Kavita Bodke^1,*, Sunil Bhirud¹, Keshav Kashinath Sangle²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1547-1562, 2025, DOI:10.32604/sdhm.2025.069239 - 17 November 2025

Abstract Structural Health Monitoring (SHM) systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity. There is a need for more efficient techniques to detect defects, as traditional methods are often prone to human error, and this issue is also addressed through image processing (IP). In addition to IP, automated, accurate, and real- time detection of structural defects, such as cracks, corrosion, and material degradation that conventional inspection techniques may miss, is made possible by Artificial Intelligence (AI) technologies like Machine Learning (ML) and Deep Learning… More > Graphic Abstract

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1529-1545, 2025, DOI:10.32604/sdhm.2025.069003 - 17 November 2025

Abstract This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality More > Graphic Abstract

Xiaobo Jiang, Hongchao Zheng^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1695-1716, 2025, DOI:10.32604/sdhm.2025.063890 - 17 November 2025

Abstract Construction failures caused by unforeseen circumstances, such as natural disasters, environmental degradation, and structural weaknesses, present significant challenges in achieving durability, safety, and sustainability. This research addresses these challenges through the development of advanced emergency rescue systems incorporating wood-derived nanomaterials and IoT-enabled Structural Health Monitoring (SHM) technologies. The use of nanocellulose which demonstrates outstanding mechanical capabilities and biodegradability alongside high resilience allowed developers to design modular rescue systems that function effectively even under challenging conditions while providing real-time failure protection. Experimental data from testing showed that the replacement system strengthened load-bearing limits by 20% while… More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1145-1165, 2025, DOI:10.32604/sdhm.2025.068922 - 05 September 2025

Abstract In the fabrication and monitoring of parts in composite structures, which are being used more and more in a variety of engineering applications, the prediction and fatigue failure detection in composite materials is a difficult problem. This difficulty arises from several factors, such as the lack of a comprehensive investigation of the fatigue failure phenomena, the lack of a well-defined fatigue damage theory used for fatigue damage prediction, and the inhomogeneity of composites because of their multiple internal borders. This study investigates the fatigue behavior of carbon fiber reinforced with epoxy (CFRE) laminated composite plates… More > Graphic Abstract

Yuhao Zhang^1,2,3, Peiqiang Zhao^1,2, Xing Chen^1,2, Shaoxuan Zhang⁴, Xinglin Zhang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1343-1365, 2025, DOI:10.32604/sdhm.2025.066558 - 05 September 2025

Abstract The structural integrity monitoring of high-density polyethylene (HDPE) geomembranes in landfill containment systems presents a critical engineering challenge due to the material’s vulnerability to mechanical degradation and the complex vibration propagation characteristics in large-scale installations. This study proposes a dual-stream deep learning framework that synergistically integrates raw vibration signal analysis with physics-guided feature extraction to achieve precise rupture detection and localization. The methodology employs a hierarchical neural architecture comprising two parallel branches: a 1D convolutional network processing raw accelerometer signals to capture multi-scale temporal patterns, and a physics-informed branch extracting material-specific resonance features through continuous More >

Caiping Huang^*, Hui Li, Zihang Yu

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1287-1304, 2025, DOI:10.32604/sdhm.2025.065930 - 05 September 2025

Abstract Semantic segmentation of concrete bridge defect images frequently encounters challenges due to insufficient precision and the limited computational capabilities of mobile devices, thereby considerably affecting the reliability of bridge defect monitoring and health assessment. To tackle these issues, a concrete defects dataset (including spalling, crack, and exposed steel rebar) was curated and multiple semantic segmentation models were developed. In these models, a deep convolutional network or a lightweight convolutional network were employed as the backbone feature extraction networks, with different loss functions configured and various attention mechanism modules introduced for conducting multi-angle comparative research. The… More >

Wenda Ma, Qizhi Tang^*, Huang Lei, Longfei Chang, Chen Wang

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1265-1286, 2025, DOI:10.32604/sdhm.2025.065294 - 05 September 2025

Abstract Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring (SHM). However, traditional methods struggle to address the reconstruction of acceleration responses with complex features, resulting in a lower reconstruction accuracy. This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks (FCN) to achieve precise reconstruction of acceleration responses. In the designed network architecture, the incorporation of skip connections preserves low-level details of the network, greatly facilitating the flow of information and improving training efficiency and accuracy. Dropout techniques are employed to reduce… More >

Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025

Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >

Zongqi Li¹, Hongwei Zhao^2,*, Jianyong Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 345-357, 2025, DOI:10.32604/cmes.2025.066175 - 31 July 2025

Abstract Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain More >

Yao Lu, Xintong Huo, Guangzhen Qu, Yanjun Li, Lei Wang^*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 919-935, 2025, DOI:10.32604/sdhm.2025.062537 - 30 June 2025

Abstract A response surface method was utilized for the finite element model updating of a cable-stayed bridge in this paper to establish a baseline finite element model (FEM) that accurately reflects the characteristics of the actual bridge structure. Firstly, an initial FEM was established by the large-scale finite element software ANSYS, and the modal analysis was carried out on the dynamic response measured by the actual bridge structural health monitoring system. The initial error was obtained by comparing the dynamic characteristics of the measured data with those of the initial finite element model. Then, the second-order… More >