Influence of the operational conditions on static and dynamic stiffness of rail pads. (September 2020) - Sainz-Aja, Jose A.; Carrascal, Isidro A.; Ferreño, Diego; Pombo, Joao; Casado, Jose A.; Diego, Soraya
Highlights: Full-scale characterization of fastening systems under in-service conditions. Temperature increments notably reduce rail pad stiffness. Toe load increments significantly increase rail pad stiffness. Axle load increments increase rail pad stiffness. Frequency increments slightly increase rail pad stiffness. Abstract: The track has a crucial role in the performance of the rail network as it provides support and guidance to the rolling stock. During train operation, the vehicle-track interaction generates high impact loads and fatigue, which lead to degradation of vehicle components and rail infrastructure. These loads tend to increase the maintenance needs and, consequently, the life-cycle-costs of the rail assets. In order to minimize these consequences, rail pads are generally used between the rails and the sleepers in order to provide flexibility to the track and to damp the transmission of noise and vibrations. In ballasted tracks, this flexibility is a combination of the mechanical properties of the ballast and the rail pads. However, in slab tracks, the flexibility of the infrastructure is almost exclusively dependant on the rail pads. These materials exhibit non-linear, dissipative characteristics that are affected by the service conditions such as temperature, frequency, toe load and axle load. This work aims to investigate experimentally different pad materials widely used in the rail industry in order to characterize the influence of these factors on the mechanical characteristics of the rail supporting elements. The detailed characterization of the rail pads enables not only better understanding of their performance in realistic service conditions, but also provides good perspectives for use of these well quantified mechanical properties in studying the vehicle-track dynamic behaviour in different scenarios and predicting the long-term performance of the infrastructure components.