@Article{sdhm.2024.055265,
AUTHOR = {Yao Jin, Yuan Ren, Chong-Yuan Guo, Chong Li, Zhao-Yuan Guo, Xiang Xu},
TITLE = {A Deep Learning Estimation Method for Temperature-Induced Girder End Displacements of Suspension Bridges},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {19},
YEAR = {2025},
NUMBER = {2},
PAGES = {307--325},
URL = {http://www.techscience.com/sdhm/v19n2/59291},
ISSN = {1930-2991},
ABSTRACT = {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 temperature-induced 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<sup>2</sup> increases from 0.683 to 0.930, which corresponds to a 74.51% decrease in MSE and a 36.14% improvement in R<sup>2</sup>. Compared to ANN, with an MSE of 4.6371 and an R<sup>2</sup> of 0.807, LSTM shows a decrease in MSE of 63.75% and an increase in R<sup>2</sup> of 13.23%, demonstrating a significant enhancement in predictive performance.},
DOI = {10.32604/sdhm.2024.055265}
}