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== Abstract ==
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In presence of financial constraints and dire need for inspecting deteriorated sewer pipelines, inspection prioritization tools are required. This paper presents a defect based risk assessment model for prioritizing inspection of sewer pipelines. Different defects that could be present in sewer pipelines are used to build a deterioration model that employs dynamic Bayesian belief network from which the probability of a pipeline to be in a certain condition state with respect to age can be determined. The consequence of failure for sewer pipelines are studied from a cost benefit analysis point of view, where the costs resulting from sewer pipelines failure are studied, and the benefits from avoiding such failures are analyzed. Sugeno-fuzzy inference system is used to integrate both the probability and consequence of failure. It is expected that the resulting risk map would help key personnel in municipalities to identify sewer pipelines that require immediate interventions and would assist in better planning for inspection programs especially in cases of limited funds. - 2018 American Society of Civil Engineers. Utility Engineering and Surveying Institute (UESI) of the American Society of Civil Engineers (ASCE) Scopu
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== Original document ==
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The different versions of the original document can be found in:
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* [http://ascelibrary.org/doi/pdf/10.1061/9780784481653.001 http://ascelibrary.org/doi/pdf/10.1061/9780784481653.001],
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: [http://dx.doi.org/10.1061/9780784481653.001 http://dx.doi.org/10.1061/9780784481653.001]
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* [http://dx.doi.org/10.1061/9780784481653.001 http://dx.doi.org/10.1061/9780784481653.001],
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: [http://hdl.handle.net/10576/12869 http://hdl.handle.net/10576/12869]
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* [https://qspace.qu.edu.qa/handle/10576/12869 https://qspace.qu.edu.qa/handle/10576/12869],
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: [https://ascelibrary.org/doi/10.1061/9780784481653.001 https://ascelibrary.org/doi/10.1061/9780784481653.001],
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: [https://academic.microsoft.com/#/detail/3000676655 https://academic.microsoft.com/#/detail/3000676655]
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Document information

Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1061/9780784481653.001
Licence: CC BY-NC-SA license

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