Open Access
ARTICLE
Jianhua Du1, Shaofeng Wang1, Ting Gao2, Huiwen Sun2, Wenjing Liu1,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071189
（This article belongs to the Special Issue: High Resolution Ultrasonic Non-Destructive Testing of Complex Structures)
Abstract In ultrasonic non-destructive testing of high-temperature industrial equipment, sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy. Conventional approaches that rely on room-temperature sound velocities introduce systematic errors, potentially leading to misjudgment of safety-critical components. Two primary challenges hinder current methods: first, it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient; second, traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields. Here, we propose a defect localization correction method based on… More >
Open Access
ARTICLE
Xiaobo Jiang, Hongchao Zheng*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.063890
（This article belongs to the Special Issue: Construction Failures and Prevention under Unforeseen Circumstances)
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 >
Open Access
ARTICLE
Huaiqin Liu1, Meng Li1, Jianwen Shao2, Weishen Zhang1, Qifan Yang1, Yutong Li1, Tian Su1,3,*, Xuefeng Mei4
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.073009
（This article belongs to the Special Issue: Durability Assessment of Engineering Structures and Advanced Construction Technologies)
Abstract Rock collapse is a significant geological disaster that poses a serious threat to life and property in
mountainous regions worldwide. Investigating the response of protective structures to rockfall impacts can provide
valuable references for the design and placement of such structures. In this study, RocPro3D and ABAQUS were
employed to comprehensively analyze rockfall movement trajectories and the structural response upon impact.
The results indicate that when the impact velocity of rockfall at the protective structure reaches 20–30 m/sec, the
corresponding bounce height ranges from 5 to 8 m, and most rockfall accumulates at the slope More >
Graphic Abstract
Open Access
ARTICLE
Benyu Wang*, Ke Chen, Bingjian Wang#,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070137
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 >
Open Access
ARTICLE
Weichen Wang1, Shaofeng Wang1, Wenjing Liu1,*, Luncai Zhou2, Erqing Zhang1, Ting Gao3, Grigory Petrishin4
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071278
Abstract In dry-coupled ultrasonic thickness measurement, thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy. Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise. This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference. By decomposing A-scan signals into Intrinsic Mode Functions (IMFs), the framework employs energy contribution thresholds (>85%) and kurtosis indices (>3) to autonomously select IMFs containing valid specimen echoes. Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing. More >
Open Access
ARTICLE
Chuang Meng1,*, Yutong Bi1, Bingji Zhang1, Wentao Fu2, Guang Chen3
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.068732
Abstract The lift-and-transverse type parking equipment, with its core advantages such as high space utilization, modular and flexible layout, and intelligent operation, has become an efficient solution to alleviate the urban parking problem. However, existing research still lacks a systematic evaluation of its structural performance, particularly in areas such as the fatigue characteristics of steel frame materials, stress distribution under dynamic loads, and resonance risk analysis. The stress amplitude (S) and fatigue life (N) relationship curve of Q235 steel, the material used in the steel frame of the lift-and-transverse type parking equipment, was obtained through fatigue… More >
Open Access
ARTICLE
Hua-Qin Wu1,2, Hao Yan1,2, Hong Zhang1,2,*, Shun-Wu Xu1,2, Feng-Yu Gao1,2, Zhao-Wen Chen1,2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070589
（This article belongs to the Special Issue: Artificial Intelligence and Data Mining Applications in Fault Diagnosis and Damage Identification of Infrastructure)
Abstract In industrial manufacturing, efficient surface defect detection is crucial for ensuring product quality and production safety. Traditional inspection methods are often slow, subjective, and prone to errors, while classical machine vision techniques struggle with complex backgrounds and small defects. To address these challenges, this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset. Three key improvements are introduced in the proposed model. First, a lightweight Guided Attention Feature Module (GAFM) is incorporated to enhance multi-scale feature fusion, allowing the model to better capture and integrate semantic and… More >
