Bayesian Monte Carlo approach for developing stochastic railway track degradation model using expert-based priors

Abstract

In the present paper, a novel method is proposed to consider the inherent uncertainties in railway track degradation model using data from eliciting expert prior beliefs in the Bayesian framework. This leads the infrastructure manager to more integrated and updatable maintenance decisions. Despite the extensive observation of this phenomenon in the literature, there are still growing appeals to consider the effect of several variables on the degradation model. Moreover, implementing prior experience and data in maintenance decisions have rarely been studied directly. To illuminate this uncharted area, Bayesian approach with informative priors is put forward. Since experts are thoroughly conversant with the actual behaviour of the system, systematic questionnaires were designed to elicit their experience comprehensively and to construct informative priors. Subsequently, by collecting further inspection data from the network, posterior distributions for the parameters have been estimated. Accordingly, the probability that a track section hits the threshold values in a specific time is calculated using Monte Carlo simulations. To show the feasibility, comprehensive case studies were presented, and the potential impacts of this methodology in outsourcing maintenance contracts are highlighted. The results have significant implications on the allocation of limited budget and resources for scheduling predictive maintenance actions.