ReviewSensors (Basel, Switzerland)2023
Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Supervised and Unsupervised AI-Driven Structural Health Monitoring Framework for Additively Manufactured Metal Components.Sensors (Basel, Switzerland) · 2026Article
- Evaluating the Influence of Normalisation Procedures on a Negative Selection Algorithm to Enhance Damage Detection.Sensors (Basel, Switzerland) · 2026Article
- A data-driven framework for structural health monitoring using reinforcement learning and deep autoencoders.Scientific reports · 2026Article
- Research on structural damage identification based on temporal power flow graph network.Scientific reports · 2026Article
- [The application of machine learning in the auxiliary diagnosis of specific learning disorder].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder.Sensors (Basel, Switzerland) · 2025Article
- Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network.Sensors (Basel, Switzerland) · 2025Review
- Dmg2Former-AR: Vision Transformers with Adaptive Rescaling for High-Resolution Structural Visual Inspection.Sensors (Basel, Switzerland) · 2024Article
- Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.Sensors (Basel, Switzerland) · 2024Article
- Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.Journal of imaging · 2024Review
- Enhancing Seismic Damage Detection and Assessment in Highway Bridge Systems: A Pattern Recognition Approach with Bayesian Optimization.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.
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