# Calibration of discrete element parameters for cohesive soils at different moisture contents

## TL;DR

This paper develops a precise method to calibrate discrete element parameters for cohesive soils at different moisture levels, improving simulation accuracy for loader operations.

## Contribution

A novel parameter calibration method for cohesive soils using PSO optimization and response surface analysis is introduced.

## Key findings

- Angle of repose increases from 30.83° to 37.13° with higher moisture content.
- Key parameters include JKR surface energy, restitution coefficient, and static friction.
- PSO optimization reduces simulation error to within 2.2%.

## Abstract

To obtain discrete element method simulation parameters for cohesive soil during the scrapping process of loaders, this paper calibrates parameters of cohesive soil with different moisture contents based on the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) Cohesion contact model in Experts in Discrete Element Modeling (EDEM). First, five cohesive soil samples with different moisture contents were prepared. By combining vibration sieving, the inclined plane method, and angle of repose experiments, the measured data ranges of particle size distribution, soil-steel friction coefficient, and angle of repose were obtained. Secondly, the JKR V2 adhesion model was constructed in EDEM. Significant parameters were screened using the Plackett-Burman test. Finally, the response surface analysis range was determined by integrating climbing experiments, and a quadratic regression model was established through Box-Behnken design to optimize parameter combinations. Furthermore, a Particle Swarm Optimization (PSO) algorithm was introduced for single-objective optimization of the angle of repose. The experimental results show that with the increase of moisture content, the angle of repose increases from 30.83° to 37.13°, and the significant parameters are JKR surface energy, soil-soil restitution coefficient, and static friction coefficient. The simulation error of the PSO algorithm is reduced from the maximum 3.38% in the response surface method to within 2.2%. This study provides a high-precision parameterization method for DEM modeling of cohesive soil, offering references for establishing DEM simulations of loaders scraping cohesive soil.