No abstract

No abstract

A holistic and sustainable strategy for the management of urban water distribution networks should be composed of two equally important pillars: (1) efficient methods for monitoring, repairing or replacing aging infrastructure, and (2) effective tools for modelling the deterioration in the network and for proactively assessing the risk of failure of its components so as to devise preventive measures for avoiding such failures. The paper presents a framework for devising such a proactive risk-based integrity-monitoring strategy for the management of urban water distribution networks. The framework presented is based on a combination of artificial neural network, parametric and nonparametric survival analysis and it is utilized in the estimation of time-to-failure metrics for pipe networks.

Among the most important components of sustainable management strategies for water distribution networks is the ability to integrate risk analysis and asset management decision-support systems (DSS), as well as the ability to incorporate in the analysis financial and socio-political parameters that are associated with the networks in study. Presented herein is a neurofuzzy decision-support system for the performance of multi-factored risk-of-failure analysis and pipe asset management, as applied to urban water distribution networks. The study is based on two datasets (one from New York City and the other from the city of Limassol, Cyprus), analytical and numerical methods, and artificial intelligence techniques (artificial neural networks and fuzzy logic) that capture the underlying knowledge and transform the patterns of the network's behaviour into a knowledge-repository and a DSS.

No abstract