@Article{sdhm.2021.011922,
AUTHOR = {Xueping Fan, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu},
TITLE = {Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {15},
YEAR = {2021},
NUMBER = {1},
PAGES = {69--83},
URL = {http://www.techscience.com/sdhm/v15n1/41899},
ISSN = {1930-2991},
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.},
DOI = {10.32604/sdhm.2021.011922}
}