Learning

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Training

Customized Engineering Training Courses

Software Tutorials

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.

An Introduction to Python Scripting: Part 2

Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 2 focuses on classes and objects plus lists and dictionaries.

Using Python in Itasca Software

Python scripting is built into current versions of FLAC3D, 3DEC, and PFC. This video introduces users of Itasca software to working with Python and FLAC3D, 3DEC, and PFC types (zones, blocks, ball, structural elements, and so on). The Itasca Module, a comparison with FISH scripting, and object-oriented and array-oriented interfaces are reviewed and demonstrated.

Technical Papers

The role of rock mass heterogeneity and buckling mechanisms in excavation performance in foliated ground at Westwood Mine, Quebec

Operations at Westwood mine in Quebec, Canada were temporarily halted in May 2015 after three large-magnitude seismic events occurred over two days. The mechanisms leading to these events, which caused severe damage to several accesses, were not well understood at first. This paper presents the key aspects of FLAC3D back-analysis modelling, which include (1) an anisotropic rock mass strength model with properties derived from field and laboratory strength testing, and (2) a scheme to account implicitly for the deconfinement that accompanies buckling around excavations.

FLAC3D mesh and zone quality

Mesh quality is crucial for the stability, accuracy, and fast convergence of numerical simulations. However, given the geometrical complexity of some models and the tools available for mesh creation, it is often necessary to accept meshes that deviate significantly from the known ideal shape.

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.