Uncertainty in Rail-Track Geometry Degradation: Lisbon-Oporto Line Case Study

Andrade, António R.; Teixeira, Paulo Fonseca

doi:10.1061/(asce)te.1943-5436.0000206

Cited by 61 publications

(36 citation statements)

References 6 publications

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“…Based on the results of the model for the unrepaired curve segments (Table 3), −1 and are significant at a 95 percent confidence level ( value is less than 0.05) to estimate These results are consistent with previous findings on rail track degradation models [25,26] which demonstrate that geometry condition of a track within its life time is strongly dependent on its initial level. Also these studies [2,27] support the results of this research about the influence of rail type and track surface on rail track degradation.…”

Section: Regression Analysessupporting

confidence: 80%

Rail Degradation Prediction Models for Tram System: Melbourne Case Study

Falamarzi

Moridpour

Nazem

et al. 2018

Journal of Advanced Transportation

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Tram is classified as a light rail mode of transportation. Tram tracks experience high acceleration and deceleration forces of locomotives and wagons within their service life and also share their route with other vehicles. This results in higher rates of degradation in tram tracks compared to the degradation rate in heavy rail tracks. In this research, gauge deviation is employed as a representative of track geometry irregularities for the predication of the tram track degradation. Data sets used in this research were sourced from Melbourne's tram system. For model development, the data of approximately 250 km of tram tracks are used. Two different models including a regression model and an Artificial Neural Networks (ANN) model have been applied for predicting tram track gauge deviation. According to the results, the performances of the regression models are similar to the ANN models. The determination coefficients of the developed models are above 0.7.

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Section: Regression Analysessupporting

confidence: 80%

Rail Degradation Prediction Models for Tram System: Melbourne Case Study

Falamarzi

Moridpour

Nazem

et al. 2018

Journal of Advanced Transportation

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“…In fact, this idea followed from an initial exploratory work previously conducted in [15], showing that spatial correlation between deterioration rates and initial quality were statistically significant. In that sense, none of the previous statistical models takes advantage of these spatial correlations between deterioration rates and the initial qualities for consecutive track sections, in order to improve the current predictive models.…”

Section: -Current Limitations In Statistical Modelling Of Railway Tramentioning

confidence: 99%

Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models

Andrade

Teixeira

2015

Reliability Engineering & System Safety

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Railway maintenance planners require a predictive model that can assess the railway track geometry degradation.The present paper uses a hierarchical Bayesian model as a tool to model the main two quality indicators related to railway track geometry degradation: the standard deviation of longitudinal level defects and the standard deviation of horizontal alignment defects. Hierarchical Bayesian Models (HBM) are flexible statistical models that allow specifying different spatially correlated components between consecutive track sections, namely for the deterioration rates and the initial qualities parameters. HBM are developed for both quality indicators, conducting an extensive comparison between candidate models and a sensitivity analysis on prior distributions. HBM is applied to provide an overall assessment of the degradation of railway track geometry, for the main Portuguese railway line Lisbon-Oporto.

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“…10 defects commonly appeared in railway networks reside in (Coenraad Esveld 2001). Eight data samples, each corresponding to a short time series for an individual plain track, extracted from (Andrade and Teixeira, 2011) were used in the testing. A time series has a length of 14 independent observations (data points) representing a standard deviation of the chosen parameter for a 200-meter track segment.…”

Section: An Examplementioning

confidence: 99%

Uncertainty Propagation Assessment in Railway-Track Degradation Model Using Bayes Linear Theory

Osman

Kaewuruen

2018

J. Transp. Eng., Part A: Systems

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Uncertainty in Rail-Track Geometry Degradation: Lisbon-Oporto Line Case Study

Cited by 61 publications

References 6 publications

Rail Degradation Prediction Models for Tram System: Melbourne Case Study

Rail Degradation Prediction Models for Tram System: Melbourne Case Study

Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models

Uncertainty Propagation Assessment in Railway-Track Degradation Model Using Bayes Linear Theory

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