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

Finally, the results show that at the design stage, the uncertainty associated with degradation rates is very high, but it reduces drastically as more inspection data is collected. Significant impacts on the definition of maintenance cost allocation inside railway business models are discussed, especially for the case of Public and Private Partnerships. Moreover, potential impacts of this methodology in maintenance contracts are highlighted. For the case of a new infrastructure, it is proposed that maintenance costs assessments related to track geometry degradation are no longer assessed at the design stage based only on the prior probability distributions of the degradation model parameters, but renegotiated instead after a 'warm-up' period of operation based on their posterior probability distributions.