Drill and Blast Decision Support to Optimize Hole Position, Explosives, and Drill Bit Clusters
Bibliographic record
Abstract
Abstract The locations and sizes of drill holes are two critical decisions in drill and blast design. These decisions should balance the trade-off between different blast objectives such as fragmentation, cost, and ease of operational execution. Additionally, selecting the correct hole diameters allows operators to use the optimal explosive amount without exceeding limits on ground vibration. Simulations show that fragmentation can be significantly improved by using a non-uniform grid for the hole locations (variable distance between holes). However, a complex non-uniform pattern hinders explosive trucks from traversing both diagonally and horizontally across the blast. Keeping navigation options open is important because obstacles in mines, such as pit walls, often limit truck movement. We propose a heuristic method for automating pattern design that optimizes fragmentation while accounting for truck navigation. Simulation results demonstrate that, by optimizing and clustering drill bit size selection across the blast, the new approach achieves 3–4% improved fragmentation with fewer drill holes, particularly when the blast is near vibration-sensitive zones.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".