Water pipe failure prediction and risk models: State-of-the-art review

Thikra Dawood, Emad Elwakil, Hector Mayol Novoa, José Fernando Gárate Delgado

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research.

Original languageEnglish
Pages (from-to)1117-1127
Number of pages11
JournalCanadian Journal of Civil Engineering
Volume47
Issue number10
DOIs
StatePublished - 2020

Bibliographical note

Funding Information:
This work is supported by the collaboration of the Universidad Nacional de San Agustín (UNSA) in Arequipa, Peru, and Purdue University in Indiana, USA through Discovery Park’s Center for the Environment (C4E).

Funding Information:
This work is supported by the collaboration of the Universidad Nacional de San Agust?n (UNSA) in Arequipa, Peru, and Purdue University in Indiana, USA through Discovery Park?s Center for the Environment (C4E).

Publisher Copyright:
© 2020, Canadian Science Publishing. All rights reserved.

Keywords

  • Infrastructure
  • Pipe failure
  • Prediction models
  • Risk analysis
  • State-of-the-art review
  • Water main deterioration
  • Water main rehabilitation

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