Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing
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VIEW THE REPLYPublication: Journal of Bridge Engineering
Volume 19, Issue 10
Abstract
This paper proposes a new framework for predicting remaining bridge strength that integrates a Bayesian network and in situ load testing. It discusses the uncertainty of important factors on corrosion damage and develops a stiffness degradation model for corroded beams based on experimental investigations. Following this, the authors develop a Bayesian network that includes corrosion damage, stiffness degradation, load-deflection response, and other factors to predict structural strength degradation. A numerical example using an existing RC bridge demonstrates the general procedures. The comparison between the theoretical and the experimental deflections from load testing shows that the proposed methodology can efficiently improve prediction accuracy and reduce prediction uncertainty.
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Acknowledgments
This work reported here was conducted with the financial support from the National Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 14JJ1022), New Century Excellent Talents in University of Ministry of Education of China (Grant No. NCET-12-0724), and the Special Fund of Excellent Doctoral Dissertations of China (Grant No. 201247). This support is gratefully acknowledged. The assistance of Hui Peng, Jianxin Peng, Jie Liu, Xin Jiang, Chang Yu, and Hao Huang with the load testing is also gratefully acknowledged.
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Information & Authors
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Published In
Journal of Bridge Engineering
Volume 19 • Issue 10 • October 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 7, 2013
Accepted: Feb 11, 2014
Published online: Mar 19, 2014
Discussion open until: Aug 19, 2014
Published in print: Oct 1, 2014
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