Open Access
ARTICLE
Yao Jin1, Yuan Ren1, Chong-Yuan Guo2, Chong Li3, Zhao-Yuan Guo1,4, Xiang Xu1,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.055265
（This article belongs to the Special Issue: Advanced Data Mining in Bridge Structural Health Monitoring)
Abstract To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network (ANN) model, this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory (LSTM) network, to predict temperature-induced girder end displacements of the Dasha Waterway Bridge, a suspension bridge in China. First, to enhance data quality and select target sensors, preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data. Furthermore, to eliminate the high-frequency components from the displacement signal, the wavelet transform is… More >
Open Access
ARTICLE
Yushan Ye1, Tao Gao1, Liankun Wang2, Junjie Ma2, Yingchun Cai2, Heng Liu2,*, Xiaoge Liu2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053756
（This article belongs to the Special Issue: Advanced Data Mining in Bridge Structural Health Monitoring)
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 >
Open Access
REVIEW
Chao Zhang1, Shang-Xi Lai1, Hua-Ping Wang1,2,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053662
（This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
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 >
Open Access
ARTICLE
Nurlan Zhangabay1,*, Ulzhan Ibraimova2, Marco Bonopera3,*, Ulanbator Suleimenov1, Konstantin Avramov4, Maryna Chernobryvko4, Aigerim Yessengali1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053391
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
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 >
Open Access
ARTICLE
Yi Wang1, Bing Wang2, Changwen Li2, Feng Zheng1, Yong Liu2, Shaohua He3,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.054761
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution, which carries inherent risks. Consequently, a variety of engineering practices are employed to monitor bridge construction. This paper presents a case study of a large-span prestressed concrete (PC) variable-section continuous girder bridge in China, proposing a feedback system for construction monitoring and establishing a finite element (FE) analysis model for the entire bridge. The alignment of the completed bridge adheres to the initial design expectations, with maximum displacement and pre-arch differences from the ideal state measuring 6.39 and 17.7 mm, respectively, which More >
Open Access
ARTICLE
Sabrina Meddah1,2,*, Sid Ahmed Tadjer3, Abdelhakim Idir4, Kong Fah Tee5,6,*, Mohamed Zinelabidine Doghmane1, Madjid Kidouche1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053541
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 >
Open Access
ARTICLE
Yadong Xu1, Weixing Hong2, Mohammad Noori3,6,*, Wael A. Altabey4,*, Ahmed Silik5, Nabeel S.D. Farhan2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053763
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 >
Open Access
ARTICLE
Hao Xu1, Jing Wang2, Rubin Zhu2, Alfred Strauss3, Maosen Cao4, Zhanjun Wu1,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.051393
（This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >
Open Access
ARTICLE
Yongfeng Tai1, Xingyu Yan2, Xiangyi Geng3, Lin Mu4, Mingshun Jiang2, Faye Zhang2,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053998
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >
Open Access
ARTICLE
Yanxue Ma1, Xiaoling Liu1,*, Bing Wang2, Ying Liu1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.052683
Abstract In the bridge technical condition assessment standards, the evaluation of bridge conditions primarily relies on the defects identified through manual inspections, which are determined using the comprehensive hierarchical analysis method. However, the relationship between the defects and the technical condition of the bridges warrants further exploration. To address this situation, this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges. Firstly, collect the inspection records of highway bridges in a certain region of China, then standardize the severity of diverse defects in accordance with relevant specifications. Secondly, in order… More >
Open Access
ARTICLE
Zhenyu Zhang1, Quan Jin1, Haitao Zhang1, Zhao Liu1, Yuyang Wu2, Longfei Zhang2, Renzhang Yan2,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.053190
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 >
Open Access
ARTICLE
Chaozhi Cai*, Tiexin Xu, Jianhua Ren, Yingfang Xue
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.052813
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 >
Open Access
ARTICLE
Lianhua Ma1, Min Huang1, Linfeng Han2,*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.051374
Abstract Given the complexities of reinforced soil materials’ constitutive relationships, this paper compares reinforced soil composite materials to a sliding structure between steel bars and soil and proposes a reinforced soil constitutive model that takes this sliding into account. A finite element dynamic time history calculation software for composite response analysis was created using the Fortran programming language, and time history analysis was performed on reinforced soil retaining walls and gravity retaining walls. The vibration time histories of reinforced soil retaining walls and gravity retaining walls were computed, and the dynamic reactions of the two types More >
Open Access
ARTICLE
Xueyan Lin1,#, Mingtao Wu2,#, Guodong Li1,*, Nan Guo3, Lidan Mei1
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.051033
Abstract In this paper, a new type of bamboo scrimber column embedded with steel bars (rebars) was proposed, and the compression performance was improved by pre-embedding rebars during the preparation of the columns. The effects of the slenderness ratio and the reinforcement ratio on the axial compression performance of reinforced bamboo scrimber columns were studied by axial compression tests on 28 specimens. The results showed that the increase in the slenderness ratio had a significant negative effect on the axial compression performance of the columns. When the slenderness ratio increased from 19.63 to 51.96, the failure… More >
Open Access
REVIEW
Husam AlShannaq, Aly Mousaad Aly*
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.054731
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 >
Open Access
ARTICLE
Huayi Zhang1, Maobin Song2, Lei Shen1,*, Nizar Faisal Alkayem1, Maosen Cao3
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.052869
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 >
Open Access
ARTICLE
Giada Faraco, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro*, Arcangelo Messina
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.052663
Abstract The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring, such as that carried out by a series of accelerometers placed on the structure, is certainly a goal of extreme and current interest. In the present work, the results obtained from the processing of experimental data of a real structure are shown. The analyzed structure is a lattice structure approximately 9 m high, monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels. The data used refer to continuous monitoring that lasted for a total of 1… More >
Open Access
ARTICLE
Liangwei Jiang1,2, Wei Zhang2, Hongyin Yang1,2,3,*, Xiucheng Zhang1, Jinghan Wu2, Zhangjun Liu2
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2024.051125
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract Aiming at the problem that it is difficult to obtain the explicit expression of the structural matrix in the traditional train-bridge coupling vibration analysis, a combined simulation system of train-bridge coupling system (TBCS) under earthquake (MAETB) is developed based on the cooperative work of MATLAB and ANSYS. The simulation system is used to analyze the dynamic parameters of the TBCS of a prestressed concrete continuous rigid frame bridge benchmark model of a heavy-haul railway. The influence of different driving speeds, seismic wave intensities, and traveling wave effects on the dynamic response of the TBCS under More >
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