DEM simulation data containing micro- and macro-scale quantities of granular materials under triaxial compression

The database contains the micro scale (raw) simulation data and the postprocessed data at the macro scale. Each dataset is created by a different set of parameter that corresponds to a certain type of soil. Note, only drained triaxial stress paths, starting from an initial void ratio of 0.68 are considered here. The dataset can be recreated using the open-source software Yade (Release version: 2020.01a). The source code that executes the simulation can be found at github.com/chyalexcheng/grainLearning. The database can be used by GrainLearning to find the first estimate of probability distribution of DEM model parameters for calibration and optimization purposes. The micro- and macro-scale data is intended to build data-driven micro-macro transition laws.

本数据库包含微观尺度（原始）仿真数据与宏观尺度后处理数据。每组数据集均由对应特定土壤类型的一套差异化参数生成。请注意，本次研究仅考虑以初始孔隙比0.68为起点的排水三轴应力路径。该数据集可通过开源软件Yade（发布版本：2020.01a）复现，执行该仿真的源代码可在github.com/chyalexcheng/grainLearning获取。本数据库可借助GrainLearning工具，获取用于模型校准与参数优化的离散元模型（DEM，Discrete Element Method）参数概率分布的初始预估结果。该微观与宏观尺度数据旨在构建数据驱动的微观-宏观转换规律。