Structural Health Monitoring-An International Journal

Papers
(The H4-Index of Structural Health Monitoring-An International Journal is 35. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-03-01 to 2024-03-01.)
ArticleCitations
A review of computer vision–based structural health monitoring at local and global levels285
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights138
Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage134
Machine learning paradigm for structural health monitoring122
Efficient attention-based deep encoder and decoder for automatic crack segmentation114
A research on an improved Unet-based concrete crack detection algorithm106
A new fault diagnosis method based on adaptive spectrum mode extraction89
Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders86
Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm77
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks74
Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating70
Vibration-based damage detection for bridges by deep convolutional denoising autoencoder64
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions63
Three decades of statistical pattern recognition paradigm for SHM of bridges60
Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study58
An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis57
Imaging-based crack detection on concrete surfaces using You Only Look Once network57
Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network55
Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure55
Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis53
Analytical approach for crack identification of glass fiber reinforced polymer–sea sand concrete composite structures based on strain dissipations45
Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks44
Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds44
Bolt-looseness detection by a new percussion-based method using multifractal analysis and gradient boosting decision tree44
Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model44
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges43
Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves43
Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure42
Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data41
A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems41
Bayesian dynamic regression for reconstructing missing data in structural health monitoring41
Augmented reality for enhanced visual inspection through knowledge-based deep learning36
Assessment and visualization of performance indicators of reinforced concrete beams by distributed optical fibre sensing36
Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer35
Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring35
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