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Probabilistic Calibration of a Discrete Particle Model for Geomaterials
A discrete element model DEM capable of reproducing the mechanistic behavior of a triaxial compressive test performed on a Vosges sandstone specimen is presented, considering for the simulations similar experimental testing conditions and the use of densely packed spherical elements with low lock-in stress. The main aim of this paper is to illustrate the calibration process of the model's micro-parameters when obtained from the experimental meso-parameters measured in the lab. For this purpose, a probabilistic inverse method is introduced to fully define the micro-parameters of the particle models through a joint probability density function, which is conditioned on the experimental observations obtained during a series of tests performed at the L3S-R, France. The DEM captures successfully some of the rock mechanical behavior features, including the global stress-strain and failure mechanisms. Results include a detailed parametric analysis consisting of varying each DEM parameter at the time and measuring the model response on the strain-stress domain. First order statistics on preliminary probabilistic results show the adequacy of the model to capture the experimental data, including a measure of the DEM performance for different parameters combinations.
Probabilistic Calibration of a Discrete Particle Model for Geomaterials
A discrete element model DEM capable of reproducing the mechanistic behavior of a triaxial compressive test performed on a Vosges sandstone specimen is presented, considering for the simulations similar experimental testing conditions and the use of densely packed spherical elements with low lock-in stress. The main aim of this paper is to illustrate the calibration process of the model's micro-parameters when obtained from the experimental meso-parameters measured in the lab. For this purpose, a probabilistic inverse method is introduced to fully define the micro-parameters of the particle models through a joint probability density function, which is conditioned on the experimental observations obtained during a series of tests performed at the L3S-R, France. The DEM captures successfully some of the rock mechanical behavior features, including the global stress-strain and failure mechanisms. Results include a detailed parametric analysis consisting of varying each DEM parameter at the time and measuring the model response on the strain-stress domain. First order statistics on preliminary probabilistic results show the adequacy of the model to capture the experimental data, including a measure of the DEM performance for different parameters combinations.
Probabilistic Calibration of a Discrete Particle Model for Geomaterials
Zhang, Y. B. (author) / Medina-Cedina, Z. (author) / Khoa, H. D. V. (author)
Geo-Frontiers Congress 2011 ; 2011 ; Dallas, Texas, United States
Geo-Frontiers 2011 ; 4204-4213
2011-03-11
Conference paper
Electronic Resource
English
Probabilistic Calibration of a Discrete Particle Model for Geomaterials
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