Learning

Software Tutorials

FLAC3D 6.0 PFC Plugin Conveyor

Working with Building Blocks in FLAC3D 6 (Part 1)

This video demonstrates using a library set of Building Blocks as a starting point for creating a new model. In this example, cylindrical blocks are snapped together to represent a tunnel and intersected with other blocks representing a nearby wall.

FLAC3D 6.0 Built-in Model Generation Tools and Workflow

Building Blocks works seamlessly with the FLAC3D 6.0 extruder tool and new Model Pane. Building Blocks includes a library of model primates and users can also add and load their own building block sets.

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.

Blast Movement Simulation Through a Hybrid Approach of Continuum, Discontinuum, and Machine Learning Modeling

This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.

Connectivity, permeability, and channeling in randomly distributed and kinematically defined discrete fracture network models

A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e., Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth, and fracture arrest.