Vol.15, No.1, 2021, pp.69-83, doi:10.32604/sdhm.2021.011922
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 Author: Xueping Fan. Email: ; Yuefei Liu. Email:
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.
Keywords
Dynamic extreme deflection data; serviceability reliability prediction; structural health monitoring; multivariate Bayesian dynamic linear models; Gaussian copula technique
Cite This Article
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.
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