OOIR: Observatory of International Research

Papers

(The H4-Index of Structural Health Monitoring-An International Journal is 34. 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 2020-11-01 to 2024-11-01.)

Article	Citations
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights	211
Machine learning paradigm for structural health monitoring	166
Efficient attention-based deep encoder and decoder for automatic crack segmentation	162
Three decades of statistical pattern recognition paradigm for SHM of bridges	97
A new fault diagnosis method based on adaptive spectrum mode extraction	95
Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm	95
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions	84
An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis	81
Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study	73
Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure	66
Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis	63
Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model	57
Bayesian dynamic regression for reconstructing missing data in structural health monitoring	55
Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds	53
Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring	52
A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques	50
Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves	48
Augmented reality for enhanced visual inspection through knowledge-based deep learning	47
Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series	46
Assessment and visualization of performance indicators of reinforced concrete beams by distributed optical fibre sensing	46
Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing	44
A novel data-driven method for structural health monitoring under ambient vibration and high-dimensional features by robust multidimensional scaling	43
Analytical approach for crack identification of glass fiber reinforced polymer–sea sand concrete composite structures based on strain dissipations	43
Guided wave–based rail flaw detection technologies: state-of-the-art review	41
An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation	41

A review of structural health monitoring of bonded structures using electromechanical impedance spectroscopy	40
Techniques of corrosion monitoring of steel rebar in reinforced concrete structures: A review	40
A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification	39
Recent progress in aircraft smart skin for structural health monitoring	39
Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network	39
The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem	38
Non-parametric empirical machine learning for short-term and long-term structural health monitoring	37
A review on diagnostic and prognostic approaches for gears	37
Investigation on the data augmentation using machine learning algorithms in structural health monitoring information	35