Search

The impact of deteriorating pipes on water quality (WQ) in the distribution network has not been consistently taken into account in decision making related to pipe renewals. In this paper, a detailed modeling approach based on fuzzy cognitive maps is developed using fuzzy rule-based models and fuzzy measures theory to investigate potential of WQ (physical, chemical, and biological) failure in distribution networks. Based on information and data obtained from preliminary analysis, literature, and expert opinion, a decision support tool named Q-WARP (water quality -- water main renewal planner) is developed to consider uncertain, subjective/linguistic and/or incomplete data. Q-WARP provides a plausible way to represent and comprehend ill-defined and complex relationships such as those that govern WQ in the distribution network. The proposed model has the capacity to perform the 'baseline analysis' that performs risk assessment and risk evaluation; and the 'decision analysis' that performs risk management and guides decision making. The developed model has also been applied to a case study in Stanley Street (Philadelphia) to evaluate the model's capability. The results manifested that the model can efficiently assess the 'potential' for WQ failures and can also be used in decision making for WQ improvements by making infrastructure changes in distribution systems [ABSTRACT FROM AUTHOR]

Loss of water due to leakage is a common phenomenon observed in all water distribution systems. However, the water loss can be reduced to an acceptable limit by improved understanding of leakage mechanisms and pathways, and the influencing factors that lead to leakage. This study proposes a methodology to estimate leakage potential (LP) in a water distribution system (WDS). The study has identified various factors which are directly and/or indirectly associated with LP in a WDS. The research has defined a selected set of influencing factors as fuzzy variables and has developed a fuzzy rule-based (FRB) model using Mamdani inferencing algorithm. The impact of influencing factors varies under different operating pressure, therefore a generalized methodology has been proposed. The proposed methodology has been implemented for a real WDS in Bangkok (Thailand). To evaluate the impacts of uncertainties of influencing factors on LP, Monte Carlo simulation-based sensitivity analysis has been carried out. It has been concluded that the operating system pressure and age of the network are the two most important factors that contribute to LP of a WDS. The proposed model will help water utility managers to prioritize their network rehabilitation strategies and to establishing an efficient active leakage control program. [ABSTRACT FROM AUTHOR]

The deterioration of water quality can adversely affect consumers' health as well as the aesthetic properties of water (taste, odour, colour). To date, little consideration has been given to the impact of deteriorating (ageing) water mains on water quality as a major decision driver for the renewal/rehabilitation of water mains. The main objective of this research was to identify major deterioration mechanisms in distribution networks that may contribute to water quality failures (WQF) and develop a model to quantify overall potential for WQF as a function of this deterioration. Numerous factors affect water quality in the distribution network and interactions among them are complex and often not well understood. Water quality failures in distribution networks are relatively scarce, which makes it difficult to establish statistically significant generalizations. In such data-sparse circumstances, expert knowledge and judgement can serve as a supplement or even as alternative source(s) of information. This paper discusses major deterioration mechanisms that may contribute to WQFs in distribution networks, and proposes a modelling framework using fuzzy-based methods. The following two papers in this series will provide a mathematical formulation of the proposed model and its application using two case studies, respectively. [ABSTRACT FROM AUTHOR]

Dearth of data is the greatest acknowledged obstacle to the deterioration modeling of the buried infrastructure assets. In the last two decades numerous models have been proposed with a greater emphasis on Markovian Deterioration Processes (MDP). The MDP requires that the condition of the deteriorating system be encoded as an ordinal condition rating, based on numerous distress indicators obtained possibly from direct and indirect observations, as well as from non-destructive tests. The encoding of distress indicators into condition rating is inherently imprecise and involves subjective judgment. This imprecision is not considered, let alone propagated in the traditional application of the MDP. In this paper a new approach is presented to model the deterioration of buried infrastructure assets using a fuzzy rule-based, non-homogeneous Markov process. This deterioration model yields the ‘possibility’ of failure at every time step along the life of the asset. The use of fuzzy sets and fuzzy techniques help to incorporate the inherent imprecision and subjectivity of the data, as well as to propagate these attributes throughout the model, yielding more realistic results. This paper is the first of two companion papers that describe a complete method of managing failure risk of large buried infrastructure assets. The second companion paper describes how the condition ratings along the life of the asset are converted to risk values and how effective decisions can be made about the renewal and/or scheduling the next inspection of the asset. [ABSTRACT FROM AUTHOR]

