Open Access

ARTICLE

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: ; Yuefei Liu. Email:

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

Received 05 June 2020; Accepted 14 August 2020; Issue published 22 March 2021

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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Keywords

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