Considering Soil Parameters in Prediction of Remaining Service Life of Metallic Pipes: Bayesian Belief Network Model
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 7, Issue 2
Abstract
Water mains are the essential component of a water supply system (WSS) buried underground, and they account for 80% of total system expenditures. Deterioration of this asset, as a result of an aggressive soil environment, is inevitable over a pipe’s service life. Although an increasing number of studies have estimated the remaining service life (RSL) of metallic pipes, most of them either are data intensive or consider limited soil and pipe parameters. In this paper, a Bayesian belief network (BBN) model is proposed to handle the problem of varying data availability and the dependency between parameters. First the proposed approach uses a combination of empirical data, experimental data, expert opinion, and a mathematical model to predict soil corrosivity and pit depth. Then a simple programming logic is used to predict RSL. Finally, the performance of the model is evaluated using a BBN sensitivity analysis. Monte Carlo simulations (MCSs) are performed using randomly generated input from measured statistical parameters (i.e., mean and standard deviation) to indicate the effect of input parameters on metallic pipe RSL and safety index (SI).
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Acknowledgments
The financial support given to the second and third authors through the Natural Science and Engineering Research Council of Canada (NSERC) Collaborative Research and Development Grant (CRDG) (Number: CRDPJ 434629-12) program is acknowledged.
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© 2015 American Society of Civil Engineers.
History
Received: Sep 25, 2014
Accepted: Oct 6, 2015
Published online: Dec 31, 2015
Published in print: May 1, 2016
Discussion open until: May 31, 2016
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