Integrating Diverse Sampling Sources for Enhanced Discrete Fracture Network Modeling of Open Pit Mine Slopes

This research project consists of the application of Discrete Fracture Network (DFN) modeling techniques to model the geological fractures within rock formations, with a specific focus on open pit mines. The central challenge lies in accurately representing the spatial distribution of fractures in rock masses. To overcome this challenge, advanced statistical methods and computational simulations are utilized to construct create fractures in 3D domains. The integration of DFN with advanced numerical methods, such as the discrete element method (DEM), enhances stability analyses, offering valuable insights. However, the computational demands associated with 3D models present constraints on fully exploring the probabilistic nature of these numerical models.
The primary objective of this research is to develop an advanced DFN model for open pit mines, leveraging information from multiple geological sampling sources (boreholes and rock outcrop mapping). This includes establishing an automated DFN input database, creating a model capable of capturing spatial variability and addressing uncertainties, and formulating a methodology for DFN-based slope stability analysis in computer clouds. This research not only contributes to advancing DFN methodologies but also addresses practical engineering challenges in large-scale projects.

Faculty Supervisor:

Pedro Cacciari

Student:

Partner:

ArcelorMittal (Longueuil, QC)

Discipline:

Engineering

Sector:

Manufacturing; Mining

University:

Polytechnique Montréal

Program: