Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines
Publication: Journal of Performance of Constructed Facilities
Volume 22, Issue 5
Abstract
The Federation of Canadian Municipalities reported that approximately 55% of sewer infrastructure in Canada did not meet current standards. Therefore, the burden on Canadian municipalities to maintain and prioritize sewers is increasing. One of the major challenges is to develop a framework to standardize the condition assessment procedures for sewer pipelines. Lack of detailed knowledge on the condition of sewer networks escalates vulnerability to catastrophic failures. This research presents a proactive methodology of assessing the existing condition of sewers by considering various physical, environmental, and operational influence factors. Based on historic data collected from two municipalities in Canada, structural and operational condition assessment models for sewers are developed using the multiple regression technique. The developed regression models show 82 to 86% accuracy when they are applied to the validation data set. These models are utilized to generate deterioration curves for concrete, asbestos cement, and polyvinyl chloride sewers in relation to traffic loads, bedding materials, and other pipe characteristics. The developed models are expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections, and rehabilitation requirements.
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Acknowledgments
The writers would like to express their gratitude to the Quebec funding agency NATEQ/FQRNT (Fonds Québécois de la Recherche sur la Nature et les Technologies) for its appreciated financial support to this research. The writers would also like to extend their appreciation to all municipal engineers who facilitated their research by positive participation and providing the required data, particularly Niagara Falls, Ontario and Pierrefonds, Quebec municipalities.
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 22 • Issue 5 • October 2008
Pages: 333 - 341
Copyright
© 2008 ASCE.
History
Received: Sep 20, 2007
Accepted: Dec 27, 2007
Published online: Oct 1, 2008
Published in print: Oct 2008
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