Sewer Structural Condition Prediction Integrating Bayesian Model Averaging with Logistic Regression
Publication: Journal of Performance of Constructed Facilities
Volume 32, Issue 3
Abstract
Utility managers and other authorities often rely on sewer structural condition prediction models for the effective execution of long-term and short-term sewer management strategies; however, it is challenging to predict the structural condition effectively because of the intrinsic uncertainties in modeling. In this research, a Bayesian framework is developed to predict the structural condition of sewers considering model uncertainties. Bayesian model averaging (BMA) techniques are used for identifying significant covariates for different sewers considering model uncertainties, whereas Bayesian logistic regression models are applied for predicting the structural condition of sewers. To validate the effectiveness of the proposed framework, the structural condition of 12,728 sewer mains of the wastewater network of the city of Calgary, Canada, is predicted. The results show that the BMA approach provides a transparent statement of the posterior probabilities to represent the effect of the significant explanatory covariates, and the performance of the Bayesian logistic regression model improves with informative priors.
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Acknowledgments
The third author acknowledges the financial support through Natural Science Engineering Research Council Canada Discovery Grant Program (RGPIN-2014-05013).
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Published In
Journal of Performance of Constructed Facilities
Volume 32 • Issue 3 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jan 2, 2017
Accepted: Nov 16, 2017
Published online: Mar 30, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 30, 2018
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