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Identification of Critical Pipes for Proactive Resource-Constrained Seismic Rehabilitation of Water Pipe Networks

机译:确定用于水管网的主动资源受限地震修复的关键管道

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摘要

Utility managers in charge of water pipe networks that are exposed to high seismicity make difficult decisions regarding the allocation of a limited rehabilitation budget to be most effective in enhancing a network's postearthquake serviceability. Seismic vulnerability models are typically integrated with simple prioritization methods to identify the critical pipes subjected to earthquakes. These methods do not distribute resources at the system level and may not provide an economical solution. The objective of this paper is to develop an approach to identify the critical pipes for proactive seismic rehabilitation that will enhance a network's postearthquake serviceability when only a finite length of pipes can be rehabilitated. To achieve this objective, a proper stochastic combinatorial optimization was formulated and then solved, using a genetic algorithm that was integrated with a network-level seismic vulnerability model. The approach was implemented to identify critical links for proactive seismic rehabilitation of two benchmark networks. The results showed that this approach outperforms the simple length-based prioritization methods used by the utilities, as well as the latest proposed methodology in the literature, in identifying the critical pipes in a water pipe network subjected to an earthquake. (C) 2018 American Society of Civil Engineers.
机译:面临高地震活动的负责水管网络的公用事业管理人员在分配有限的修复预算方面做出艰难的决定,以最有效地增强网络的震后可服务性。地震易损性模型通常与简单的优先级排序方法集成在一起,以识别遭受地震的关键管道。这些方法不会在系统级别上分配资源,并且可能无法提供经济的解决方案。本文的目的是开发一种方法,用于识别用于主动地震修复的关键管道,当仅有限长度的管道可以修复时,将增强网络的地震后可服务性。为了实现此目标,使用与网络级地震易损性模型集成的遗传算法,制定了适当的随机组合优化方法,然后进行求解。实施该方法是为了确定两个基准网络的主动地震修复的关键环节。结果表明,在识别遭受地震的水管网络中的关键管道时,该方法优于公用事业公司使用的基于长度的简单优先级排序方法以及文献中最新提出的方法。 (C)2018美国土木工程师学会。

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  • 来源
    《Journal of Infrastructure Systems》 |2018年第4期|04018024.1-04018024.13|共13页
  • 作者单位

    Univ Texas Arlington, Dept Civil Engn, 416 S Yates St, Arlington, TX 76019 USA;

    Univ Texas Arlington, Dept Civil Engn, 416 S Yates St, Arlington, TX 76019 USA;

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  • 正文语种 eng
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