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Fuzzy cognitive maps for decision support to maintain water quality in ageing water mains

From National Research Council Canada

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Affiliation
  1. National Research Council of Canada. NRC Institute for Research in Construction
FormatText, Article
ConferenceDMUCE 4, 4th International Conference on Decision Making in Urban and Civil Engineering: 28 October 2004, Porto, Portugal
Subjectdistribution networks; fuzzy cognitive maps; aging water mains; water quality; Water quality
Abstract
The prioritization of water mains for renewal requires the simultaneous consideration of their structural integrity and hydraulic performance as well as their contributions to the deterioration of water quality. Presently, several decision models exist for water main renewal. Most consider the structural integrity of the pipes as the sole decision criterion, although some consider also their hydraulic capacity and others consider only network reliability. Various attempts have been made by water utilities to consider multiple decision drivers in their prioritization process, however, these are done through simple point scoring methods, which are essentially qualitative, inherently subjective and do not consider input uncertainties. Theimpact of deteriorating pipes on water quality in the distribution network has not been considered in decision process. This paper outlines a framework for assessing risk associated with water quality failures indistribution networks due to ageing mains. The available field data are both quantitative and qualitative, and when available, they are often uncertain and vague. Numerous factors affect water quality in the distribution system and the interactions amongst them are complex and often not well understood. For these and other reasons, fuzzy cognitive maps are examined as a decision support tool to develop an integrated approach for the renewal of water mains.
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  • DMUCE 4, 4th International Conference on Decision Making in Urban and Civil Engineering [Proceedings] (1 October 2004): 1–10.
LanguageEnglish
Peer reviewedYes
NRC numberNRCC 47305
NRC-IRC-16491
NPARC number20377032
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Record identifierfe1eda37-fcb1-47fe-a0f2-18f9e36981ed
Record created2012-07-24
Record modified2020-04-17
Date modified: