Statistical failure models for water distribution pipes - A review from a unified perspective.

Statistical failure models for water distribution pipes - A review from a unified perspective.
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DOI:
10.1016/j.watres.2015.06.027
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发表时间:
2015-10
期刊:
影响因子:
12.8
通讯作者:
A. Scheidegger;J. Leitão;L. Scholten
A. Scheidegger;J. Leitão;L. Scholten
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
A. Scheidegger;J. Leitão;L. Scholten

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本文从系统和统一的角度对配水管道的统计失效模型进行了描述和比较。这种比较的结构提供了科学家和从业者选择适合他们特定需求的失效模型所需的信息。该模型是在一个新的框架下提出的,包括:1)澄清模型假设。原来用不同数学形式表述的模型都以故障率表示。这使得可以看到模型之间的差异和相似之处。此外,我们提出了一个新的失效率概念,一个最优模型将表示,并与每个模型的失效率进行比较。2)规范无偏模型校准所需的详细数据假设,包括数据的结构和完整性。3)每种模型可获得的不同类型的概率预测。本文介绍了九种不同的模型及其变体或进一步发展。对于每个模型,都提供了在科学期刊上发表的应用概述和可用的软件实现。统一视图为模型选择提供指导。此外,本文提出的模型比较能够确定需要进一步研究的领域。
This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective. The way the comparison is structured provides the information needed by scientists and practitioners to choose a suitable failure model for their specific needs.The models are presented in a novel framework consisting of: 1) Clarification of model assumptions. The models originally formulated in different mathematical forms are all presented as failure rate. This enables to see differences and similarities across the models. Furthermore, we present a new conceptual failure rate that an optimal model would represent and to which the failure rate of each model can be compared. 2) Specification of the detailed data assumptions required for unbiased model calibration covering the structure and completeness of the data. 3) Presentation of the different types of probabilistic predictions available for each model.Nine different models and their variations or further developments are presented in this review. For every model an overview of its applications published in scientific journals and the available software implementations is provided.The unified view provides guidance to model selection. Furthermore, the model comparison presented herein enables to identify areas where further research is needed.