A Bayesian model to assess rail track geometry degradation through its life-cycle

One of the major drawbacks in rail track investments is the high level of uncertainty in maintenance, renewal and unavailability costs for the Infrastructure Managers (IM) during the life-cycle of the infrastructure. Above all, rail track geometry degradation is responsible for the greatest part of railway infrastructure maintenance costs. Some approaches have been tried to control the uncertainty associated with rail track geometry degradation at the design stage, though little progress has improved the investors' confidence. Moreover, many studies on rail track life-cycle cost modelling tend to forget the dynamic perspective in uncertainty assessments and do not quantify the expected reduction of the uncertainty associated with degradation parameters as more inspection data is collected after operation starts.