Infrastructure management: Integrated AHP/ANN model to evaluate municipal water mains' performance

Hassan Al-Barqawi, Tarek Zayed

Research output: Journal article publicationJournal articleAcademic researchpeer-review

90 Citations (Scopus)

Abstract

Canadian municipalities have noted that 59% of their water systems needed repair and the status of 43% of these systems is unacceptable. In the United States, ASCE assigned a near failing grade of D- to the condition of water system infrastructure. Therefore, municipalities face a great challenge of managing the expected large replacement and new installation projects of water mains. This research aims at designing a robust model in order to assess the condition and predict the performance of water mains. Data are collected from three different Canadian municipalities: (1) Moncton (New Brunswick); (2) London (Ontario); and (3) Longueiul (Québec). An integrated model and framework, using an analytic hierarchy process (AHP) and artificial neural network (ANN), are developed. In addition, an automated, user-friendly, web-based infrastructure management tool (CR-Predictor) is developed based on the integrated AHP/ANN model to assess water main condition. The developed tool and models are validated in which they show robust results (98.51%)-the average validity percent. They are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritize inspection and rehabilitation planning for existing water mains.
Original languageEnglish
Pages (from-to)305-318
Number of pages14
JournalJournal of Infrastructure Systems
Volume14
Issue number4
DOIs
Publication statusPublished - 27 Nov 2008
Externally publishedYes

Keywords

  • Canada
  • Deterioration
  • Infrastructure
  • Neural networks
  • Rehabilitation
  • Water distribution systems

ASJC Scopus subject areas

  • Civil and Structural Engineering

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