MINEDW Tutorial (Part 3: Boundary Conditions)

In this tutorial we will take a look at the different boundary conditions available to the user, and we will go over some examples of different scenarios in which they would be used.

3DEC 5.2 Introductory Webinar

This video is a recording of a one hour webinar reviewing the latest features in Version 5.2 of 3DEC. Presented by Dr. Jim Hazzard, 3DEC Product Manager and Lead Developer.

Using Python in FLAC3D 6

The Python programming language is embedded inside FLAC3D 6 and extended to allow FLAC3D models to be manipulated from Python programs. This webinar recording provides a brief introduction to Python scripting and includes many examples of using Python with FLAC3D.

A Discrete Fracture Network Model With Stress-Driven Nucleation: Impact on Clustering, Connectivity, and Topology

The realism of Discrete Fracture Network (DFN) models relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. In this study, we introduce correlations between fractures by enhancing the genetic model (UFM) of Davy et al. [1] based on simplified concepts of nucleation, growth and arrest with hierarchical rules.

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.