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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Investigation of the Manufacturability of Smart Composite Piping Structures Using Life Cycle Cost Modeling and Uncertainty Analysis

Engineering Technology Conference on Energy, Parts A and B ◽

10.1115/etce2002/cmda-29070 ◽

2002 ◽

Cited By ~ 1

Author(s):

Rahul R. Maharsia ◽

H. Dwayne Jerro

Keyword(s):

Life Cycle ◽

Uncertainty Analysis ◽

Life Cycle Cost ◽

Cost Model ◽

Cost Comparison ◽

Inspection System ◽

Pipe Failure ◽

Maintenance Costs ◽

Comparison Model ◽

Reliable Monitoring

FRP composites are finding increasing use in the civilian applications such as highways, bridges, pipes etc. This analysis focuses on the FRP piping systems used in the Petrochemical industries under extreme conditions. Due to the high operational and maintenance costs involved with pipes made from traditional materials, there is a need to develop a smart inspection system that replaces or eliminates the traditional inspection and maintenance techniques, providing continuous and reliable monitoring of the structure. Smart FRP pipes have an embedded smart sensor system incorporated in them, providing continuous and reliable monitoring of the pipe structure. This helps in preventing catastrophic failure of pipes thereby reducing the costs involved with the pipe failure. Smart FRP systems have a very high initial investment cost, and therefore a cost comparison model is needed in order to justify their use against traditionally used materials. A Life Cycle Cost (LCC) comparison model has been developed in this paper, which shows that despite high initial investment costs, large savings could be made in the operational and maintenance costs with the use of Smart FRP pipes. This cost model Calculates the life cycle costs of Steel, FRP and Smart FRP pipes, and determines the alternative with the lowest life cycle cost. To deal with an uncertainty associated with the cost factors, used in calculating the LCC of the three alternatives, an uncertainty analysis has been performed. An computer spreadsheet has been programmed in order to perform the LCC and Uncertainty Analysis. This analysis has laid down the basic foundations for a larger cost model, wherein; several other alternatives materials and factors could be included. This would further help in widening the scope of use of Smart Structures in various industries. Certain aspects of the data used in this analysis may be disputable, however for the purpose of modeling and procedural demonstration, the gathered and available information was used to perform our analysis. Therefore, use of this data outside of the scope and context of this report is not warranted.

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