Open Access

REVIEW

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 >

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ARTICLE

Giada Faraco, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro^*, Arcangelo Messina
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 739-762, 2024, DOI:10.32604/sdhm.2024.052663 - 20 September 2024
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 >

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259

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ARTICLE

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 >

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258

Open Access

ARTICLE

Hao Xu¹, Jing Wang², Rubin Zhu², Alfred Strauss³, Maosen Cao⁴, Zhanjun Wu^1,*
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393 - 20 September 2024
（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 >

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180

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ARTICLE

Xueyan Lin^1,#, Mingtao Wu^2,#, Guodong Li^1,*, Nan Guo³, Lidan Mei¹
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 805-833, 2024, DOI:10.32604/sdhm.2024.051033 - 20 September 2024
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 >

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202

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ARTICLE

Liangwei Jiang^1,2, Wei Zhang², Hongyin Yang^1,2,3,*, Xiucheng Zhang¹, Jinghan Wu², Zhangjun Liu²
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 835-851, 2024, DOI:10.32604/sdhm.2024.051125 - 20 September 2024
（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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744

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208

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ARTICLE

Lianhua Ma¹, Min Huang¹, Linfeng Han^2,*
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 853-869, 2024, DOI:10.32604/sdhm.2024.051374 - 20 September 2024
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 >

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224

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ARTICLE

Yanxue Ma¹, Xiaoling Liu^1,*, Bing Wang², Ying Liu¹
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 871-889, 2024, DOI:10.32604/sdhm.2024.052683 - 20 September 2024
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 >

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