Automated Shape Analysis and DEM Study on Graded Crushed Stone

Abstract

Graded crushed stone (GCS), as a cheap and essential component, is of great importance in road construction. The irregularity and variability of particle shape is known to affect the packing characteristics of GCS, such as compactness and void ratio. In this study, the realistic particle outline is first automatically extracted based on digital image processing and deep learning algorithms. Then, the elongation (EI), roundness (Rd), and roughness (Rg) of GCS are quantified by shape evaluation algorithms. Moreover, based on the establishment of the GCS shape library, the gravity deposition with various elongations is simulated using the discrete element method to study the packing features of GCS. The elongation effects on the macroscopic and microscopic quantities are explored. Finally, the shear behavior of GCS is studied. The results illustrate that elongation has a significant effect on the packing of GCS.

Mix Design and Field Detection of Large-Particle-Size Graded Crushed Stone Mixtures for Pavement Reconstruction

“…Graded crushed stone (GCS) is a kind of mixture composed of coarse aggregates, fine aggregates, and sand in certain proportions [1]. As a kind of granular material without binders, GCS has the advantages of easy access to its raw materials, low cost, and convenient construction [2]. In addition, an excellent effect of stress dissipation can be achieved through the application of GCS in a pavement structure [3].…”