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 well-structured 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
Modern transport organizations have shifted their focus from construction and expansion of the transport infrastructure towards how to intelligently maintaining them. This has taken place due to many reasons such as budget restrictions and running out of land space. Transport organizations currently focus on exploring the solutions for developing a maintenance management system that will help them accurately predict the time and location where maintenance should be carried out. This will assist transport infrastructure authorities in optimizing the cost and time of maintenance.
Different types of degradation prediction models have been presented in the literature, and these models have been mainly developed for the heavy rail system. Since there are differences in the structure and performance of heavy and light rail systems, it is impossible to use such degradation prediction models to predict the degradation of the light rail system. Consequently, it is needed to develop models which are capable of predicting the degradation of light rail tracks. This particular research will focus on developing a degradation prediction model for light rail network with the focus on tram network of Melbourne, Australia. The map of the current Melbourne tram network is shown in Figure
Melbourne tram network.
The maintenance data of the Melbourne tram network has been collected through on-sight inspection and stocked in a nondigitized way for a long time. Traditionally, the rail maintenance used to be planned based on the experience of experts in the field. This procedure has changed since the introduction of new rail track inspection vehicles. These vehicles run through rail tracks and detect a large amount of data related to gauge and twist values from infrastructure condition. Based on this data, prediction models will be developed to predict the degradation of rail tracks and estimate the maintenance procedures needed in the future.
In this paper, a review of the previous models on rail degradation prediction is presented. Afterwards, an ANFIS model is proposed to predict the Melbourne tram track degradation. Results from the models for both curves and straight sections are presented then. Finally, conclusions of this research and directions for further work on this topic are presented.
According to the literature, the models which have been used to predict rail track degradation could be categorized into three main categories including mechanistic, statistical, and artificial intelligence (AI) models. Out of these three main types, some of them are further categorized into subcategories as in Figure
Rail track degradation prediction models.
Previous studies on rail track degradation have represented some models that are capable of predicting degradation using a common set of parameters such as the age of the rail, axle load in Million Gross Tone (MGT), speed, and track curvature.
The mechanistic approach is the oldest model type in predicting the rail track degradation. Mechanistic models are based on the knowledge and understanding of the behaviour of the mechanical components. Mechanistic models involve establishing the mechanical properties either by theory or by testing. These types of models could mainly develop based on laboratory experiment data or collecting data from the field by observing the sections that will be used for modeling for a long period of time. Since such type of models has the capability of predicting rail track settlement and degradation with greater accuracy, according to the literature that is one of the key strengths of this type of models. So this is a type of model more suitable for utilizing in a situation where just one particular section of the track needed to be repaired. However, with this positive, there are few drawbacks that limit the possibility of using this type of models for predicting the future degradation and settlement values. The main drawback of this type of models is that it will not provide predictions with greater accuracy when you try to apply the model to the different sections of the rail network. The reason behind this is because the mechanical properties of the track and external factors they face may vary from place to place. Since it requires an extensive amount of data to develop a model small section of the rail track, developing a model for different sections of the network could be challenging and very time-consuming. Some of the models developed by the Japanese researchers [
The statistical models are based on the data collected from monitoring the track performances and the variables affecting such performances (e.g., traffic, rail type, and maintenance data). Those variables are used as inputs to develop a model which predicts track degradation. Statistical models provide the ability to deal with the considerably large amount of datasets when developing degradation models and they provide more accurate results when applied to an entire rail network compared to a mechanistic model. However, it also has some downsides such as being able to create a large database which these types of models require due to the lack of availability of historical track data. Some of these statistical models could be further categorized into deterministic [
The artificial intelligence models are a group of machine learning models which primarily could be categories into two groups’ including artificial neural networks (ANNs) and neurofuzzy models. Artificial neural networks were originally inspired by the biological neural networks which are present in the human brain. These models are both simple computational devices which are highly interconnected, and in both models the connections between neurons determine the function of the network. Although ANNs are quite new to tram track degradation predictions, they have been extensively used in other fields of engineering [
In this research, at first the data are preprocessed, and all the outliers are eliminated. Following that, a fuzzy model is proposed due to the nonlinear and noisy nature of the data according to the data which were available on the Melbourne tram network. In the next stage, the model is validated on the test samples. The final year gauge values have been considered as the measured value for both training and testing experiments. Model validation/testing was done by comparing the final year gauge values as observed values and the gauge values for the final year estimated by the model.
Since gauge value represents the physical shape of railways, it can be considered as a factor which comprises various important features of rail degradation in its nature. Thus, rail degradation maintenance scheduling could be done by considering gauge degradation as a paramount factor. In this paper, gauge value degradation prediction modeling is done which could be a breakthrough into degradation hotspot detection which leads to lower amount of investment in both rail monitoring and preventive maintenance.
