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Forward and backward mixed-mode crack estimation using artificial neural network

Abdolghafour Khademalrasoul (Shahid Chamran University of Ahvaz, Ahvaz, Iran)
Zahra Hatampour (Shahid Chamran University of Ahvaz, Ahvaz, Iran)
Masoud Oulapour (Shahid Chamran University of Ahvaz, Ahvaz, Iran)
Seyed Enayatollah Alavi (Shahid Chamran University of Ahvaz, Ahvaz, Iran)

International Journal of Structural Integrity

ISSN: 1757-9864

Article publication date: 28 November 2022

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Abstract

Purpose

In this manuscript, the authors aimed to demonstrate the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue.

Design/methodology/approach

In this manuscript, the authors demonstrated the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue. Furthermore, three different scenarios for crack growth are considered. In reality, edge-cracked plate, center-cracked plate and cracked plate in the presence of void and inclusion are studied. In fact, by designing suitable artificial neural network's (ANN) architectures all the three aforementioned conditions are trained and estimated through those architectures with very good agreement with input data. Also by conducting a series of sensitivity analysis, the most affecting factors in mixed-mode crack propagation in different situations are demonstrated. The obtained results are very interesting and useful for other researchers and also the authors hope the results would be cited by researchers.

Findings

The influential parameters on mixed-mode crack propagation were found in this paper.

Originality/value

The computer code using MATLAB was prepared to study the mixed-mode crack paths. Also using ANNs toolbox, the crack path estimation was investigated.

Keywords

Acknowledgements

The authors gratefully acknowledge the Research Council at Shahid Chamran University of Ahvaz for the funding (No: SCU.C1401.31071) and for having used the services and facilities.

Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Citation

Khademalrasoul, A., Hatampour, Z., Oulapour, M. and Alavi, S.E. (2022), "Forward and backward mixed-mode crack estimation using artificial neural network", International Journal of Structural Integrity, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJSI-09-2022-0114

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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