Discrete Element Modeling of Full-Scale Ballasted Track Dynamic Responses from an Innovative High-Speed Rail Testing Facility

Feng, Bin; Park, Eun Hyun; Huang, Haohang; Li, Wei; Tutumluer, Erol; Hashash, Youssef M. A.; Bian, Xuecheng

doi:10.1177/0361198119846475

Cited by 24 publications

(12 citation statements)

References 19 publications

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“…While gathering such data in the field is impossible, it is quite feasible in DEM simulations. As a proven methodology to simulate physical conditions, DEM has already been used widely in many track-related research studies ( 2 , 3, 33– 38 , 43 ). In our study, a one-crosstie ballast track model, similar to the one by Hou et al ( 2 ), was prepared to perform DEM simulations, and ballast particle responses were captured at every 2.90 × 10 −6 s time step during train passes for all the particles used in the model.…”

Section: Data-driven Ballast Layer Clusteringmentioning

confidence: 99%

Data-Driven Railway Crosstie Support Condition Prediction Using Deep Residual Neural Network: Algorithm and Application

Feng

Liu

Tutumluer

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Ballasted track substructure is designed and constructed to provide uniform crosstie support and serve the functions of drainage and load distribution over trackbed. Poor and nonuniform support conditions can cause excessive crosstie vibration which will negatively affect the crosstie flexural bending behavior. Furthermore, ballast–tie gaps and large contact forces at the crosstie–ballast interface will result in accelerated ballast layer degradation and settlement accumulation. Inspection of crosstie support condition is therefore necessary while very challenging to implement using current methods and technologies. Based on deep learning artificial intelligence techniques and a developed residual neural network (ResNet), this paper introduces an innovative data-driven prediction approach for crosstie support conditions as demonstrated from a full-scale ballasted track laboratory experiment. The discrete element method (DEM) is leveraged to provide training and testing data sets for the proposed prediction model. K-means clustering is applied to establish ballast layer subsections with representative ballast particles and provide additional insights on layer zoning for dynamic behavior trends. When provided with DEM simulated particle vertical accelerations, the proposed deep learning ResNet could achieve 100% training and 95.8% testing accuracy. Fed with vertical acceleration measurements captured by advanced “SmartRock” sensors from a full-scale ballasted track laboratory experiment, the trained model could successfully reach a high accuracy of 92.0%. Based on the developed deep learning approach and the research findings presented in this paper, the innovative crosstie support condition prediction system is envisioned to provide railroaders accurate, timely, and repeatable inspection and monitoring opportunities without disrupting railway network operations.

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Section: Data-driven Ballast Layer Clusteringmentioning

confidence: 99%

Data-Driven Railway Crosstie Support Condition Prediction Using Deep Residual Neural Network: Algorithm and Application

Feng

Liu

Tutumluer

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…Ballast particles undergo cyclic loading from trains [ 26 , 27 ]. The relative ballast movements produce fines, especially under high-speed train loading [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Review of Ground Penetrating Radar (GPR) Applications for Railway Ballast Inspection

Wang

Liu

Jing

et al. 2022

Sensors

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In the past 20 years, many studies have been performed on ballast layer inspection and condition evaluation with ground penetrating radar (GPR). GPR is a non-destructive means that can reflect the ballast layer condition (fouling, moisture) by analysing the received signal variation. Even though GPR detection/inspection for ballast layers has become mature, some challenges still need to be stressed and solved, e.g., GPR indicator (for reflecting fouling level) development, quantitative evaluation for ballast fouling levels under diverse field conditions, rapid GPR inspection, and combining analysis of GPR results with other data (e.g., track stiffness, rail acceleration, etc.). Therefore, this paper summarised earlier studies on GPR application for ballast layer condition evaluation. How the GPR was used in the earlier studies was classified and discussed. In addition, how to correlate GPR results with ballast fouling level was also examined. Based on the summary, future developments can be seen, which is helpful for supplementing standards of ballast layer evaluation and maintenance.

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“…The discrete element method (DEM) pioneered by Cundall and Strack [8] provides a powerful tool to explore the performance of granular materials from a particulate perspective [4, 10,25,29,43]. By adopting the DEM, Lim and McDowell [26] and Lobo-Guerrero and Vallejo [31] numerically studied the effect of ballast degradation on permanent deformation in the cubical box test with satisfactory results, albeit the simulated ballast particles are in the form of assumed simplified shapes.…”

Section: Introductionmentioning

confidence: 99%

A discrete element study on the deformation and degradation of coal-fouled ballast

et al. 2022

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This paper presents the results of Discrete Element Modelling (DEM) which quantitively examine the effect of coal fouling on the deformation and degradation of ballast upon cyclic loading. The degradation model described herein considers the Weibull distribution effects in tandem with a granular medium hardening law that incorporates the maximum contact criterion to capture surface abrasion and corner breakage of angular ballast. The DEM model had been calibrated initially with laboratory data obtained from large-scale direct shear testing. Subsequently, a series of cubical shear test simulations have been carried out using DEM to understand the behaviour of fouled ballast whereby the numerical particle degradation modelling could simulate the experimental response of the ballast assembly at various fouling levels. The results show that the increased level of fouling exacerbates the sleeper settlement, while decreasing the resilient modulus and the particle breakage. Ballast beneath the sleeper experiences significant breakage compared to the crib ballast, and not surprisingly, the extent of damage decreases with depth. Rigorous microscopic analysis is also presented in relation to inter-particle contacts, particle velocity and anisotropy of the ballast assembly. This micromechanical examination highlights that the decrease in ballast breakage for fouled assemblies is predominantly attributed to the inevitable decrease in inter-particle contact pressures as effected by the coating of ballast aggregates by the coal fines.

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Discrete Element Modeling of Full-Scale Ballasted Track Dynamic Responses from an Innovative High-Speed Rail Testing Facility

Cited by 24 publications

References 19 publications

Data-Driven Railway Crosstie Support Condition Prediction Using Deep Residual Neural Network: Algorithm and Application

Data-Driven Railway Crosstie Support Condition Prediction Using Deep Residual Neural Network: Algorithm and Application

State-of-the-Art Review of Ground Penetrating Radar (GPR) Applications for Railway Ballast Inspection

A discrete element study on the deformation and degradation of coal-fouled ballast

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