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.
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.
The influential parameters on mixed-mode crack propagation were found in this paper.
The computer code using MATLAB was prepared to study the mixed-mode crack paths. Also using ANNs toolbox, the crack path estimation was investigated.
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.
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
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