Issue 68

A. Aabid et alii, Frattura ed Integrità Strutturale, 68 (2024) 310-324; DOI: 10.3221/IGF-ESIS.68.21

Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms

Abdul Aabid Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia Md Abdul Raheman NITTE (Deemed to be University), Dept. of Electrical and Electronics Engineering, NMAM Institute of Technology, Nitte - 574110, Karnataka, India Meftah Hrairi* Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50725 Kuala Lumpur, Malaysia Muneer Baig Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia

Citation: Aabid, A., Raheman, M. A., Hrairi, M., Baig, M., Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms, Frattura ed Integrità Strutturale, 68 (2024) 310-324.

Received: 31.01.2024 Accepted: 27.02.2024 Published: 05.03.2024 Issue: 04.2024

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 . Bonded composite repair, Cracks, Reinforced patch, Analytical model; Machine learning.

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