Modeling Degradation of Tracks for Maintenance Planning on a Tram Line

Yousefikia, Mohammad; Moridpour, Sara; Setunge, Sujeeva; Mazloumi, Ehsan

doi:10.12720/jtle.2.2.86-91

Cited by 37 publications

(32 citation statements)

References 18 publications

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“…The knowledge on degradation analysis and modelling of tram tracks is still rudimentary [17,18]. The adoption of best practices in the establishment of a modern tram track maintenance system is certainly preferable than starting the process from scratch.…”

Section: The Possibility Of Developed Models Application On Tram Systemsmentioning

confidence: 99%

Track Gauge Degradation Modelling on Small Urban Rail Networks: Zagreb Tram System Case Study

Ahac¹,

Lakušić²

2017

Urban Transport Systems

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Track gauge is the most significant tram track geometry parameter. Its degradation, which manifests as gradual increase of gauge deviation from prescribed values during track exploitation, causes poor ride quality, reduces safety and triggers most of the maintenance activities. To optimize tram track maintenance procedures, it is necessary to increase the proportion of preventive maintenance at the expense of corrective maintenance. This requires creation of physical model of track degradation. Conducted survey of conventional track degradation models showed that, in order to quantify the influence of track design, construction and exploitation characteristics on gauge degradation, it is most favourable to adopt the mechanistic-empirical modelling approach. Zagreb high-capacity tram network presents an optimal testing ground for exploration of the possibilities for tram track gauge degradation model development. Analysis of modelling results gave new, practical insights about the effects of tram track design and construction elements and exploitation characteristics on gauge degradation. These models represent the first step towards predictive maintenance system establishment on Zagreb tram tracks.

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Section: The Possibility Of Developed Models Application On Tram Systemsmentioning

confidence: 99%

Track Gauge Degradation Modelling on Small Urban Rail Networks: Zagreb Tram System Case Study

Ahac¹,

Lakušić²

2017

Urban Transport Systems

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“…Models based on Japanese studies [21 -24] Models based on Austrian studies analysing the development of track quality from a passenger's point of view [3,24,25].…”

Section: Mechanistic Modelsmentioning

confidence: 99%

“…rail type, sleeper type), time and rail geometry (including tamping activities) are the main variables used in stochastic modelling. Yousefikia et al [24] proposed a review of stochastic models based on their data analysis of rail tracks along tram routes in Melbourne, Australia. From this study's viewpoint, the rail track is considered to be regular when it carries out its function under operating conditions for a certain period of time; if this is not the case, the rail track fails [24].…”

Section: Stochastic Modelsmentioning

confidence: 99%

Degradation Prediction of Rail Tracks: A Review of the Existing Literature

Elkhoury¹,

Hitihamillage²,

Moridpour³

et al. 2018

TOTJ

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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.

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“…Data was collected from Melbourne tram network from 2009 to 2013, covering different types of segments of four routes such as straights, curves, H-crossings, and crossovers [12]. Out of these segments, curves were the focus since they have a higher failure rate than some other segments [13,14]; this section's data was utilized in case of light rail degradation modeling using a nonlinear method [15]. However, the results presented in previous researchs were far less accurate than what this paper has gained due to the higher nonlinear capability of ANFIS model.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Approach in Rail Track Degradation Prediction

Karimpour

Hitihamillage

Elkhoury

et al. 2018

Journal of Advanced Transportation

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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.

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Modeling Degradation of Tracks for Maintenance Planning on a Tram Line

Cited by 37 publications

References 18 publications

Track Gauge Degradation Modelling on Small Urban Rail Networks: Zagreb Tram System Case Study

Track Gauge Degradation Modelling on Small Urban Rail Networks: Zagreb Tram System Case Study

Degradation Prediction of Rail Tracks: A Review of the Existing Literature

Fuzzy Approach in Rail Track Degradation Prediction

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