
recorded
% pipes with
material
recorded
Total length of
mains (km)
% pipes with
pipe diameter
recorded
% pipes with soil
information
recorded
% pipes with
traffic
information
recorded
% pipes with
pipe location
recorded
Approximate
number of pipe
segments files
% pipes with
joint type
recorded
% pipes with
water pressure
recorded
a
£>
_j
1993 ->
1982 ->
S: 62%
C: 21% (2)
100%
S: 98% C:
50% (2)
3000
100%
100%
100%
98%
50000
NA
20%
on going
x
a>
1
M
1951 ->
1970 ->
85%
100%
95%
3000
99%
60%
100%
90%
10000
10%
95%
a>
a1""1
rt
1926 ->
99%
100%
99%
700
100%
0%
0%
0%
7000
50%
100%
o .3
§8 1
1994 ->
NA
100%
Su: 86%
Di: 95% (1)
890 (1)
(su. + di.)
100%
NA
NA
100% (3)
15886
NA
(4)
M
ffl
1995 ->
~ 50% (5)
100%
51%
7694
95%
100% (6)
Partly
100%
76 161
11%
NA
VI
O
1976 ->
99%
100%
100%
1600
100%
Thematic
0%
100%
37000
NA
100%
•53
$
d
^
1988 ->
95%
100%
95%
750
100%
Thematic
0%
100%
7000
80%
100%
Q
1994 ->
50%
100%
90%
1800
90%
Soil 60%
Bedding
20%
0%
50%
NA
60%
100%
e
60
^
t/5
1978-
99%
100%
99%
1326
100%
31%
0%
100%
16531
100%
100%
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models
and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
Notes: (1): Su: supply, Di: distribution; (2)C: City, S: Suburbs; (3) average of total length, one orthogonal measurement from the
properties each 120m of length; (4) pressure spot measurements, permanent district flow metering; (5) 50% known, others
estimated from pipe material; (6) corrosivity and fracture potential class. NA = not available.
26
-------
Table 2-6. Types of data collected by Swedish water services
Description of the Data Collected
Type of leak/failure
Date of leak/failure
ID number for pipe node
Pipe diameter
Date of pipe installation
Cause of leak/failure
Description of corrective action taken
Cost of corrective action
Length of pipe segments
Date of last repair
Street name
Pipe material
Pipe condition
Soil information
Fill type
Traffic information
Depth of pipes
Type of joints
Water pressure
Malmo
1
1
1
1
1
1
1
1
1
1
8
,0
0>
!-H
,0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Eskilstuna
1
1
1
1
1
1
1
1
1
1
1
Vasteras
1
1
1
1
1
1
1
1
1
1
1
cs
o>
5
1
1
1
1
1
1
1
60
VH -5
o> u
•e ^ a
S "o S
£ O Q
5
5
5
5
4
3
3
3
3
3
3
2
1
1
1
1
1
1
0
Pipe age, temperature, soil temperature and water content, and observed pipe defects are
examples of random, time-dependent factors that may influence the breakage rate of
underground pipes.20 In addition to being classified as time-dependent or static, the
factors influencing pipe failures can be classified as shown in Table 2-7.
The following sections summarize various European research papers that analyze the
significance of the factors that influence pipe failure and leak rates.
Influence of Pipe Age on the Structural Deterioration of Pipes
At the heart of most rehabilitation models is the premise that as pipes get older, they
require more maintenance and repairs. Therefore, many rehabilitation plans have often
been based solely on the age of the pipe. As in the US, European research21 has shown
Kleiner, Y., and B. Rajani. Considering Time-dependent Factors in the Statistical Prediction of Water Main Breaks. Ottawa, Ontario, Canada:
Institute for Research in Construction, National Research Council Canada, Infrastructure Management Conference Proceedings, American Water
Works Association Research Foundation, Baltimore, MD. 2000.
21
Sundahl, Ann Christin. Diagnosis of Water Pipe Conditions, Lund University, Department of Water Re sources Engineering, Lund, Sweden, 1996,
ISSN 1101-9824.
27
-------
Table 2-7. Factors that influence pipe failure rates
Category of Factors that Influence Pipe
Failure Rate
Pipe section factors
Operational and maintenance factors
Environmental and climate factors
Factors
pipe material
pipe diameter
joint type
pipe age
pipe depth below surface
pipe condition (wall thickness, defects, etc.)
operating pressure (water distribution)
nature and date of last failure (e.g., type, cause,
severity)
nature of maintenance operations (e.g., TV
inspections, pipe cleaning, cathodic protection)
nature and date of last repair (e.g., type, length)
water quality
construction method (e.g., fill type)
soil type
soil temperature or frost depth
rainfall
soil moisture content
temperature
traffic and loading
that pipe age is significant, but not the only indicator of pipe failure rates. Other factors,
such as current condition, diameter, and location contribute to the significance of pipe
age.
For example, Figure 2-4 shows the leak frequency distributed according to pipe age for
gray cast iron (GCI) pipes based on a study22 of Swedish water companies. Swedish
researchers studied leak frequencies for five municipalities over a five-year period and
determined that the number of leaks in GCI pipes increased with pipe age up until the
pipes were about 30 years old. After this, and until the pipes were about 80 years old,
there was no significant correlation between leak frequency and pipe age. The same
pattern also emerged when each municipality was studied separately.
Ibid.
28
-------
Leaks/km-year
0.03
Correlation exists for pipe
ages 0 to 30 years
Q+m
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Pipe Age in Years
Figure 2-4. Leak frequency versus pipe age for GCI water distribution pipes measured
over a five-year period for Malmo, Sweden.
Figure 2-4 shows that, on average, a water distribution pipe that is 50 years old can have a
lower leak rate than a pipe that is 30 years old.
European research has shown that pipe age is a fairly good indicator of pipe breaks in
wastewater collection pipes. Swedish researchers conducted a study23 in Malmo, Sweden
to investigate factors that influence changes in the structural condition of wastewater
pipes. The resulting study presents a model for the structural deterioration of sewer pipes
over time based on the team's observations (TV inspections) of the changes in pipe
defects over an average of 5-year intervals for 5800 meters of concrete sewer pipes, 4200
meters of PVC pipe, and 1200 meters of PE pipe. As shown in Figure 2-5, they
concluded that pipe age, as grouped into two age categories by installation date, was a
good indicator of failure rate. Pipes installed before 1950 (class O pipes) demonstrated a
higher change in observed damage over a 7-year period than pipes installed after 1950
(class N pipes).
Lidstrom, Viveka, Diagnos Av Avloppsledningars Rendition (The Diagnosis of the Condition of Sewer Pipelines), Rapport 3194, Institutionen
for Teknisk Vattenresurslara, Lunds Tekniska Hogskola, Lunds Universitet, Lund, Sweden,1996.
29
-------
Damage points
250
200"
150--
100 --
50--
dassO
\
Sp readout
cracks
Class M
Pointcracks
/ Damage-free
'=\ 1
1st (Hiring
Time
2iMl lilniim
Figure 2-5. Change in observed damage over 7 years for sewer pipes installed before
1950 (class O) and sewer pipes installed after 1950 (class N) for Malmo, Sweden.
Results from a Norwegian study also supported the significance of pipe age in predicting
failure rates for sewer pipes. Figure 2-6 shows that the older the pipe, the worse the
condition. However, researchers did not conclude that this was due solely to pipe age.
Influence of Pipe Materials on Pipe Failure Rates
European research has shown, similar to research in the US, that failure rates and leak rates
differ for various pipe materials. Table 2-8 contains an analysis of relative failure rates for
different pipe materials, as compared to the failure rate of gray cast iron pipes based on a
study24 of the information collected from and the software tools used by European water
Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models and Databases,
AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
30
-------
Accumulated failures rate
(breaks/ 1000km)
0.016
o.ou
Pipes installed before 1945
Pipes installed after 1985
Pipes installed 1945-1970
ipes installed 1970-1985
0.002
0.000 -i
1980 1982 1984 1986 1988 1990 1992 1994 1996
1981 1983 1935 1987 1989 1991 1993 1995 1997
Year
Source: Saegrov, S., Selseth, I., and Schilling W., Management of Sewer System Data in Norway. EWPCA Symposium,
"Sewerage Systems - Costs and Sustainable Solutions." 4-6 May, 1999 in conjunction with 12th IF AT 1999 Exhibition,
Munich, Germany.
Figure 2-6. Accumulated failures versus pipe installation year for concrete sewer pipes in
Trondheim, Norway.
services. In Reggio Emilia (Italy), the failure rate is larger for gray cast iron and asbestos
cement (AC) pipes. In this case, the average age of the pipe is not taken into account.
Researchers note that the cause of the failure rate for GCI pipes decreasing from 1994 to
1996 may be due to a policy change that resulted in decreasing water pressure in the
distribution systems. For Bordeaux, ductile iron and GCI pipes are compared. Even after
eliminating the influence of age, it shows that GCI pipes break more than ductile iron
pipes. The table also shows that, in Norway, asbestos cement and unprotected ductile iron
pipes are more vulnerable than GCI.
31
-------
Table 2-8. Sample relative failure rates* for different pipe materials used in water
distribution systems
PE
PVC
Asbestos Cement
Steel
Gray Cast Iron
Ductile Iron
(no corrosion
protection)
Ductile Iron
(corrosion
protection)
Reggio Emilia
1994
0.01
0.21
0.34
0.08
1
1995
0.11
0.25
0.64
0.11
1
1996
0.25
0.3
0.68
0.15
1
Failnet
Bordeaux
1
0.81
NTNU/SINTEF
Trondheim
1988-1996
0.06
0.01
1.92
1
1.75
0.22
Oslo
1976-
1998
0.22
0.33
1
Bergen
1978-
1999
0.06
0.12
1.44
1
0.12
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models
and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
* Relative failure rate = (Failure rate of the material concerned / Failure rate of Cast Gray ton) for Reggio-Emilia and
NTNU-SINTEF; and relative failure rate =[h (ductile iron)] / [h(gray cast iron)], with h the hazard function calculated with
Failnet model. If the value is more than 1, the material will break more than the GCI.
Influence of Pipe Diameter on Pipe Failure Rates
European researchers have found that pipe diameter significantly influences pipe failure rates.
Specifically, the failure rates for a particular pipe material increase as pipe diameter decreases.
Researchers found (Table 2-9) that the relative failure rate for different pipe diameters can be quite
different based on the data collected from one model to another and from one municipality to
another, but that the same trend appears with the exception of the NTNU/SINTEF study.
Researchers there noted that other location and maintenance factors may have contributed to the low
relative failure values. The researchers also qualified their conclusions by noting that the different
model databases define and use the data element 'pipe diameter' differently. For example, the Failnet
model considers the diameter as a quantitative variable, whereas AssetMap considers it as a
qualitative variable by grouping diameters into ranges.
Other research supports this conclusion that failure rates are greater for smaller diameter pipes. A
study on leak rates for water distribution pipes in Malmo, Sweden, also showed that leak
frequency per kilometer of pipe decreased as pipe diameter increased (Figure 2-7).
32
-------
Table 2-9. Sample relative failure rates* based on diameter of pipes used in water
distribution systems
Failnet
Bordeaux
(GCI, 1st fail.)
2.5
Charente M.
(GCI, 1st fail.)
2.08
Sub. Paris
(GCI, 1st fail.)
