Data-driven time-variant reliability assessment of bridge girders based on deep learning
Qingkai Xiao, Yiping Liu, Chengbin Chen, Licheng Zhou, Zejia Liu, Zhenyu Jiang, Bao Yang, Liqun Tang
Abstract
This article presents a new data-driven reliability analysis framework through combining the sample convolution and interaction network (SCINet) with Bayesian probability recursion to predict the time-variant reliability of bridge girders. The structural response predicted by SCINet and the state parameter (i.e., the variance of normal distribution) estimated by Bayesian dynamic linear model (BDLM) are used to form the limit state function to predict time-variant reliability. The results with a practical bridge show that the proposed method can predict future structural responses and time-variant reliability more accurately than BDLM, long short-term memory (LSTM), and long- and short-term time-series network (LSTNet) methods.