A Deep Learning Estimation Method for Temperature-Induced Girder End Displacements of Suspension Bridges  

作　　者：Yao Jin Yuan Ren Chong-Yuan Guo Chong Li Zhao-Yuan Guo Xiang Xu 

机构地区：[1]School of Transportation,Southeast University,Nanjing,210096,China [2]Department of Engineering,Guangzhou Expressway Operation Management Co.,Ltd.,Guangzhou,510000,China [3]Department of Engineering,CCCC Highway Bridge National Engineering Research Center Co.,Ltd.,Beijing,100088,China [4]Department of Engineering,Jiangsu Provincial Transportation Engineering Construction Bureau,Nanjing,210004,China

出　　处：《Structural Durability & Health Monitoring》2025年第2期307-325,共19页结构耐久性与健康监测(英文)

基　　金：The National Key Research and Development Program of China grant No.2022YFB3706704 received by Yuan Ren;the National Natural and Science Foundation of China grant No.52308150 received by Xiang Xu.

摘　　要：To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R^(2) increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R^(2).Compared to ANN,with an MSE of 4.6371 and an R^(2) of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R^(2) of 13.23%,demonstrating a significant enhancement in predictive performance.

关 键 词：Suspension bridges thermal response girder end displacement deep learning 

分 类 号：U44[建筑科学—桥梁与隧道工程]