Numerical modeling of geomaterial fracture using a cohesive crack model in grain-based DEM

This paper aims to study the mechanical behavior of rocks using the cohesive crack model (CCM) and grain-based model (GBM) within the distinct element method (DEM) simulations. In the GBM-DEM, the Voronoi tessellation scheme is used and the intact material is simulated as assemblies of a number of p...

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Bibliographic Details
Published in:Computational particle mechanics 2020-07, Vol.7 (4), p.645-654
Main Authors: Fathipour-Azar, Hadi, Wang, Jianfeng, Jalali, Seyed-Mohammad Esmaeil, Torabi, Seyed Rahman
Format: Article
Language:English
Subjects:
Artificial neural networks
Bonding
Classical and Continuum Physics
Cohesion
Compression tests
Compressive strength
Computational Science and Engineering
Computer simulation
Contact angle
Discrete element method
Engineering
Mathematical models
Mechanical properties
Parameters
Shear strength
Shear tests
Soft computing
Tessellation
Theoretical and Applied Mechanics
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Summary:This paper aims to study the mechanical behavior of rocks using the cohesive crack model (CCM) and grain-based model (GBM) within the distinct element method (DEM) simulations. In the GBM-DEM, the Voronoi tessellation scheme is used and the intact material is simulated as assemblies of a number of particles bonded together at their contact areas. Implemented CCM defines the nonlinear behavior of the grain interfaces under various modes of loading. Numerical simulation of a tension–compression and direct shear tests is conducted to verify the correct implementation of CCM. Moreover, in the application of such a numerical framework, the uniaxial and biaxial compression tests as well as the Brazilian disc test are simulated in the universal distinct element code using a calibration process and then the results are compared with the relevant responses of the rock sample. Finally, the capability of soft computing method, i.e., artificial neural network (ANN) for predicting macromechanical parameters of rock, i.e., UCS based on input cohesive contact shear strength parameters, i.e., cohesion and friction angle, is explored. Results indicate that the ANN model can be quite accurate predictor of uniaxial compressive strength (UCS) and therefore used as a potential tool to aid in estimating target values.
ISSN:2196-4378
2196-4386
DOI:10.1007/s40571-019-00295-4