Benyu Wang^*, Ke Chen, Bingjian Wang^#,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070137 - 08 January 2026

Abstract To examine stress redistribution phenomena in bridges subjected to varying operational conditions, this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge. An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns. XGBoost (eXtreme Gradient Boosting), a gradient-boosting machine learning (ML) algorithm, was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution. Unlike traditional numerical models that rely on extensive assumptions and idealizations, XGBoost effectively captures nonlinear and time-varying relationships between stress… More >

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 >

Chao Luo¹, Zhengsong Xiang^1,2, Yin Zhou^1,*, Dingsong Qin³, Tianlei Cheng⁴, Qizhi Tang¹

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 683-703, 2025, DOI:10.32604/sdhm.2025.061549 - 03 April 2025

Abstract Failure tests were conducted on two concrete-filled steel tubular (CFST) truss arch bridges with a span of approximately 12 m to investigate the influence of initial geometric defects on the in-plane bearing capacity of CFST truss arch bridges. The effects of antisymmetric defect on the ultimate bearing capacity, failure mode, structural response, and steel–concrete confinement effect of CFST truss arch bridges under quarter-point loading were analyzed. On this basis, numerical simulations were conducted to investigate the in-plane bearing capacity of CFST truss arch bridges further under different scenarios. The initial defect form of the arch… 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 >

Yang Wang¹, Zhe Wu^2,*, Kaixing Zhang³, Youzhi Wang^2,*

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 613-642, 2025, DOI:10.32604/sdhm.2024.058451 - 03 April 2025

Abstract A steel-concrete composite cable-stayed bridge features integrated steel girders and concrete decks linked by shear connectors to support loads, but stress concentration in wet joints can lead to cracking. In-situ tests were conducted on key sections of steel-concrete composite cable-stayed bridges to analyze the stress-strain evolution of wet joints under environmental factors, constraints, and complex construction processes. The coordinated working performance of the bridge decks was also analyzed. The results indicate that temperature is the key factor affecting the stresses and strains in wet joint concrete. Approximately 7 days after casting the wet joint concrete, the… More >

AUGUSTO FUSCO¹, STEFANO BONOMI^2,*, LUCA PADUA^1,2

BIOCELL, Vol.49, No.1, pp. 1-5, 2025, DOI:10.32604/biocell.2024.058418 - 24 January 2025

Abstract Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis, including mechanical properties. Mechanical load influences extracellular matrix (ECM)—cell interactions and modulates cellular behavior. Mechano-regulation processes involve matrix modification and cell activation to preserve tissue function. The ECM remodeling is crucial for force transmission. Cytoskeleton components are involved in force sensing and transmission, affecting cellular adhesion, motility, and gene expression. Proper mechanical loading helps to maintain tissue health, while imbalances may lead to pathological processes. Active and passive movement, including manual mobilization, improves connective tissue elasticity, promotes ECM-cell homeostasis, and More > Graphic Abstract

Menghui Hao¹, Shanshan Zhou¹, Yongchao Han¹, Zhanwei Zhu¹, Qiang Yang², Panxu Sun^2,*, Jiajun Fan²

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 399-415, 2025, DOI:10.32604/sdhm.2024.056250 - 15 January 2025

Abstract As an evaluation index, the natural frequency has the advantages of easy acquisition and quantitative evaluation. In this paper, the natural frequency is used to evaluate the performance of external cable reinforced bridges. Numerical examples show that compared with the natural frequencies of first-order modes, the natural frequencies of higher-order modes are more sensitive and can reflect the damage situation and external cable reinforcement effect of T-beam bridges. For damaged bridges, as the damage to the T-beam increases, the natural frequency value of the bridge gradually decreases. When the degree of local damage to the… 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 >

Yadong Xu¹, Weixing Hong², Mohammad Noori^3,6,*, Wael A. Altabey^4,*, Ahmed Silik⁵, Nabeel S.D. Farhan²

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 763-783, 2024, DOI:10.32604/sdhm.2024.053763 - 20 September 2024

Abstract This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor More >

Yanxue Ma¹, Xiaoling Liu^1,*, Bing Wang², Ying Liu¹

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 871-889, 2024, DOI:10.32604/sdhm.2024.052683 - 20 September 2024

Abstract In the bridge technical condition assessment standards, the evaluation of bridge conditions primarily relies on the defects identified through manual inspections, which are determined using the comprehensive hierarchical analysis method. However, the relationship between the defects and the technical condition of the bridges warrants further exploration. To address this situation, this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges. Firstly, collect the inspection records of highway bridges in a certain region of China, then standardize the severity of diverse defects in accordance with relevant specifications. Secondly, in order… More >