## 4 Results
These rail pads exhibited glass transition temperatures of around \(-40\)\({}^{\circ}\)C to \(-45\)\({}^{\circ}\)C.
* Mechanical Fatigue Tests (Row 2): Rail pads A and B show a more linear behavior with limited dispersion. In contrast, Type C rail pads, made from EVA, exhibit greater variability and display non-linear behavior. * Ultraviolet-Light Exposure Tests (Row 3):Type A and B rail pads show a predominantly increasing linear trend with low dispersion, while Type C rail pads, again, exhibit greater dispersion in dynamic stiffness values and non-linear behavior. * Hydrocarbon Exposure Tests (Row 4): Type A rail pads, as in previous cases, exhibit linear behavior with low dispersion. Type B pads show linear behavior under condition HC2 and non-linear behavior under condition HC1. Type C pads show low dispersion under condition HC1, with increased dispersion under condition HC2. This condition has been studied and explained in greater depth by other authors [10]. Figure 6 shows a matrix of subplots that provides more detailed insight into the data, which are separated by aging type and show the relationship between static and dynamic stiffnesses for the three types of rail pads (A, B, and C) and the following four test conditions: temperature (Temp), mechanical load (Mec), ultraviolet radiation exposure (UV), and hydrocarbon exposure (HC). Each column corresponds to a type of rail pad, while each row represents a type of test condition. In this way, introducing a new dummy variable representing the test condition to which each rail pad was subjected should improve the model's performance. To achieve this, an optimized GB model was re-evaluated for each rail pad, yielding the results shown in the Tables 6 and 7. It can be observed that the test conditions introduce valuable new information into the model, improving the results and reducing error in all cases. Figure 6: Matrix of subplots with the relationship between static and dynamic stiffnesses for each rail pad and condition. To verify that the models were not overfitting due to the presence of only three frequency values (5, 10, and 20 Hz) while the rest of the variables remained constant, a model was trained using only the data corresponding to a frequency of 5 Hz. As shown in Table 8, despite being trained on only one-third of the data, the results remain robust, indicating that overfitting is not occurring due to this factor. However, it can be observed that frequency is a particularly relevant variable for rail pad C, which is consistent with the findings from the analyses in Figures 7 and 8. This is evident as the deterioration in the metrics for rail pad C is even greater than the deterioration observed when the test condition variable was removed from the analysis, as shown by comparing the results with those in Table 4.  
To interpret the impact of the different variables on the predictions of the optimized GB model, the SHAP (SHapley Additive exPlanations) interpretability technique was used. This methodology quantifies the effect of each feature on the model's output, providing a clear explanation of how the input variables influence the prediction of the target variable, in this case, dynamic stiffness. The SHAP bar chart presented in Figure 7 shows the average importance of each variable in the model. In this chart, the features are ordered from the highest to lowest impact on the prediction. This allows us to identify the most relevant factors in the estimation of dynamic stiffness. Based on Figure 7, it can be observed that rail pads A and B, both made of EPDM but differing in the thickness of their protuberances, as explained in Section 2, rely primarily on just two variables to predict their dynamic stiffness. In pad B, the toe load stands out with an importance far exceeding that of the other variables, while in pad A, static stiffness is the