Open Access

REVIEW

Recent Applications of Unsupervised Machine Learning in Structural Health Monitoring

Abdullah Alariyan¹, Abdulhadi Alzabout², Mohammed Alariyan³, Anas Alaryan⁴, Mahmoud Alhashash⁵, Abdulrahman Ahmed⁶, Mohammed Abdulaal⁷, Ahed Habib^8,*

1 Department of Civil Engineering, Eastern Mediterranean University, Famagusta, Northern Cyprus via Mersin 10, Mersin, Turkey
2 Department of Civil Engineering, Isra University, Amman, Jordan
3 Department of Civil Engineering, University of Science and Technology Yemen, Aden, Yemen
4 Department of Civil Engineering, Cairo University, Giza, Egypt
5 Department of Civil Engineering, Cyprus International University, Famagusta, Cyprus
6 Department of Civil Engineering, Fahd Bin Sultan University, Tabuk, Saudi Arabia
7 Department of Architecture, Eastern Mediterranean University, Famagusta, Northern Cyprus via Mersin 10, Mersin, Turkey
8 Sustainable Systems, Technologies, and Infrastructure Research Center, Research Institute of Sciences & Engineering, University of Sharjah, Sharjah, United Arab Emirates

* Corresponding Author: Ahed Habib. Email:

Structural Durability & Health Monitoring 2026, 20(3), 4 https://doi.org/10.32604/sdhm.2026.076012

Received 12 November 2025; Accepted 11 March 2026; Issue published 18 May 2026

Abstract

Unsupervised machine learning has recently gained attention in structural health monitoring as engineers seek methods that can interpret large and complex data sets without prior labeling. Traditional diagnostic approaches often rely on predefined models or manual analysis, which limits their adaptability and efficiency when dealing with evolving structural behaviors or unforeseen conditions. Despite the growing interest in this domain, the literature remains fragmented, with limited systematic and bibliometric reviews that consolidate progress, identify prevailing trends, and clarify methodological limitations. This study addresses this gap through a comprehensive systematic and bibliometric review of research on unsupervised machine learning applied to structural health monitoring. The review examines the evolution of techniques, their applications in various structural systems, and the frequency and quality of their adoption in scientific literature. The aim is to present a consolidated understanding of how unsupervised learning contributes to data-driven monitoring, fault detection, and predictive maintenance. This research is important as it provides an evidence-based overview that supports future methodological improvements and guides the selection of suitable algorithms for practical engineering applications, ensuring a stronger link between data science and structural reliability.

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Keywords

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