Open Access
ARTICLE
Yang Lei1,*, Bo Jiang1, Yucai Zhao2, Gaofeng Fu3, Falin Qi1, Tian Tian1, Qiankuan Feng1, Qiming Qu1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069415
（This article belongs to the Special Issue: AI-Enhanced Low-Altitude Technology Applications in Structural Integrity Evaluation and Safety Management of Transportation Infrastructure Systems)
Abstract Detecting internal defects, particularly voids behind linings, is critical for ensuring the structural integrity of aging high-speed rail (HSR) tunnel networks. While ground-penetrating radar (GPR) is widely employed, systematic quantification of performance boundaries for air-coupled (A-CGPR) and ground-coupled (G-CGPR) systems within the complex electromagnetic environment of multilayer reinforced HSR tunnels remains limited. This study establishes physics-based quantitative performance limits for A-CGPR and G-CGPR through rigorously validated GPRMax finite-difference time-domain (FDTD) simulations and comprehensive field validation over a 300 m operational HSR tunnel section. Key performance metrics were quantified as functions of: (a) detection distance (A-CGPR:… More >
Open Access
ARTICLE
Wu Feng1,2,*, Tengku Anita Raja Hussin1, Xu Yang3
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071664
（This article belongs to the Special Issue: Advanced Strategies for Structural and Non-Structural Seismic Protection and Damage Prediction in Reinforced Concrete Structures)
Abstract This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700°C by integrating experimental testing and advanced numerical modeling. A temperature-indexed Concrete Damage Plasticity (CDP) framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution. Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation. Results demonstrated a distinct two-stage degradation regime: moderate stiffness and strength reduction up to ~400°C, followed by sharp deterioration beyond 500°C–600°C, with residual capacity at… More >
Open Access
ARTICLE
Pengwei Guo1, Xiao Tan2,3,*, Yiming Liu4
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071317
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train… More >
Open Access
ARTICLE
Ping Wu1, Jie Zou2, Wangxin Li1,*, Yidong Xu1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070873
Abstract The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical prediction models. Consequently, early anomalies (e.g., minor cracks) are often misjudged, and warnings lag by about 24 h without automated spatial localization. This study establishes a technical framework for requirements analysis, architectural design, and data-integration protocols. Revit parametric modelling is used to build a 3D tunnel model with structural elements, pipelines and 18 monitoring points (for displacement and joint width). Custom Revit API code integrated real-time sensor data into the BIM platform via an automated… More >
Open Access
ARTICLE
Si-Kai Wang1, Ti-Cai Wang1, Di-Fei Yi2, Jia Rui3, Peng-Fei Cao4, Hua-Ping Wang1,5,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070034
（This article belongs to the Special Issue: Smart Sensors and Smart CFRP Components for Structural Health Monitoring of Aerospace, Energy and Transportation Structures)
Abstract As a key storage facility, the structural safety of large oil tanks is directly related to the stable operation of the energy system. The static pressure caused by the change of liquid level is one of the main loads in the service process of storage tanks, which determines the structural deformation and damage risk. To explore the structural deformation properties under the change of liquid levels and provide a theoretical basis for the prevention and control of damage risk, this paper systematically analyzes the mechanical response of storage tanks under the pressures induced by different… More >
Graphic Abstract
Open Access
REVIEW
Abdullah Alariyan1, Rawand A. Mohammed Amin2, Ameen Mokhles Youns3, Mahmoud Alhashash4, Favzi Ghreivati5, Ahed Habib6,*, Maan Habib7
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069821
（This article belongs to the Special Issue: Resilient and Sustainable Infrastructure: Monitoring, Safety, and Durability)
