Abstract
Significant investment is required to upgrade deteriorating underground sewer networks. Sewer failure and the subsequent rehabilitation process can have economic, social, and environmental impacts. It can disrupt critical urban function and adjacent utilities, such as telecom, electric, gas, and water supply lines. This paper identifies 48 indicators to assess the renewal complexity and the failure consequence of buried sewer pipes. A Bayesian belief network (BBN) model is used to capture dependencies among indicators, quantify uncertainty, and update belief when new information becomes available. Geographic information system (GIS) applications are used to collect and process model input data as well as visualize analysis results. The framework can identify locations where trenchless rehabilitation may be cost effective. Finally, the proposed method is demonstrated on a storm sewer network in the city of Vernon, Canada.
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Data Availability Statement
Some data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments. Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
We acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Alexander Graham Bell Canada Graduate Scholarship—Doctoral. We also thank the City of Vernon’s Infrastructure Management Department for providing data and support for the project. Maps throughout this paper were created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 11 • Issue 2 • May 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Mar 27, 2019
Accepted: Aug 8, 2019
Published online: Dec 26, 2019
Published in print: May 1, 2020
Discussion open until: May 26, 2020
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