Clone granular soils with mixed particle morphological characteristics by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process

Abstract

Computers have been taught to clone granular soil particles for discrete element method simulations to alleviate difficulties of using three-dimensional imaging techniques for scanning a large number of particles. In this process, computers analyze a few scanned particles to extract morphological characteristics of the target soil, which are used to clone as many particles as necessary. However, many natural granular soils contain a wide range of particle shapes mixing more than one type of morphological characteristics, causing difficulties in cloning. This research aims to address this challenge by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process. Spherical harmonics coefficients are used to characterize morphological information of the granular soil. Gaussian mixture model is used to fit a function to the mixed morphological characteristics. The expectation–maximization and Dirichlet process are used to estimate the fitting parameters in Gaussian mixture model. Then, Gaussian mixture model is used to generate new spherical harmonics coefficients and then generate new particles. The effectiveness and accuracy of the proposed methodology are verified using a Griffin sand. Although this approach is developed for granular soils, the proposed technique can also be used to clone other particulate materials.