1.37
AssetMap
Lyon
(GCI)
2.94
NTNU/SINTEF
Trondheim
0.99
Oslo
0.80
Bergen
0.14
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European Models and
Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
* relative failure rate for AssetMap is = [(average failure rate for pipe diameter A x (60-80 mm))]/[ (average failure rate for group of
pipes with diameter B x (150-175 mm))]; relative failure rate for Failnet model is = [h(60 mm)]/[/i(150mm)], where h is the hazard
function calculated by the Failnet model; relative failure rate for the NTNU/SINTEF model is = [average fail rate for group of pipes
with diameter A (A <100)] / [average fail rate for group of pipes with diameter B (100
-------
The results of a study25 of the performance of sewer pipes in Trondheim, Norway, are shown in
Figures 2-8 to 2-10. Figures 2-8 and 2-9 present trends for concrete sewer pipe collapses in
Trondheim, relative to pipe diameter. As can be seen in Figure 2-8, the majority of the failures have
occurred for pipes with a diameter less than 400 mm. Figures 2-9 and 2-10 show blockage statistics
for groups of sewers, i.e., the two common pipe materials concrete and PVC. The results show that
the small diameter pipes have a higher failure rate than the larger ones. Also, small diameter concrete
pipes have a higher failure rate than similar diameter PVC pipes. Researchers concluded that this
might be explained by the fact that the PVC pipes were constructed within the last decade, while
many of the concrete pipes were much older.
Accumulated failures rate
(burst/ 1 000km)
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0.000
Pipe d ia < 200 m m
Pipe dia. 200 - 400 m m
Pipe dia. > 400 mm
1980 1982 1934 1966 1983 1990 1992 1994 1996 199S
Note: Annual length of pipes changed during study. Therefore, years with ma jo r pipe
installation may show decreasing accumulation rates.
Figure 2-8. Collapses of concrete sewer pipes versus classes of pipe diameter in Trondheim,
Norway.26
Saegrov, S., Selseth, I., and Schilling W., Management of Sewer System Data in Norway. EWPCA Symposium, "Sewerage Systems - Costs
and Sustainable Solutions." 4-6 May, 1999 in conjunction with 12th IFAT 1999 Exhibition, Munich, Germany.
26 Ibid
34
-------
Accumulated failure rate
(failures/1 ODD km )
D.U
D.12
D.1D
D.DS
D.D6
D.Dt
D.D2
D.DD
-*- f f'i dia -: 2DD mm
—— P (>s dia. 2DD - inn m m
*^** P EJ dia. > tPD mm
z
z_
19T2 19TI 19T6 19TS 19SD 19S2
19S6 19SS 199D 1992 1991 1996 199S
Nala: Annual hnglh nlpipas c h a n g a d during study, f h a ro In ra . / a a rs wilh maja
in s la Ma I in n rr n / s h n w dacraasing accurrulalbn ralas.
Figure 2-9. Concrete sewer blockages versus pipe diameter in Trondheim, Norway.
27
Accumulated failures
(breaks/1 000 km)
D.12
D.1D
D.D8
0.06
0.04
0.02
0.00
t Pipe dia < 200 mm
-^±- Pipe dia. 200 - 400 mm
-•- Pipe dia. > 400 mm
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Figure 2-10. PVC sewer blockages for different diameters in Trondheim, Norway.
Ibid
35
-------
Influence of Soil Conditions on Pipe Failure Rates
European researchers have found that as for diameter, the surrounding soil conditions significantly
affect pipe failure rates. Table 2-10 presents relative failure rates for corrosive, alluvial, and clay soil
characteristics. The low relative failure rate for alluvial soils, as determined from the AssetMap data,
is noteworthy. This value contradicts the common assumption that alluvial soils result in increased
pipe failures. In this case, some factors correlated with the pipes located in alluvial soils are creating
an opposite effect.
Table 2-10. Sample relative failure rates for pipes in water distribution systems based on
surrounding soil conditions
Failnet
Relative Failure Rate =
[h(corrosive soil)] / [h(non-corrosive soil)]
Bordeaux
(GCI, 1st fail.)
1.75
Charente M.
(GCI, 1st fail.)
3.64
Suburb of Paris
(GCI, 1st fail.)
1.33
AssetMap
Relative Failure Rate =
[BR* (alluvium)3 ]/
[BR(other)]
Lyon
(GCI)
0.72
NTNU/SINTEF
Relative Failure Rate =
[h(clay)] / [h(non-day)]
Trondheim
3.09
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European
Models and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
* BR = average break or failure rate for each group of pipes.
Researchers also attributed the difference in the relative risk values to the fact that in each model,
the definition of soil is different. In Trondheim, using the model NTNU-SINTEF, a rough
classification has been applied to represent the "soil:"
+ Very aggressive: (Tidal zone, high ground water level, natural soil with resistivity under 750
Ohm cm, pH less than 5, polluted by chemicals, stray current, etc.)
+ Moderate aggressive: (Clay, wetland, nonhomogeneous, etc.)
+ Not aggressive: (Natural soil resistivity over 2500 Ohm cm, dry conditions, sand, moraine).
In Lyon, researchers scanned geological maps (scale 1/50 000) to develop information about the soil
type. In addition, areas with a history of soil movements (geotechnical risks) were defined in a
previous study. This variable appears highly significant in explaining problems with joints and leak
frequency. In Bordeaux, the soil type was defined from a specific study that sampled the resistivity
of the soil in half of the covered infrastructure area. In a suburb of Paris and in Charente-Maritime,
information about the soil was entered based on staff knowledge of soil corrosiveness.
36
-------
Influence of Traffic and Loading Conditions on Pipe Failure Rates
European research has also shown that traffic load is a significant factor affecting pipe failure rates.
Table 2-11 presents sample relative failure rates for high versus low traffic rates as recorded in the
studies applying the Failnet approach. In the Failnet approach, traffic is taken into account as a
qualitative variable according to the number of vehicles per hour or the type of road. As shown in
the table, failure rates increase with traffic load in all three systems.
Table 2-11. Sample relative failure rates for pipes in water distribution systems based on
traffic loads from Failnet
Relative Failure Rate = fhfhigh traffic)] /
[h(low traffic)]
Bordeaux
(GCI, 1st fail.)
2.30
Charente M.
(GCI, 1st fail.)
3.00
Suburb of Paris
(GCI, 1st fail.)
1.77
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European
Models and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland,
2000.
For AssetMap and the case of Lyon, researchers developed six types of traffic conditions. These
results display low differences between light and heavy or very heavy traffic, and can be considered
a conservative estimate of relative risks. Studies28 have shown that the location of pipes must be
considered as uncertain data. In studying this uncertainty on a sample (a 211 km networks in
Villeurbanne) with a Bayesian approach, the point estimate of the relative risk increased from 1.6
to 4 (see Tables 2-12 and 2-13).
Table 2-12. Sample relative failure rates for pipes in water distribution systems based on
traffic loads from AssetMap model
(Traffic) x (Pipe Location)
Case of Lyon
EO: pipe under pavement
El : under roadway with light traffic (<25 trucks/day)
E2: under roadway with heavy traffic (25 - 300 trucks/ day)
E4: under secondary road
E3: under roadway with very heavy traffic (>300 trucks/day)
E5: under main (national) road
Point Estimate of Relative Failure Rate
= BR* (Ei) / BR* (EO)
and 95% confidence interval
1
1.13; [0.98; 1.30]
1.35 ;[1.18; 1.56]
1.80; [1.37; 2.35]
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European
Models and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
* BR = average break or failure rate for each group of pipes.
28
Malandain J., Le Gauffre P., MiramondM. Organizing A Decision Support System For Infrastructure Maintenance: Application To Water Supply
Systems, Proceedings. First International Conference on New Information Technologies for Decision-making in Civil Engineering. Montreal
(Canada) 11-13 Oct. 1998, pp. 1013-1024. ISBN 2-921145-14-6
37
-------
Table 2-13. Sample relative failure rates based on traffic loads from AssetMap
model
Pipe Location
LO: pipe under pavement
LI : pipe under roadway
Point estimate of relative failure rate and
95% confidence interval, without
considering uncertainty of data
1
1.64; [1.06; 2. 54]
Point estimate of relative failure rate and
95% confidence interval, considering
uncertainty of data (Bayesian approach)
1
4 ; [2.68 ; 5.96]
Source: Eisenbeis, P., P. Le Gauffre, and S. Saegrov. Water Infrastructure Management: An Overview of European
Models and Databases, AWWARF Infrastructure Conference and Exhibition Proceedings, Baltimore, Maryland, 2000.
Influence of Initial Pipe Condition and Previous Breaks on Pipe Failure Rates
Research29 in the US has shown that generally, each time a pipe is repaired, the time to the next
repair is increasingly shorter. Although a few European studies discuss this phenomenon, we found
little empirical data to support the hypothesis. One related study30 was conducted on the sewers in
Malmo, Sweden. Swedish researchers conducted TV inspections at least five years apart on 40
segments of pipes. Most of the pipes were old and had small dimensions. On initial inspection,
researchers developed a score for the observed defects and assigned the pipes to one of three
condition categories: good, medium, and poor. During the second inspection, researchers developed
a new damage score based on observed defects.
To express the rate of structural decay, the researchers compared the damage scores between the two
events and created the following three categories of decay rates:
SD 0 = no change in damage score
SD 1 = a small (undefined) change in damage score
SD 2 = a large (undefined) change in damage score
The result of this analysis is summarized in Figure 2-11. As can be seen, the rate of deterioration
was greater for pipes in the poor initial condition.
European Experience Using GIS to Conduct Spatial Analysis of Pipe Failure Rates
Geographic Information Systems (GIS) are computer technologies that combine mapping and
technical information to generate maps and reports. They provide an effective framework to collect,
store, and use location-based information to improve planning and decision making. They can also
create links among geographical data (e.g., network drawings), relational databases (e.g., database
of network characteristics such as diameter, age, material, condition, etc.), and modeling tools
Clark, R.M., and J.A. Goodrich, Developing a Data Base on Infrastructure Needs. Journal of the American Water Works Association (AWWA).
Vol. 81, No. 7. (1989) pp. 81 -87.
30
Lidstrom, V. "Investigation of Sewer Condition," Urban Underground Water and Waste-Water Infrastructure: Identifying Needs and Problems,
Cost Action C3 Workshop, 18-19 June 1996, pp. 101-107.
38
-------
Pipes with Good
Initial Condition
Pipes with Medium
Initial Condition
Pipes with Poor Initial
Condition
Figure 2-11. Relationship between rate of structural decay and initial condition of pipe, Malmo,
Sweden31
(e.g., probabilistic tools that estimate chances of particular pipes failing based on pipe
characteristics). The ability to integrate this disparate information makes GIS tools
particularly useful for infrastructure asset management and rehabilitation planning.
Similar to US studies, Swedish32 and English33 GIS-based studies have found that leaks
tend to occur in clusters rather than being evenly distributed throughout the network. For
example, researchers in Sweden used a GIS system to display the annual average leak
frequency (based on all leaks recorded from 1985 to 1994) in a subdivision of the city of
Malmo. Figure 2-12 demonstrates how the spatial analysis of the leak frequencies can be
used to identify subdivisions that experience extremely high leak rates. This fact can
assist infrastructure managers in prioritizing rehabilitation plans.
However, it is important to note that GIS systems are resource-intensive, both in terms of
cost (expensive), and in terms of data management (require large volumes of high-quality
Lidstrom, V. Diagnos Av Avloppsledningars Kondition (The Diagnosis of the Condition of Sewer Pipelines) , Rapport 3194, Institution en for
Teknisk Vattenresurslara, Lunds Tekniska Hogskola, Lunds Universitet, Lund, 1996.
Sundahl, Ann Christin. Diagnosis of Water Pipe Conditions, Lund University, Department of Water Resources Engineering, Lund, Sweden,
1996, ISSN 1101-9824.
