Learning

Customized Training

Customized Engineering Training Courses

Itasca Educational Partnership

ITASCA Academics

Software Tutorials

FLAC3D Quick Start Tutorial

This tutorial steps through the actions necessary to quickly create and solve a FLAC3D model. The focus of this tutorial is to provide you with a basic familiarity with the user interface and recommended work flow.

FLAC3D 6.0 Interactive Model Pane

Rockmass Integration

This example describes how to import and use structural data generated by Rockmass Technologies mapping instrumentation.

Technical Papers

Caving in the Fabian Orebody —from Mining Stope to Cave Crater in Malmberget

The Fabian orebody is a non-daylighting iron orebody in the LKAB Malmberget Mine in northern Sweden. During 2010, a prognosis of the cave development in the Fabian area was developed, based on compilation and analysis of all available material. In March 2012, a new cave crater formed on the ground surface above the Fabian orebody, similar to what was predicted. The prognosis is compared with observations of the caving and the differences and implications quantified. A program for continued monitoring of mining-induced deformation in Malmberget is also described and a criterion for allowable mining-induced surface deformations is proposed.

Numerical Models as Important Component of EGS Design and Operation

Calibration of geomechanics models using microseismic data is key to creating reliable predictive tools. This presentation reviews the geomechanical model used for: stress characterization, microseismic modeling to assess the risk associated with faults activation and induced seismicity, and evaluation of designs and operational strategies. Both hydraulic fracturing and hydro-shearing of discrete fracture network were important components of stimulation of EGS and zonal isolation can play a key role in effective stimulation of an EGS along the entire length of the horizontal well.

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.