In the past few decades, the railway infrastructure has been widely expanded in urban and rural areas, making it the most complex matrix of rail transport networks. Safe and comfortable travel on railways has always been a common goal for transportation engineers and researchers, and requires railways in excellent condition and well-organized maintenance practices. Degradation of rail tracks is a main concern for railway organizations as it affects the railway’s behaviour and its parameters, such as track geometry, speed, traffic and loads. Therefore, the prediction of the degradation of rail tracks is very important in order to optimise maintenance needs, reduce maintenance and operational costs of railways, and improve rail track conditions.This paper provides a comprehensive review of rail degradation prediction models, their parameters, and the strengths and weaknesses of each model. A comprehensive discussion of existing research and a comparison of different models of degradation of rail tracks is also provided. Finally, this review presents concluding remarks on the limitations of existing studies and provides recommendations for further research and appraisal practices.

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work. With the restrictions on financial support, the rail transport authorities are in pursuit of improved modern methods, which can provide a precise prediction of rail maintenance timeframe. The expectation from such a method is to develop models to minimise the human error that is strongly related to manual prediction. Such models will help rail transport authorities in understanding how the track degradation occurs at different conditions (e.g., rail type, rail profile) over time. They need a wellstructured technique to identify the precise time when rail tracks fail to minimise the maintenance cost/time. The rail track characteristics that have been collected over the years will be used in developing a degradation prediction model for rail tracks. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use the data in prediction model development. An accurate model can play a key role in the estimation of the long-term behaviour of rail tracks. Accurate models can increase the efficiency of maintenance activities and decrease the cost of maintenance in long-term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curves and straight sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. The results from the developed model show that it is capable of predicting the gauge values with 2 of 0.6 and 0.78 for curves and straights, respectively.

No abstract