Baghbani, A.; Costa, S.; Faradonbeh, R.S.; Soltani, A.; Baghbani, H. Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence. Appl. Sci.2023, 13, 4363.
Baghbani, A.; Costa, S.; Faradonbeh, R.S.; Soltani, A.; Baghbani, H. Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence. Appl. Sci. 2023, 13, 4363.
Baghbani, A.; Costa, S.; Faradonbeh, R.S.; Soltani, A.; Baghbani, H. Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence. Appl. Sci.2023, 13, 4363.
Baghbani, A.; Costa, S.; Faradonbeh, R.S.; Soltani, A.; Baghbani, H. Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence. Appl. Sci. 2023, 13, 4363.
Abstract
This paper reports on a series of dynamic simple shear tests conducted to investigate the influence of particle shape on the damping ratio of dry sand. The tests were conducted on sand samples subjected to simple cyclic shear tests to evaluate their cyclic behavior. The particle shape was quantified using three shape parameters: roundness, sphericity, and regularity. The sand samples were subjected to twelve different scenarios with varying vertical stresses and cyclic stress ratios (CSR), in both constant and controlled stress states. Each scenario involved five cyclic tests, using the same sand that was reconstructed from its previous cyclic test. After each cyclic test, hysteresis loops were created to determine the damping ratio. The results showed that the shape of the sand particles changed during cyclic loading, becoming more rounded and spherical, which resulted in an increase in damping ratio. Moreover, the paper presents two artificial intelligence models, an artificial neural network (ANN) and a support vector machine (SVM), which were developed to predict the effect of grain shape on the damping ratio. The models were found to be effective in predicting the damping ratio based on the shape of the grain, vertical stress, CSR, and number of loading cycles. Furthermore, a parameter analysis was conducted to identify the most important shape parameter, which was found to be vertical stress and regularity, while parameter CSR was the least important. Overall, this study contributes to a better understanding of the relationship between particle shape and damping ratio, which could have practical implications for geotechnical engineering applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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