The objective of this study has been to develop an approach to the allocation of an effective maintenance limit for track geometry maintenance that leads to a minimisation of the total annual maintenance cost. A cost model was developed by considering the cost associated with inspection, preventive maintenance, normal corrective maintenance and emergency corrective maintenance. The standard deviation and extreme values of isolated defects of the longitudinal level were used as quality indicators for preventive and corrective maintenance activities. The Monte Carlo technique was used to simulate the track geometry behaviour under different maintenance limit scenarios and the effective limit was determined which minimises the total maintenance cost. The applicability of the model was tested in a case study on the Main Western Line in Sweden. Finally, a sensitivity analysis was carried out on the inspection intervals, the emergency corrective maintenance cost and the maintenance response time. The results show that there is an optimal region for selecting an effective limit. However, by considering the safety aspects in track geometry maintenance planning, it is suggested that the lower bound of the optimal region should be selected.

This study has been dedicated to the optimization of opportunistic tamping scheduling. The aim of this study has been to schedule tamping activities in such a way that the total maintenance costs and the number of unplanned tamping activities are minimized. To achieve this, the track geometry tamping scheduling problem was defined and formulated as a mixed integer linear programming (MILP) model and a genetic algorithm was used to solve the problem. Both the standard deviation of the longitudinal level and the extreme values of isolated defects were used to characterize the track geometry quality and to plan maintenance activities. The performance of the proposed model was tested on data collected from the Main Western Line in Sweden. The results show that different scenarios for controlling and managing isolated defects will result in optimal scheduling plan. It is also found that to achieve more realistic results, the speed of the tamping machine and the unused life of the track sections should be considered in the model. Moreover, the results show that prediction of geometry condition without considering the destructive effect of tamping will lead to an underestimation of the maintenance needs by 2%.

The aim of this study has been to predict the track geometry degradation rate using artificial neural network. Tack geometry measurements, asset information, and maintenance history for five line sections from the Swedish railway network were collected, processed, and prepared to develop the ANN model. The information of track was taken into account and different features of track sections were considered as model input variables. In addition, Garson method was applied to explore the relative importance of the variables affecting geometry degradation rate. By analysing the performance of the model, we found out that the ANN has an acceptable capability in explaining the variability of degradation rates in different locations of the track. In addition, it is found that the maintenance history, the degradation level after tamping, and the frequency of trains passing along the track have the strongest contributions among the considered set of features in prediction of degradation rate.

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In order to evaluate the railway track geometry condition and plan maintenance activities, track inspection cars run over the track at specific times to monitor it and record geometry measurements. Applying an adequate inspection interval is vital to ensure the availability, safety and quality of the railway track, at the lowest possible cost. The aim of this study has been to investigate the effect of different inspection intervals on the track geometry condition. To achieve this, an integrated statistical model was developed to predict the track geometry condition given different inspection intervals. In order to model the evolution of the track geometry condition, a piecewise exponential model was used which considers break points at the maintenance times. Ordinal logistic regression was applied to model the probability of the occurrence of severe isolated defects. The Monte Carlo technique was used to simulate the track geometry behaviour given different inspection intervals. The results of the proposed model support the decision-making process regarding the selection of the most adequate inspection interval. The applicability of the model was tested in a case study on the Main Western Line in Sweden.