Automated Drill and Blast Design using Data from Autonomous Drills*
Bibliographic record
Abstract
At the Rio Tinto Pilbara iron ore mines of Western Australia, drill and blast operations use autonomous drills. These drills have automated control functions such as tramming (propulsion using tracks), levelling and drilling. An operator located at the control centre, more than a thousand kilometres away, directs the drills on where to drill the blast holes. Prior to drilling, a drill and blast engineer determines the locations of these holes using ground hardness values and other information manually prepared by the geology team. The drill pattern takes many hours to design and uses geological data that is highly uncertain. This paper presents an algorithm for automating this process. The algorithm takes under one minute to automatically optimise the locations of drill holes using information collected from autonomous drills. The simulated automated design outcomes from 10 case studies indicate that total energy consumption can be reduced by 6.2%, with a 6.6% reduction in the number of holes, compared to traditional designs. The long-term goal is to move from static pattern design to dynamic real-time updates as each new hole is drilled.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".