Rapid Prediction of Railway Ballast Particle Motion Using an Acceleration-Based Continuous Convolutional Neural Network

Abstract

The discrete element method (DEM) can describe the mesoscopic motion and contact behavior of irregular railway ballast particles, but its low computational efficiency limits long-duration simulation of ballast-bed evolution under repeated loading. To address this limitation, this study proposes an acceleration-based continuous convolutional neural network (CCNN) for rapid ballast particle motion prediction. Different from position-based prediction methods, the proposed method reformulates the network output from particle position correction to acceleration correction, and updates particle velocity and position through kinematic integration, thereby reducing the error amplification caused by velocity back-calculation through positional differencing. A calibrated DEM model evaluated against laboratory angle-of-repose tests was used to generate 100,000 training samples, and the trained model was subsequently assessed using same-scenario and cross-scenario ballast deposition cases. For a 50 s prediction involving 433 ballast particles, the proposed method achieved a computational efficiency approximately 542 times higher than DEM, with a 9.88% relative error in the angle of repose and a 75.6% reduction in average centroid drift compared with the position-based method. In a cross-scenario case where the particle number increased by a factor of 2.5, the proposed method remained approximately 402 times faster than DEM and reduced the centroid drift error by 97.75%. These results indicate that the acceleration-based CCNN can substantially improve DEM-based ballast particle motion prediction efficiency while maintaining stable particle-position evolution and preliminary cross-scenario adaptability. The proposed method provides a technical basis for rapid DEM-based ballast particle motion prediction and future extension to ballast-bed structural optimization and evaluation of maintenance effectiveness.