Abstract
Stress relaxation plays an important role in the design of underground stopes. The aim of this paper is to assess the stope stability in connection with the stress relaxation using a classification approach. Three types of stress relaxation were clearly defined, namely partial relaxation, tangential relaxation and full relaxation. A neural network classifier was implemented to assess the stability of the stopes on the basis of case histories of stope performances. The results of the classification were compared to existing empirical methods of quantifying the stress relaxation. Overall, the present study shows higher classification accuracies, especially when the stress relaxation was considered. The results suggested that the relaxation type can be a good predictor of stability. Relaxed stope (full and tangential stress relaxation) cases are the most critical in the sense that lower accuracies were obtained and the probability of correct classification is rather erratic.
| Original language | English |
|---|---|
| Pages (from-to) | 30-39 |
| Number of pages | 10 |
| Journal | Mining Technology: Transactions of the Institute of Mining and Metallurgy |
| Volume | 129 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 4 2020 |
Funding
This study was supported by the Faculty Development Competitive Research Grant program of Nazarbayev University, Grant N? 090118FD5338; and the Advanced Mining Technology Center (AMTC), University of Chile, through the Basal Project FB-0809. The authors wish to acknowledge the contribution of the anonymous reviewers.
Keywords
- Mathew’s stability graph
- mine stope stability
- neural network classifier
- Stress relaxation
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Geology
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