Risk-based asset management of water piping networks using neurofuzzy systems
Risk-based asset management of water piping networks using neurofuzzy systems
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DOI：
10.1016/j.compenvurbsys.2008.12.001
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
S. Christodoulou;A. Deligianni;P. Aslani;A. Agathokleous
中科院分区：
文献类型：
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作者：
S. Christodoulou;A. Deligianni;P. Aslani;A. Agathokleous
公用事业网络的有效和有组织的管理对网络的生存能力和可靠运作至关重要。合适的网络管理策略的关键组成部分之一是综合风险分析和资产管理决策支持系统（DSS）的利用，该系统既包括网络组件的失效风险分析的科学方面，也包括与所研究的网络相关的金融和社会政治参数。本文提出了一种神经模糊决策支持系统，用于城市配水管网的多因素失效风险分析和资产管理。该研究基于两个数据集（一个来自纽约市，另一个来自塞浦路斯利马索尔市），分析和数值方法，以及人工智能技术（人工神经网络和模糊逻辑），这些技术捕获底层知识，并将网络行为模式转换为知识库和决策支持系统。在报告的研究结果中，有一种评估网络故障风险的方法，影响管段可靠性的因素，以及一种神经模糊方法来分析损坏数据，分层和维护优先级。在城市配水管网中，管道破损历史、管道材料、管道年龄和管径是重要的危险因素。
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.