Towards optimising ground support systems in underground mines
Bibliographic record
Abstract
There is a large body of literature describing different methods for designing ground support systems for underground excavations. Ground support design methods are commonly grouped in three classes—namely, analytical, empirical and numerical modelling methods. A comprehensive review of practices in Australian and Canadian mines (Potvin & Hadjigeorgiou 2016) has shown that the design of ground support is largely the result of the evolution of and adjustments to an initial design. The initial design relies mainly on two methods: the Grimstad–Barton (Grimstad & Barton 1993) empirical chart and the RocScience software UnWedge, based on a limit equilibrium wedge analysis defined by continuous large geological structure. The optimisation process is largely reactive and involves modifying the ground support systems to cater for ground conditions that differ from those initially anticipated or replacing ground support elements to improve performance. Sometimes the decision is backed up by numerical modelling analyses in anticipation of stress changes as a result of mining. Nevertheless, a systematic engineering methodology is rarely employed to optimise ground support systems in underground mines. An optimum ground support system can be defined as the lowest cost system, including installation cost and productivity factors such as development mining rates, which can achieve a tolerable Probability of Failure (PoF) during the service life of an excavation. A further requirement is that such a system will minimise the need for rehabilitation. The above definition implies that no matter which design method is applied, a probabilistic assessment is required to ascertain whether the ground support design meets the target PoF. This paper outlines userfriendly mXrap-based tools developed within the scope of the Australian Centre for Geomechanics’s Ground Support System Optimisation research project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".