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Abdullah Alariyan¹, Rawand A. Mohammed Amin², Ameen Mokhles Youns³, Mahmoud Alhashash⁴, Favzi Ghreivati⁵, Ahed Habib^6,*, Maan Habib⁷
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1387-1410, 2025, DOI:10.32604/sdhm.2025.069821 - 17 November 2025
（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 >

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Si-Kai Wang¹, Ti-Cai Wang¹, Di-Fei Yi², Jia Rui³, Peng-Fei Cao⁴, Hua-Ping Wang^1,5,*
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1411-1432, 2025, DOI:10.32604/sdhm.2025.070034 - 17 November 2025
（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 >

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Roman Shults^1,2,*, Natalia Kulichenko³, Andriy Annenkov³, Oleksandr Adamenko³
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1433-1456, 2025, DOI:10.32604/sdhm.2025.068099 - 17 November 2025
（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 >

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Zheda Zhao, Tao Xu, Tong Yang, Yunpeng Wu^*, Fengxiang Guo^*
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1457-1472, 2025, DOI:10.32604/sdhm.2025.069611 - 17 November 2025
（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 >

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Mingxing Wu¹, Chengzhen Li¹, Xinyan Feng¹, Fei Chen², Yingchun Feng¹, Huihui Song¹, Wenyu Wang³, Faye Zhang^3,*
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1473-1487, 2025, DOI:10.32604/sdhm.2025.069811 - 17 November 2025
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 >

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Jian Xia^1,2, Xianzhong Hu¹, Yan Li¹, Wei Zhang^3,*
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1489-1506, 2025, DOI:10.32604/sdhm.2025.065997 - 17 November 2025
（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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242

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REVIEW

Yidong Xu¹, Weijie Zhuge^1,2, Jialei Wang¹, Xiaopeng Yu^3,*, Kan Wu⁴
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1507-1527, 2025, DOI:10.32604/sdhm.2025.069021 - 17 November 2025
（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 >

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262

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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 >

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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
（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 >

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Huaiqin Liu¹, Meng Li¹, Jianwen Shao², Weishen Zhang¹, Qifan Yang¹, Yutong Li¹, Tian Su^1,3,*, Xuefeng Mei⁴
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1563-1588, 2025, DOI:10.32604/sdhm.2025.073009 - 17 November 2025
（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 >

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Qi Feng^1,2, Weijie Hu¹, Lu Liu^3,*, Junhui Luo⁴
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1589-1605, 2025, DOI:10.32604/sdhm.2025.072955 - 17 November 2025
（This article belongs to the Special Issue: Innovative and Sustainable Materials for Reinforced Concrete Structures)
Abstract Ultra-high performance fiber-reinforced concrete (UHPFRC) has received extensive attention from scholars and engineers due to its excellent mechanical properties and durability. However, there is a mutually restrictive relationship between the workability and mechanical properties of UHPFRC. Specifically, the addition of fibers will affect the workability of fresh UHPFRC, and the workability of fresh UHPFRC will also affect the dispersion and arrangement of fibers, thus significantly influencing the mechanical properties of hardened UHPFRC. This paper first analyzes the research status of UHPFRC and the relationship between its workability and mechanical properties. Subsequently, it outlines the test… More >

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Qiang Ma^1,2,3,4, Zepeng Li^1,2, Kai Yang^1,2,*, Shaofeng Zhang^1,2, Zhuopei Wei^1,2
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876 - 17 November 2025
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 >

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220

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Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025
（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 >

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291

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Yang Lei^1,*, Bo Jiang¹, Yucai Zhao², Gaofeng Fu³, Falin Qi¹, Tian Tian¹, Qiankuan Feng¹, Qiming Qu¹
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1657-1679, 2025, DOI:10.32604/sdhm.2025.069415 - 17 November 2025
（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 >

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Chuang Meng^1,*, Yutong Bi¹, Bingji Zhang¹, Wentao Fu², Guang Chen³
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1681-1694, 2025, DOI:10.32604/sdhm.2025.068732 - 17 November 2025
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 >

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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
（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 >

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Dongyu Teng^1,2,*, Hao Tang^1,3,*, Peng He^1,2, Zhen Hao^1,2
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1717-1731, 2025, DOI:10.32604/sdhm.2025.069751 - 17 November 2025
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 >

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Weixi Zhu^1,2,3, Yongdong Meng^1,3,*, Jindong Xie², Zhenglong Cai^1,3, Yu Lyu², Xiaowei Xu^1,3
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1733-1744, 2025, DOI:10.32604/sdhm.2025.070098 - 17 November 2025
（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 >

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Xinrui Ma, Shizhi Chen^*
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1745-1767, 2025, DOI:10.32604/sdhm.2025.071148 - 17 November 2025
（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 >

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