Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT
Volume 227, Issue 4, Pages 364-375Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954409713486619
Keywords
Track degradation; track geometric quality; uncertainty; life cycle; Bayesian model
Funding
- Portuguese Foundation for Science and Technology (FCT) [PTDC/SEN-TRA/112975/2009]
- MIT Portugal (FCT) [SFRH/BD/33785/2009]
- Fundação para a Ciência e a Tecnologia [PTDC/SEN-TRA/112975/2009] Funding Source: FCT
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available