A Deep Learning Estimation Method for Temperature-Induced Girder End Displacements of Suspension Bridges
Yao Jin1, Yuan Ren1, Chong-Yuan Guo2, Chong Li3, Zhao-Yuan Guo1,4, Xiang Xu1,*
Structural Durability & Health Monitoring, Vol.19, No.2, pp. 307-325, 2025, DOI:10.32604/sdhm.2024.055265
- 15 January 2025
(This article belongs to the Special Issue: Advanced Data Mining in Bridge Structural Health Monitoring)
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… More >