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Grain Learning: Bayesian Calibration of DEM Models and Validation Against Elastic Wave Propagation

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  • First Online:
Proceedings of China-Europe Conference on Geotechnical Engineering

Part of the book series: Springer Series in Geomechanics and Geoengineering ((SSGG))

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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.

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References

  1. Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K.W., Schindelin, J., Cardona, A., Sebastian Seung, H.: Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 9, 676–682 (2017)

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  2. Cheng, H., Shuku, T., Thoeni, K., Yamamoto, H., Radjai, F., Nezamabadi, S., Luding, S., Delenne, J.: Calibration of micromechanical parameters for DEM simulations by using the particle filter. EPJ Web Conf. 140, 12011 (2017). EDP Sciences

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  3. Cheng, H., Shuku, T., Thoeni, K., Yamamoto, H.: Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter Granul. Matter 20, 11 (2018)

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Acknowledgments

This work was financially supported by Eni S.p.A.

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Authors and Affiliations

  1. Multi Scale Mechanics (MSM), Faculty of Engineering Technology, MESA+, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands

    Hongyang Cheng, Stefan Luding & Vanessa Magnanimo

  2. Graduate School of Environmental and Life Science, Okayama University, 3-1-1 Tsushima naka, Kita-ku, Okayama, 700-8530, Japan

    Takayuki Shuku

  3. Centre for Geotechnical and Materials Modelling, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Klaus Thoeni

  4. Division of Exploration and Production, Eni SpA, Milano, Lombardy, Italy

    Pamela Tempone

Authors
  1. Hongyang Cheng
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  2. Takayuki Shuku
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  3. Klaus Thoeni
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  4. Pamela Tempone
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  5. Stefan Luding
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  6. Vanessa Magnanimo
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Corresponding author

Correspondence to Hongyang Cheng .

Editor information

Editors and Affiliations

  1. Institut für Geotechnik, Universität für Bodenkultur, Vienna, Austria

    Wei Wu

  2. Faculty of Engineering, University of Leeds, Leeds, United Kingdom

    Hai-Sui Yu

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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: Wu, W., Yu, HS. (eds) Proceedings of China-Europe Conference on Geotechnical Engineering. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-319-97112-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-97112-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97111-7

  • Online ISBN: 978-3-319-97112-4

  • eBook Packages: EngineeringEngineering (R0)

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