Peijun Xie^1,2, Shoulong Zhang^1,2,3,#,*, Pengfei Huang^1,2, Jintuan Zhang^4,#,*

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.072958

Abstract To ensure the safety of the integral hoisting of precast pier boxes for sea-crossing bridges, this study focused on the sidewall height of the pier box and the width of the hoisting sling as core variables, established a finite element model using ABAQUS, and conducted a safety analysis of the hoisting process. The results showed that optimal structural safety and cost-effectiveness were achieved by first casting the concrete base plate of the pier box, then constructing the sidewalls to a height of 500 mm, and subsequently using REE-100T eye & eye round slings for hoisting. More >

Yiyan Chen^1,2, Yihu Chen^1,2,*, Min Zhang³, Xiaogang Ye⁴, Jindan Zhang^1,2

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.073581

Abstract The large thickness of the common composite precast base slab leads to difficulties in construction through reinforcement installation and pipeline laying. To solve this problem, this paper proposes a lightweight ribbed base slab, reducing the base slab thickness to 30 mm compared to the ordinary precast base slab, adding concrete ribs to improve the mechanical properties of the base slab, and analyzing its damage pattern, stiffness change, and deflection deformation through static loading experiments. Based on the experimental conditions, the effect of concrete rib height, rib width, and top chord reinforcement diameter on the short-term… More >

Zhuangqiang Wen¹, Min Zhang², Zhekun Shou^3,*

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071300

Abstract Roadbed disease detection is essential for maintaining road functionality. Ground penetrating radar (GPR) enables non-destructive detection without drilling. However, current identification often relies on manual inspection, which requires extensive experience, suffers from low efficiency, and is highly subjective. As the results are presented as radar images, image processing methods can be applied for fast and objective identification. Deep learning-based approaches now offer a robust solution for automated roadbed disease detection. This study proposes an enhanced Faster Region-based Convolutional Neural Networks (R-CNN) framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation (2D-DFT) for… More >

Giada Faraco^*, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro^*, Arcangelo Messina

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.075327

Abstract Damage detection and localization analysis have gained increasing importance over the years, due to the growing number of catastrophic events and the associated risks that small, undetected cracks in structures may evolve into severe failures if not identified in time. In this context, vibration-based methods have been extensively investigated for structural damage detection. Among them, one of the most widely used approaches since its introduction is the curvature method. It has been successfully employed in numerous studies, consistently providing reliable results. However, the use of second-order or higher-order derivatives can be challenging when dealing with… More >

Shuigen Hu^1,2, Hao Wang³, Qingyang Wei^4,*, Maosen Cao^2,4, Drahomír Novák⁵

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.073665

Abstract As vital hydraulic infrastructures, concrete dams demand uncompromising safety assurance. Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams, making seismic performance analysis crucial for structural integrity. Numerical simulation based on damage mechanics is usually considered as the approach for investigating the seismic damage behavior of concrete dams. To address the limitations of existing studies and extract the key dynamic characteristics of concrete arch dams, a concrete elastoplastic damage mechanics model is adopted, a seismic load input technique involving the viscoelastic boundary along with equivalent nodal forces is generated,… More >

Trinh Thi Nhu Quynh¹, Hoa-Cuc. Nguyen², Bich-Ngoc. Mach², Thanh Q. Nguyen^3,4,*

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070202

Abstract This study introduces a method for processing various types of random real-world signals from bridges in both experimental models and real-world scenarios using a wireless sensor system. By analyzing and processing signals collected during actual traffic on bridges, the study identifies and provides parameters that meet current quality inspection requirements to ensure the safety of bridge users. The parameters investigated in this study include deformation, natural frequency, amplitude, impact factor, and damping coefficient. Research has determined and highlighted key parameters to assess the quality of bridge spans to meet quality inspection standards. Using actual traffic More >

Xiansong Xie^1,*, Xiaoqian Qian²

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.074148

Abstract Correlation function of acceleration responses-based damage identification methods has been developed and employed, while they still face the difficulty in identifying local or minor structural damages. To deal with this issue, a robust structural damage identification method is developed, integrating a modified holistic swarm optimization (MHSO) algorithm with a hybrid objective function. The MHSO is developed by combining Hammersley sequence-based population initialization, chaotic search around the worst solution, and Hooke-Jeeves pattern search around the best solution, thereby improving both global exploration and local exploitation capabilities. A hybrid objective function is constructed by merging acceleration correlation… More >

Xianghui Li^1,2, Xin Fu³, Libo Pan², Fancong Zeng^1,2,*, Zhijiang Zuo^1,2

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.073124

Abstract The present study proposes an autonomous visual inspection system based on Wall-Climbing Robot (WCR), with a view to addressing the shortcomings of traditional building crack detection methods, namely their low measurement accuracy, high manual dependence and insufficient environmental adaptability. The system has been developed to construct a crack recognition model with robust illumination adaptation by fusing the improved YOLOv5s target detection algorithm with the Canny edge enhancement algorithm. The system has been realized as a lightweight deployment on an embedded device (MaixCAM). The robot platform employs a design scheme integrating a dual-chamber negative pressure adsorption… More >

Qi Feng^1,2, Dan Wang^3,*, Weijie Hu¹, Wenhao Zhao²

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.072968

Abstract Engineered Cementitious Composites (ECC) represent an advanced class of fiber-reinforced cement-based materials developed over the past three decades, characterized by remarkable tensile strain-hardening and multiple-cracking behavior. By incorporating hybrid fibers, Hybrid Fiber engineered cementitious composites (HFECC) can be tailored to meet specific engineering demands in terms of strength, deformation, dynamic mechanical performance, and cost-effectiveness. This paper provides a comprehensive review of the critical fiber volume theory, experimental investigations into quasi-static and dynamic mechanical properties, and the structural performance of HFECC. Furthermore, current research gaps and future directions for the development and application of HFECC are More >

Lishuai Zhu¹, Guangcai Zhang^1,*, Qun Xie^1,*, Zhen Peng², Li Ai³, Ruijun Liang¹, Taochun Yang¹

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.074620

Abstract Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features, making it difficult for traditional methods to ensure high accuracy and robustness. Therefore, this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm. By combining deep learning techniques with traditional template matching methods, the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination. Specifically, in the edge-orientation gradient matching stage, the Canny-Devernay sub-pixel edge detection technique and… More >