Prediction of uniaxial compression PFC3D model micro-properties using artificial neural networks

DOI10.1002/nag.809zbMath1273.74311OpenAlexW2118933861WikidataQ111522866 ScholiaQ111522866MaRDI QIDQ2854696

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Publication date: 24 October 2013

Published in: International Journal for Numerical and Analytical Methods in Geomechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nag.809

zbMATH Keywords

DEMneural networkscalibrationnumerical modelingmaterial strengthPFC3D

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Soil and rock mechanics (74L10)

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Field-enriched finite element method for simulating of three-dimensional crack propagation ⋮ Weibull grain-based model (W-GBM) for simulating heterogeneous mechanical characteristics of salt rock ⋮ Application of supervised machine learning as a method for identifying DEM contact law parameters

Uses Software

• PFC3D

Cites Work

• Prediction of surface crown pillar stability using artificial neural networks
• A logical calculus of the ideas immanent in nervous activity

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