Conference Proceedings
Seventh International Conference & Exhibition on Mass Mining (MassMin 2016)
Conference Proceedings
Seventh International Conference & Exhibition on Mass Mining (MassMin 2016)
The Application of Rock Engineering Systems to Block Caving to Identify the Effective Parameters
An economic block cave is dependent to the characteristics of the rock mass at stages of caving. These are cave initiation, propagation and ore flow. In this research, the rock engineering system (RES), first established by Hudson (1992) to solve complex rock engineering problems, is introduced and verified against the well-established caving parameters established by Laubscher. There are two types of factors influencing caving and these are natural (cannot be controlled) and induced (operator controlled). A RES based interaction matrix is developed by determining the rating parameters required to reproduce the caving database using Laubscher's caving parameters. This provides the relative importance of the parameters in the interaction matrix. After discussing the rock mass caving process and identifying all the effective parameters, an interaction matrix is introduced to study the cave initiation of rock masses. The expert-semi-quantitative (ESQ) coding method is utilised to assign the numerical value to each parameter's interaction. Thereafter, the interaction intensity and dominance of each factor in the system is determined to classify these factors. Finally, all the parameters affecting caving operation and their weights on the RES system are identified. This new application of RES in block caving is very efficient and useful for any stage of caving studies and helps experts to make decisions more accurately.CITATION:Mohammad Khani, M, 2016. The application of rock engineering systems to block caving to identify the effective parameters, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 853-860 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Contributor(s):
M Mohammad Khani
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- Published: 2016
- PDF Size: 0.808 Mb.
- Unique ID: P201602091