Train operations generate high impact and fatigue loads that degrade the rail infrastructure and the vehicle components. Rail pads are installed between the rails and the sleepers in order to damp the transmission of vibrations and noise and to provide flexibility to the track. These components play a crucial role in maximizing the durability of the railway assets and minimizing maintenance costs. Rail pads can be fabricated with different polymeric materials that exhibit non-linear mechanical behaviours, which strongly depend on the service conditions. Therefore, it is extremely difficult to estimate their mechanical properties, in particular the dynamic stiffness. In this work, several machine learning methodologies (multilinear regression, K nearest neighbours, regression tree, random forest, gradient boosting, multi-layer perceptron and support vector machine) were used to determine the dynamic stiffness of rail pads depending on their in-service conditions (temperature, frequency, axle load and toe load). 720 experimental tests, under different realistic operating conditions, were performed to produce a dataset that was then used for the training and testing of the machine learning methods. The optimal algorithm was gradient boosting for EPDM (R² of 0.995 and mean absolute percentage error of 5.08% in the test dataset), TPE (0.994 and 2.32%) and EVA (0.968 and 4.91%) pads. This model was implemented in an application, available for the readers of this journal, developed on the Microsoft .Net platform that allows the dynamic stiffness of the pads study to be estimated as a function of the temperature, frequency, axle load and toe load.

火车运行会产生高冲击力和疲劳负荷，从而降低铁路基础设施和车辆零部件的性能。滑轨垫安装在滑轨和轨枕之间，以衰减振动和噪音的传递并为滑轨提供灵活性。这些组件在最大化铁路资产的耐用性和最小化维护成本方面起着至关重要的作用。滑轨垫可以用表现出非线性机械性能的不同聚合材料制成，这在很大程度上取决于使用条件。因此，很难估计它们的机械性能，特别是动态刚度。在这项工作中，我们采用了几种机器学习方法（多线性回归，K最近邻，回归树，随机森林，梯度提升，多层感知器和支持向量机）用于根据其使用条件（温度，频率，轴负载和脚趾负载）确定滑垫的动态刚度。在不同的实际操作条件下进行了720次实验测试，以生成一个数据集，然后将其用于训练和测试机器学习方法。最佳算法是EPDM的梯度提升（R ²为0.995，测试数据集中的平均绝对百分比误差为5.08％），TPE（0.994和2.32％）和EVA（0.968和4.91％）垫。该模型是在Microsoft .Net平台上开发的应用程序中实现的，该应用程序可供本期刊的读者使用，该平台允许根据温度，频率，轴负载和脚趾负载估算垫板的动态刚度。