33 Newport, R. 1981. Factors Influencing the Occurrence of Bursts in Iron Water Mains. Journal of AQUA No. 3,1981, pp. 274-278.
39
-------
data). Hardware typically includes workstations, plotters/printers, GIS software, and
computer network servers with extensive storage space. Software typically consists of a
computer-aided design system (e.g., AutoCAD™), a database application (e.g.,
Microsoft® Access™), and a GIS interfacing program (e.g., Arclnfo™) to marry the
graphical data stored as maps with the data stored in relational databases. These
applications are usually available in both high-powered versions for mainframe, or as
lower-powered versions for desktop computers. No studies were found that compare
costs and benefits of using a GIS.
Leak Frequency per Year
Low
N o rm a I
High
Extremely High
Figure 2-12. Spatial analysis of leak frequency for the subdivisions of Malmo, Sweden
From a data management perspective, GIS consists of three major elements: data entry, data
manipulation, and data output. Graphical data, as well as location-specific data, must be
entered into the GIS. When a utility moves to GIS (as many have), they must manually enter
much of the required data. Data manipulation consists of evaluating and mode ling the data
40
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entered into the GIS, allowing the water utility to evaluate spatial data and alternatives.
Finally, the data output element graphically displays the results of the data manipulation
element. Data output can be printed or plotted and is usually in the form of maps, tables, and
digital files.
Summary of European Models to Assess Pipe Condition and Support Proactive
Rehabilitation Planning
Through a review of European research papers, company product literature, and interviews with
European researchers and practitioners, eight models or methodologies that are designed to support
rehabilitation planning were identified. To provide an overview of each of the European models
discussed in this paper, its capabilities to quantify and rank the condition of a pipeline from factors
such as structural deterioration (e.g., failure rate), hydraulic capacity, water quality, and economics
are described. Table 2-14 provides a summary of the capabilities of each model.
It is important to note that the summary of system capabilities was based solely on a review of
documentation and previous studies and not on independent validation or verification of the software
function. Neither USEPA nor LMI makes any endorsement of the products discussed.
Conclusions
Based on the review and analysis of European research and product literature related to the use of
models for rehabilitation management, it was found that:
+ There is still not a widespread use of modeling applications34 in Europe. Each model
presented in this paper has been applied in select urban or rural water services, but not on a
large national scale. UtilNets is the most comprehensive model. It contains capabilities to
model pipe failures, water quality, and rehabilitation scenarios. However, it is only in the
prototype development stage.
+ Although the studies reviewed pointed to the high cost of data collection in Europe, no
studies were found to compare the collection costs to the benefits received. However, some
did give an indication of the magnitude of costs. For example, the East of Scotland Water
Service estimated that the cost of data capture was about 80% of the total cost of its GIS and
rehabilitation management system prior to rolling it out to operational staff. Because data
collection costs are high, water services must avoid the unnecessary collection of data that
will rarely, if ever, be used, e.g., the number of step irons in a manhole. Therefore, managers
must ensure that the data they collect has a business requirement. One approach to
minimizing data collection costs is to collect only the minimum data elements (pipe material,
Eisenbeis, P., P. Le Gauffre, and S. Saegrov, Water Infrastructure Management: An Overview of European Models and Databases, AWWARF
Infrastructure Conference, Baltimore, MD, 2000.
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Table 2-14. Summary of capabilities of European water and wastewater infrastructure rehabilitation software
applications/models
Model Name
AQUA-
WertMin 4.0
AssetMap
EPAREIV
EPANET
Failnet
Gemini VA
KANEW
KureCad
UtilNets
(prototype)
Assets
Water Distribution
X
X
X
X
X
X
X
X
Wastewater Collection
X
X
X
Approach to Assess Current Network Conditions
Observed
Defects
X
X
X
X
X
X
X
X
'I
Failure/Break/Burst Rate Anal
X
X
X
X
X
X
X
X
^
Hydraulic Capacity/ Vulnerabi
Analysis
X
X
X
X
X
T3
Current Asset Value, O&M, a
Estimation
X
X
X
X
X
X
X
Water Quality Modeling
(Water Only)
X
X
Prioritization System to Selec
Critical Pipes for Rehabilitatio
X
X
X
X
X
X
X
Approach to Predict Future Network Conditions
Predicts Failure
Rates/Replacement Dates
X
X
X
X
X
X
X
Predicts Hydraulic Capacity or
Vulnerability
X
X
X
X
X
X
X
Predicts Future Asset Value
X
X
X
X
X
Predicts Future O&M and
Rehabilitation Costs
X
X
X
X
X
-o
Predicts Changes to Distribute
Water Quality (Water Only)
X
X
Compares Future Network
Rehabilitation Scenarios
X
X
X
X
X
X
User
Interface/
Output
CD
X
X
X
X
Report Generator
X
X
X
X
X
X
X
X
age, section length, number of breaks or bursts, and diameter) required by the models to
develop a prioritized list of pipes (as shown in Table 2-4). Water authorities can then use
this prioritized list of pipes to direct the collection of the additional data elements listed in
Table 2-4. Also, managers can modify maintenance worksheets to enable site crews to
capture the appropriate data as part of their routine site repair operations.
Spatial analysis plays an important role in rehabilitation planning since the research shows
that a significant number of failures appear in geographic clusters. However, only four of
the models (AssetMap, Gemini VA, KureCad, and UtilNets) integrated a GIS user interface.
All of the models had the capability to produce reports and graphics for comparison and
trending purposes.
42
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The concept of modeling the impact of pipe failures on water quality and using that
information for rehabilitation planning has not yet been implemented in practice. Only the
EPAREL/EPANET and UtilNets models integrated a water quality module and they are still
in the development stage.
High quality, consistent data is essential for developing accurate models. Water and
wastewater infrastructure operations and maintenance decisions must be based on analysis
of reliable data that reflects the true status of a pipe system. It is evident from the European
research that if a model or application is to gain the support of the engineering staff who use
the records on a daily basis, the data used by the application or model must be accurate. At
a minimum, the quality of the data should be flagged to warn the user of possible
inaccuracies. European researchers note that even if water services in a region do use the
same model, existing data collection methods vary considerably from service to service since
the data entered into the models has typically been inherited from historic paper-based
record-keeping approaches.
Finally, European researchers note that sharing data across water services would reduce costs
of data collection and improve modelling accuracy.
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Chapter 3
Summary of European Performance Indicators for Water Distribution and
Wastewater Collection Infrastructure
The historical performances of water distribution, sewer collection pipes, or networks have been
used as indirect estimations of pipe or network conditions and rehabilitation needs. Examples of such
performance indicators (Pis) are the number of distribution pipe bursts, distribution system leakage,
sewer collapses, and sewer blockages. In Europe, three initiatives were found that use performance
indicators for asset management and recapitalization purposes: the Italian Reggio water distribution
system, the Scandinavian Six-Cities Group performance benchmarking consortium, and the United
Kingdom's (UK's) Office of Water (OFWAT) annual and five-year system serviceability
assessments. Each case description includes the Pis used, the types of decisions made, and provides
a summary list of the indicators.
Although the intent was to also provide a cost-benefit evaluation regarding the use of indicators, data
collection cost or benefit information was unable to be identified.
Background on the Use of Performance Indicators for O&M and Rehabilitation
Planning
How is the effectiveness of water and wastewater infrastructure measured? The answer to this
question is not easy to come by, but is an essential one if a meaningful framework for assessing its
performance can be created. A National Research Council (NRC) study35 on measuring and
improving infrastructure stated that:
".. .performance was the degree to which infrastructure provides the services that the
community expects of that infrastructure [and] can be defined as a function of
effectiveness, reliability and cost.... "
This general concept of using performance measures as a management tool is straightforward: you
can't improve the performance of a system unless you measure it. However, the NRC implies that
there is no single definition of good performance. Rather, good performance is determined by
meeting the expectations of the community stakeholders. Therefore, understanding the expectations
of community stakeholders is essential if infrastructure managers are to clearly demonstrate how a
particular infrastructure system is performing against indicators of effectiveness, reliability, and
costs.
Since there are no standards for infrastructure performance, infrastructure managers and community
35
National Research Council, Committee on Measuring and Improving Infrastructure Performance, Measuring and Improving Infrastructure
Performance, National Academy Press, Washington D.C., 1995. ISBN 0-309-050987
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stakeholders have found it difficult to identify performance targets, establish meaningful indicators
of performance, and use the information to make decisions or communicate results to the public.
However, there are general guidelines36 that can assist infrastructure managers and community
stakeholders with their efforts. Managers should look to select indicators of effectiveness, reliability
and cost that enable them to:
+ Compare what the water authority did related to O&M, rehabilitation, and new
construction with what they planned to do. For example, this can be demonstrated by
listing the planned rehabilitation activities in relation to the annual accomplishments.
+ Compare the infrastructure network's present performance with past performance to
observe the trends of key performance indicators. Is it more effective, reliable, and less
costly to maintain than before? The number of pipe breaks is one of the most commonly
used indicators of effectiveness, but to be meaningful, managers should assess its trend
over time to determine if O&M and rehabilitation policies are having a positive impact on
system costs, water quality, or meeting customer expectations.
+ Compare the water authority's performance to other similar water authorities. This
"benchmarking" approach is an important internal tool for monitoring best practices, and
ensuring that the water authority is keeping up with best internal and external practices.
Although trend analysis of performance metrics is an important exercise for a water
authority to undertake individually, it can provide more useful information if the results
are compared across many water authorities by a third-party organization or government
agency.
+ Compare the water authority's processes and performance to existing protocols - of which
there are many. In addition to the water authority's internal standards, such as
maintenance procedures and repair goals, there are a variety of industry recommendations
and a host of criteria offered by a wide range of organizations such as the American
Water Works Association, the American Society of Civil Engineers, and many others.
Whichever framework is used to measure the water authority's performance in operating,
maintaining, and rehabilitating its infrastructure, both management and external watchdogs should
look to performance benchmarking as a method to achieve the community's expectation for
effectiveness and reliability at the lowest possible life-cycle costs.
European Case Studies
In reviewing the European experience, focus was put on case studies where managers used
performance indicators as they relate solely to the management and rehabilitation of water
Friend, Gil, Evaluating Corporate Environmental Performance, The New Bottom Line, Issue 5.22, Berkeley, CA, October 21,1996.
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distribution and wastewater collection pipe networks. It is important to note that these performance
indicators are usually developed as part of a comprehensive performance assessment program aimed
at improving operations across a water or wastewater authority. In addition, examples are provided
on how performance indicators can be used by one water authority, a regional consortium of water
authorities, and by a federal government.
Italy Reggio Water Distribution Network
One of the simplest uses of performance indicators to improve operations comes from a case study37
of the water distribution network in Reggio, Italy. It involves the use of performance indicators by
AGAC, a private water authority, to reduce leakage rates. AGAC management believed that the
distribution system was experiencing excessive leakage rates and implemented a performance
improvement program to reduce the amount of water lost. Since the target audience for this effort
was the system operators or management, the indicators selected focused strictly on reducing the
system leakage, both in terms of total amounts and as a percentage of total water produced. To
calculate the values for the selected indicators, AGAC had to measure the water produced and the
water delivered to the consumer, in total and for each district of the network. Table 3-1 lists the
performance indicators and required data for the Reggio case study.
