Issue 75

A. Aabid et alii, Fracture and Structural Integrity, 75 (2025) 55-75; DOI: 10.3221/IGF-ESIS.75.06

Prediction of crack length in thin-walled plates under different fracture mode conditions using machine learning algorithms

Abdul Aabid Department of Engineering Management, College of Engineering, Prince Sultan University, P.O. BOX 66833, Riyadh 11586, Saudi Arabia aaabid@psu.edu.sa, https://orcid.org/0000-0002-4355-9803

Citation: Aabid, A., Prediction of crack length in thin-walled plates under different fracture mode conditions using machine learning algorithms, Fracture and Structural Integrity, 75 (2026) 55-75.

Received: 29.05.2025 Accepted: 11.10.2025 Published: 16.10.2025 Issue: 01.2026

Copyright: © 2026 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 . SIF, Fracture modes, Thin-walled plates, Machine learning, damages/cracks.

I NTRODUCTION

aterial structures can fail due to mechanical and thermal loads. The crack can be initiated through the thickness or only on the surface of the structures. When it comes to the thin structures, the crack can initiate through the thickness, while in the thick structures, the surface cracks can occur in many cases. Early researchers have conducted a vast number of studies on damaged structures considering the fundamentals of fracture mechanics (FM). On the other hand, the researcher used a different approach to show the results of damaged structures [1]. This can be characterized by different scenarios for thick and thin structures. The damage or crack can be initiated in three modes that have Mode I, II, and III. These modes of propagation can be predicted through the fracture parameters such as stress intensity, stress concentration, J-integral evaluation, or fracture toughness. The FM has been classified into two major M

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