Optimisation in the design of underground mine access
Bibliographic record
Abstract
Efficient methods to model and optimise the design of open cut mines have been known for many years. The design of the infrastructure of underground mines has a similar potential for optimisation and strategic planning. Over the last five years our group has developed two pieces of software to tackle this problem – UNO (Underground Network Optimiser) and DOT (Decline Optimisation Tool). The idea is to connect up a system of declines, ramps, drives and possibly shafts, to minimise capital development and haulage costs over the lifetime of a mine. Constraints that can be handled by the software include: gradient bounds (typically 1:7), turning circle restrictions for navigability, and obstacle avoidance. The latter constraint keeps development at stand off distances from orebodies and ensures it avoids regions that involve high cost, such as faults, voids and other geological features. The software is not limited to only interconnecting fixed points. It has the useful feature that a group of points can be specified such that the development is required to connect to one member of the group. So for example, if an existing ventilation rise must be accessed at some level, then a group of points along the rise can be selected. Similarly, this gives the opportunity to use variable length cross-cuts from a decline to an orebody. The latter gives important flexibility and can significantly reduce the development and haulage cost of a design. Finally, the goals for the next phase of development for this project will be discussed, including speeding up the algorithms and allowing for heterogeneous materials, such as aquifers and faults, as additional costs rather than obstacles.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".