Zhongyi Liu

Railroad ballast is uniformly graded coarse aggregate placed between and immediately underneath the crossties. The purpose of ballast is to provide drainage and structural support in a ballasted track. As ballast ages, degradation takes place under increased traffic when aggregate particles become more rounded and smaller in size as a result of abrasion and breakage. In this study, 10-crosstie train-track discrete element method (TT-DEM) models, containing up to 160,203 particles for clean ballast and 942,472 for degraded ballast, were generated to investigate the effect of ballast degradation on track dynamics caused by the passage of the ACELA Express passenger train used in North America. Realistic gradations and particle shape properties of clean and degraded ballast were adopted to build the TT-DEM models. Through a comparative analysis of the results of clean and degraded ballast models, the following key observations were made: 1) crossties in the degraded model experienced 30% smaller displacements and slightly lower vibration velocities as a result of higher ballast layer density and stiffness; 2) degraded ballast had more particles greater than 5 mm (0.2 in.) that indicated large movements when compared those in the clean ballast; 3) a critical particle size of 25.4 mm (1 in.) could classify the primary and secondary load-carrying structures in both clean and degraded ballast; and 4) for ballast in the crib area, two times wider ranges of principal stress rotation were observed for clean ballast than the degraded. In conclusion, the TT-DEM models developed in this study captured realistic track dynamic behavior trends much better than the individual train-track or discrete element method models.

Nonuniform track support and differential settlements are commonly observed in bridge approaches where the ballast layer can develop gaps at crosstie-ballast interfaces often referred to as a hanging crosstie condition. Hanging crossties usually yield unfavorable dynamic effects such as higher wheel loads, which negatively impact the serviceability and safety of railway operations. Hence, a better understanding of the mechanisms that cause hanging crossties and their effects on the ballast layer load-deformation characteristics is necessary. Since the ballast layer is a particulate medium, the discrete element method (DEM), which simulates ballast particle interactions individually, is ideal to explore the interparticle contact forces and ballast movements under dynamic wheel loading. Accurate representations of the dynamic loads from the train and track superstructure are needed for high-fidelity DEM modeling. This paper introduces an integrated modeling approach, which couples a single-crosstie DEM ballast model with a train–track–bridge (TTB) model using a proportional–integral–derivative control loop. The TTB–DEM model was validated with field measurements, and the coupled model calculates similar crosstie displacements as the TTB model. The TTB–DEM provided new insights into the ballast particle-scale behavior, which the TTB model alone cannot explore. The TTB–DEM coupling approach identified detrimental effects of hanging crossties on adjacent crossties, which were found to experience drastic vibrations and large ballast contact force concentrations.

The development of a large-scale high-fidelity model of train, rail, crosstie, and ballast offers a virtual laboratory for studying train–track dynamics. Currently, Train–Track (TT) models integrate the whole train and track system together, but lack explicit representation of ballast particles and simplify them as one-degree-of-freedom mass blocks only moving vertically, whereas models based on Discrete Element Method (DEM) for detailed ballast granular mechanics rarely include detailed representations of the rail and train because these multi-body systems are difficult to model within a DEM framework. To overcome these shortcomings, a large-scale TT-DEM coupled model with more than 480,000 polyhedron ballast particles was established to simulate track dynamic responses. To make this size model feasible with available computing resources, the TT and DEM models were coupled with a proportional–integral–derivative (PID) algorithm to eliminate the need for iteration within each time step. Additionally, the DEM time step was increased, cross-software communication was streamlined, and DEM data extraction was improved. Collectively, these improvements resulted in a model speed-up of about 200 times. The proposed TT-DEM model was validated by comparing predicted and field measured crosstie displacements. These comparisons showed that the TT-DEM model more closely represents the nonlinear system behavior than the conventional TT model and offers the advantage of studying the ballast at the particle level. A study of the thirty-crosstie TT-DEM ballast particle response to train track loading identified significant horizontal ballast forces that are not included in the TT model or single-crosstie TT-DEM models.

Train-track interaction models are widely used to simulate the dynamic responses of train and track components. In a conventional train-track analytical model, the ballast layer is often simplified as mass blocks, interacting with other components through a spring and dashpot system. Such an idealization ignores the particle-level information, that is interaction of different sized and shaped aggregates and related degradation characteristics linked to fouling behavior of the ballast layer. On the other hand, realistic ballast models based on the discrete element method (DEM) can capture the particle-level information but require predefined external loading patterns as inputs to mimic the train passages. To overcome such drawbacks of train-track and DEM models, this paper proposes to couple the two calibrated models together to build a more realistic ballasted track model for predicting dynamic responses of the train and track. The coupled model was first validated with detailed field data collected from Amtrak’s Northeast Corridor. The validated model was then used to study the effects of crosstie spacing realizing that a smaller crosstie spacing than regular often results in a higher construction cost. Increasing crosstie spacing, however, was found to result in larger track displacements, crosstie accelerations and reaction forces, particle accelerations, and local average normal contact forces. Therefore, vibration patterns with a smaller crosstie spacing were more stable. Such observations suggest that crosstie spacing plays an essential role in controlling track dynamic responses, and an optimum crosstie spacing could be determined by using the newly introduced coupled model.

Ballast degradation refers to the traffic use of in-service track when aggregate particles become smaller in size and fines are generated because of particle breakage and abrasion to cause geometry defects and influence the serviceability of ballasted track affecting train operations. To evaluate effects of ballast degradation on track substructure maintenance needs, single crosstie models were developed to simulate new and degraded ballast behavior under train loading. Particle size distributions and shape properties of the new and degraded ballast materials studied were adopted from previous laboratory tests conducted at the University of Illinois, U.S. Crosstie vibrations, ballast particle contact force distributions and particle rotations, and localized contact force chain networks were compared among different single crosstie models incorporating new and degraded ballast. The following key observations were made based on the simulation results: (1) crosstie on degraded ballast exhibits larger vibrations and more inconsistent vibration patterns; (2) degraded ballast shows a higher number of localized force chains with more particle contacts while the characteristic trend observed for new ballast is the presence of more symmetric force chain networks, which might be attributed to the new ballast layer’s uniform gradation and good interlock; (3) average normal contact forces are distributed more uniformly in new ballast in both horizontal and vertical directions as opposed to these forces concentrated more in vertical direction in degraded ballast; and (4) more ballast particles experience large rotations in degraded ballast and the related impact zone, which encloses all particles with large rotations, is also broader and deeper.

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.