Slope Movement Prediction of Open-pit Mine Using Machine Learning and Field Data

In BC open pit porphyry mines, pit wall deformation rate has been closely monitored to evaluate the stability of open pits and determine proper operational response. This research aims to improve our understanding towards the deformation behaviours of pit walls considering both the slope characteristics and external influence factors. The project will collect a large dataset of pit wall displacements measured by robotic total stations and slope stability radars in Gibraltar mine as well as targeted pit walls’ geological conditions (e.g. rock type/strength) and operation parameters (e.g. blasting parameters, mining equipment/rate, and groundwater). By incorporating a machine learning method with a slope deformation function, a prediction model will be developed to predict the displacement of open pit walls subjected to external factors, and mostly importantly, identify their deformation stage for proper mine operation responses.

Faculty Supervisor:

Wenbo Zheng

Student:

Partner:

Taseko | Gibraltar Mines Ltd

Discipline:

Engineering

Sector:

Mining

University:

University of Northern British Columbia

Program: