To quantitatively investigate the morphological features of railway ballast, a new statistical index, Curvature Index (CI), was presented. With a set of 584 digitized railway ballast particles obtained through 3D scanning, the statistical distribution functions of CI and existing global shape indices (i.e., long axis, middle axis, short axis and sphericity index) were obtained. Then, based on Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) Neural Network, a new shape reconstruction method was proposed to generate an arbitrary number of ballast particles that met the desired probability density distribution of morphological indices. And based on local curvature distribution on particle surface, a new simplification algorithm was implemented to reduce the number of contour points of each particle to no more than 31 on the premise of preserving global and local morphological features, which improved the efficiency of numerical simulations. These methods were used to reconstruct and simplify the arbitrary shapes of gravel ballast particles. Then, a DEM-FEM coupling model of ballasted track-subgrade was established by applying the regenerated ballast particles to analyze the contact stress at the ballast-soil interface. The numerical simulations can effectively reflect the contact stress distributions resulted from laboratory tests, which further confirmed that the reconstruction of the ballast particles reflected the morphological characteristics of real ballast.

为了定量研究铁路道ast的形态特征，提出了一种新的统计指标曲率指数（CI）。通过3D扫描获得的一组584个数字化铁路道ast颗粒，CI的统计分布函数并获得现有的整体形状指数（即长轴，中轴，短轴和球形度指数）。然后，基于适当的正交分解（POD）和径向基函数（RBF）神经网络，提出了一种新的形状重构方法，以生成满足期望的形态指标概率密度分布的任意数量的道ast颗粒。并且基于粒子表面局部曲率分布，在保留全局和局部形态特征的前提下，采用一种新的简化算法，将每个粒子的轮廓点数量减少到不超过31个，从而提高了数值模拟的效率。这些方法被用来重建和简化砾石压载颗粒的任意形状。然后，通过应用再生的道ast颗粒分析道ast-土界面处的接触应力，建立道track路基的DEM-FEM耦合模型。数值模拟可以有效反映实验室测试产生的接触应力分布，进一步证实了压载颗粒的重建反映了真实压载物的形态特征。