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Prediction of uniaxial compression PFC3D model micro‐properties using artificial neural networks
Author(s) -
Tawadrous A. S.,
DeGagné D.,
Pierce M.,
Mas Ivars D.
Publication year - 2009
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.809
Subject(s) - artificial neural network , materials science , macro , radius , particle (ecology) , perceptron , algorithm , composite material , structural engineering , biological system , computer science , artificial intelligence , engineering , geology , oceanography , computer security , biology , programming language
Artificial neural networks are used to predict the micro‐properties of particle flow code in three dimensions (PFC3D) models needed to reproduce macro‐properties of cylindrical rock samples in uniaxial compression tests. Data for the training and verification of the networks were obtained by running a large number of PFC3D models and observing the resulting macro‐properties. Four artificial networks based on two different architectures were used. The networks used different numbers of input parameters to predict the micro‐properties. Multi‐layer perceptron networks using Young's modulus, Poisson's ratio, uniaxial compressive strength, model particle resolution and the maximum‐to‐minimum particle ratio showed excellent performance in both training and verification. Adding one more variable—namely, minimum particle radius—showed degrading performance. Copyright © 2009 John Wiley & Sons, Ltd.