Automated particle shape identification and quantification for DEM simulation of rockfill materials in subgrade construction

Abstract

Rockfill materials, conducted by impermeable stone, are frequently used in subgrade construction projects. The irregularity and variability of particle shape are demonstrated to affect the mechanical properties of rockfill subgrade, such as void ratio and coordination number. This study first identifies the subgrade rockfill particle contour by machine learning algorithms, including AdaBoost, Cascade, and sliding windows. Then, the shape evaluation indexes of length flatness, edge angle, and roughness are quantified, and the statistical analysis of each index is presented. In addition, the discrete element method (DEM) simulation is implemented on the compaction of rockfill subgrade to explore the impact of roundness on characteristics of particles. Finally, the macroanalysis on the void ratio and cumulative settlement and the microanalysis on particle coordination number, rotation momentum, and displacement are studied. The results illustrate that roundness has a significant effect on the mechanical characteristics of subgrade rockfill materials. With the increase of rolling passes, the porosity of packing decreases, whereas the settlement increases gradually. The change rate starts fast and ends slowly.