The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/16632559

Cheng, Hongyang (2021): DEM simulation data containing micro- and macro-scale quantities of granular materials under triaxial compression. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16632559.v1

Dataset

• Civil Engineering
• Mechanical Engineering
• Condensed Matter Physics
• Geophysics
• Other Earth Sciences

DEM, GrainLearning, Granular materials, Multi-scale, Triaxial compression, YADE

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Hongyang Cheng

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.

• 2021-09-23 first online, published, posted

4TU.ResearchData

Raw data (micro-scale) in .yade.gz format Processed data (macro-scale) in .npy format

An iterative Bayesian filtering framework for fast and automated calibration of DEM models

• https://github.com/chyalexcheng/grainLearning
• http://yade-dem.org
• https://scikit-learn.org/stable

University of Twente, Faculty of Engineering Technology, Department of Civil Engineering