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

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    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 publicationProceedings of China-Europe Conference on Geotechnical Engineering. Volume 1
    EditorsWei Wu, Hai-Sui Yu
    Place of PublicationVienna, Austria
    PublisherSpringer
    Pages132-135
    Number of pages4
    ISBN (Electronic)978-3-319-97112-4
    ISBN (Print)978-3-319-97111-7
    DOIs
    Publication statusPublished - 3 Aug 2018
    EventChina-Europe Conference on Geotechnical Engineering, 2016 - Vienna, Austria
    Duration: 13 Aug 201616 Aug 2016

    Publication series

    NameSpringer Series in Geomechanics and Geoengineering
    PublisherSpringer Publishers
    ISSN (Print)1866-8755

    Conference

    ConferenceChina-Europe Conference on Geotechnical Engineering, 2016
    CountryAustria
    CityVienna
    Period13/08/1616/08/16

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