## 1 Introduction
The effects of such geometric and topographic parameters had been considered in previous studies, such as those of [PERSON] and [PERSON] (2006) and [PERSON] et al. (2018) [50; 51]. However, such specific site effects are beyond the scope of this study,and they are not included in basic code specifications. Consequently, for the sake of efficiency, simplification, and direct comparison with codes and the main body of literature, 1D modelling of soil and wave propagation is considered in this study. Furthermore, this avenue offers the possibility of obtaining a simple analytical solution to the wave propagation problem. Soil nonlinearity is incorporated using shear modulus degradation curves that show the variations in shear modulus (G) with respect to shear strain (\(\gamma\)) levels and loading history (cycling), as detailed hereafter. Shear modulus degradation under cyclic loading has been studied by several researchers using resonant column or enhanced triaxial tests. [PERSON] and [PERSON] (1970) were pioneers in developing and widely applying shear modulus reduction and damping curves specifically for sand ([PERSON] and [PERSON], 1970). In 1991, [PERSON] and [PERSON] introduced the plasticity index (IP) to define shear modulus degradation curves for clay ([PERSON] and [PERSON], 1970; [PERSON] and [PERSON], 1971). In 1993, [PERSON] and [PERSON] added another parameter, the effective confining pressure, used alongside the IP to better capture the behaviour of soils with low plasticity indices ([PERSON] and [PERSON], 1970). That same year, the Electric Power Research Institute (EPRI) created its own soil degradation curves by combining shear modulus reduction and damping curves, allowing for broader applicability across cohesionless soils, from gravelly sands to low plasticity silts and sandy clays ([PERSON] and [PERSON], 1970). More recently, [PERSON] (2001) proposed a new set of degradation curves, indicating that the degree of linearity increases with the plasticity index (IP), mean effective stress, and overconsolidation ratio (OCR) ([PERSON], 2001). Such a model, while considered more appropriate for representing soil nonlinearity, requires a set of additional soil parameters to those used in our study which will extend the number of required nonlinear analyses. In order to streamline our analysis, limit the number of analyses, and focus on identifying effective proxies for soil profiles, we selected simpler degradation models proposed by [PERSON] et al. (1988) ([PERSON] et al., 1988) for clay and by [PERSON] and [PERSON] (1970) ([PERSON] and [PERSON], 1970) for sand. These models require minimal soil properties while still representing the nonlinear behaviour of soils under cyclic loading accurately enough ([PERSON] and [PERSON], 1970; [PERSON] et al., 1988). Additionally, we incorporate the damping curves from the study by [PERSON] (1990), which provide insights into how damping ratios vary with strain levels ([PERSON] and [PERSON], 1970). This combination allows us to simplify our approach while retaining the critical features for predicting the seismic response of clay and sand, which ultimately should facilitate the identification of the most suitable proxies for soil characterization in seismic assessments. To explore the correlation between the average amplification factor and various sets of soil characteristics, we use an artificial neural network (ANN) approach. This method effectively captures complex relationships and nonlinear interactions among input variables. To enhance our analysis, a sensitivity analysis is conducted using the neural network model. This analysis aims to identify which soil characteristics serve as the best site proxies for predicting the amplification factor. By examining how changes in these variables (proxies) impact the output, we can pinpoint the most influential parameters, thereby improving our understanding of soil behaviour in seismic contexts and facilitating better site characterization in future studies.