Project Details
Grant Program
Faculty Development Competitive Research Grant Program 2018-2020
Project Description
The project aims at developing a generalized model or unified model applicable for ore dilution minimization and stope stability, regardless of the stope size, mining methods and site conditions. It is motivated by the need for further studies to continuously improve understanding towards the complex rock mass behavior surrounding stopes as unplanned stope instabilities and dilution pose a serious threat to production and safety. This project has two major components (work packages): stope performance prediction using the stability graph methods, and stope stability analysis based on numerical modelling. The tasks to be delivered include: compilation of a database of stope performance and characterization of stope performance; improving the graph methods: probabilistic, machine learning and rock engineering system approaches; investigating the effect of stress relaxation; approach toward a generalization of the method; determining how the influencing factors (orebody geometry, stress relaxation, faults, etc...) affect the stope performance via numerical modelling using FLAC3D, Map 3D and/or Rocscience.
| Status | Finished |
|---|---|
| Effective start/end date | 1/1/18 → 12/31/21 |
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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Predicting rockburst damage scale in seismically active mines using a classifier ensemble approach
Toksanbayev, N. & Adoko, A. C., 2023, In: IOP Conference Series: Earth and Environmental Science. 1124, 1, 012102.Research output: Contribution to journal › Conference article › peer-review
Open Access4 Link opens in a new tab Citations (Scopus) -
Reliability Analysis of Rock Supports in Underground Mine Drifts: A Case Study
Adoko, A. C., Yakubov, K. & Kaunda, R., Apr 2022, In: Geotechnical and Geological Engineering. 40, 4, p. 2101-2116 16 p.Research output: Contribution to journal › Article › peer-review
5 Link opens in a new tab Citations (Scopus) -
Unplanned Dilution Prediction in Open Stope Mining: Developing New Design Charts Using Artificial Neural Network Classifier
Korigov, S., Adoko, A. C. & Sengani, F., 2022, In: Journal of Sustainable Mining. 21, 2, p. 157-168 12 p.Research output: Contribution to journal › Article › peer-review
Open Access3 Link opens in a new tab Citations (Scopus)