Table 3-1. Summary of performance indicators and required data for Reggio Water System,
Italy, 1994 Leakage Study
Performance Indicator
Definition
Total amount of leakage = total water produced minus the total
water demand from billing data (total and by district)
Percentage of leakage = total leakage divided by total produced
(total and by district)
Unit
Cubic
meter
%
Required Data
Definition
Water produced - total annual (total
and by district)
Water billed - total from billing data
(total and by district)
Unit
Cubic
meter
Cubic
meter
To collect the necessary data, AGAC divided the network into districts that were served by one or
two water mains and installed flow meters on those pipes. With the meters in place, AGAC
collected data on the volume of water flowing to a district, and collected billing data from consumer
meter readings. To calculate the leakage amount for the system, AGAC compared flow
measurements into each district with the consumer meter readings for each district. Through the
analysis of this data by district, AGAC was able to identify districts with high leakage or faulty
point-of-use meters. Those districts with high leakage rates were then given priority for detailed pipe
evaluations, through which operators identified specific leaking pipes. This effort resulted in an
overall annual reduction of water losses of 52% in 1994, as compared to 1989. Table 3-2 shows the
performance of the leak monitoring system by comparing the water loss data for 1989 to 1994 for
the Reggio water system. AGAC realized an additional benefit from installing district meters: it
Schiatti, Marcello, Ing., "Active Control of an Urban Distribution Network", Urban Underground Water and Waste-Water
Infrastructure: Identifying Needs and Problems, Cost Action C3 Workshop, 18-19 June 1996, pp. 109-117.
49
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created a long-term flow monitoring system that enabled it to continuously monitor leakage rates.
Table 3-2. Leakage data for Reggio E. Water System, Italy (1989 versus 1994)
Year
1989
1994
Water Produced
(cubic meter)
16,153,000
14,079,000
Water Billed
(from billing meters)
(cubic meter)
9,486,000
10,857,000
Leakage
(produced - consumed)
(cubic meter)
6,667,000
3,222,000
5 -year reduction water lost
Percentage Lost
41%
23%
52%
This case study is an example of a simple use of Pis in that it only involved an intra-system
comparison of a very specific indicator of network effectiveness (leakage). AGAC used the trend
in performance over time to judge success. In this case, success was a reduction in leakage rates
from 1989 to 1994.
Scandinavia Six-Cities Group
Another example38 of the use of performance indicators comes from the Six-Cities Group, a
consortium of water authorities from four Scandinavian countries. This case study involves a group
of six water authorities joining together in a private consortium to identify and use performance
indicators as a mechanism to improve operations among members.
The six cities are Copenhagen in Denmark, Oslo in Norway, Helsinki in Finland, and Stockholm,
Gothenburg, and Malmo in Sweden.
At the inception of the Six-Cities Group, the utilities were owned by the cities. In the 1990s,
discussion arose regarding the privatization of the water authorities. At this meeting, the utility
managers found that they could not demonstrate that their utilities performed well. As a result, the
managers decided to form a consortium, the Six-Cities Group, to share information and to develop
indicators of performance. They selected seven business areas in which to measure performance:
+ Business-wide management,
+ Production of drinking water,
* Distribution of drinking water,
+ Collection of wastewater and stormwater,
38
Helland, B., and J. Adamsson, Performance Benchmarking Among 6 Cities in Scandinavia. Oslo, Norway: Oslo Water and Sewage Works,
unpublished white paper.
50
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* Treatment of wastewater,
+ System construction and rehabilitation, and
* Finances.
For each business area, they looked for indicators of customer satisfaction, cost-effectiveness, and
environmental impact. Table 3-3 provides a description of the performance indicators for the
business areas directly related to water distribution and wastewater collection.
The Six-Cities Group continues to develop and refine the performance indicators each year.
Managers meet at the beginning of each year to refine the PI definitions and begin the data collection
process. Each water authority completes the data collection form electronically and submits it to the
consortium committee. At the end of each year, managers meet and present the results for their
system in an agreed-upon format.
To date, the participants have found that despite the differences that exist among the cities (e.g.,
different languages and currencies) it is possible to compare the performance. However, managers
noted that to do so, it is essential to clearly define the data collection requirements and the indicators.
Although the Six-Cities Group continues to refine its approach, this case study demonstrates how
a voluntary association can adopt a wide variety of Pis for both intra-system and inter-system
comparisons. Its approach has enabled participants to identify best-in-class practices during annual
reviews and to identify trends for each participant as they continue to measure performance over a
5-to 10-year period.
United Kingdom's Office of Water (OFWA T) System Serviceability and Performance Assessments
One of the best and most developed examples39 of PI usage comes from the UK. Since the
privatization of water and wastewater systems in the UK, the Office of Water (OFWAT) has required
the private companies to maintain their extensive infrastructure of water mains and sewers in a
manner that provides "adequate" services to current and future customers. The goal is to ensure that
private companies provide adequate investments in infrastructure while maintaining competitive
rates. OFWAT reviews company performance annually and at each 5-year review of company price
rates and request for license renewal.
For the 5-year license renewal performance assessment, OFWAT reviews each company's Pis for
the previous 5 years, as well as its plans for the future O&M and rehabilitation of its infrastructure.
Based on this review, OFWAT in effect approves each company's capital reinvestment budget.
39
Office of Water (OFWAT), Comparing Company Performance. OFWAT Information Note No. 5, London, England, July, 1995 (revised
February 1998). This paper and additional information can be obtained from the OFWAT web site at
http://www.open.gov.uk/ofwat/pubslist/pubsinih.htm
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Specifically, it approves a water company's request to set future prices at a level that will provide
sufficient funds to maintain its network. Companies are required to carry out any work needed to
Table 3-3. Description of Pis related to water and sewer pipes for Swedish Six-Cities Group
PI Category
Business-wide
Distribution of drinking
water
Collection of wastewater
and stormwater
System construction and
rehabilitation
Financial indicators
PI Description
Customer inquiries — under development
Energy consumption per customer
Energy production per customer
Cost of chemicals per cubic meter of water produced
Cost of chemicals per cubic meter of treated wastewater
Number of employees per 1,000 customers
Personnel cost per customer
Percent of "In-house work" of total cost
Cost per cubic meter of water sold (distributed on type of cost
and activity)
Cost per cubic meter of wastewater treated (distributed on type of cost and activity)
Income (distributed on type of activity)
Interruptions (minute/customer)
Number of breaks per 10 km of pipe length
Leakage (1/min/km)
Cost per cubic meter of water sold (distributed on type of cost)
O&M cost per meter of pipe length
Number of blockages per 10 km of pipe length
Number of flooding per 1 ,000 consumers
O&M cost per meter of pipe length
Reconstruction per renovation of water pipes (percent of total
length)
Rehabilitation (spending per cubic meter water sold)
New construction (spending per cubic meter water sold)
Rehabilitation of sewers (percent rehabilitated of total length)
Rehabilitation of sewers (percent rehabilitated of total length)
Under development
rectify deteriorating serviceability to customers, either before license transfers or as part of the new
license, but at no cost to customers. The need for such work at a license transfer would be reflected
in the company value at transfer. Such a potential liability should provide an incentive for the
companies to ensure that they maintain the serviceability of the water main and sewer networks.
OFWAT's 5-year assessment is based on the concept of serviceability to customers. It examines the
overall trends for a range of Pis that describe the performance of the distribution and collection
systems. By examining the trends over several years, OF WAT determines whether the O&M and
rehabilitation carried out by the company has resulted in stable, improving, or deteriorating services
to customers.
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* If the assessment shows stable serviceability, then OFWAT's initial judgment would be that
a continuation of past levels of O&M and rehabilitation activity should be sufficient for the
next price limit period.
* If the assessment shows improving serviceability, then OFWAT's initial judgment would be
that slightly lower levels of O&M and rehabilitation activity should be sufficient to deliver
stable serviceability in the next price limit period.
* If the assessment shows deteriorating serviceability, then OFWAT's initial judgment would
be that past levels of O&M and rehabilitation activity have not been adequate. OFWAT
considers a decline in serviceability a serious shortfall in company performance.
OFWAT also conducts an annual review of company performance against various predefined Pis.
Although OFWAT does not use this information to review pricing or licensing issues, it does publish
a "Level of Service Report"40 which compares company performance in delivering customer service
and in providing water supplies and sewerage services. The report provides an intra-company
assessment of performance trends, an inter-company comparison of performance and industry
averages and an extra-company comparison to select benchmarks from other industries. These
assessments provide information on the quality of individual services delivered to customers and
allow OFWAT, the public, and the customers to judge how well the companies are performing.
DESCRIPTION OF PERFORMANCE INDICATORS41
The OFWAT assessment of company performance focuses on the delivery of services to customers.
There are six key categories of Pis for assessing the water and wastewater companies:
+ water supply,
* water distribution,
+ sewerage service,
+ customer service,
* environmental impact, and
* infrastructure costs.
40
Office of Water (OFWAT), 1998-99 Report on Levels of Service for the Water Industry for England and Wales, London, England,
September 1999. This paper is available from the OFWAT web site at http://www.open.gov.uk/ofwat/pubslist/pubsinfri.htm
41 Office of Water (OFWAT), Level of Service Indicators, OFWAT Information Note No. 40, London, England, March 1998. This paper is
available from the OFWAT web site at http://www.open.gov.uk/ofwat/pubslist/pubsinfri.htm
53
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In each of these areas, OFWAT has developed specific output Pis based on a number of
considerations. These considerations are: services measured should be of real importance to
customers and, where possible, based on those used in the published reports; Pis should be
meaningful to companies and customers; Pis should be supported by high-quality data; and Pis that
can be objectively assessed are preferred.
The specific Pis OFWAT has developed related to water and sewer networks are described further
in the following paragraphs and summarized in Table 3-4.
Indicators of Customer Service
Data on current levels of customer service is available from two key sources — the companies and
the customer service centers (CSCs). It exists in a number of forms, from the objective data provided
annually by companies in the July return, and from the assessments of service made by the CSCs.
Four objective and independently audited measures of customer contact are available and have been
included in this overall assessment — response to billing contacts, replying to written complaints,
issuing bills for me tered customers (DG8), and speed of response to telephone contacts. Two of these
metrics — response to billing contacts and replying to written complaints — have a long enough
history to be used in the analysis of performance improvement. Customer service clearly goes wider
than the speed of response to complaints and frequency of reading meters. The assessment is mostly
based on objective facts about the service offered. The assessment of information to customers is
currently based on information obtained through the billing process. Members of the CSCs and
companies have expressed concern that there are currently no measures reflecting the quality of
replies to complaints or the quality of the telephone service provided. With respect to written
complaints, the limited results of CSC audits are being used to update the 1996 analysis of company
complaint handling procedures.
Billing Contacts
This indicator shows the total number of written and telephone-billing contacts received by a
company, and the number dealt with in 2, 5, 10, 20, and more than 20 working days. A billing
contact is any inquiry (but not a complaint) about a bill - for example, an account query, change of
address, or request for alternative payment arrangements. Complaints are covered by the metric
associated with the company performance in replying to written complaints.
Replying to Written Complaints
This indicator shows the total number of written complaints received by a company, and the number
dealt with in 2, 5, 10, 20, and more than 20 working days. A written complaint is any letter that
draws attention to any service provided or action taken by a company (or its representatives) which
falls short of the customer's expectations. Complaints that the company considers unjustified must
still be included.
