Forecasting the Remaining Useful Life of Cast Iron Water Mains
Publication: Journal of Performance of Constructed Facilities
Volume 23, Issue 4
Abstract
Effective asset management strategy of civil infrastructure systems requires integration of technical and financial plans. This is particularly true in managing water mains, which requires knowledge of their current condition and their forecasted remaining useful life. This paper presents a model designed to forecast the remaining useful life of cast iron water mains. The model is easy to use and its generated results are utilized in determining condition rating of the water mains being considered. The model considers factors related to pipe properties, its operating conditions, and the external environment that surrounds the pipe. In addition, it overcomes limitations associated with existing models. Three different data-driven techniques are considered in the model development; each is used to study the relationship between remaining useful life and a set of deterioration factors, and to forecast remaining useful life of cast iron water mains. These techniques are multiple regression and two types of artificial neural networks: multilayer perceptron; and general regression neural network. The data used in model development were acquired from 16 municipalities in Canada and the United States. The results produced by the developed models correlate well with the actual conditions.
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Acknowledgments
The writers wish to acknowledge the financial support provided by the Natural Sciences and Engineering Research Council of Canada,NRC and the internal research grant provided by the Faculty of Engineering and Computer Science, Concordia University. They would also like to thank Dr. R. Sadiq, for sharing his valuable knowledge and time.
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 23 • Issue 4 • August 2009
Pages: 269 - 275
Copyright
© 2009 ASCE.
History
Received: Aug 9, 2007
Accepted: Mar 31, 2009
Published online: Jul 15, 2009
Published in print: Aug 2009
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