About the Journal
In order to maintain a reasonable cost for large scale structures such as airframes, offshore structures, nuclear plants etc., it is generally accepted that improved methods for structural integrity and durability assessment are required. Structural Health Monitoring (SDHM) had emerged as an active area of research for fatigue life and damage accumulation prognostics.
This journal is a member of the Committee on PublicationEthics (COPE).
Indexing and Abstracting
Scopus Citescore (Impact per Publication 2023): 2.4; SNIP (Source Normalized Impact per Paper 2023): 0.579; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; INSPEC Databases; Mechanics; Science Navigator; Portico, etc...
- Latest Articles
- Most Viewed Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
- Most Cited Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
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Open Access
REVIEW
Review of Artificial Neural Networks for Wind Turbine Fatigue Prediction
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 >- 751
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Open Access
ARTICLE
Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages
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 >- 585
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Open Access
ARTICLE
Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group
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 >- 490
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Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating
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 >- 356
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Open Access
ARTICLE
Experimental Study on the Axial Compression Performance of Bamboo Scrimber Columns Embedded with Steel Reinforcing Bars
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 >- 451
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Open Access
ARTICLE
Simulation and Traffic Safety Assessment of Heavy-Haul Railway Train-Bridge Coupling System under Earthquake Action
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 >- 628
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Open Access
ARTICLE
Time-History Dynamic Characteristics of Reinforced Soil-Retaining Walls
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 >- 457
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Open Access
ARTICLE
Intelligent Diagnosis of Highway Bridge Technical Condition Based on Defect Information
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 >- 434
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Copyright © 2024 The Author(s). Published by Tech Science Press.