Jingxiang Zhou¹, Qiaoling Zhou^1,*, Chunlian Liang¹, Yulu Guo¹, Zhao Xiao², Yingfeng Xu¹

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 953-983, 2025, DOI:10.32604/sdhm.2024.058501 - 30 June 2025

Abstract Streamlined box girders serve as a prevalent choice for the primary structural elements in large-span suspension bridge designs. With the increase in traffic demands, the design of such girders is evolving towards wider bridge decks and larger aspect ratios (B/D). To obtain more effective and aerodynamic design shapes for streamlined box girders, it is essential to investigate the impact of B/D on their aerodynamic performance. Accordingly, in this study we investigate the buffeting responses of large-span suspension bridges using girders of varying aspect ratios (B/D of 7.5, 9.3, and 12.7). First, the aerodynamic coefficients… More >

Zihang Wang¹, Ying Peng¹, Xiong Lan², Xiaoyu Bai³, Chao Deng¹, Yuan Ren^1,*

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 643-664, 2025, DOI:10.32604/sdhm.2025.060302 - 03 April 2025

Abstract To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge, this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis. This bridge employs an unprecedented tower-girder constraint method, with all vertical supports placed at the transition piers at both ends. This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure, relying on finite element (FE) analysis. Initially, based on the Weigh In Motion (WIM) data, a random vehicle load… More >

Yao Jin¹, Yuan Ren¹, Chong-Yuan Guo², Chong Li³, Zhao-Yuan Guo^1,4, Xiang Xu^1,*

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 307-325, 2025, DOI:10.32604/sdhm.2024.055265 - 15 January 2025

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 >