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.

This study has been dedicated to the optimization of opportunistic tamping scheduling. The aim ofthis study has been to schedule tamping activities in such a way that the total maintenance costs andthe number of unplanned tamping activities are minimized. To achieve this, the track geometry tampingscheduling 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 thelongitudinal level and the extreme values of isolated defects were used to characterize the trackgeometry quality and to plan maintenance activities. The performance of the proposed model wastested on data collected from the Main Western Line in Sweden. The results show that different scenariosfor controlling and managing isolated defects will result in optimal scheduling plan. It is alsofound that to achieve more realistic results, the speed of the tamping machine and the unused life ofthe track sections should be considered in the model. Moreover, the results show that prediction ofgeometry condition without considering the destructive effect of tamping will lead to an underestimationof the maintenance needs by 2%.

A large part of the railway track geometry maintenance burden concerns local maintenance activates conducted to rectify isolated defects. Isolated defects are short irregularities in the track geometry that can dramatically increase the dynamic forces between the wheel and rail, which in turn will accelerate the growth or occurrence of internal rail defects. The dynamic force between the wheel and rail is dependent on the shape of the isolated geometry defects. However, the severity of isolated defects is mainly defined only by their amplitude. Therefore, in addition to amplitude, other characteristics of geometry defects must be considered to analyze severity of defects and to prioritize local maintenance actions. This study aims to use the first- and second-order derivatives of the longitudinal level defects to plan local maintenance activities. The derivatives of geometry defects provide useful information about the shape of defects. The information is used to categorize the isolated defects based on their severities and prioritize maintenance actions. In this regard, K-means clustering technique is applied. The results of this study will support the decision-making process regarding the planning of local maintenance activities. The foot-by-foot track geometry data collected from Main Western Line in Sweden is used to implement and test the model.

A great part of the cost of railway maintenance is related to the track geometry. Reliable and complete track geometry data form the foundation of an efficient and effective condition-based maintenance strategy. Generally, track geometry measurement data suffer from positional errors. Because the aim of track geometry degradation modeling has been to analyze the evolution of location-specific defects over time, accurate information on the position of defects is of crucial importance. Therefore, collected track geometry measurements in different inspection runs need to be pre-processed before they are used to model geometry degradation or implement a condition-based maintenance strategy. In this study, three relative position-based approaches are applied to align the positional measurement data obtained from different inspection runs. The two main types of data positional errors are shifted and stretched waveforms. In this regard, three time series alignment algorithms, i.e., crosscorrelation function, dynamic time warping, and dynamic time alignment kernel, are applied to align both shifted and stretched waveforms. Foot-by-foot track geometry data obtained from the main Western line in Sweden are used to implement and test the models. On the basis of the results, dynamic time warping outperforms the other two techniques to align shifted and stretched datasets.

Track geometry degradation is a function of several interacting factors (e.g. traffic, environmental conditions, track structure, and maintenance history) and each interaction may follow several interrelating rules. Artificial Neural Network (ANN) showed a high capability in prediction of track geometry degradation, by analysing a set of features that explain the behaviour of geometry evolution. The aim of this study is to develop an approach using ANN to predict the degradation of track geometry along a track line. In the ANN model, maintenance history, curvature, annual traffic, presence of level cross, speed, frequency of trains passing along the track and material type and age are inputs to the model and track geometry degradation rate is the output of the model. A set of data from railway network in Sweden are used to train and validate the model. The results indicate that ANN is able to predict the degradation of track geometry.

Railway track geometry subjects to degradation with age and usage and highly affects track functionality. An accurate prediction of track geometry degradation is essential to enhance the performance of maintenance planning and scheduling. In this study, artificial neural network is used to predict track geometry degradation rate. The data of five lines from Swedish railway network are used to develop the model. The standard deviation of the longitudinal level is used as quality indicator for track geometry degradation. For all track sections, a set of features which may contribute in track geometry degradation are collected. Principal component analysis method is used to reduce the dimension of the features to facilitate the training of the ANN model. Then, the new principal features considered as inputs to ANN model. The results indicate that the proposed method can be used to predict track geometry degradation along the track line.

Accurate prediction of track geometry degradation is essential for an efficient track geometry maintenance planning and scheduling. Track geometry prediction is a complex task as many quantitative and qualitative parameters affect track geometry degradation. Artificial neural networks (ANNs) have shown a great capability in prediction of such complex systems, although they are not complicated. In this paper, a three-layered feedforward network is employed to predict track geometry degradation for each track section with-in a track line for a period of two years. A set of influencing factors along with track geometry degradation history are used as inputs to the model. The weight and bias values in the ANN model are optimised using Levenberg and Marquardt optimization algorithm.  Data from the Swedish railway network are used to train and verify the proposed model. The relative importance of input parameters on track geometry degradation is determined by Garson`s algorithm. The results indicate that ANN is able to predict the future state of the track in next two years even in the existence of tamping activity within the degradation history.