Influences of ballast degradation on railway track buckling

Ngamkhanong, Chayut; Kaewunruen, Sakdirat; Baniotopoulos, Charalampos

doi:10.1016/j.engfailanal.2021.105252

Cited by 15 publications

(5 citation statements)

References 38 publications

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“…It is clear from this figure that increasing misalignment tends to shift the buckling mode from snap-through buckling to progressive buckling for all cases of unconstrained length since the buckling temperatures are quite close to the safe temperatures. These results agree well with the previous FE results [33,34].…”

Section: Figure 7 Multilayer Network With Weight Matrixsupporting

confidence: 94%

“…The datasets have been collected from the previous numerical studies [33,34]. Table 1 presents the input parameters considered in this study.…”

Section: Data Collectionmentioning

confidence: 99%

“…This paper presents the optimal architecture for estimating the safe and buckling temperatures of ballasted railway tracks. The datasets have been collected from the previous numerical studies based and are input into different ANN models to quantify the performances and obtain the best model [33,34]. The optimal ANN models are found to be capable of managing the complicated relationships between the inputs and outputs and of designing track components to encounter the extreme temperature.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Thermal-Induced Buckling Failures of Ballasted Railway Tracks Using Artificial Neural Network (ANN)

Ngamkhanong

Kaewunruen

2022

Int. J. Str. Stab. Dyn.

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This paper investigates the possibility for implementing machine learning-aided prediction in analyzing the buckling phenomena of ballasted railway tracks induced by extreme temperature. In this study, artificial neural networks (ANNs) have been developed to identify the relationship between various ballasted track conditions and outputs, namely safe temperature and buckling temperature. The variables included in the objective function of the optimization problems are the lateral resistance of ballasted track provided by ballast-sleeper interaction, torsional resistance provided by fastening systems, and misalignment of the track. Due to its complexity in parameter combinations, the objective of this study is to create predictive models with the aim of minimizing the usage of scarce resources. Thus, this paper is the first to develop a novel machine learning-aided prediction of railway track buckling due to extreme temperature. Comprehensively, all 353 datasets of the safe and buckling temperatures derived from previous finite element (FE) simulation results have been collected and trained. Note that the mean squared error (MSE) and the coefficient of determination ([Formula: see text] are considered to quantify the performance of the ANN architectures. The optimal ANN architecture with a very high rate of accuracy has been determined and highlighted. Thus, the suggested neural network model can be applied conveniently to help estimate safe and buckling temperatures of the complex track models in order to improve track conditions and thus prevent track buckling in summer.

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Section: Figure 7 Multilayer Network With Weight Matrixsupporting

confidence: 94%

“…The datasets have been collected from the previous numerical studies [33,34]. Table 1 presents the input parameters considered in this study.…”

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Thermal-Induced Buckling Failures of Ballasted Railway Tracks Using Artificial Neural Network (ANN)

Ngamkhanong

Kaewunruen

2022

Int. J. Str. Stab. Dyn.

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“…As examples, the influence of sleeper shape was investigated in [6] using numerical simulations, and in [7] using both experiments and simulations. In [8] the influence of ballast fouling was investigated through finite element simulations that featured lateral track resistance for fouled ballast estimated from discrete element simulations. Finite element analyses have also been employed to study the effect of mixed timber and concrete sleepers [9].…”

Section: Introductionmentioning

confidence: 99%

“…In ref. 8 the influence of ballast fouling was investigated through finite element simulations that featured lateral track resistance for fouled ballast estimated from discrete element simulations. Finite element analyses have also been employed to study the effect of mixed timber and concrete sleepers.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of track buckling resistance

Kabo,

Ekberg

2024

Proceedings of the Institution of Mechanical Engineers, Part F:

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This study sets out with a network-wide analysis of the risk for track buckling that concludes that track buckling relates to a few “weak points” along the track. To identify these, a numerical model to predict track buckling resistance is developed. The influences of key track parameters are evaluated and quantified using the innovative concept of an equivalent temperature. This allows to quantify the large influence of for example, curves and hanging sleepers in terms of an equivalent increase in temperature. Influence of less well-defined track parameters such as nearby fixed points or recent track work is estimated through statistical assessment of track buckling reports. Predicted influence generally agrees with empirical knowledge with some exceptions that are discussed in the report. Developed models and produced results should be valuable in identifying track sections at risk of track buckling and in assessing effectiveness of mitigating actions.

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Data driven machine learning prognostics of buckling failure modes in ballasted railway track

Wongkaew,

Muanyoksakul,

Ngamkhanong

et al. 2024

Discov Appl Sci

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This study explores the development and application of a machine learning (ML) approach to predict buckling failure modes in ballasted railway tracks. With the growing demand for safer and more reliable railway systems, the ability to foresee and mitigate track failures is of paramount importance. Our study focuses on harnessing advanced ML algorithms to analyse and interpret complex data sets, aiming to identify potential buckling failures before they occur. The methodology employed involves collecting extensive data from previous advanced numerical studies. Faced with the inadequacy of field data collection on track buckling and the limited availability of data related to track conditions, our study has relied on simulation data for insight and analysis. This data is then processed and analysed using sophisticated ML models, trained to recognise patterns and anomalies indicative of potential buckling failures. A novel aspect of our approach is the integration of environmental factors, acknowledging their significant influence on the likelihood of both snap-through and progressive buckling in railway tracks. We compare the effectiveness of various ML algorithms in accurately predicting these failure modes, evaluating their performance in simulated and real-world scenarios. The findings demonstrate the models' proficiency in identifying early signs of both snap-through and progressive buckling, leading to timely interventions. This capability not only improves railway safety but also aids in efficient maintenance scheduling and asset management. Additionally, a case study in Thailand's railway system demonstrates the model's effectiveness in predicting buckling failures under tropical environmental conditions. This paper contributes a novel perspective to the field of railway infrastructure maintenance. By providing a reliable method for predicting specific buckling failure modes, it paves the way for enhanced operational safety and efficiency in railway networks, particularly in the face of dynamic environmental conditions.

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Influences of ballast degradation on railway track buckling

Cited by 15 publications

References 38 publications

Prediction of Thermal-Induced Buckling Failures of Ballasted Railway Tracks Using Artificial Neural Network (ANN)

Prediction of Thermal-Induced Buckling Failures of Ballasted Railway Tracks Using Artificial Neural Network (ANN)

Characterisation of track buckling resistance

Data driven machine learning prognostics of buckling failure modes in ballasted railway track

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