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Table 3-4. Description of the UK OF WAT's performance indicators related to water
distribution and wastewater collection systems
PI Category
Customer Service
Water Distribution
Wastewater Collection
Infrastructure Costs
Performance Indicator
Billing contacts
Written complaints
Bills for metered customers
Ease of telephone contact
Number of properties reporting low water pressure
Water leakage in ml/day
Km of mains relined
Km of mains renewed
Total km mains relined & renewed
Number of burst mains per 1 000 km
Unplanned interruptions
Number of pollution incidents at sewers
Number of sewer collapses per 1 000 km
Number of properties affected by flooding (overloaded sewers),
weather
except due to the effects of extreme
Km of sewers renovated
Km of sewers replaced
Total km of sewers renovated & replaced
Total % of properties reporting internal sewage flooding
Water Infrastructure Main Installation Costs (average, maximum and minimum actual unit cost/unit
length by type)
Water Infrastructure Main Rehabilitation Costs (average, maximum and minimum actual unit
cost/unit length by type)
Sewerage Main Installation Costs (average, maximum and minimum actual unit cost/unit length by
type)
Sewerage Main Rehabilitation Costs (average, maximum and minimum actual unit cost/unit length
by type)
Bills for Metered Customers
This indicator shows the percentage of metered customers who receive at least one bill during the
year based on an actual meter reading. An actual meter reading is a reading taken by the water
company, or one provided to the company by the customer (in response to an estimated bill, or as
a result of a request for the information). Companies also report the number of meters that they
have not read in two years or more.
Ease of Telephone Contact
This indicator identifies the ease with which customers can make telephone contact with their
local water company, showing speed of response within 15 and 30 seconds, the number of
55
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abandoned calls, and the amount of time all lines to the company were engaged. Incoming
telephone traffic on the main, advertised customer contact numbers (e.g., the customer service
department, accounts section, or the main switchboard) is monitored.
Indicators of Water Distribution and Quality
Two important aspects of company performance in supplying water are pressure and interruptions
to supply. OFWAT reviews performance in these areas annually against predefined standards for
pressure and interruptions - the results are published in OFWAT's annual Level of Service Report.
The PI related to inadequate pressure measures the total number of properties at risk of receiving
water below a prescribed rate of flow and pressure. The data is derived from a company assessment
of risk and allows exclusions for abnormal demand. Performance improvement is measured by the
total number of pressure problems solved through company action since 1992 (this excludes
properties added to or removed from the 'at risk' categories because of select information). The
methodologies associated with company risk assessment are now generally sound, and the data is
considered suitable for comparative purposes.
Companies provide data in their July returns to OFWAT on planned and unplanned interruptions to
supply. The Level of Service report concentrates on the latter, and uses a scoring system to reflect
the number and duration of interruptions in order to produce comparative performance assessments.
These results have been used in this analysis. Planned interruptions have not been included because
of the difficulty in accounting for the impact of different maintenance techniques used by the
companies.
Performance improvement in the area of interruptions to supply is based on a comparison of the
rolling average figure for interruptions in excess of 12 hours (the only data available with a history
since 1992) for 1992-1995 and 1994-1997.
Inadequate Pressure
This indicator shows the number of residential properties which have received (and are likely to
continue to receive) pressure below a certain reference level when demand for water is not abnormal.
The reference level of service is defined as 10 meters head of pressure at the boundary stop tap, with
a flow of 9 liters per minute. This should be sufficient to fill a 4.5-liter container in 30 seconds from
a ground-floor kitchen tap. Because it is impractical to measure the pressure and flow at the
boundary of every customer's property, companies are allowed to report against an alternative
reference level, which is normally 15 meters head of pressure in the distribution main supplying the
property. This is a sufficiently high pressure, even allowing for the connection from the water main
to the property boundary. Companies are expected to keep registers that identify the properties at risk
of receiving low pressure.
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Supply Interruptions
This indicator shows the number of properties experiencing interruptions lasting longer than 6 hours,
12 hours, and 24 hours, which are the responsibility of the water company and are unplanned and
without warning. Supply interruptions are excluded if a third party causes them. Companies also
provide information on the number of supply interruptions that result from planned maintenance
work and overrun the stated restoration time. Companies are required to keep registers that identify
those properties affected by supply interruptions.
Restrictions on Use of Water
This indicator shows the percentage of a company's population that has experienced restrictions in
using water. There are several categories:
+ hosepipe (residential water use) restrictions,
* sprinkler/unattended hosepipe restrictions, and
+ drought orders restricting non-essential use of water.
Companies are required to report the percentage of their population affected by any of the above
water restrictions.
Indicators of Sewer Performance
OF WAT collects and publishes annual data on company performance for flooding due to inadequate
sewer capacity (including an assessed risk of flooding, as well as actual incidents), and flooding
incidents related to the condition of sewers and associated equipment. The former results from long-
term problems that generally can only be resolved by capital investment; the latter are generally the
result of insufficient, ongoing maintenance and are more within companies' control.
Combined sewer overflows are also part of the sewage collection system and, as such, might be
expected to appear in this part of the assessment. However, OFWAT has included this PI in the
environmental impact section, as failures will have their major effect on the receiving rivers and
coastal waters.
Flooding from Sewers
There are two measures covered by this PI. First, this indicator shows the number of properties at
risk of internal flooding from sewers due to overloading more than twice in 10 years and more than
once in 10 years. Second, it lists the number of properties that are internally flooded due to either
temporary problems, such as blockages or sewer collapses, or overloading.
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Indicators of Environmental Impact
Customers are clearly interested in understanding the environmental impacts of the activities of
companies, especially since these are amajor driving force behind the increases in customers' bills.
Therefore, OFWAT measures:
+ Sewage treatment works failing their permit limits. Failures considered by the UK
enforcement agency not to be reflective of company performance are excluded.
* Data on unsatisfactory combined sewer overflows. This reflects company progress in
dealing with the problem of overflows that the UK enforcement agency considers
unsatisfactory.
The annual total of major and significant incidents is expressed as a percentage of the resident
equivalent population served by sewage treatment works to allow for the different size of companies.
While OFWAT acknowledges that it may have been better to use the number of outlets where an
incident could take place as a denominator, this data is not available.
Indicators of Infrastructure Costs
OFWAT uses the cost base method,42 which it developed as part of its 1994 annual review, to
determine the performance of the companies as it relates to installing new pipes or rehabilitating
existing pipes. Company performance for each cost category is compared to the range of costs
experienced by all companies for a particular cost component. OFWAT looks at the costs companies
experienced to install new water mains and sewers, and to rehabilitate existing ones. Tables 3-5 to
3-8 present the definition of each PI and actual costs for water and sewer infrastructure in the UK
for 1999.43
42 Office of Water (OFWAT), Infrastructure Renewals Accounting. OFWAT Information Note No. 36, London, England, February 1997. This paper
is also available on the OFWAT web page at http://www.open.gov.uk/ofwat/pubslist/pubsinfn.htm
43 Office ofWater (OFWAT), Annual Report 1999-2000, House ofCommons Paper 455, London, England, ISBN 0 10 556761 2, May 2000. This
paper is also available on the OFWAT web page at http://www.open.gov.uk/ofwat/pubslist/pubsinfn.htm
58
-------
Table 3-5. Water infrastructure standard costs in the UK - mains laying
Standard (std)
cost
Description
Number of
std costs
submitted
Range of std
costs
submitted
(£/meter)
Benchmark
General specification for mains laying:
New water pipes laid in normal site conditions at a depth of no greater than 900 mm without any adverse complications. Pipe
material is based on companies 'own practices. Costs include all fixtures and fittings, ancillary works and reinstatement. Diameters
relate to the nominal internal bore of the pipe.
Grassland
100mm
150 mm
200 mm
300 mm
450 mm
600mm
Mains laid in urban/rural verges, new development
sites or open field normally used for grazing.
Excludes the cost of traffic management.
25
25
25
24
20
17
25 to 62
28 to 76
40 to 89
60 to 130
96 to 237
141 to 342
31
37
41
65
113
176
Rural/suburban
100mm
150mm
200mm
300mm
450 mm
600mm
Mains laid in secondary or minor roads and housing
estates. Type 3 or 4 reinstatement and non-traffic
sensitive in accordance with the New Roads and
Street Works Act 1991.
25
25
25
25
18
15
62 to 118
73 to 133
83 to 149
105 to 205
159 to 344
286 to 546
71
85
95
118
232
315
Urban
100mm
150mm
200mm
300mm
450 mm
600mm
Mains laid in cities and town center trunk roads.
Type 2 reinstatement and traffic sensitive in
accordance with the New Roads and Street Works
Act 1991.
21
21
23
22
16
14
73 to 160
88 to 174
101 to 195
126 to 253
191 to 367
339 to 587
81
99
112
141
236
345
59
-------
Table 3-6. Water infrastructure standard costs in the UK - mains rehabilitation
Standard (std)
cost
Description
Number of
std costs
submitted
Range of std
costs
submitted
(£/meter)
Benchmark
General specification for mains rehabilitation:
Existing water pipes rehabilitated using particular techniques at a depth of no greater than 900 mm. All fixtures and fittings,
ancillary works and reinstatement are included.
Epoxy resin
100mm
150mm
200mm
300mm
Encrustation removed andpipe lined internally using
an epoxy seal coat.
17
17
16
12
30 to 42
29 to 45
31 to 54
31 to 73
34
38
42
46
Slip lining
100mm
150mm
200mm
Encrustation removed and a non- structural medium-
density polyethylene pipe is inserted into the existing
pipe.
4
4
4
37 to 63
40 to 71
46 to 96
44
52
62
Pipe insertion
100mm
150mm
200mm
300mm
450 mm
600mm
Encrustation removed and a smaller structural pipe is
inserted into the existing pipe.
6
6
6
6
7
7
28 to 59
36 to 63
55 to 77
88 to 148
117 to 180
134 to 250
50
53
67
88
135
227
Pipe bursting
100mm
150 mm
200 mm
Encrustation removed and the existing pipe broken
using an expander attached to a mole that compresses
the resulting fragments of the existing pipe into the
surrounding soil. As the pipe is broken, a new pipe
is drawn behind the mole.
18
18
12
42 to 69
51 to 97
70 to 96
42
51
70
60
-------
Table 3-7. Sewer infrastructure standard costs in the UK - sewer laying
Standard (std)
cost
Description
Number of
std costs
submitted
Range of std
costs
submitted
(£/meter)
Benchmark
General specification for sewer laying:
New sewers laid assuming a depth of cover to the sewer is 2.0 meters to the crown of the pipe. Costs include a sewer junction and
cap at 10 meter intervals and 50 meter intervals between manholes. Costs are based on open-trench pipe laying, with all other
assumptions consistent with the relevant design and construction guidelines in Sewers for Adoption (4th Edition) . Diameters relate
to the nominal internal bore of the pipe.
Grassland
150mm
225 mm
300mm
450 mm
600mm
900mm
Sewers laid in urban/rural verges, new development
sites or open fields normally used for grazing.
Excludes the cost of traffic management.
10
10
10
9
9
9
76 to 150
108 to 166
121 to 192
154 to 240
196 to 316
268 to 502
87
114
141
181
232
346
Rural/suburban
150mm
225 mm
300mm
450 mm
600mm
900mm
Sewers laid in secondary or minor roads and housing
estates. Type 3 or 4 reinstatement and non-traffic
sensitive in accordance with the New Roads and
Streets Works Act 1991.
10
10
10
9
9
9
132 to 250
157 to 284
186 to 326
250 to 388
318 to 521
449 to 775
179
210
241
255
348
515
Urban
150mm
225 mm
300 mm
450 mm
600 mm
900 mm
Sewers laid in cities and town center trunk roads.
Type 2 reinstatement and traffic sensitive in
accordance with the New Roads and Street Works
Act 1991.
