AModel for Large Scale Near-Real Time Simulation of Granular Material Flow

摘要

This paper describes ongoing development of a methodology to model largedisplacement behavior of non-cohesive soils, such as sands and gravels. A set of artificial neural networks are trained to learn the underlying knowledge behind the large displacement behavior of granular media. Data from a large number of discrete element simulations is used to train the neural networks. Particularly, the NN’s are used to relate the stresses at any point inside the soil to the history of stress at some surrounding points as well as soil property and configuration in vicinity of that point. A numerical framework is developed to utilize the trained NN’s in simulation environments of granular material flow. The results of 2D and 3D simulations are presented.