Melbourne tram network is the world’s largest urban operating tram network and covers 250 km of double tracks which includes 25 routes. There are 1700 stops across the network spread out with more than 400 level access stops. Seventy-five percent of the Melbourne’s tram network operates on shared roads with other vehicles and manages to provide a service with a punctuality percentage around the high seventies to mid-eighties. To meet the current demands of the rapid growth of Melbourne city, its iconic tram network also needs to evolve accordingly. Along with the increase in Melbourne tram patronage, it has been understood that the expenses related to rail infrastructure maintenance have grown gradually and constantly.
Rail track degradation occurs due to many reasons. Rail vehicles travel at various speeds while carrying various loads. This will cause a wide range of stresses on the rail structure resulting in its decay. When it comes to the light rail tracks, the degradation of the track occurs due to few more other reasons such as the damage occurring by road sharing and weathering due to climate change. Since all these changes influence the decay and are embedded in gauge value, it is considered as the key value for the degradation. Gauge value is defined as the distance between the inner sides of two rails on a railway system.
In this particular study, data are provided by Yarra Trams (which is the operator of the Melbourne tram network and is responsible authority for its maintenance). This data is utilized to develop a degradation model which will provide the ability to predict the future gauge values. The dataset which was provided by them for this research comprises different section types including curves, straight sections, H-crossing, and crossovers. The gauge values have been collected by Yarra Trams for the whole tram network at different years. In addition, curve radius, annual tonnage in Million Gross Tone (MGT), track surface (asphalt and concrete surfaces), rail profile (the cross-sectional shape of a rail which is represented by kilogram per metre), rail type (grooved and T-shapes), rail support (or rail ties categorized into concrete and steel sleepers), location of routes, and track installation date have also been provided by Yarra Trams. These data were gathered over a period of 6 years from 2009 to 2015. Curve and straight sections where the two major groups of the track sections used in this study to analyze and develop the degradation prediction model since they represent the majority of the tram network.
In this paper, ANFIS is used to estimate the gauge value for
An adaptive network can be considered as Figure
Simple adaptive network.
In this paper, 2,700 gauge observations were used to train the system. The input data are gauge values for
Membership function of the antecedents, i.e., the gauge values for
In this article firstly the datasets are randomly divided into training and testing datasets; following that ANFIS model is utilized using 5 membership functions for trio inputs. The number of membership functions and their shape are selected to have the Least Mean Squared Error (MSE) and higher
The observed and predicted age values for the test data (30% of the data).
Curve sections
Straight sections
As shown in Figure
Mean and standard deviation for curves and straights. Real data is abbreviated as rd and estimated data is abbreviated as ed.
Curves | Straights | |
---|---|---|
| 1.3579 | 2.8207 |
| 0.9766 | 2.4594 |
| 3.5320 | 3.9185 |
| 3.6160 | 4.0932 |
Table
To show the accuracy of the model, observed gauge values versus the predicted gauge values are plotted in Figure
The observed versus predicted gauge values.
Curve sections
Straight sections
Moreover, Figure
As Table
Statistical parameters of model.
Criteria | Curves | Straights |
---|---|---|
| 0.6001 | 0.7808 |
MSE | 0.7350 | 1.7335 |
Total Samples | 3860 | 3860 |
Training Samples | 2700 | 2700 |
Testing Samples | 1160 | 1160 |
Number of inputs | 3 | 3 |
Predicting rail track degradation on time and carrying out maintenance accordingly are a plan that all the major authorities that are responsible for maintaining and managing the rail networks try to implement right across the world. The intention behind this exercise is to improve the cost efficiency by reducing the unnecessary costs related to carrying out maintenance work too early or too late. There are many types of research that have been done on developing such models for heavy rail tracks. However, there is a need for a proper model which can predict the degradation in light rail tracks. In this paper, firstly the raw data was captured by connecting the MATLAB to the database, then it is preprocessed, and the outliers are eliminated due to the messy structure of the data environment. Following that to cover all noisy environment ANFIS model was utilized; the model has five membership functions in the antecedent for all three inputs and is put forward to model rail track degrading using the data for Melbourne tram network between 2010 and 2015. The data is consisting of gauge values for two previous years and the MGT value. For modeling the system 70% of the data is used for the training purpose, and the remaining was used for testing the system. Results show that the model can predict the gauge values for the coming year by the
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to acknowledge the support and collaboration of Yarra Trams for providing this research with the required data.