Estimating DEM microparameters for uniaxial compression simulation with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{DESIMONE:2019:IJRMMS,

• author = "Marcelo {De Simone} and Lourdes M. S. Souza and Deane Roehl",
• title = "Estimating {DEM} microparameters for uniaxial compression simulation with genetic programming",
• journal = "International Journal of Rock Mechanics and Mining Sciences",
• volume = "118",
• pages = "33--41",
• year = "2019",
• ISSN = "1365-1609",
• DOI = "doi:10.1016/j.ijrmms.2019.03.024",
• URL = "http://www.sciencedirect.com/science/article/pii/S1365160918307123",
• keywords = "genetic algorithms, genetic programming, Discrete element method, Calibration, Uniaxial compression simulation, Young's modulus, Compressive strength",
• abstract = "Among the steps in modeling with the Discrete Element Method (DEM), one of the most important is parameter calibration. The commonly used trial-and-error approach brings drawbacks such as user dependence and high computational cost. As an alternative, artificial intelligence methods, such as neural networks and genetic algorithms, have been adopted. In this work, a new methodology based on Genetic Programming (GP) is presented as an alternative to calibrate DEM microparameters. From DEM models, GP provides functions relating microparameters and macro-properties. Given target macro-properties, the microparameters are obtained by an optimization procedure. The calibration procedure was evaluated for a uniaxial compression simulation and showed good accuracy for data sets with a reduced number of models. In addition, GP is less user dependent and less computationally intensive than other calibration methods. The methodology proved to be effective for DEM calibration and can be extended to other multiscale models",

}

Genetic Programming entries for Marcelo De Simone Lourdes M S Souza Deane Roehl

Citations