Stochastic rail life cycle cost maintenance modelling using Monte Carlo simulation

The need for decision support systems to guide maintenance and renewal decisions for infrastructure is growing due to tighter budget requirements and the concurrent need to satisfy reliability, availability and safety requirements. The rail of the railway track is one of the most important components of the entire track structure and can significantly influence maintenance costs throughout the life cycle of the track. Estimation of life cycle cost is a popular decision support system. A calculated life cycle cost has inherent uncertainty associated with the reliability of the input data used in such a model. A stochastic life cycle cost model was developed for the rail of the railway track incorporating imperfect inspections. The model was implemented using Monte Carlo simulation in order to allow quantification of the associated uncertainty within the life cycle cost calculated. For a given set of conditions, an optimal renewal tonnage exists at which the rail should be renewed in order to minimise the mean life cycle cost. The optimal renewal tonnage and minimum attainable mean life cycle cost are dependent on the length of inspection interval, weld type used for maintenance as well as the cost of maintenance and inspection activities. It was found that the distribution of life cycle cost for a fixed renewal tonnage followed a log-normal probability distribution. The standard deviation of this distribution can be used as a metric to quantify uncertainty. Uncertainty increases with an increase in the length of inspection interval for a fixed rail renewal tonnage. With all other conditions fixed, it was found that the uncertainty in life cycle cost increases with an increase in the rail renewal tonnage. The relative contribution of uncertainty of the planned and unplanned maintenance costs towards the uncertainty in total life cycle cost was found to be dependent on the length of inspection interval.

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  • English

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Filing Info

  • Accession Number: 01667734
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 6 2018 3:45PM