9
9
9
9
9
9
171 to 275
201 to 313
233 to 359
290 to 427
382 to 573
533 to 851
185
217
249
310
382
556
61
-------
Table 3-8. Sewer infrastructure standard costs in the UK - sewer rehabilitation
Standard (std)
cost
Description
Number of
std costs
submitted
Range of std
costs
submitted
(£/meter)
Benchmark
General specification for sewer rehabilitation:
Existing sewers rehabilitated using particular techniques. All sewers rehabilitated at a depth of cover to sewer of 2.0 meters. Costs
include a sewer junction and cap at 10 meter intervals. Costs assume that linings are installed in 100 meter lengths and that
adequate water supply is available on site. Diameters relate to the nominal internal bore of the pipe.
Pipe bursting
225mm
300mm
450 mm
Existing sewer is broken out by an expander attached
to a mole and a new pipeline is drawn in behind.
5
4
4
124 to 196
163 to 255
220 to 349
139
163
220
Insituform
150mm
225 mm
300mm
450 mm
600mm
A flexible lining is inserted into the sewer, via
existing manholes, under pressure of water and then
cured by circulating hot water.
8
10
10
10
9
99 to 161
92 to 165
108 to 183
150 to 250
189 to 398
99
114
127
150
204
Main entry
900mm
Gunite, Glass Reinforce Cement or Glass Reinforced
Plastic installed inside the sewer in short or
continuous lengths
4
203 to 564
409
APPROACHES TO COLLECT DATA AND EVALUATE Pis
OFWAT requires all companies to submit a standard data collection worksheet for the annual
reporting and 5-year license reviews. They also work with all the companies and encourage them
to improve the consistency and comparability of information. OFWAT reviews the companies'
values for Pis and assigns them a combined performance score of 5 to 50 as described in Table 3-9.
OFWAT also differentiates between companies that achieve a high level of performance based on
sound information, and those whose performance is based on less-reliable data. OFWAT assigns
a confidence grade to the reliability and accuracy of information companies submit. These grades
have two parts: a reliability band based on how the data was gathered and a number indicating its
likely range of error.
62
-------
Table 3-9. The UK's OFWAT scoring criteria for assessing company performance
Performance
Indicator
Water pressure
1996-97
Unplanned
interruptions to
water supply
Sewer flooding
incidents due to
overloaded sewers
Sewer flooding
incidents with
causes other than
capacity
Properties at risk of
sewer flooding
Data Source
Company
data
Company
data
Company
data
Company
data
Company
data
Description
Company assessment
of properties at risk of
receiving low pressure
Properties affected by
unplanned interruption
to supply greater than
six hours
Properties flooded
internally by sewage
as a result of an
overloaded company
sewer
Properties flooded
internally by sewage
— caused by
blockages, sewer
collapses, equipment
failure etc.
Properties at risk of
internal flooding from
sewers more than once
in ten years
Performance Range
From 5.5%
properties at risk
(worst) to zero at risk
Performance scores
(combination of 6,12,
and 24 hour
interruptions) from
2.77 (worst) to 0.14
(best)
Percentage of
connected properties
flooded from 0.01
(worst) to 0.001
(best)
Percentage of
connected properties
flooded from 0.035
(worst) to 0.005
(best)
Percentage of
connected properties
at risk from 0.244
(worst) to 0.1 2 (best)
Scoring Criteria
Percentage at risk figure
scored from 5 (poorest
performance) to 50 (best)
Comparison of interruption
scores as used in the OFWAT
Levels of Service report.
Interruption scores scored
from 5 (poorest performance)
to 50 (best)
Sewer flooding incidents due
to hydraulic incapacity
excluding extreme weather
events. Percentage figure
scored from 5 (poorest
performance) to 50 (best)
Sewer flooding incidents due
to hydraulic incapacity
excluding extreme weather
events. Percentage figure
divided into ten bands and
scored from 5 (poorest
performance) to 50 (best)
Sum of properties at risk of
flooding more than once in
ten and twice in ten years
expressed as a per- cent.
Percentage figure scored from
5 (poorest performance) to 50
(best)
SUMMARY OF ACTUAL PIS FOR THE UK'S WATER MAIN NETWORKS
OFWAT's 1998 assessment44 of the entire UK water distribution system found that overall, the
serviceability of underground networks is improving (see Figure 3-1). Based on that review of
performance indicators, OFWAT determined that at an industry level, the companies' current
levels of capital reinvestment should be sufficient to maintain serviceability to customers in the
next price limit period.
OFWAT aggregated the information provided by the companies to create an industry picture of
the water main and sewer networks, covering the inventory of asset stock, and its valuation,
condition,and individual performance (see Tables 3-10 and 3-11).
Office of Water (OFWAT), 1998-99 Report on Levels of Service for the Water Industry for England and Wales, London, England, September 1999.
This paper is available from the OFWAT web site at http://www.open.gov.uk/ofwat/pubslist/pubsinfa.htm
63
-------
Table 3-10. Inventory of UK water main pipes by diameter (March 1998)
<=300
bore
(km)
245000
<=600
bore
(km)
51000
<=900
bore
(km)
21000
>900
bore
(km)
8000
Total
Stock
(km)
325000
Table 3-11. Condition assessment of water mains in the UK (March 1998)
Proportion
Grade 1
46%
Grade 2
31%
Grade 3
12%
Grade 4
6%
Grade 5
5%
Notes on Condition Grades for Water Mains:
Condition grade 1: No failures, fully complies with modern standards.
Condition grade 2: No significant failures (minimal impact on service performance), not quite consistent with
modern standards.
Condition grade 3: Deterioration beginning to be reflected in service levels or increased operating costs.
Condition grade 4: Considerable corrosion affecting service performance, nearing end of useful life, frequent bursts.
Condition grade 5: Substantially derelict and source of service problems, no residual life.
Companies estimated that the gross replacement cost of all the potable water mains with modern
equivalent assets (MEA) to be about £39 billion. Around 11% of the potable water mains were
assessed as poor condition (condition grades 4 and 5), compared to 9% reported as poor condition
in 1993. Many companies have attributed small changes in the reported proportion of water mains
in poor condition over the last 5 years to improvements in their management information systems
and reporting methods. Analyses of the companies' Business Plans confirm OF WAT's assessment
that there is no evidence of a significant deterioration in the condition of the aggregate potable water
main network stock.
SUMMARY OF ACTUAL PERFORMANCE METRICS FOR THE UK'S SEWER
NETWORK
OFWAT's 1998 assessment45 of the entire UK wastewater collection system found that overall, the
serviceability of underground sewer networks is stable and, in some companies, is improving (see
Figure 3-2). Based on that review of performance indicators, OF WAT determined that at an industry
Office of Water (OFWAT), 1998-99 Report on Levels of Service for the Water Industry for England and Wales, London, England, September
1999. This paper is available from the OFWAT web site at http://www.open.gov.uk/ofwat/pubslist/pubsinfri.htm
64
-------
3.S
1.0 -
11,5 -
^
0,0
WATER MAINS NETWORK
Woraanng
far B7ffiS Mid SELF'S
TO-
GO
81-
82
83-
m
-«- Pressure (DG2)
83-
86
87-
88
• lutenupticiiis
B9-
year
Pi
(pG3> -a-BuMs
93-
S4
93-
96
9T-
PB
-*- Water quality (Fezooal failures]}
00
Figure 3-1. Summary analysis of trends of Pis for UK water mains for 1979-2000.
46
level, the companies' current levels of capital reinvestment should be sufficient to maintain
serviceability to customers in the next price limit period.
This approach, based on serviceability to customers, is a top-down method using a standardized
approach to compare the detailed asset management plans of the individual water companies.
OFWAT also recommends that companies' asset management plans link serviceability to
customers with information on the performance and condition of the networks, so that work on
the networks is effectively prioritized.
OFWAT sets price limits to enable sufficient maintenance of the water main and sewer networks
such that a prudent and well-managed water company will be able to achieve stable
serviceability. By accepting the price limit, the company commits itself to carrying out sufficient
maintenance to achieve stable or improving serviceability. In August 1998, each company was
required to assess its asset stock as of March 1998 in the Asset Inventory and System
46 Office ofWater (OFWAT), Annual Report 1999-2000, House ofCommons Paper 455, London, England, ISBN 0 10 556761 2, May 2000. This
report is also available from the OFWAT web site at http://www.open.gov.uk/ofwat/pubslist/pubsinfn.htm
65
-------
4.0
SEWER NETWORK
o
£.0
o
o
0.0
Worsening
Improving
Incomplete datasets for
87/88, 88/89 and 89/90
~niiir
~~i 1 1 r
75-
76
77-
78
79-
80
81-
82
83-
84
85-
86
87-
89-
90
91-
92
93-
94
year
Flooding -*- Pollution incidents -a- Sewer collapses
95-
96
97-
98
Figure 3-2. Summary analysis of trends of Pis for UK sewer mains for 1975-1998/
47
Performance submission. OFWAT summarizes that information to create an industry picture of
the water main and sewer networks covering the asset stock, and its valuation, condition, and its
individual performance (see Tables 3-12 to 3-15).
Table 3-12. Kilometers of critical sewers by size in the UK (March 1998)
<=150bore
5,800
<=300 bore
27,600
<=600 bore
18,700
<=900 bore
7,900
>900 bore
9,400
Total Stock
69,400
47
Ibid.
66
-------
Table 3-13. Summary of critical sewer condition by grade in the UK (March 1998)
Critical Sewers Asset Condition
Grade 1
58%
Grade 2
18%
Grade 3
14%
Grade 4
8%
Grade 5
2%
Definition of Condition Grades:
Grade 1: No structural defects.
Grade 2: Minor cracking in brick sewers but no deformation or loss of bricks, line and level as built; for other
sewers, some circumferential cracking or moderate joint defects.
GradeS: Somedeformationinbricksewers, displaced bricks, occasional connection defects; for other sewers, some
deformation (up to 5 percent), cracking, fractures, joint defects or minor loss of level, or badly made connections.
Grade4: Deformation in brick sewers up to lOpercent, some brick loss or moderate loss of level; for other sewers,
deformation of up to 10 percent, cracked or fractured, or serious loss of level.
Table 3-14. Kilometers of non-critical sewer pipes in the UK by Diameter
(March 1998)
<=150bore
98,000
<=300 bore
115,900
=600 bore
18,100
Total Stock (km)
232,000
Table 3-15. Condition of non-critical sewer pipes in the UK (March 1998)
Non-Critical Sewers Asset Condition
Grade 1
57%
Grade 2
20%
Grade 3
14%
Grade 4
7%
Grade 5
2%
Definition of Condition Grades:
Grade 1: No structural defects.
Grade 2: Minor cracking in brick sewers but no deformation or loss of bricks, line and level as built; for other
sewers, some circumferential cracking or moderate joint defects.
Grade 3: Some deformation in brick sewers, displaced bricks, occasional connection defects; for other sewers, some
deformation (up to 5 percent), cracking, fractures, joint defects or minor loss of level, or badly made connections.
Grade 4: Deformation in brick sewers up to 10 percent, some brick loss or moderate loss of level; for other sewers,
deformation of up to 10 percent, cracked or fractured, or serious loss of level.
Grade 5: Collapsed or severely deformed sewers or missing inverts, or extensive areas of missing fabric/bricks.
67
-------
The gross replacement cost of sewers with modern equivalent assets was estimated by the companies
to be more than 96 billion. Around 9-10 percent of sewers were assessed to be in poor condition
(condition grades 4 and 5). The aggregate reinvestment activity in kilometers on the critical sewer
network over the last 8 years is summarized in Table 3-16 below.
