 This study examined how the shape of sand particles affects the damping ratio of a dry sand material under cyclic loading conditions. The results showed that the shape of the sand particles became progressively more rounded and spherical over time, leading to an increase in the damping ratio. Additionally, artificial intelligence models such as an artificial neural network, ANN, and a support vector machine, SVM, were developed to predict the effects of sand particle shape on the damping ratio. The ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors, roundness, sphericity, and regularity, vertical stress, cyclic stress ratio, CSR, and number of loading cycles. Furthermore, a sensitivity analysis was conducted to identify the importance of the input variables. The vertical stress and regularity were ranked as first and second in terms of importance, while the CSR was found to be the least important parameter. This article was authored by Abalfazal Bighbani, Susanga Kosta, Ruhilas Shirani Faridamba, and others. We are article.tv, links in the description below.