Issue 69

A. Anjum et alii, Frattura ed Integrità Strutturale, 69 (2024) 43-59; DOI: 10.3221/IGF-ESIS.69.04

Civil structural health monitoring and machine learning: a comprehensive review

Asraar Anjum, Meftah Hrairi* Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia asraar.anjum@live.iium.edu.my and meftah@iium.edu.my Abdul Aabid Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia aaabid@psu.edu.sa, http://orcid.org/0000-0002-4355-9803 Norfazrina Yatim Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia noorfazrina@iium.edu.my Maisarah Ali Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia maisarahali@iium.edu.my

Citation: Anjum, A., Hrairi, M., Aabid, A., Yatim, N., Ali, M., Civil structural health monitoring and machine learning: a comprehensive review, Frattura ed Integrità Strutturale, 69 (2024) 43-59.

Received: 02.01.2024 Accepted: 08.04.2024 Published: 17.04.2024 Issue: 07.2024

Copyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

K EYWORDS . Concrete structures, Machine learning, Electromechanical impedance, Damage detection, Damage repair.

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