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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

153 Downloads (Pure)

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	Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1
Editors	Wei Wu, Hai-Sui Yu
Place of Publication	Vienna, Austria
Publisher	Springer
Pages	132-135
Number of pages	4
ISBN (Electronic)	978-3-319-97112-4
ISBN (Print)	978-3-319-97111-7
DOIs	https://doi.org/10.1007/978-3-319-97112-4_29
Publication status	Published - 3 Aug 2018
Event	China-Europe Conference on Geotechnical Engineering, 2016 - Vienna, Austria Duration: 13 Aug 2016 → 16 Aug 2016

Publication series

Name	Springer Series in Geomechanics and Geoengineering
Publisher	Springer Publishers
ISSN (Print)	1866-8755

Conference

Conference	China-Europe Conference on Geotechnical Engineering, 2016
Country/Territory	Austria
City	Vienna
Period	13/08/16 → 16/08/16

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

Cheng2018_Chapter_GrainLearningBayesianCalibratiFinal published version, 1.04 MBLicence: Taverne

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.), Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1 (pp. 132-135). (Springer Series in Geomechanics and Geoengineering). Springer. https://doi.org/10.1007/978-3-319-97112-4_29

@inproceedings{1514d6b4d5d244578d7b94c283a7ec35,

title = "Grain learning: Bayesian calibration of DEM models and validation against elastic wave propagation",

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",

year = "2018",

month = aug,

day = "3",

doi = "10.1007/978-3-319-97112-4_29",

language = "English",

isbn = "978-3-319-97111-7",

series = "Springer Series in Geomechanics and Geoengineering",

publisher = "Springer",

pages = "132--135",

editor = "Wei Wu and Hai-Sui Yu",

booktitle = "Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1",

address = "Germany",

note = "China-Europe Conference on Geotechnical Engineering, 2016 ; Conference date: 13-08-2016 Through 16-08-2016",

}

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), Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1. Springer Series in Geomechanics and Geoengineering, Springer, Vienna, Austria, pp. 132-135, China-Europe Conference on Geotechnical Engineering, 2016, Vienna, Austria, 13/08/16. 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.
Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1. ed. / Wei Wu; Hai-Sui Yu. Vienna, Austria: Springer, 2018. p. 132-135 (Springer Series in Geomechanics and Geoengineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Cheng, Hongyang

AU - Shuku, Takayuki

AU - Thoeni, Klaus

AU - Tempone, Pamela

AU - Luding, Stefan

AU - Magnanimo, Vanessa

PY - 2018/8/3

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AB - 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.

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T3 - Springer Series in Geomechanics and Geoengineering

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BT - Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1

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Y2 - 13 August 2016 through 16 August 2016

ER -

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 HS, editors, Proceedings of China-Europe Conference on Geotechnical Engineering. Volume 1. Vienna, Austria: Springer. 2018. p. 132-135. (Springer Series in Geomechanics and Geoengineering). doi: 10.1007/978-3-319-97112-4_29

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

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