Learning

Software Tutorials

Converting Plots to Data Files

Any model plot that you create interactively by adding plot-items and adjusting settings can be represented by an equivalent set of commands. This is useful should you want to include command-driven plotting in your modeling run.

Homogeneous Embankment Dam Analysis (Part 2 of 3)

This FLAC 8.1 tutorial demonstrates how to conduct a steady-state seepage analysis to calculate the pore water pressures in the embankment due to the reservoir.

MINEDW Tutorial (Part 4: Meshing)

In this tutorial we will go over meshing, from the creation of a 2D mesh and how to import it to MINEDW, to the inclusion of topography, layers, and pinch-outs to different areas of interest in the model.

Technical Papers

A DFN–DEM Multi‑scale Modeling Approach for Simulating Tunnel Excavation Response in Jointed Rock Masses

Based on the concept of the representative elementary volume (REV) and the synthetic rock mass (SRM) modeling technique, a DFN–DEM multi-scale modeling approach is proposed for modeling excavation responses in jointed rock masses. Based on the DFN models of various scales, equivalent rock mass properties are obtained using 3DEC SRM models. A tunnel excavation simulation using data from the Äspö TAS08 tunnel is conducted to demonstrate the applicability of the proposed multi-scale modeling approach.

Graph-based flow modeling approach adapted to multiscale discrete-fracture-network models

In this study, we address the issue of using graphs to predict flow as a fast and relevant substitute to classical DFNs. We consider two types of graphs, whether the nodes represent the fractures or the intersections between fractures.

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.

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