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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]

The evaluation of the risk of water quality failures in a distribution network is a challenging task given that much of the available data are highly uncertain and vague, and many of the mechanisms are not fully understood. Consequently, a systematic approach is required to handle quantitative-qualitative data as well as a means to update existing information when new knowledge and data become available. Five general pathways (mechanisms) through which a water quality failure can occur in the distribution network are identified in this article. These include contaminant intrusion, leaching and corrosion, biofilm formation and microbial regrowth, permeation, and water treatment breakthrough (including disinfection byproducts formation). The proposed methodology is demonstrated using a simplified example for water quality failures in a distribution network. This article builds upon the previous developments of aggregative risk analysis approach. Each basic risk item in a hierarchical framework is expressed by a triangular fuzzy number, which is derived from the composition of the likelihood of a failure event and the associated failure consequence. An analytic hierarchy process is used to estimate weights required for grouping noncommensurate risk sources. The evidential reasoning is proposed to incorporate newly arrived data for the updating of existing risk estimates. The exponential ordered weighted averaging operators are used for defuzzification to incorporate attitudinal dimension for risk management. It is envisaged that the proposed approach could serve as a basis to benchmark acceptable risks in water distribution networks. [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]