Application of supervised machine learning as a method for identifying DEM contact law parameters

DOI10.3934/MBE.2021370zbMath1505.74208OpenAlexW3198001532MaRDI QIDQ2092159

Piotr Klejment

Publication date: 2 November 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021370

zbMATH Keywords

discrete element methodrandom forest algorithmmultiple linear regression algorithmnumerical model calibrationuniaxial compression strength test

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Contact in solid mechanics (74M15) Numerical and other methods in solid mechanics (74S99)

Uses Software

• DEMPack
• ESyS-Particle
• TensorFlow

Cites Work

• Combination of discrete element and finite element methods for dynamic analysis of geomechanics problems
• Prediction of uniaxial compression PFC3D model micro-properties using artificial neural networks
• Calibration of a discrete element model for intact rock up to its peak strength
• Selecting a suitable time step for discrete element simulations that use the central difference time integration scheme
• A study on the effects of microparameters on macroproperties for specimens created by bonded particles
• Random forests

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