This paper explores hierarchical Bayesian models that can be used to predict rail track geometry degradation and thus guide planning maintenance and renewal actions. Hierarchical Bayesian models allow great flexibility in their specification, especially if they are combined with conditional autoregressive terms that can take into account spatial dependencies between model parameters. For rail track geometry degradation, conditional autoregressive terms are specified to tackle spatial interactions between consecutive rail track sections in rail track lines. An analysis of inspection, operation and maintenance data from the main Portuguese line (Lisbon–Oporto) motivates and illustrates the proposed predictive models. Inference is then conducted based on Markov Chain Monte Carlo (MCMC) simulation, which is proposed for fitting different model specifications. Finally, model comparison and a sensitivity analysis on prior distribution parameters are assessed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hierarchical Bayesian modelling of rail track geometry degradation




    Publication date :

    2013-07-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Hierarchical Bayesian modelling of rail track geometry degradation

    Andrade, Antnio Ramos | Online Contents | 2013


    Rail Track Geometry Measurement

    MOELLER RICHARD B | European Patent Office | 2017

    Free access

    Rail track geometry measurement

    MOELLER RICHARD B | European Patent Office | 2018

    Free access

    Track geometry degradation and maintenance modelling: A review

    Soleimanmeigouni, Iman / Ahmadi, Alireza / Kumar, Uday | SAGE Publications | 2018


    Track geometry degradation and maintenance modelling: A review

    Soleimanmeigouni, I | Online Contents | 2016