Probabilistic analysis of numerical simulated railway track global stiffness

The aim of this work is to assess numerically the influence of track geomaterials variability on the railway track stiffness. A non-intrusive probabilistic methodology based on in situ cone resistance tests is implemented in a 2D bidimensional finite element model with a modified plane strain condition. This model is used to estimate the track response to a moving load, which is characterized by the track stiffness measurement rolling stock. Spatial variability is taken into account by considering the cone resistance of each track layer as independent random fields, each one characterized by a marginal probability density function obtained from a statistical description of measured in situ data and a theoretical autocorrelation function. Despite input data variability, results presented much less variability than the input, which could be explained by both: load repartition over steppers, i.e. homogenization of the track stiffness measure, and deterministic characteristic of rail pads. Moreover, reduction of variance is observed, which means that less variance is observed for smaller correlation distances. In addition, a sensitivity analysis is also performed based on the Fourier Amplitude Sensitivity Test (FAST) and it showed, for the present case study, that the platform presents the highest first-order sensitivity index for all analyzed cases.