Learning

Software Tutorials

Working with Email in Itasca Software

Learn how you can use commands and functions to send email messages and attachments via Itasca software. Use this capability to inform you when a model has finished running, a result is available (even attach a plot), or the model run is interrupted.

Tutorial: Simple Slope Stability

Using UDEC 6 and the shear-reduction method to calculate the factor-of-safety, this tutorial will show you how to analyze the stability of a simple slope containing: (1) no discrete jointing (continuum), (2) fully-continuous jointing (discrete blocks), and (3) noncontinuous, en echelon jointing.

MINEDW Tutorial (Part 1: Menu Options)

In this tutorial we will briefly cover the MINEDW user interface, its components, and the MINEDW Menu with the different options and tools it provides to build numerical models.

Technical Papers

FLAC3D Soil-structure Model of a Building

SOIL – STRUCTURE INTERACTION | FLAC3D - midas GEN DIRECT LINK

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.

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.

Latest News
  • Itasca at Balkanmine 2025! Itasca is pleased to announce its participation in the Balkanmine 2025 Conference. Our experts Lauriane...
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  • Itasca has announced the release of FLAC2D v9 Itasca has announced the release of FLAC2D v9, revolutionizing the way we analyze and predict...
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  • 6th Itasca Symposium on Applied Numerical Modeling The next Itasca Symposium will take place June 3 - 6, 2024, in Toronto, Canada....
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