Measurement of train-induced vibration for track parameter identification: Bayesian probabilistic approach

Abstract

The train-induced vibration response is used to develop a comprehensive finite element model for the railway track, which considered rail-sleeper-ballast interaction. This model can be further use in the development of an operational monitoring system for railway track by following the Bayesian statistical system identification framework. However, it is believed that the uncertainties associated with the measured train-induced test data and model parameters are relatively high, therefore, the corresponding model updating problem will very likely to be in the category of unidentifiable case. Therefore, Markov chain Monte Carlo (MCMC) simulation was used in generating the samples for the approximation of the posterior uncertainties. The measurements also provide not only reference values suitable for model fitting, but also a good insight into the main features of the dynamic behaviour of the railway track system. Finally, the reliability and accuracy of the proposed method are demonstrated by numerically reproducing the system responses. Modelling assumptions are discussed, together with their implications on numerical results of the time-domain analysis, which were found to be in good agreement with experimental results.