Website
http://dataportal.arc.gov.au/NCGP/API/grants/LP200100038
Funding Status
Active
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Funded Activity Summary
A Machine Learning driven flow modelling of fragmented rocks in cave mining. The project aims to develop an integrated method that uses micro scale and macro scale information to predict block scale behaviour so that a better cave mining design can be established. The role of various mineral composition on the energy storage and fracture properties of rocks will be investigated to examine rock fragmentation for block cave mining. Later Machine Learning based models will be developed to establish various predictive models for Block Scale rock mass behaviour and caveability of ore deposit. Finally, we will develop a new constitutive model based on a dual damage concept that will capture the rock fragmentation and simulate the cave propagation in a large scale mine layout using Smoothed-particle hydrodynamics.Funded Activity Details
Start Date: 07-2020
End Date: 06-2024
Funding Scheme: Linkage Projects
Funding Amount: $516,000.00
Funder: Australian Research Council
Research Topics
ANZSRC Field of Research (FoR)
Geomechanics and Resources Geotechnical Engineering | Resources Engineering and Extractive Metallurgy | Mining Engineering
