Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence

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.

Keywords

• cyclic simple shear testing
• damping ratio
• sand particle shape
• artificial neural network
• support vector machine