Modeling Deformation Induced by Thermal Loading Using Long-Term Bridge Monitoring Data
Publication: Journal of Performance of Constructed Facilities
Volume 32, Issue 3
Abstract
An accurate correlation model between thermal loading and deformation is required for facilitating a reliable deformation-based condition evaluation in bridge service periods. In this paper, a general approach for modeling closed-form thermal correlation of deformation based on monitoring data is proposed and applied in a long-span arch bridge. First, samples of all available thermal variables and deformation induced by thermal loading are prepared by averaging preprocessed monitoring records at a 10-min interval. Then these available thermal variables are reduced to several predominant thermal variables, each of which represents a cluster of thermal variables with statistical similarity and has the strongest relationship with the thermal deformation in concern. Finally, the model of thermal deformation is formulated as a weighted sum of fitted functions of predominant thermal variables. The weighted coefficients are calculated by the back-propagation neural network technique combined with the mean impact value method and the fitted functions are estimated by the nonlinear least-squares method. The proposed approach is applied to 1 year of monitoring data obtained from a sophisticated structural health monitoring system deployed on the Jiubao Bridge. The proposed method is validated and a closed-form thermal correlation model of vertical deformation in the Jiubao Bridge is established.
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Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 51625802 and 51678218), the 973 Program (Grant No. 2015CB060000), and the Fundamental Research Fund for the Central Universities (Grant No. 2015B17914).
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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: Feb 26, 2017
Accepted: Nov 3, 2017
Published online: Feb 16, 2018
Published in print: Jun 1, 2018
Discussion open until: Jul 16, 2018
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