Statistical Analysis and Reconstruction of Morphological Characteristics of Railway Ballast

Abstract:

The geometrical shape characteristics of the particles have a significant influence on the mechanical properties of the ballast. To quantitatively study the morphological characteristics of railway ballast， this paper took the first-order ballast particles of the real scan as an example， used 3D laser scanning to obtain the point cloud data of the ballast particles， and introduced the description indices of overall morphological characteristics （long axis， middle axis， short axis， sphericity index）， and proposed the local morphological characteristics index （curvature index） of the ballast granules. The probability density distributions of above-mentioned overall and local morphological characteristic indicators were established. On this basis， based on the intrinsic orthogonal decomposition （POD） and radial basis function （RBF） neural networks， a regenerative algorithm based on the probability density distribution of particle morphological indicators was proposed to reconstruct the ballast particle sample library. The above-mentioned re-generation algorithm reconstructed 600 and 4 000 particle turnouts， respectively. The results show that the statistical distribution of the morphological characteristics of the rebuilt ballast sample were close to those of the scanned samples， indicating that the method can quickly establish any number of turnout samples based on the probability density distribution of the particle morphological indicators.