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REVIEW

Ali Akbar Firoozi^1,*, Ali Asghar Firoozi²
Structural Durability & Health Monitoring, Vol.18, No.1, pp. 1-18, 2024, DOI:10.32604/sdhm.2023.044573
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures. This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment. After a detailed exploration of damage tolerance concepts and their historical progression, the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures. The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures, marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification… More >

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ARTICLE

Wei Guo¹, Yanliang Liu¹, Xuqiang Wang¹, Jiazheng Meng², Mengqin Hu², Bo He^2,*
Structural Durability & Health Monitoring, Vol.18, No.1, pp. 19-35, 2024, DOI:10.32604/sdhm.2023.042944
Abstract As an important lightning protection device in substations, lightning rods are susceptible to vibration and potential structural damage under wind loads. In order to understand their vibration mechanism, it is necessary to conduct flow analysis. In this study, numerical simulations of the flow field around a 330 kV cylindrical lightning rod with different diameters were performed using the SST k-ω model. The flow patterns in different segments of the lightning rod at the same reference wind speed (wind speed at a height of 10 m) and the flow patterns in the same segment at different reference wind speeds were investigated. The variations… More >

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ARTICLE

Jiajie He^1,2, Fuzheng Liu³, Xiangyi Geng³, Xifeng Liang¹, Faye Zhang^3,*, Mingshun Jiang³
Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428
Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the… More >

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ARTICLE

Gang Niu^1,2, Guangwei Zhang¹, Zhaoyang Jin¹, Wei Zhang^3,*, Xiang Liu³
Structural Durability & Health Monitoring, Vol.18, No.1, pp. 55-71, 2024, DOI:10.32604/sdhm.2023.044832
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract In the realm of subway shield tunnel operations, the impact of tunnel settlement on the operational performance of subway vehicles is a crucial concern. This study introduces an advanced analytical model to investigate rail geometric deformations caused by settlement within a vehicle-track-tunnel coupled system. The model integrates the geometric deformations of the track, attributed to settlement, as track irregularities. A novel “cyclic model” algorithm was employed to enhance computational efficiency without compromising on precision, a claim that was rigorously validated. The model’s capability extends to analyzing the time-history responses of vehicles traversing settlement-affected areas. The research primarily focuses on how… More >

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ARTICLE

Jie Li^1,3,*, Rongwen Wang², Yongtao Hu^1,3, Jinjun Li¹
Structural Durability & Health Monitoring, Vol.18, No.1, pp. 73-90, 2024, DOI:10.32604/sdhm.2023.044023
Abstract The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains. However, in real-world scenarios, accurate predictions are challenging due to various interferences. This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter (KF). The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments. By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals, it becomes possible… More >

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