Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103426
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dc.contributorDepartment of Building and Real Estateen_US
dc.creatorKaddoura, Ken_US
dc.creatorZayed, Ten_US
dc.date.accessioned2023-12-11T00:33:50Z-
dc.date.available2023-12-11T00:33:50Z-
dc.identifier.isbn978-0-7844-8165-3 (PDF)en_US
dc.identifier.urihttp://hdl.handle.net/10397/103426-
dc.descriptionPipelines 2018 Conference, Tornoto, Canada, July 15-18, 2018en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineersen_US
dc.rights© ASCEen_US
dc.rightsThis material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://ascelibrary.org/doi/10.1061/9780784481653.009.en_US
dc.titleCriticality model to prioritize pipeline rehabilitation decisionsen_US
dc.typeConference Paperen_US
dc.identifier.spage75en_US
dc.identifier.epage85en_US
dc.identifier.doi10.1061/9780784481653.009en_US
dcterms.abstractSewer networks are comprised of a huge maze of underground pipelines. They are designed and laid to transfer sewage medium to treatment plants or disposal areas. However, their conditions are subject to deterioration owing to ageing. The American Society of Civil Engineers claimed that the sewer infrastructure grade is D+. Inspection, assessment, and effective decisions are required to enhance their performance through their service life. There are several inspection methods and assessment models that can evaluate the condition of the pipelines. When evaluating the global network, the city or municipality will end up having thousands of pipeline conditions. Therefore, they confront obstacles in deciding which pipelines to tackle first. The objective of this study is to design a criticality model that is based on multiple factors that could affect the criticality of one pipeline to another. The study evaluates the weights for the environmental, economic, and public factors as well as their subfactors by using the analytic network process (ANP). The results concluded that the most important factor is the economic factor. The criticality model is implemented on an actual case study brought from the city of Edmonton, Canada. Based on the results, 20% of the pipelines are of low criticality; 70% are of medium criticality; and 10% are of high criticality. This study is expected to enhance the prioritization of the pipelines in the network for efficient future decisions.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn C C Macey, & J S Lueke (Eds.), Pipelines 2018 : condition assessment, construction, and rehabilitation : proceedings of Sessions of the Pipelines 2018 Conference, July 15-18, 2018, Toronto, Ontario, Canada, p. 75-85. Reston, Virginia: American Society of Civil Engineers, 2018en_US
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85063492480-
dc.relation.ispartofbookPipelines 2018 : condition assessment, construction, and rehabilitation : proceedings of Sessions of the Pipelines 2018 Conference, July 15-18, 2018, Toronto, Ontario, Canadaen_US
dc.publisher.placeReston, Virginiaen_US
dc.description.validate202312 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberBRE-0850-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS24314598-
dc.description.oaCategoryGreen (AAM)en_US
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