The pathways, through which water quality in the distribution network can be compromised, may be classified into five categories: intrusion of contaminants into the distribution system (e.g. through cross connection); regrowth of bacteria in pipes and distribution storage tanks; water treatment breakthrough; leaching of chemicals or corrosion products from system components (pipes, tanks, liners, etc.); and permeation of organic compounds through plastic pipe and pipe components in the system. Quantification and characterization of the various risk factors in water distribution systems is a difficult task. Many kilometer of pipes of different ages and materials, uncertain operational and environmental conditions, unavailability of reliable data, and lack of understanding of some factors and processes affecting pipe performance make it extremely challenging. It is often difficult to identify or validate specific cause(s) for water contamination or waterborne disease outbreak because real-time data are rarely, if ever, available. For these reasons, high uncertainties are inherent in any risk measure that may be assigned to the distribution system. Further, the current inability to precisely quantify most of these risks may warrant the usage of a quantitative-qualitative framework. In this paper, a framework is presented for the analysis of aggregative risk associated with water quality failure in the distribution system. Each risk item is defined by the product of the likelihood of a failure event and its consequence (peril). Both the likelihood and the consequences of a failure event are defined using fuzzy numbers to capture vagueness in the qualitative linguistic definitions. A multi-stage hierarchical model of aggregative risk for water quality failure is developed. An analytic hierarchy process is used for estimating the priority matrix (weights) for grouping risk attributes. The framework is applied on a simplified structure of risk hierarchies for a water distribution system. [ABSTRACT FROM AUTHOR]

The prioritization of water mains for renewal requires the consideration of their impact on the deterioration of water quality, in addition to their structural integrity and hydraulic capacity. The deterioration of water mains may lead to structural failure that may have grave economic impacts. This paper develops a fuzzy-based decision support system (DSS) to identify the vulnerable locations in water distribution network (WDN) that may cause overall system failure not only to compromise structural integrity, but also include failures related to water quality and hydraulic capacity. The developed DSS was applied to Al-Khobar WDN located in the eastern part of the Kingdom of Saudi Arabia. To achieve the objectives of the study, an aggregate vulnerability index representing the likelihood of system failure was developed using multi-criteria decision models. In addition, the potential impacts in terms of sensitivity index were also evaluated using advanced soft computing methods. Finally, a risk index, based on both vulnerability and sensitivity indices, was developed to help water managers to prioritize the water mains based on the overall risk of failure. [ABSTRACT FROM AUTHOR]

This paper explores the application of the quality function deployment (QFD) approach to identify and prioritize factors to reconcile 'actual' risk with 'perceived' risk for drinking water in a distribution network. Consumers' complaints (regarding water odour, taste, colour and other problems) and consumers' perception about water safety are used to define customers' requirements (aka 'whats'). Twelve water quality parameters and distribution network properties (e.g. turbidity, pipe breaks) are used as indicators for drinking water quality -- the product characteristics (aka 'hows'). Correlations between 'whats' and 'hows' are established using data obtained from a case study. Opinions of experts in drinking water and consumers are used to define correlations between the 'what' 'microbial safety' and the 'hows'. The analytic hierarchy process allows prioritizing customer requirements. Three QFD-based methods are applied to prioritize factors affecting water quality in a distribution network. The proposed approach is demonstrated through a case study of a water distribution network in Quebec City (Canada). Results show that the factors that primarily affect customers' requirements include free residual chlorine, turbidity and trihalomethanes. Sensitivity analyses using three scenarios confirm the robustness of the proposed approach. Utility managers must take action, especially concerning these product characteristics, to satisfy customers' requirements. [ABSTRACT FROM AUTHOR]

