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

doi:10.1007/978-3-319-97112-4_29

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 proceeding › Conference 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 language	English
Title of host publication	Springer Series in Geomechanics and Geoengineering
Editors	Wei Wu, Hai-Sui Yu
Publisher	Springer Verlag
Pages	132-135
Number of pages	4
Edition	216849
ISBN (Print)	9783319971117
DOIs	https://doi.org/10.1007/978-3-319-97112-4_29
Publication status	Published - 2018
Event	China-Europe Conference on Geotechnical Engineering, 2016 - Vienna, Austria Duration: Aug 13 2016 → Aug 16 2016

Publication series

Name	Springer Series in Geomechanics and Geoengineering
Number	216849
ISSN (Print)	1866-8755
ISSN (Electronic)	1866-8763

Other

Other	China-Europe Conference on Geotechnical Engineering, 2016
Country/Territory	Austria
City	Vienna
Period	8/13/16 → 8/16/16

ASJC Scopus subject areas

Geotechnical Engineering and Engineering Geology
Mechanics of Materials

Access to Document

10.1007/978-3-319-97112-4_29

Cite this

Cheng, H., Shuku, T., Thoeni, K., Tempone, P., Luding, S., & Magnanimo, V. (2018). Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation. In W. Wu, & H-S. Yu (Eds.), Springer Series in Geomechanics and Geoengineering (216849 ed., pp. 132-135). (Springer Series in Geomechanics and Geoengineering; No. 216849). Springer Verlag. https://doi.org/10.1007/978-3-319-97112-4_29

Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation. / Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus et al.
Springer Series in Geomechanics and Geoengineering. ed. / Wei Wu; Hai-Sui Yu. 216849. ed. Springer Verlag, 2018. p. 132-135 (Springer Series in Geomechanics and Geoengineering; No. 216849).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cheng, H, Shuku, T, Thoeni, K, Tempone, P, Luding, S & Magnanimo, V 2018, Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation. in W Wu & H-S Yu (eds), Springer Series in Geomechanics and Geoengineering. 216849 edn, Springer Series in Geomechanics and Geoengineering, no. 216849, Springer Verlag, pp. 132-135, China-Europe Conference on Geotechnical Engineering, 2016, Vienna, Austria, 8/13/16. https://doi.org/10.1007/978-3-319-97112-4_29

Cheng H, Shuku T, Thoeni K, Tempone P, Luding S, Magnanimo V. Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation. In Wu W, Yu H-S, editors, Springer Series in Geomechanics and Geoengineering. 216849 ed. Springer Verlag. 2018. p. 132-135. (Springer Series in Geomechanics and Geoengineering; 216849). doi: 10.1007/978-3-319-97112-4_29

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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.",

author = "Hongyang Cheng and Takayuki Shuku and Klaus Thoeni and Pamela Tempone and Stefan Luding and Vanessa Magnanimo",

note = "Funding Information: This work was financially supported by Eni S.p.A. Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; China-Europe Conference on Geotechnical Engineering, 2016 ; Conference date: 13-08-2016 Through 16-08-2016",

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

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