## 6 Conclusions  
The main objective of this study was to identify the key soil parameters influencing 1D site seismic response and amplification factors (_AFs_) at the free surface. To achieve this, we computed the nonlinear site response using a linear equivalent approach under vertically incident plane waves, utilizing a representative set of real input accelerograms that cover a broad range of peak frequencies and intensities. We focused on monolayer soil profiles with variable thicknesses and shear wave velocities, situated above a semi-infinite bedrock with varying shear wave velocities. Soil nonlinearity was modelled using degradation curves proposed by [PERSON] et al. (1988) [58] for clay and [PERSON] and [PERSON] (1970) [52] for sand, which are simple and require minimal soil parameters and are effective enough. In total, 324 soil profiles of sand and an equal number of profiles of clay were analyzed under 14 records at 11 _PGA_ levels each for a total of 99,792 time-history analyses. For each soil profile, we calculated geometric average amplification factors for short to mid and long period ranges, denoted as F\({}_{\rm a}\), F\({}_{\rm v}\), and F\({}_{\rm l}\) for periods of [0.1 s, 0.2 s], [0.75 s, 1.5 s], and [2.82 s, 5.65 s], respectively. Two types of neural networks were utilized in our analysis: (1) the Generalized Regression Neural Network (GRNN) was used to identify the most effective combination of soil proxies in predicting _AFs_, and (2) the Radial Basis Function neural network (RBF) was used to develop regression equations between these proxies and _AFs_. This combination of methods facilitated a comprehensive investigation into the factors that govern site response. The main findings can be summarized as follows:  
* The pair (_PGA_,\(f\)0) was identified as effective for predicting _AFs_, achieving reductions in the standard deviation of 64% and 65% for clay and sand profiles, respectively. \begin{table}
\begin{tabular}{l c c c} \hline
**Participation of Synaptic Weights** & **(\%)_PGA_** & **(\%)_f_0** & **(\%)_V_30** \\ \hline For F\({}_{\rm a}\) (\(\theta=0\)) (soil type: clay) & 30.51 & 46.82 & 22.67 \\ \hline For F\({}_{\rm v}\) (\(\theta=0\)) (soil type: clay) & 32.73 & 45.81 & 21.45 \\ \hline For F\({}_{\rm l}\) (\(\theta=0\)) (soil type: clay) & 33.44 & 45.26 & 21.30 \\ \hline For F\({}_{\rm a}\) (\(\theta=1\)) (soil type: sand) & 30.76 & 51.10 & 18.14 \\ \hline For F\({}_{\rm v}\) (\(\theta=1\)) (soil type: sand) & 31.75 & 47.11 & 21.14 \\ \hline For F\({}_{\rm l}\) (\(\theta=1\)) (soil type: sand) & 41.52 & 37.62 & 20.86 \\ \hline \end{tabular}
\end{table} 
Table 7: Participation of synaptic weights for different site parameters used in RBF model. * The triplet (\(PGA\), \(\text{Cv},f_{0}\)) proved to be particularly powerful in predicting actual \(A\)Fs, resulting in standard deviation reductions of over 71% for both clay and sand. Other combinations, such as (\(PGA\), \(\text{Cv}2,f_{0}\)), (\(PGA\), \(V_{sm},f_{0}\)), and (\(PGA\), \(V_{s30},f_{0}\)), also yielded promising results and can be utilized. * Because parameters \(\text{Cv}\) and \(\text{Cv}2\) are often difficult and costly to measure in engineering practices, we recommend using the combination (\(PGA\), \(V_{s30},f_{0}\)), which, while offering slightly lower performance, still provides a significant reduction in the standard deviation, making it a practical alternative for field applications. Furthermore, the prediction equations derived from the GRNN were found to be quite complex due to the high number of neurons in the hidden layer, making them challenging to implement in practice. As a result, we opted for the RBF neural network, which offers simpler prediction equations, albeit at the cost of a more intensive and prolonged training phase. We focused on the most effective site proxies identified through the GRNN analysis: \(PGA,f_{0}\), and \(V_{s30}\).