Risk-based asset management of water piping networks using neurofuzzy systems

Abstract

The efficient and organized management of public utility networks is of paramount importance to a network’s viability and reliable functioning. One of the key components of a suitable network management strategy is the utilization of integrated risk analysis and asset management decision-support systems (DSS) that incorporate both the scientific aspects of risk-of-failure analysis for the network components but also the financial and socio-political parameters that are associated with the networks in study. The study reported on presents a neurofuzzy decision-support system for performing multi-factored risk-of-failure analysis and asset management related 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 behavior into a knowledge-repository and a DSS. Among the findings reported on, is a methodology to assess the risk of failure in a network, the factors affecting the reliability of pipe segments, and a neurofuzzy approach to breakage-data analysis, stratification and maintenance prioritization. Pipe-breakage history, pipe material, pipe age, and pipe diameter are shown to be significant risk factors in urban water distribution networks.