- Title
- Modeling the effects of particle shape on damping ratio of dry sand by simple shear testing and artificial intelligence
- Creator
- Baghbani, Abolfazl; Costa, Susanga; Faradonbeh, Roohoollah; Soltani, Amin; Baghbani, Hasan
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194649
- Identifier
- vital:18381
- Identifier
-
https://doi.org/10.3390/app13074363
- Identifier
- ISSN:2076-3417 (ISSN)
- Abstract
- This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter. © 2023 by the authors.
- Publisher
- MDPI
- Relation
- Applied Sciences (Switzerland) Vol. 13, no. 7 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- MD Multidisciplinary; Artificial neural network; Cyclic simple shear testing; Damping ratio; Sand particle shape; Support vector machine
- Full Text
- Reviewed
- Funder
- The first author gratefully acknowledges Deakin University for making this research possible through the provision of the Australian Government Research Training Program (RTP) Scholarship.
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