## 5 Conclusions  
This study implemented machine learning models to predict the dynamic stiffness of elastomeric (EPDM and EVA) railway pads, which are key components for vibration absorption and for the proper functioning of railway superstructures. These pads are subjected to various mechanical and environmental conditions that influence their stiffness, making accurate evaluation essential for optimizing performance and service life. By leveraging a dataset of 1080 dynamic stiffness tests and 30 static stiffness tests conducted under different operating and degradation conditions, predictive models were developed and optimized, significantly reducing the need for extensive laboratory testing. The degradation conditions studied included different temperatures (0 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C), mechanical fatigue (250,000; 750,000; and 2,000,000 cycles), and chemical exposure (UV radiation and exposure to hydrocarbons for 100 and 500 h). The results confirm that static stiffness is strongly correlated with dynamic stiffness, though the strength of this relationship varies by pad type and degradation condition. Type A rail pads exhibit a nearly linear correlation with low dispersion, whereas Type B and, especially, Type C pads show increased variability and non-linearity, highlighting the influence of additional factors beyond static stiffness. Among the machine learning models evaluated, gradient boosting achieved the best predictive performance, with R\({}^{2}\) values of nearly 0.99 on the test dataset when all test conditions were included as input variables. The final models demonstrated excellent predictive capabilities, achieving root mean squared errors of 2.69 kN/mm for pad A, 8.35 kN/mm for pad B, and 12.02 kN/mm for pad C. These results highlight the robustness of the method and the importance of considering different degradation mechanisms, such as temperature, mechanical fatigue, and chemical exposure. The predictive models showed a strong capacity to assess variations in dynamic stiffness across different mechanical scenarios and in the presence of various environmental and degradation factors. This accuracy enables the implementation of predictive maintenance strategies, optimizing resource utilization by reducing both inspection frequency and preventive replacements, thus contributing to a more efficient and sustainable management of railway infrastructure. Furthermore, the SHAP interpretability analysis provided valuable information on the relative influence of various operational and environmental factors on the performance of railway pads. Toe load and static stiffness were identified as the most influential variables in predicting dynamic stiffness. However, temperature had a more significant impact on Type C pads, made of EVA, compared to Pads A and B, made of EPDM, highlighting the importance of proper material selection according to operating conditions. The SHAP interpretability analyses confirmed these findings, showing that low temperatures (0 \({}^{\circ}\)C) increase the SHAP value of Pad C's stiffness by up to 100 kN/mm. This research provides a scalable, data-driven methodology for evaluating rail pad performance, contributing to the development of more efficient and sustainable railway systems. Although studies such as that by [PERSON] et al. [8] show that factors like temperature induce non-linear variations in the static stiffness of materials other than those used in the present research, such as chloroprene rubber (CR) and thermoplastic polyurethane elastomer (TPE), suggesting that the proposed approach could be extrapolated to a broader range of materials. Future work should extend its application to different materials, investigate the combined effects of multiple degradation agents, study the possibility of extrapolating results among pads, and validate the models under real service conditions. Conceptualization, I.R., J.A.S.-A. and V.C. ; methodology, I.R., J.A.S.-A., D.F., V.C., I.C., J.C. and S.D. ; software, I.R. and J.A.S.-A.