Box-Behnken test data processing.

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.

为获取装载机铲装作业过程中黏性土的离散元法（Discrete Element Method, DEM）仿真参数，本文基于含约翰逊-肯德尔-罗伯茨（Johnson-Kendall-Roberts, JKR）黏聚力接触模型的赫兹-明德林接触模型，在离散元建模专家（Experts in Discrete Element Modeling, EDEM）中对不同含水率的黏性土参数开展标定工作；首先制备5组不同含水率的黏性土试样，结合振动筛分法、斜面法与休止角试验，获取了粒度分布、土-钢摩擦系数以及休止角的实测数据范围；其次在EDEM中构建JKR V2黏附模型，通过普拉凯特-伯曼（Plackett-Burman）试验筛选出影响仿真结果的显著参数；最后结合爬坡试验确定响应面分析的取值范围，通过Box-Behnken试验设计建立二次回归模型以优化参数组合，此外引入粒子群优化（Particle Swarm Optimization, PSO）算法实现休止角的单目标优化。实验结果表明，随着含水率升高，黏性土的休止角从30.83°提升至37.13°，显著参数包括JKR表面能、土-土恢复系数与静摩擦系数；采用PSO算法后的仿真误差从响应面法的最大3.38%降至2.2%以内。本研究为黏性土的DEM建模提供了高精度参数化方法，可为装载机铲装黏性土的DEM仿真构建提供参考依据。