Learning

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Software Tutorials

Python and Pore Pressure Initialization

In this tutorial we will demonstrate how to map a random point cloud with pore pressure values onto the grid points of a FLAC3D model using Python.

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.

Homogeneous Embankment Dam Analysis (Part 1 of 3)

This FLAC 8.1 tutorial demonstrates how to establish the stresses in the dry embankment prior to the formation of the upstream reservoir.

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.

Quasi-Static Nonlinear Seismic Assessment of a Fourth Century A.D. Roman Aqueduct in Istanbul, Turkey

This paper presents a model of a stone masonry Roman aqueduct (the Valens Aqueduct), constructed in the fourth century A.D. in Istanbul, Turkey, to explore the seismic capacity and behavior using the discrete element method (DEM).

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.