Prediction of Water Pipe Asset Life Using Neural Networks
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Volume 13, Issue 1
Abstract
This paper describes investigations into a development of a new application of neural networks (NN) for prediction of pipeline failure. Results show higher correlations with recorded data when compared with the two existing statistical models. The shifted time power model gives results in total number of failures and the shifted time exponential model gives results in number of failures per year. The database was large but neither complete and nor fully accurate. Factors influencing pipeline deterioration were missing from the database. Using the NN technique on this database produced models of pipeline failure, in terms of failures/km/year, that more closely matched the number of failures of a particular asset recorded for the period.
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Acknowledgments
The writers acknowledge the support provided by the Australian Research Council and City West Water for this work. They are also grateful to George Ruta and Julian Beasley from City West Water Ltd. and Professor Tim Hendtlass of Swinburne University of Technology.
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Published In
Journal of Infrastructure Systems
Volume 13 • Issue 1 • March 2007
Pages: 26 - 30
Copyright
© 2007 ASCE.
History
Received: Dec 14, 2004
Accepted: Mar 13, 2006
Published online: Mar 1, 2007
Published in print: Mar 2007
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