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Evaluating risk of water mains failure using a Bayesian belief network model

机译:使用贝叶斯信念网络模型评估水管故障的风险

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摘要

It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter. (C) 2014 Elsevier B.V. All rights reserved.
机译:据报道,自2000年以来,仅加拿大和美国平均每天有700条供水总中断,每年花费超过100亿加元。此外,水管泄漏会影响其他邻近基础设施,可能导致灾难性故障。为此,市政当局或利益相关者更加关注预防措施,而不是对故障事件做出反应。本文提出了一种贝叶斯信念网络(BBN)模型,该模型使用结构完整性,水力容量,水质和后果因素来评估金属水管的失效风险。 BBN是一个概率图形模型,代表一组变量及其概率关系,该模型还捕获了有关这些依存关系的历史信息。所提出的模型能够对分配网络内的水管进行排名,从而可以识别出脆弱和敏感的管道,从而为维护/修复/更换(M / R / R)采取适当的决策行动。为了证明该模型的应用,对基洛纳市的供水网络进行了研究。结果表明,在夏季和冬季,全部259条金属管中几乎有9%处于高风险状态。 (C)2014 Elsevier B.V.保留所有权利。

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