Predicting the Timing of Water Main Failure Using Artificial Neural Networks
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 4
Abstract
Effective management of aging water distribution infrastructure is essential for preserving the economic vitality of North American municipalities. Historical failures within Scarborough, Ontario, Canada, reveal a seasonal pattern to water main failures, with the majority of failures occurring during the very cold winter months. Extensive installation of cement mortar lining and cathodic protection have extended the life span of aging water mains and reduced escalating failure rates. Artificial neural networks are found to be capable of predicting the time to failure for individual pipes using a range of pipe-specific attributes, including diameter, length, soil type, construction year, and the number of previous failures. The developed models have correlation coefficients ranging from 0.70–0.82 on instances reserved for evaluating predictive performance and have utility on an asset-by-asset basis when planning water main inspection, maintenance, and rehabilitation. Simulated failure scenarios indicate a return to high failure rates if cement mortar lining and cathodic protection are not extended to all candidate pipes in the distribution system.
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Acknowledgments
The authors would like to thank the district of Scarborough for their contribution in the data collection phase. Thank you to Colin Priestley for his assistance with figure development and to Ahmad Asnaashari for his data analysis contributions. This research was funded by the University of Guelph, the Natural Sciences and Engineering Research Council of Canada, and the Canada Research Chairs program.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 4 • April 2014
Pages: 425 - 434
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Sep 11, 2012
Accepted: Feb 5, 2013
Published online: Feb 7, 2013
Discussion open until: Jul 7, 2013
Published in print: Apr 1, 2014
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