Application of a fuzzy Markov model to plan the renewal of large-diameter buried pipes: a case study

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ConferenceComputer and Control in the Water Industry (CCWI): 05 September 2005, Exeter, UK
Pages16; # of pages: 6
SubjectPipes and pipelines
AbstractThe lack of sufficient historical data on the deterioration of large-diameter buried transmission water mains is an obstacle to formulating an effective strategy for managing their failure risk. These historical data are required to model their rate of deterioration in order to anticipate and prevent future failures without resorting to frequent inspections that are both very costly and disruptive. The National Research Council of Canada (NRC), with the financial support of the American Water Works Association Research Foundation (AwwaRF) has developed a new fuzzy-based approach. Fuzzy synthetic evaluation is used to discern the ?condition rating' of a pipe by aggregating the effects of various distress indicators observed (or estimated) during inspection. A rule-based fuzzy Markov approach, introduced in earlier publications, is used to model and predict the risk of pipe failure. This approach comprises three main concepts: (a) modeling the deterioration of a buried pipe as a fuzzy Markov process, (b) combining the possibility of failure with the fuzzy consequences to obtain the fuzzy risk of failure throughout the life of the pipe, and (c) using the fuzzy risk model to anticipate elevated risk levels and to make effective decisions on pipe renewal. These decisions include when to renew a deteriorated pipe, or alternatively, when to schedule the next inspection and condition assessment, and if renewal is required, what renewal alternative should be selected. In this paper the approach is demonstrated through a detailed case study. Inspection data were obtained from a North American water purveyor on a large-diameter pressure cylinder concrete pipe (PCCP). The case study highlights the use of limited data, as well as the limitations and caveats that can be expected in the implementation of the model to improve renewal decisions.
Publication date
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number48348
NPARC number20378426
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Record identifier4320ccb6-58c0-40d3-bcd8-a72c0271c23f
Record created2012-07-24
Record modified2016-05-09
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