Enhancing Sustainable Mining Practices Through Fracture-Informed Blasting Strategies: A Case Study of the Aïn El Kebira Limestone Quarry in Algeria
Bibliographic record
Abstract
Optimizing fragmentation in mining operations is a multifaceted challenge, compounded by natural and induced fractures, cleavage, and the inevitable approximations in blast design parameters.Current research predominantly emphasizes the optimization of mining progression with respect to power and grade distribution; however, the dynamic behavior of fractures within the rock mass also plays a crucial role.These fractures not only impact the rock mass stability but also dictate the choice of blasting techniques.This study aims to enhance the control over blasting outcomes by incorporating the evolution of fractures, thereby improving the technical efficiency, economic viability, and safety of mining operations.We apply geometric modeling that utilizes digital cartography and stereography to account for these evolving fractures.The primary goal is to reduce oversized material volume, hence contributing to more sustainable mining practices.We illustrate our approach with a case study at the Aï n El Kebira limestone quarry in Setif province, northeastern Algeria, where we measure rock fragmentation post-blasting and discuss implications for sustainable mining.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".