Table 3-16. Kilometers of critical sewers recapitalized in the UK (by year)
Activity
Renovation
Replacement
New Sewers
Total Activity
90-91
152
3
384
539
91-92
131
165
507
803
92-93
89
146
455
690
93-94
59
111
334
504
94-95
80
68
350
498
95-96
104
76
228
408
96-97
143
105
272
520
97-98
178
92
212
482
Summary of Pis Used in Europe
Although the general concept of using Pis as a management tool is straightforward, the approaches
to define, collect, and use PI information vary dramatically between the case studies from Europe.
The Pis used in each case study were grouped into one of the following six categories:
* Table 3-17 lists indicators of plant and network size and type,
* Table 3-18 lists indicators of customer service,
* Table 3-19 lists indicators of water distribution system effectiveness and reliability,
* Table 3-20 lists indicators of wastewater collection system effectiveness and
reliability,
* Table 3-21 lists indicators of environmental impact, and
* Table 3-22 lists indicators of infrastructure construction, maintenance, and
rehabilitation cost-effectiveness.
Each table lists the PI used, identifies applicable type of infrastructure asset (water or wastewater),
and identifies which organization utilizes it. As can be seen through these tables, the UK's OFWAT
has adopted the most comprehensive list of Pis.
68
-------
Table 3-17. Summary list of Pis related to plant size and type
Performance Indicator
Plant capacity
Length of pipes by type and section
Population served
System area covered
Cost - treatment cost per million gallons pumped
Cost of operations - total
Total value of system assets
Value of system per length of pipe
Quality of water at intake and treatment
Sewered area per mile of main
Unit
volume/day
length
count
total area
currency per volume
currency
currency
currency/pipe
varies
area/length
_fe
"ro
g
X
X
X
X
X
X
X
X
X
Wastewater
X
X
X
X
X
X
X
UK OFWAT
X
X
X
X
X
X
X
X
X
X
>*
s
o
'o>
D)
&
X
X
X
5
X
w
X
X
X
X
X
X
Table 3-18. Summary list of Pis for customer service
Performance Indicator
Complaints - number calls about interrupted service
Complaints - water taste
Complaints - other
Complaints - odor
Complaints per capita
Complaints - water color
Complaints - water pressure
Number of new services connected, by customer type
Service interruption time per customer
Properties affected by unplanned interruption > 6 hours
Service interruption - hosepipe bans
Service interruptions - low flow restrictions
Service interruptions - planned
Service interruptions - unplanned
Unit
count
count
count
count
complaints/person
count
count
count
minute/consumer
count
count
count
count
count
i_
£
"m
X
X
X
X
X
X
X
X
X
X
X
X
Wastewater
X
X
X
X
UK OFWAT
X
X
X
X
X
X
X
X
X
X
X
X
X
_>*
£
0
'o>
D)
0)
UL
0)
O
X
w
X
69
-------
Table 3-19. Summary list of Pis for water distribution system effectiveness and reliability
Performance Indicator
Pipe age
Pipe material
Pipe diameter
Pipeline length in total, by type and section
Pipe condition grade by type, size and location
Breaks
Breaks per pipe length per year (by area, severity, and type of pipe)
Distribution network delivery rate
Earning ability
Fire delivery pressure
Leakage - average rate
Leakage - total volume
Leakage - per unit length
Maximum daily demand/system capacity
Maximum head (pressure)
Number of breaks, leaks, etc. repaired
Per capita water consumption
Percentage breaks, leaks, etc., repaired within x hours of notification
Percentage of leakage versus total produced
Percentage of total water volume metered
Pressure
Volume pumped, metered, and treated
Water billed - commercial consumption from billing data
Water volume billed
Unit
years
varies
varies
unit length
score
count
count/length/year
cubic meter/length/year
billed/length
volume/second
volume/second
cubic meter
volume/sec/length
volume/day
pressure
count
volume/person
%
%
%
pressure
unit volume
liter/sec
cubic meter
UK OFWAT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
o
•a .
60 ^>
&&
X
X
X
X
X
X
X
X
X
M
0>
o
X
t/5
X
X
X
X
X
X
X
X
X
70
-------
Table 3-20. Summary list of Pis for wastewater collection system effectiveness and reliability
Performance Indicator
Pipe condition grade by type, size and location
Average daily flow/max daily treatment capacity
Number of days volume of influent exceeded treatment plant capacity
Backups per capita
Blockages or stoppages/pipe length
Blockages/year/pipe length
Collapses/year/length
Pipe age
Pipe material
Pipe diameter
Length of pipe by section and type
Sewer flooding residences incidents, due to capacity
Sewer overflows - combined
Sewer overflows - incidents due to sewer capacity
Sewer overflows - incidents due to blockages, etc.
Sewer overflows - incidents (other causes)
Sewer overflows/pipe length
Sewer overflows/1 000 consumers
Projected needed capacity in 5 years/current capacity
Properties flooded internally by sewage - caused by blockages, sewer collapses,
equipment failure, etc.
Properties flooded internally by sewage as a result of overloaded company sewer
Properties flooded internally by sewage - other causes
Unit
score
%
count
backups/person
count/unit length
count/year/length
count/year/length
years
varies
varies
length
count
count
count
count
count
count/length
number/1000
consumers
%
count
count
count
UK OFWAT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
_>*
£
0
'o>
D)
0)
£
X
«)
0)
O
X
w
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
71
-------
Table 3-21. Summary list of Pis for environmental impact
Performance Indicator
Water usage per capita
Pollution incidents/million residents
Sewer bypasses
Major and serious sewer overflows
Sewer overflows, estimated volume
Sewer overflows/reporting period
Unit
volume per customer
count/million residents
count
count
volume
count/reporting period
1
X
0>
"8
t/J
X
X
X
X
X
"^
fe
o
X
X
X
X
X
X
' cd
o
•a
X
.2
O
t/2
X
X
Table 3-22. Summary list of Pis for construction, maintenance, rehabilitation costs and
effectiveness
Performance Indicator
Cost - distribution cost per million gallons pumped
Cost - O&M costs
Costs - cost per household or type of service
Costs - cost per length of new pipe installed by type, location andpipe
diameter
Costs - cost per length of repaired pipe installed by type of repair and
pipe diameter
Costs - per volume sold
Length of new line constructed
Length of existing line rehabilitated
Percentage of interruptions cleared in goal time
Maintenance - pressure problems solved by company action
Unit
currency per volume
currency
currency per house
currency per length
currency per length
currency/volume
unit length
unit length
%
count
u
1
X
X
X
X
X
X
X
X
X
0>
>
£
0>
cd
£
X
X
X
X
X
X
X
H
•<
£
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o
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72
-------
Conclusions
Based on the review of the three case studies and European research papers, the following
conclusions have been reached:
+ The practice of using performance indicators as a management tool is not widespread or
standardized across the different European countries. There are, however, cases where
individual companies, regional consortiums, and national governments have used Pis to
make management decisions about infrastructure investments. However, these
companies and governments do not use a set of standard Pis. Only the UK is using a well-
defined and nationally standardized approach. A set of well-defined and standard
performance indicators is essential for comparing performance across countries, regions,
and different systems.
* The Pis used in the case studies varied considerably, but could be grouped in the
following categories:
>• Indicators of network type and size,
>• Indicators of customer service,
>• Indicators of water distribution system effectiveness and reliability,
>• Indicators of wastewater collection system effectiveness and reliability,
>• Indicators of environmental impact, and
>• Indicators of infrastructure construction and rehabilitation cost-effectiveness.
+ The performance measurement system in the UK is the most developed and could serve
as a model for the US. Although all of the case studies provided examples of how Pis
could be used for intra-system, inter-system, and extra-system comparisons, only the
UK's OFWAT uses Pis as one piece of information to approve rehabilitation plans and
price rate changes. Therefore, companies must demonstrate via Pis how the plans
improved the distribution or collection systems' serviceability to customers. If a
particular water company is not able to prove this to OFWAT, they will not be allowed to
use customer revenues to fund the future rehabilitation plans, and could even be denied a
license to operate the water authority on the public's behalf.
73
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Chapter 4
Recommendations for National Database of Performance
Indicators for Drinking Water and Wastewater Infrastructure
In this chapter, a recommended list of performance indicators and an approach to handle the
collection of the necessary data are provided. These recommendations are based on the assessment
of the European experience, as well as similar US studies related to the use of performance
indicators.
Proposed Indicators of Drinking Water and Wastewater Infrastructure Performance
It is recommended that the proposed indicators listed in Tables 4-1 to 4-6 be used as a basis for
developing a standardized list of performance indicators. This list is based on the literature reviewed
in this report and the two companion reports,
* Potable Water Distribution: An Assessment of European Approaches for Improving
Operations and Maintenance, and
^ Wastewater Collection: An Assessment of European Approaches for Improving
Operations and Maintenance.
To develop a standardized list of Pis, it is recommended that the proposed list be provided to
industry and academic and professional groups to verify its completeness and to establish common
definitions for the selected Pis.
Although the general concept of using Pis as a management tool is straightforward, the approaches
to define, collect, and use PI information vary dramatically between the case studies examined in
Europe. The European experience also varies greatly from the efforts in the US.
Since one of the major goals of this study was to provide a framework for a standardized national
database of performance indicators, the following is a proposed list of the Pis that would meet the
requirements set out in the European case studies, as well as the following US organizations:
+ The Governmental Accounting Standards Board (GASB) requires that state and local
governments implement new accounting practices for infrastructure assets. This
procedure involves the use of performance indicators. Specifically, state and local
governments will have to:
>• maintain an up-to-date inventory of eligible infrastructure assets,
>• perform condition assessments of the eligible infrastructure assets and summarize
the results using a standard measurement scale,
77
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>• estimate, each year, the annual amount to maintain and preserve the eligible
infrastructure assets at the condition level established and disclosed by the
government, and
>• document that condition assessments are performed consistently at least every three
years, and to provide reasonable assurance, through the use of the results of the
most recent three condition assessments, that infrastructure assets are being
preserved approximately at (or above) the condition level established and
disclosed by the government.
+ The USEPA requires municipalities holding National Pollutant Discharge Elimination
System (NPDES) permits to monitor the performance of its sanitary sewers through its
Capacity, Management, Operation and Maintenance Program (CMOM) for Municipal
Sanitary Sewer Systems. This program requires NPDES permit holders to:
>• properly manage, operate, and maintain, at all times, all parts of the collection
system that the permit holder owns or has operational control of,
>• provide adequate capacity to convey base flows and peak flows for all parts of the
collection system that the permit holder owns or has operational control of,
>• take all feasible steps to stop and mitigate the impact of sanitary sewer overflows
in portions of the system the permit holder owns, and to regain operational control
as soon as possible, and
>• notify parties that have a reasonable potential for exposure to pollutants associated
with the overflow event.
* The National Research Council's Committee on Measuring and Improving Infrastructure
Performance provides recommended Pis in its report on measuring and improving
infrastructure performance.
+ The California State University's study (sponsored by the USEPA) provides an approach
for evaluating and improving performance wastewater collection systems.
+ The Water Environment Research Foundation provides a recommended approach to
benchmark the performance of wastewater operations, collection, treatment, and biosolids
management.
Based on the analysis of the requirements laid out in these documents and in the European case
studies, a list of Pis has been proposed. The following tables contain a summary of the
recommended Pis. Each table lists the PI, the appropriate units, the asset for which it applies (as
shown in the highlighted columns), and references other studies that have applied it in the past. The
Pis recommended in each study are grouped into one of the following five categories:
78
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Table 4-1 lists indicators of customer service,
Table 4-2 lists indicators of water distribution system effectiveness and reliability,
Table 4-3 lists indicators of wastewater collection system effectiveness and reliability,
Table 4-4 lists indicators of environmental impact, and
Table 4-5 lists indicators of infrastructure construction, maintenance, and rehabilitation cost-
effectiveness.