Abstract Artificial intelligence (AI) is transforming the building and construction sector, enabling enhanced design strategies for achieving durable and sustainable structures. Traditional methods of design and construction often struggle to adequately predict building longevity, optimize material use, and maintain sustainability throughout a building’s lifecycle. AI technologies, including machine learning, deep learning, and digital twins, present advanced capabilities to overcome these limitations by providing precise predictive analytics, real-time monitoring, and proactive maintenance solutions. This study explores the benefits of integrating AI into building design and construction processes, highlighting key advantages such as improved durability, optimized resource efficiency,… More >
Open Access
REVIEW
Mahmoud Alhashash1, Abdullah Alariyan2, Ameen Mokhles Youns3, Favzi Ghreivati4, Ahed Habib5,*, Maan Habib6
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070194
（This article belongs to the Special Issue: Sustainable and Durable Construction Materials)
Abstract Concrete production often relies on natural aggregates, which can lead to resource depletion and environmental harm. In addition, improper disposal of thermoplastic waste exacerbates ecological problems. Although significant attention has recently been given to recycling various waste materials into concrete, studies specifically addressing thermoplastic recycled aggregates are still trending. This underscores the need to comprehensively review existing literature, identify research trends, and recognize gaps in understanding the mechanical performance of thermoplastic-based recycled aggregate concrete. Accordingly, this review summarizes recent investigations focused on the mechanical properties of thermoplastic-based recycled aggregate concrete, emphasizing aspects such as compressive… More >
Open Access
ARTICLE
Dongyu Teng1,2,*, Hao Tang1,3,*, Peng He1,2, Zhen Hao1,2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069751
Abstract In order to study the axial compression characteristics of brick masonry historical buildings, and to better protect and repair traditional mortar-brick masonry historical buildings, axial compression tests were carried out on three kinds of restored mortar (pure mud mortar, pure mortar, and mud mortar) brick masonry with restored mortar brick masonry as the object of study. The damage modes, axial compression chemical indexes (compressive strength and elastic modulus), load-displacement curves and stress-strain curves of the three kinds of restored mortar brick masonry were obtained. The experimental results show that the compressive strength of mud mortar… More >
Open Access
ARTICLE
Thi Kim Chi Duong1, Bich-Ngoc. Mach2, Hoa-Cuc. Nguyen2, Thi Nhu Quynh Trinh2, Thanh Q. Nguyen3,4,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070185
（This article belongs to the Special Issue: Advances in Intelligent Operation and Maintenance Applications for Bridge Structures)
Abstract This study explores theoretical insights and experimental results on monitoring load-carrying capacity degradation in bridge spans through frequency analysis. Experiments were conducted on real bridge structures, including the Binh Thuan Bridge, focusing on analyzing the power spectral density (PSD) of vibration signals under random traffic loads. Detailed digital models of various bridge spans with different structural designs and construction periods were developed to ensure diversity. The study utilized PSD to analyze the vibration signals from the bridge spans under various loading conditions, identifying the vibration frequencies and the corresponding response regions. The research correlated the… More >
Open Access
REVIEW
Kavita Bodke1,*, Sunil Bhirud1, Keshav Kashinath Sangle2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069239
（This article belongs to the Special Issue: Machine Learning Approaches for Real-Time Damage Detection and Structural Monitoring in Civil Structures)
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
Open Access
ARTICLE
Qiang Ma1,2,3,4, Zepeng Li1,2, Kai Yang1,2,*, Shaofeng Zhang1,2, Zhuopei Wei1,2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069876
Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >
Open Access
ARTICLE
Mingxing Wu1, Chengzhen Li1, Xinyan Feng1, Fei Chen2, Yingchun Feng1, Huihui Song1, Wenyu Wang3, Faye Zhang3,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069811
Abstract As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an More >
Open Access
ARTICLE
Yating Xu1, Mansheng Xiao1,*, Mengxing Gao1, Zhenzhen Liu1, Zeyu Xiao2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.068822
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >
Open Access
ARTICLE
Xinrui Ma, Shizhi Chen*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071148
（This article belongs to the Special Issue: Resilient and Sustainable Infrastructure: Monitoring, Safety, and Durability)
Abstract The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% More >
Open Access
ARTICLE
