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Software Tutorials

FLAC3D 6.0 PFC Plugin Conveyor

Loops, Splitting, and Operators

When constructing or running simulations, you may want to query or modify values associated with all, or some of, the objects in your model (such as zones, nodes, blocks, balls, contacts, rockbolts, etc.). This may be to measure results like stress or displacement, to assign a calculated extra variable for plotting, or to adjust a property value. There are several ways to identify and navigate across all these objects using loops, splitting, and operators — with each one becoming easier and faster to execute. See how you can apply all of these approaches in a tutorial where a zone property is randomly assigned for strength variability throughout the model. You will also see how much easier and faster these approaches have become. Applying model property distributions via the PROPERTY command is also reviewed.

Python and Pore Pressure Initialization

In this tutorial we will demonstrate how to map a random point cloud with pore pressure values onto the grid points of a FLAC3D model using Python.

Technical Papers

Time-Dependent Behavior of Saint-Martin-La-Porte Exploratory Galleries: Field Data Processing and Numerical Modeling of Excavation in Squeezing Rock Conditions

Field monitoring programs (e.g., convergence measurements and stress measurements in the support system) play an important role in following the response of the ground and of the support system during and after excavation. They contribute to the adaptation of the excavation and support installation method and the prediction of the long-term behavior. In the context of the Lyon–Turin link project, an access gallery (SMP2) was excavated between 2003 and 2010, and a survey gallery (SMP4) has been excavated since 2017.

Simulation of Three-Dimensional Pore-Pressure Distribution for Slope-Stability Analysis

A 3D groundwater flow model was constructed using MINEDW [1] to simulate pore pressure at the Chuquicamata open pit mine slope in Chile.

On the Density Variability of Poissonian Discrete Fracture Networks, with application to power-law fracture size distributions

This paper presents analytical solutions to estimate at any scale the fracture density variability associated to stochastic Discrete Fracture Networks. These analytical solutions are based upon the assumption that each fracture in the network is an independent event. Analytical solutions are developed for any kind of fracture density indicators.