Yushan Ye¹, Tao Gao¹, Liankun Wang², Junjie Ma², Yingchun Cai², Heng Liu^2,*, Xiaoge Liu²

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 145-166, 2025, DOI:10.32604/sdhm.2024.053756 - 15 November 2024

Abstract To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams, destructive tests were conducted on full-scale pre-stressed concrete beams. Based on the measurement and analysis of beam deflection, strain, and crack development under various loading levels during the research tests, combined with the verification coefficient indicators specified in the codes, the verification coefficients of bridges at different stages of damage can be examined. The results indicate that the T-beams experience complete, incomplete linear, and… More >

Chao Zhang¹, Shang-Xi Lai¹, Hua-Ping Wang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662 - 15 November 2024

Abstract Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods… More >

Sabrina Meddah^1,2,*, Sid Ahmed Tadjer³, Abdelhakim Idir⁴, Kong Fah Tee^5,6,*, Mohamed Zinelabidine Doghmane¹, Madjid Kidouche¹

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 77-103, 2025, DOI:10.32604/sdhm.2024.053541 - 15 November 2024

Abstract Maintaining the integrity and longevity of structures is essential in many industries, such as aerospace, nuclear, and petroleum. To achieve the cost-effectiveness of large-scale systems in petroleum drilling, a strong emphasis on structural durability and monitoring is required. This study focuses on the mechanical vibrations that occur in rotary drilling systems, which have a substantial impact on the structural integrity of drilling equipment. The study specifically investigates axial, torsional, and lateral vibrations, which might lead to negative consequences such as bit-bounce, chaotic whirling, and high-frequency stick-slip. These events not only hinder the efficiency of drilling… More >

Nurlan Zhangabay^1,*, Ulzhan Ibraimova², Marco Bonopera^3,*, Ulanbator Suleimenov¹, Konstantin Avramov⁴, Maryna Chernobryvko⁴, Aigerim Yessengali¹

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 1-23, 2025, DOI:10.32604/sdhm.2024.053391 - 15 November 2024

Abstract Using the software ANSYS-19.2/Explicit Dynamics, this study performed finite-element modeling of the large-diameter steel pipeline cross-section for the Beineu-Bozoy-Shymkent gas pipeline with a non-through straight crack, strengthened by steel wire wrapping. The effects of the thread tensile force of the steel winding in the form of single rings at the crack edges and the wires with different winding diameters and pitches were also studied. The results showed that the strengthening was preferably executed at a minimum value of the thread tensile force, which was 6.4% more effective than that at its maximum value. The analysis… More >

Zhenyu Zhang¹, Quan Jin¹, Haitao Zhang¹, Zhao Liu¹, Yuyang Wu², Longfei Zhang², Renzhang Yan^2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 167-191, 2025, DOI:10.32604/sdhm.2024.053190 - 15 November 2024

Abstract When the upper chord beam of the beam-string structure (BSS) is made of concrete-filled steel tube (CFST), its overall stiffness will change greatly with the construction of concrete placement, which will have an impact on the design of the tensioning plans and selection of control measures for the BSS. In order to accurately obtain the bending stiffness of CFST beam and clarify its impact on the mechanical properties of composite BSS during construction, the influence of some factors such as height-width ratio, wall thickness of steel tube, elasticity modulus of concrete, and friction coefficient on More >

Huayi Zhang¹, Maobin Song², Lei Shen^1,*, Nizar Faisal Alkayem¹, Maosen Cao³

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 55-75, 2025, DOI:10.32604/sdhm.2024.052869 - 15 November 2024

Abstract The concrete panel of earth-rock dams in cold regions tends to crack due to the combination effect of non-uniform foundation settlement, ice expansion loads, and freeze-thaw damage. In this work, simulations are designed to investigate the effects of freeze-thaw damage degrees on the fracture behavior caused by the partial detachment and ice expansion loads on concrete panels. Results show that the range of detached panels and freeze-thaw damage degree are the dominant factors that affect the overall load-bearing capacity of the panel and the failure cracking modes, whereas the panel slope is a secondary factor. More >

Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Zhao-Jun Zhang¹, Wen-Wei Wang^1,2,*, Jing-Shui Zhen¹, Bo-Cheng Li¹, De-Cheng Cai¹, Yang-Yang Du¹, Hui Huang²

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 105-123, 2025, DOI:10.32604/sdhm.2024.052506 - 15 November 2024

Abstract This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer (GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment zone. An experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs, and a calculation method based on the conjugate beam method was proposed. The composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab specimens. Two methods, epoxy resin bonding, and stud connection, were used to connect the composite… More >

Husam AlShannaq, Aly Mousaad Aly^*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >

Yadong Xu¹, Weixing Hong², Mohammad Noori^3,6,*, Wael A. Altabey^4,*, Ahmed Silik⁵, Nabeel S.D. Farhan²

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 763-783, 2024, DOI:10.32604/sdhm.2024.053763 - 20 September 2024

Abstract This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor More >