Hüseyin Bilgin*, Bredli Plaku
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.071007
Abstract Nonlinear static procedures are widely adopted in structural engineering practice for seismic performance assessment due to their simplicity and computational efficiency. However, their reliability depends heavily on how the nonlinear behaviour of structural components is represented. The recent earthquakes in Albania (2019) and Türkiye (2023) have underscored the need for accurate assessment techniques, particularly for older reinforced concrete buildings with poor detailing. This study quantifies the discrepancies between default and user-defined component modelling in pushover analysis of pre-modern reinforced concrete structures, analysing two representative low- and mid-rise reinforced concrete frame buildings. The lumped plasticity approach… More >
Graphic Abstract
Open Access
ARTICLE
Zheda Zhao, Tao Xu, Tong Yang, Yunpeng Wu*, Fengxiang Guo*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069611
（This article belongs to the Special Issue: AI-Enhanced Low-Altitude Technology Applications in Structural Integrity Evaluation and Safety Management of Transportation Infrastructure Systems)
Abstract Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA… More >
Open Access
REVIEW
Yidong Xu1, Weijie Zhuge1,2, Jialei Wang1, Xiaopeng Yu3,*, Kan Wu4
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069021
（This article belongs to the Special Issue: Advances in Sustainable Concrete Technologies: SCMs, Circular Economy, and AI Integration)
Abstract The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the More >
Open Access
ARTICLE
Roman Shults1,2,*, Natalia Kulichenko3, Andriy Annenkov3, Oleksandr Adamenko3
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.068099
（This article belongs to the Special Issue: Non-contact Sensing in Infrastructure Health Monitoring)
Abstract Oil tanks are essential components of the oil industry, facilitating the safe storage and transportation of crude oil. Safely managing oil tanks is a crucial aspect of environmental protection. Oil tanks are often used under extreme operational conditions, including dynamic loads, temperature variations, etc., which may result in unpredictable deformations that can cause severe damage or tank collapses. Therefore, it is essential to establish a monitoring system to prevent and predict potential deformations. Terrestrial laser scanning (TLS) has played a significant role in oil tank monitoring over the past decades. However, the full extent of… More >
Open Access
ARTICLE
Weixi Zhu1,2,3, Yongdong Meng1,3,*, Jindong Xie2, Zhenglong Cai1,3, Yu Lyu2, Xiaowei Xu1,3
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.070098
（This article belongs to the Special Issue: Sustainable and Durable Construction Materials)
Abstract To mitigate the severe abrasion damage caused by high-velocity water flow in hydraulic engineering applications in Xizang, China, this study systematically optimized key mix design parameters, including aggregate gradation, sand ratio, fly ash content, and superplasticizer dosage. Based on the optimized mix, the combined effects of an abrasion-resistance enhancement admixture (AEA) and silica fume (SF) on the abrasion resistance of self-compacting concrete (SCC) were examined. The results demonstrated that the appropriate incorporation of AEA and SF significantly improved the abrasion resistance of SCC without compromising its workability. The proposed mix design not only achieves superior… More >
Open Access
ARTICLE
Wael A. Altabey*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.069003
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
Open Access
ARTICLE
Jian Xia1,2, Xianzhong Hu1, Yan Li1, Wei Zhang3,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.065997
（This article belongs to the Special Issue: Sustainable and Durable Construction Materials)
Abstract Incorporating microencapsulated phase change materials (MPCM) into mortar enhances building thermal energy storage for energy savings but severely degrades compressive strength by replacing sand and creating pores. This study innovatively addresses this critical limitation by introducing nano-silicon (NS) as a modifier to fill pores and promote hydration in MPCM mortar. Twenty-five mixes with varying NS content from 0 to 4 weight percent and different MPCM contents were comprehensively tested for flowability, compressive strength, thermal conductivity, thermal energy storage via Differential Scanning Calorimetry, and microstructure via Scanning Electron Microscopy. Key quantitative results showed MPCM reduced mortar… More >
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