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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 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]