Selecting and prioritizing monitoring locations (zones) in a water distribution network (DN) involves complex decision-making that warrants the use of risk-based decision-making. This paper couples the concept of risk with fuzzy sets and provides a simple approach to prioritizing monitoring locations in a DN. An index-based approach is proposed to estimate vulnerability, sensitivity and risk indices at a given DN location. The approach is based on fuzzy-based techniques that include fuzzy synthetic evaluation and fuzzy rule-base. Two types of analyses (deterministic and probabilistic) are used to demonstrate the proposed approach for a DN in Quebec City (Canada). Fifteen 'input factors' related to structural integrity, hydraulics, water quality and consumer sensitivity are used in the analyses. Based on the risk index results, the DN is divided into two parts. Sensitivity analysis results reveal that four factors, including two sensitivity factors (i.e. % population < 5 and > 75 yr) and two vulnerability factors (free residual chlorine and pipe breaks), explain 85% of the risk index variability. The usefulness of the proposed approach is demonstrated and strategies to improve the model are discussed. [ABSTRACT FROM AUTHOR]

The effective management of failure risk of buried infrastructure assets requires knowledge of their current condition, their rate of deterioration, the expected consequences of their failure and the owner's (decision-maker) risk tolerance. Fuzzy-based techniques seem to be particularly suited to modeling the deterioration of buried infrastructure assets, for which data are scarce, cause-effect knowledge is imprecise and observations and criteria are often expressed in vague (linguistic) terms (e.g., ‘good’, ‘fair’ ‘poor’ condition, and so on). The use of fuzzy sets and fuzzy-based techniques helps to incorporate inherent imprecision, uncertainties and subjectivity of available data, as well as to propagate these attributes throughout the model, yielding more realistic results. This paper is the second of two companion papers that describe an entire method of managing risk of large buried infrastructure assets. The first companion paper describes the deterioration modeling of buried infrastructure assets, using a fuzzy rule-based, non-homogeneous Markov process. This paper describes how the fuzzy condition rating of the asset is translated into a possibility of failure. This possibility of failure is combined with the fuzzy failure consequences to obtain fuzzy risk of failure throughout the life of the pipe. This life-risk curve can be used to make effective decisions on pipe renewal. These decisions include when to schedule the next inspection and condition assessment or alternatively, when to renew a deteriorated pipe, and what renewal alternative should be selected. [ABSTRACT FROM AUTHOR]

An important step towards the assessment and management of failure risk in large-diameter (transmission) water mains is to observe distress indicators through scheduled inspections (using non-destructive or visual techniques) and translate these into condition ratings. Condition rating reflects an aggregate state of the pipe's health. Distress indicators are physical manifestations of the ageing process. The type (or form) and location of observed distress indicators in large-diameter mains are dependent on the pipe material and its surrounding environment. The physicochemical processes that promote ageing are often not understood well enough to merit an adequate physicochemical (based on mechanics or electrochemistry or microbiology) model. Further, the encoding of distress indicators into condition rating is inherently imprecise and involves subjective judgement. Fuzzy logic-based tools enable the use of engineering judgement, experience and scarce field data to translate the level of distress to condition ratings. This paper describes the translation of distress indicators detected by non-destructive or visual techniques into fuzzy condition ratings. Examples of distresses observed in prestressed cylinder concrete pipes (PCCP) and cast iron pipes are used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

All over the world water mains are under increasing threat due to aging, aggressive environmental factors, operational factors and inadequate maintenance. Extensive maintenance, repair and rehabilitation practices, or replacement of water mains are required to ensure an acceptable performance of water supply systems. Investment deficit for water system maintenance and rehabilitation has been estimated in billions. Small to medium sized water utilities are generally impacted more because of limited financial resources and lack of technical expertise. However, water mains integrity is of primary interest due to potential adverse consequences related to public health, safety, and heavy financial liabilities in case of a failure. This paper presents a user-friendly decision support tool to help managers of these water utilities to prioritize maintenance, repair, and replacement (M/R/R) strategies for water mains. This paper proposes a risk index approach which aggregates the water mains vulnerability and associated failure consequences. The developed tool can help decision-makers to rank the water mains and help to prioritize M/R/R strategies. The results have been integrated with a geographic information system that will allow a decision-maker to visualize the vulnerable zones within the network. The City of Kelowna water network is used to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]