## 1 Introduction  
The damping ratio is a fundamental soil property that determines its response to various dynamic loading scenarios, including earthquakes, vibrations and machine operations [1; 2; 3; 4]. The damping ratio is influenced by several factors, namely soil type, grain-size distribution, soil particle shape and confining pressure. Many researchers are interested in the effects of particle shape on the damping ratio of sand [5; 6; 7; 8; 9; 10]. Previous investigations have demonstrated that the damping ratio of sand tends to decrease with increasing strain amplitude [7; 11; 12]. As the amplitude of cyclic loading increases, the soil particles undergo more severe deformations, thereby rendering the energy dissipation through damping less effective. Accordingly, it is important to consider the effects of strain amplitude when evaluating the damping ratio of sand [7; 11; 12]. Stress history can also influence the soil's damping attributes [13; 14]. For instance, cyclic loading can result in the development of excess pore pressure, which can affect the soil's damping response [15]. The buildup of excess pore pressure can cause changes in the effective stress, which in turn alters the soil stiffness and damping behavior. Moreover, the magnitude and duration of cyclic loading can influence the stress history of the soil, thereby impacting the damping ratio [16]. The sand's saturation degree can also affect its damping properties [17; 18; 19; 20]. In general, the damping ratio tends to increase with increasing saturation, as the presence of water within the soil matrix provides a pathway for energy dissipation through viscous damping. As the saturation degree increases, the viscous damping effect becomes more prominent, leading to a higher damping ratio. However, it should be noted that the effects of saturation on damping are not always consistent and depend on other factors, including soil type and loading conditions [17; 18; 19; 20]. The confining pressure is another parameter that influences the damping ratio of sands. Referring to [PERSON] et al. [21], the damping ratio of sands decreases as the confining pressure increases. Other reported variables that can influence the damping ratio of sands include moisture content and loading frequency; an increase in these parameters has been reported to, respectively, increase [22] and decrease [23] the damping ratio. The grain-size distribution, or uniformity coefficient, has also been investigated for its influence on the damping ratio of sands. Referring to [PERSON] et al. [24], the damping ratio of sands decreases as the gradational uniformity increases. In view of the above, it is evident that several interrelated variables can influence the damping ratio of sands, including grain-size distribution, sand particle shape, confining pressure, moisture content (and saturation) and loading frequency [25; 26; 27; 28; 29; 30; 31]. Hence, the complex relationship between these interrelated parameters and the damping ratio (of sands) warrants further investigation. As of late, the application of artificial intelligence (AI) models, such as artificial neural networks (ANNs), has become increasingly popular (and accepted) in solving diverse engineering problems due to their ability to learn and generalize complex nonlinear relationships between input and output variables [20]. Conventional neural networks have been successfully applied in various engineering fields, including (but not limited to) civil, geotechnical, mechanical and electrical engineering. The success of these conventional AI-based models has led to the development of novel techniques and algorithms, such as deep learning, which have demonstrated even more promise in solving complex engineering problems. The feasibility of neural networks in engineering highlights their potential to provide insights and solutions to multifaceted problems that may not have been possible (in the past) using traditional analytical and statistical methods.