Combining recorded failures and expert opinion in the development of ANN pipe failure prediction models

Sean Kerwin, Borja Garcia de Soto, Bryan Adey, Kleio Sampatakaki, Hannes Heller

Research output: Contribution to journalArticlepeer-review

Abstract

Buried pipes comprise a significant portion of assets of a water utility. With time, these pipes inevitably fail. Failure prediction enables infrastructure managers to estimate long-term failure trends for budgetary planning purposes and identify critical pipes for preventive intervention planning. For short-term prioritization, machine learning based algorithms appear to have superior predictive performance compared to traditional survival analysis based models. These models are typically stratified by material resulting in the exclusion of newer pipe materials such as polyethylene and corrosion-protected ductile iron, despite their prevalence in modern networks. In this paper, an application of an existing methodology is presented to estimate time to next failure using artificial neural networks (ANNs). The novelties of the approach are 1) including material as an input parameter instead of training several material-specialized models and, 2) addressing right-censored data by combining soft and hard deterioration data. The model is intended for use in short-term prioritization.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalSustainable and Resilient Infrastructure
DOIs
StatePublished - 2020

Keywords

  • Artificial neural networks
  • failure prediction
  • water distribution networks

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Geography, Planning and Development
  • Safety, Risk, Reliability and Quality

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