Table 4-1. Recommended list of Pis for customer service
Recommended Performance Indicator
Complaints - odor
Complaints - water color
Complaints - water pressure
Complaints - water taste
Complaints - other
Complaint calls/1 000 customers
Complaints - total complaints
Complaints - customer complaints by
geographic area
Complaints - number of calls about
interrupted service
Service interruption time/customer
Service interruption -properties affected by
unplanned interruption > six hours
Service interruption - hosepipe bans
Service interruptions - low flow restrictions
Service interruptions - planned
Service interruptions - unplanned
Unit
count
count
count
count
count
count /1 000
customers
count
count
count
minute/
consumer
count
count
count
count
count
,
X
X
X
X
X
X
X
X
X
X
X
X
X
Wastewater
X
X
X
X
X
X
UK OFWAT
X
X
X
X
X
X
X
X
X
X
X
X
Id
o
60
0>
M
0>
O
t/2
X
X
X
1
X
X
X
X
X
X
X
X
X
U-
w
X
X
X
cd
o
X
X
X
c/2
o
X
X
X
X
X
X
X
X
X
79
-------
Table 4-2. Recommended list of Pis for water distribution system
effectiveness and reliability
Recommended Performance Indicator
Pipe condition grade by type and section
Breaks
Breaks/pipe length/year (by area, severity,
and type of pipe)
Distribution network delivery rate
Leakage - average rate
Leakage - total volume
Leakage/unit length
Maximum daily demand/system capacity
Number of breaks, leaks, etc. repaired
Number of new services connected, by
customer type
Per capita water consumption
Percentage breaks, leaks, etc., repaired
within x hours of notification
Percentage of total water volume by user
category
Percentage of leakage vs. total produced
Percentage of total water volume metered
Projected water demand in 5 years/current
capacity
Unit
score
count
count/length/year
cubic meter/
length/year
volume/second
cubic meter
volume/sec/length
volume/day
count
count
volume/person
%
%
%
%
%
fe
"S
£
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Wastewater
UK OFWAT
X
X
X
X
X
X
X
X
X
X
Jj>
Id
o
'Eb
60
0>
04
X
X
X
X
X
M
o>
O
2i
t/2
X
X
X
X
X
X
X
X
g
£
X
X
X
X
X
X
U-
04
w
£
X
X
0>
Id
55
^
o
X
ffl
•<
c/2
o
X
X
X
X
X
X
X
X
X
X
X
80
-------
Table 4-3. Recommended list of Pis for wastewater collection
effectiveness and reliability
system
Recommended Performance Indicator
Pipe condition grade by type and section
Number of days volume of influent exceeded
treatment plant capacity
Backups/capita
Blockages or stoppages/pipe length
Blockages/year/pipe length
Collapses/year/length
Collapses by pipe material, age, diameter and
date of occurrence
Projected needed capacity in 5 years/current
capacity
Properties flooded internally by sewage - caused
by blockages, sewer collapses, equipment
failure, etc.
Properties flooded internally by sewage - other
causes
Sewer overflows - incidents due to blockages,
etc.
Properties flooded internally by sewage, due to
capacity
Sewer overflows - incidents due to sewer
capacity
Sewer overflows - incidents (other causes)
Sewer overflows/pipe length
Sewer overflows/1 000 consumers
Unit
score
count
backups /person
count/unit
length
count/year/
length
count/year/
length
count/type
%
count
count
count
count
count
count
count/length
number/1000
consumers
fe
13
£
Wastewater
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
UK OFWAT
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Jj"
Id
o
'Eb
60
o>
04
X
M
o>
O
2i
t/2
X
X
X
X
X
X
g
£
X
U-
04
w
£
X
X
X
X
X
X
X
X
X
X
0>
13
55
':3
o
X
X
X
X
X
X
X
ffl
•<
c/2
o
X
X
X
X
X
X
81
-------
Table 4-4. Recommended list of Pis for environmental impact
Recommended Performance
Indicator
Water usage/consumer
Pollution incidents/million residents
Sewer bypasses
Sewer overflows - major and serious
pollution incidents
Sewer overflows - combined
Sewer overflows, estimated volume
Sewer overflows/reporting period
Unit
volume/person
count/million
residents
count
count
count
volume
count/reporting
period of time
X
£
£2
a
^
X
X
X
X
X
X
H
U-
O
^
p
X
X
X
X
X
X
13
o
60
60
0>
04
\g
O
X
t/2
X
Q
*
X
X
X
fc
04
w
^
X
X
X
X
§
c/2
rt
O
X
X
X
X
X
X
,
"3
o
'5b
60
0>
e4
M
o>
O
2;
t/2
X
X
X
X
X
X
X
g
"z,
X
X
X
X
X
U-
04
W
£
X
X
X
X
0>
B
t/2
13
O
X
X
X
X
X
X
53
c/2
O
X
X
X
X
X
X
X
X
X
X
X
-------
Table 4-5. Recommended list of Pis for construction, maintenance, rehabilitation costs and
effectiveness
Recommended Performance Indicator
Maintenance - pressure problems solved by
company action
Percentage of interruptions cleared in goal time
Percentage manholes, lines inspected visually
each year
Percentage of force mains inspected annually
Percentage of I/I flow eliminated
Percentage of inflow sources eliminated
Percentage of length maintained requiring repair
Percentage of repairs completed for each method
Percentage of sewers inspected by CCTV each
year
Percentage of system cleaned annually
Percentage of system inspected/year
Percentage of system tested for smoke, dye
Percentage rehabilitation completed
Percentage time spent on maintenance
Unit
count
%
°/
%
%
%
%
%
%
%
%
%
%
%
X
X
X
X
X
X
X
X
X
u
1
^
X
x
X
X
X
X
X
X
X
X
X
X
X
H
U-
o
M
p
X
X
X
X
X
X
X
X
13
o
•a
*
0>
u
X
t/2
o
*
P4
w
^
X
n
t/2
o
x
X
X
X
X
X
X
X
X
X
-------
Table 4-6. Recommended data for pipe failure modeling, rehabilitation planning and for
performance analysis
Category of Data
Data describing plant type, capacity,
network size and asset value
Data describing significant pipe
section characteristics
Data describing significant
operational and maintenance factors
Type of Data
Plant capacity
Volume treated
Volume billed
Length of pipes by type
Population served
System area covered
Cost of operations — total and by activity
(e.g., O&M, capitalization, etc.)
Total value of system assets — by type of asset
Quality of water at intake and treatment
Potable water storage capacity
Total employees
Pipe material
Pipe diameter
Date pipe installed
Joint type
Pipe section length
Pipe depth
Pipe section design characteristic (force main, gravity feed,
etc.)
Pipe condition (estimated or observed)
Water pressure (fire demand and average)
Customer complaints — by type
Water quality data
Nature and date of failure/leak/overflow (e.g., type, cause,
severity)
Nature and date of last repair (e.g., method, length, location,
etc.)
Nature and date of maintenance operations by type
Construction method (e.g., fill type, location, etc.)
Crew size
Data Essential
for Failure
Modeling
X
X
X
X
X
X
X
X
X
X
X
X
84
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Table 4-6. Recommended data for pipe failure modeling, rehabilitation planning and for
performance analysis
Category of Data
Data describing significant
environmental and seasonal factors
for entire system or for each pipe
section
Data describing significant cost and
economic factors
Type of Data
Soil type
Average soil moisture content
Average ground water depth
Average soil temperature and frost depth
Traffic and loading conditions
Location of co-located utilities
Cost of corrective actions (by approach and pipe diameter)
Cost to replace pipe (by installation area and pipe diameter)
Discount rate
Data Essential
for Failure
Modeling
Since proactive rehabilitation modeling requires a large volume of data to be integrated with some
type of spatial analysis tool, water authorities rely on high-performance desktop computers.
However, the collection, analysis, and communication of PI data requires a tool that enables water
authorities to share PI data in order to benchmark its performance. The world wide web (www)
provides an excellent mechanism to enable water and wastewater authorities to collect the data
required for calculating ranges for Pis, and for communicating the results to participating
municipalities and concerned stakeholders. Figure 4-1 presents a framework for collecting the
necessary data and communicating results via the www.
A national database of standard Pis is envisioned. This database would not only contain raw data,
but would establish ranges for each PI (e.g., 25th percentile to 75th percentile). In general, it is
recommended that the national database have the following characteristics:
* Participating municipalities would voluntarily submit raw data in exchange for analyzed
Pis and intra-system comparisons.
* The tool would consist of an easy-to-use www-based interface for both data entry and PI
analysis.
* The required data elements and resulting Pis would be clearly defined.
+ Users would have a secure connection and the ability to restrict the release of proprietary
information.
85
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Participating municipalities would be able to maintain historical data submissions.
Recommendations for Next Steps
The information provided in this report is only intended as a framework for going forward. It is
recommended that the information in Tables 4-1 to 4-6 be used as a basis for developing a web-based
survey of industry, state, and local government officials, and academic and professional groups. The
purpose of the survey would be to select the most important Pis, create standard definitions, and to
verify the core data elements necessary to support the selected Pis.
The results from this survey could be used as a basis to convene an expert steering committee to
provide direction to the development and use of the database. Participation by representatives of
industry, local government, and water authorities is key.
Once standard definitions are developed, volunteer water authorities could provide the data necessary
to develop a statistically significant database of infrastructure performance indicators.
86
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National database of
PI information
maintained by USEPA
Water authority voluntarily
enters and maintains raw data
Communicate
via the WWW
_,EPA provides water authorities
with analyzed Pis with inter- and
Water Authorities
maintain system
data and integrate
Pis with
rehabilitation
planning
intra-system comparisons
data for public release
I
public and stakeholders
Figure 4-1. Framework for collecting and communicating PI data.
87
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Appendix B - List of European Experts
Table B-l. List of European potable & wastewater infrastructure experts
Name
Contact information
Areas of expertise
Dr. Peter Stahre
Director
Malmo Water and Wastewater Works
S-205 80 Malmo, Sweden
Tel: 9-011-46-4034-1623
Fax: 9-011-46-4034-1448
E-mail: peter.stahrefSjnalmo.se
Potable and wastewater
infrastructure
Mr. Keith Edwards
C3, MC WGD
Network Technology Manager
Anglian Water Services Ltd.
-Henderson House
Lancaster Way, Ermine Business Park
UK-PE18 6 XZ Huntingdon
Tel: 9-011-44-1480-323996
Fax:9-011-44-1480-323993
E-mail: kedwards@anglianwater.co.uk
Industry perspective and modeling
Dr. Gerald M. A. Jones
WRC. Inc.
2655 Philmont Ave.
Huntingdon Valley, PA 19006
Email: Gerald Jones/WRcPLCfajWRcPLC
Potable and wastewater
infrastructure
Dr. Paul Conroy
WRC. Inc.
2655 Philmont Ave.
Huntingdon Valley, PA 19006
Email: conroyfajwrcplc.co.uk
COST information and modeling
Prof. Dr. -Ing Raimund
Herz
Dresden University of Technology
Chair of Urban Engineering
Nitaiberger StraBe 31 A, 5.OG
01187 Dresden,
Phone:(0351)463-2383
Fax:(0351)463-7730
Email: herz(5)yrcs.urz.tu-dresden.de
Infrastructure modeling
Dr. Sveinung
Saegrov
SINTEF Civil and Environmental Engineering, N-
7034 Trondheim, Norway
Tel: 9-011+47-73-592349
Fax: 9-011+47-73-592376
E-mail: sveinung.sagrovfajcivil.sintef.no
Overview of European Systems
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