Rail pads, typically made of polymeric materials, exhibit highly non-linear behavior, with mechanical properties dependent not only on the type of material used but also on the applied load conditions. Additionally, the polymeric materials used in these components are not entirely stable over time. External factors, such as temperature, mechanical degradation as a consequence of fatigue, or chemical deterioration due to UV or hydrocarbons exposure, can significantly modify their mechanical properties, reducing their effectiveness in vibration damping and wear resistance [4; 5; 6]. In this context, some authors, such as [PERSON] et al. [7], have analyzed how the dynamic stiffness and damping capacity of rail pads vary with factors such as temperature, load frequency, and material wear. Similarly, [PERSON] et al. [8] analyzed, in their study, how the stiffness of rail pads is affected by temperature variations, especially at certain frequencies, highlighting the importance of proper material property selection to optimize performance. Other authors have studied the effects of chemical degradation; for example, there is literature on the effect of UV light on polymeric materials. In this regard, [PERSON] et al. [9] observed a significant reduction in the contact angle, an increase in cracking, and the formation of a calcareous layer on the surface. This implies a loss of electrical resistance and changes in the mechanical properties of the material. Likewise, [PERSON] et al. [10] examined how exposure to hydrocarbons affects the mechanical properties of rail pads, demonstrating that hydrocarbon exposure has a significant effect on dynamic stiffness, especially in those made of EPDM. [PERSON] et al. [11] present a systematic and quantitative characterization of how mechanical  
Figure 1: Picture with the main elements of a railway fastening system. aging, UV radiation, and hydrocarbon exposure affect the dynamic stiffness of EPDM and EVA rail pads and demonstrate how these variables significantly influence that stiffness. This evidence shows that evaluating the dynamic behavior of these type of materials under different possible operating conditions and after exposure to degradation factors is a challenge that requires a large number of experimental tests, as well as subsequent analysis, representing a considerable consumption of resources. For instance, [PERSON] et al. [12] used static tests at different temperatures and dynamic tests also at different temperatures, with 1000 sinusoidal load cycles ranging from 20 to 95 kN at a frequency of 5 Hz, to demonstrate that pads made of thermoplastic elastomers exhibit stiffness variations due to cyclic loading from train passage and wear factors. Additionally, [13] carried out a wide-band experimental characterization of Vossloh 300 rail pads and integrated it into a 3D explicit LS-DYNA model simulating a high-speed wheel running over a corrugated rail. They concluded that design models and maintenance plans must incorporate the frequency- and environment-dependent variation in pad stiffness and damping to predict in-service behavior reliably. Until now, it had not been possible to develop a model capable of estimating the dynamic stiffness of rail pads for different combinations of operating and degradation conditions. Recent advances in machine learning enable the proposal of a model capable of predicting dynamic stiffness based on static stiffness, operating conditions, and degradation effects. This type of approach has been successfully used by other authors, such as [PERSON] et al. [14], who employed gradient boosting algorithms to predict the mechanical properties of rail pads under service conditions, considering factors such as temperature, frequency, and toe load. Other authors' recent contributions extend this line of work. [PERSON] et al. [15] optimize sensorized rail pads for real-time monitoring and predictive maintenance. [PERSON] et al. [16] show, using machine learning algorithms, a strong correlation between vertical ballast stiffness and track longitudinal.