## 1 Introduction
The available literature on the application of AI-based models to investigate the complex relationship between various governing variables and the damping ratio of sands is still fairly limited [32; 33; 34; 35; 36]. [PERSON] and [PERSON] [32] presented neural network models for predicting the damping ratio and shear modulus of sand-mica mixtures based on stress, mica content and strain. The reported models were established using experimental data from consolidated torsional resonant column tests on various mixtures of mica and Leighton Buzzard sand. The predictive performance of their models was found to be high, with \(R^{2}=0.97\) and \(0.99\) for the damping ratio and shear modulus parameters, respectively. [PERSON] and [PERSON] [33] proposed models based on gene expression programming (GEP) to predict the normalized shear modulus and the damping ratio of sands as a function of the mean effective confining pressure, void ratio and shear strain percentage; their proposed models were validated using published experimental data from the literature. Their GEP-based models demonstrated acceptable accuracy, with relative error margins lower than \(\pm 6\%\) and \(\pm 2\%\) for the normalized shear modulus and damping ratio parameters, respectively. In another study performed by [PERSON] et al. [34], a neuro-fuzzy network was developed to model the dynamic behaviors of sand-rubber mixtures under varying conditions. Three predictive systems were trained and tested, using experimental data, to predict the shear modulus and damping ratio parameters. The study found that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting the dynamic behaviors of the composite materials, and further investigations of this paradigm were encouraged. The present study contributes significantly to the research literature by being the first of its kind to systematically investigate the effects of sand particle shape on the damping ratio of dry sand where the minerals are identical and the only variable is the shape and size of the sand particles. The study's unique approach involves the application of cyclic loading tests to examine the effects of cyclic stress ratio (CSR) loading on the relationship between particle shape and the damping ratio. This type of cyclic loading is particularly useful as it can simulate the impacts of earthquakes, which are important in geotechnical engineering practice. The first objective was to experimentally investigate the effects of particle shape on the damping ratio of dry sands. To this end, a series of sand samples with different particle shapes were tested in a cyclic simple shear apparatus in both constant-stress and controlled-stress modes to determine the damping ratio under different scenarios. The analysis of the test results was then incorporated into different correlations in the form of curves. The second objective of this study involved performing a pioneering investigation into the use of AI-based models for predicting the damping ratio as a function of sand particle shape, vertical stress, CSR and the number of loading cycles. Although AI-based methods have been successfully employed in different areas of geotechnical engineering [37; 38; 39; 40; 41; 42; 43; 44], this study marks the first time they were applied to the aforementioned problem. This study represents an important step toward expanding the application of AI-based models in geotechnical engineering. Finally, in developing the AI-based models, the study focuses on analyzing the importance of the input parameters to gain a better understanding of how they impact damping ratio predictions.