The lateral stability of ballasted railway tracks is a function of the lateral resistance of the sleepers created by interaction with ballast materials. Thus, one of the approaches for increasing the lateral resistance of sleepers has been to increase bottom friction and use frictional sleepers. A review of the technical literature showed that numerous experimental studies have been performed on this type of sleeper; however, no numerical analysis has been conducted on its lateral resistance. Therefore, this paper developed a finite element numerical model for investigating frictional concrete sleepers. In this regard, a hardening elasto-plastic behavior model was developed for the ballast layer and ABAQUS software was used to numerically analyze the lateral resistance of this type of concrete sleeper. Using the developed model, some sensitivity analyses were performed on the parameters that affect the lateral resistance including the thickness and extent of the ballast shoulder, and the friction coefficient between the ballast layer and sleeper. The obtained results indicated that increasing the ballast shoulder from 25 to 40 cm resulted in about a 16-22% increase in lateral resistance, whereas increasing the friction coefficient from 0.1 to 0.8 led to about a 22-28% increase in the lateral resistance. On the other hand, on decreasing the ballast layer thickness from 30 to 20 cm, the lateral resistance increased by about 12-17%. In summary, it can be concluded that, compared with conventional concrete sleepers, frictional sleepers increased the lateral resistance by about 63-70%.

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The aim of this study has been to determine the optimal maintenance limits for one of the main railway lines in Iran in such a way that the total maintenance costs are minimized. For this purpose, a cost model has been developed by considering costs related to preventive maintenance activities, corrective maintenance activities, inspection, and a penalty costs associated with exceeding corrective maintenance limit. Standard deviation of longitudinal level was used to measure the quality of track geometry. In order to reduce the level of uncertainty in the maintenance model, K-means clustering algorithm was used to classify track sections with most similarity. Then, a linear function was used for each cluster to model the degradation of track sections. Monte Carlo technique was used to simulate track geometry behavior and determine the optimal maintenance limit which minimizes the total maintenance costs. The results of this paper show that setting an optimal limit can affect total annual maintenance cost about 27 to 57 percent.

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