Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation

Hongyang Cheng, Takayuki Shuku, Klaus Thoeni, Pamela Tempone, Stefan Luding, Vanessa Magnanimo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The estimation of micromechanical parameters of discrete element method (DEM) models is a nonlinear history-dependent inverse problem. In order to reproduce the experimental measurements with high accuracy, this work aims to develop a machine learning-based calibration toolbox named “Grain learning”, which can extract grains from X-ray computed tomography (CT) images and perform Bayesian parameter estimation for DEM models of dry granular materials.

Original languageEnglish
Title of host publicationSpringer Series in Geomechanics and Geoengineering
EditorsWei Wu, Hai-Sui Yu
PublisherSpringer Verlag
Pages132-135
Number of pages4
Edition216849
ISBN (Print)9783319971117
DOIs
Publication statusPublished - 2018
EventChina-Europe Conference on Geotechnical Engineering, 2016 - Vienna, Austria
Duration: Aug 13 2016Aug 16 2016

Publication series

NameSpringer Series in Geomechanics and Geoengineering
Number216849
ISSN (Print)1866-8755
ISSN (Electronic)1866-8763

Other

OtherChina-Europe Conference on Geotechnical Engineering, 2016
Country/TerritoryAustria
CityVienna
Period8/13/168/16/16

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Mechanics of Materials

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