## 4 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Learning Rate** & **Max Depth** & **Min Samples Split** & \(n\) **Estimators** & **Subsample** \\ \hline Pad A & 0.05 & 5 & 10 & 800 & 0.6 \\ Pad B & 0.01 & 5 & 2 & 800 & 0.8 \\ Pad C & 0.1 & 5 & 10 & 800 & 0.8 \\ \hline \end{tabular}
\end{table} 
Table 6: Parameters obtained for each of the GB models. \begin{table}
\begin{tabular}{l c c c} \hline  & **Pad A** & **Pad B** & **Pad C** \\ \hline MSE: kN\({}^{2}\)/mm\({}^{2}\) & 7.25 & 69.81 & 144.68 \\ RMSE: kN/mm & 2.69 & 8.35 & 12.02 \\ MAE: kN/mm & 1.70 & 4.85 & 9.37 \\ R\({}^{2}\) & 0.9874 & 0.991 & 0.991 \\ \hline \end{tabular}
\end{table} 
Table 7: Metrics of the tests for each of the GB models. \begin{table}
\begin{tabular}{l c c c} \hline  & & **All Frequencies** & **5 Hz** \\ \hline \multirow{4}{*}{Pad A} & MSE: kN\({}^{2}\)/mm\({}^{2}\) & 7.25 & 14.58 \\  & MAE: kN/mm & 1.70 & 2.67 \\  & RMSE: kN/mm & 2.6 & 3.81 \\  & R\({}^{2}\) & 0.9874 & 0.9746 \\ \hline \multirow{4}{*}{Pad B} & MSE: kN\({}^{2}\)/mm\({}^{2}\) & 69.81 & 211.85 \\  & MAE: kN/mm & 4.85 & 8.38 \\  & RMSE: kN/mm & 8.35 & 14.55 \\  & R\({}^{2}\) & 0.991 & 0.9720 \\ \hline \multirow{4}{*}{Pad C} & MSE: kN\({}^{2}\)/mm\({}^{2}\) & 144.68 & 1832.7 \\  & MAE: kN/mm & 9.37 & 36.20 \\ \cline{1-1}  & RMSE: kN/mm & 12.02 & 42.81 \\ \cline{1-1}  & R\({}^{2}\) & 0.991 & 0.8909 \\ \hline \end{tabular}
\end{table} 
Table 8: Metrics of the tests for each GB model across all frequencies and at 5 Hz. dominant factor, closely followed by the toe load. In contrast, the C pad, fabricated from EVA, distributes its predictive weight across a broader set of parameters; aside from toe load and static stiffness, frequency and temperature play prominent roles, with mechanical degradation and exposure to hydrocarbons also showing a modest influence. This differs notably from pads A and B, where those factors contribute to a much lesser extent. Figure 7: SHAP chart with the impact of each variable on the model predictions: (**a**) Pad A; (**b**) Pad B; (**c**) Pad C.  
To analyze how each feature influences the prediction, Figure 8 presents SHAP dependence plots for the features used by the model, showing their direct contribution to the predictions of dynamic stiffness. In this way, we can observe the magnitude and direction of the influence of each feature on the prediction and make comparisons across the different rail pads (A, B, and C). Static stiffness, with values ranging from 15 kN/mm to 275 kN/mm, shows a strong positive linear correlation with dynamic stiffness for rail pads A and C. In contrast, rail pad B exhibits a flatter relationship, consistent with Figure 7, where the impact of static stiffness is on the prediction of the dynamic stiffness. Figure 8: SHAP dependence plots of model features and their contribution to dynamic stiffness prediction: (**a**) static stiffness; (**b**) toe load; (**c**) amplitude; (**d**) frequency; (**e**) mechanical degradation (cycles); (**f**) temperature; (**g**) hydrocarbon exposure; (**h**) ultraviolet light exposure. stiffness on the model's results was much lower than for pads A and C. This suggests a weaker relationship between static and dynamic stiffnesses for this rail pad. Additionally, a set of points between 50 and 100 kN/mm for pad C displays anomalous behavior, corresponding to the effect of a temperature of 0 \({}^{\circ}\)C, indicating a clear change in behavior under this condition. Toe load, which ranges from 1 kN to 25 kN, exhibits a similar behavior across all rail pads, with consistent stiffening as its value increases. Notably, for pad B, in the range of 1 to 18 kN, the effect on the model is between \(-\)100 kN and 0 kN. However, for values of 25 kN, it has a strong positive effect on the model, with SHAP values ranging from 125 to 150 kN/mm. Frequency, ranging from 5 Hz to 20 Hz, has an impact similar to that of toe load, with consistent stiffening of the rail pads as its value increases.