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Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

Xueping Fan*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu*

School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou, 730000, China

* Corresponding Authors: Xueping Fan. Email: email; Yuefei Liu. Email: email

Structural Durability & Health Monitoring 2021, 15(1), 69-83. https://doi.org/10.32604/sdhm.2021.011922

Abstract

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

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APA Style
Fan, X., Yang, G., Shang, Z., Zhao, X., Liu, Y. (2021). Data fusion about serviceability reliability prediction for the long-span bridge girder based on MBDLM and gaussian copula technique. Structural Durability & Health Monitoring, 15(1), 69-83. https://doi.org/10.32604/sdhm.2021.011922
Vancouver Style
Fan X, Yang G, Shang Z, Zhao X, Liu Y. Data fusion about serviceability reliability prediction for the long-span bridge girder based on MBDLM and gaussian copula technique. Structural Durability Health Monit . 2021;15(1):69-83 https://doi.org/10.32604/sdhm.2021.011922
IEEE Style
X. Fan, G. Yang, Z. Shang, X. Zhao, and Y. Liu "Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique," Structural Durability Health Monit. , vol. 15, no. 1, pp. 69-83. 2021. https://doi.org/10.32604/sdhm.2021.011922



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