A different methodology to control and predict ground vibrations from mine blasting
Bibliographic record
Abstract
Ground vibration prediction and control from mine blasting is a complex task involving disciplines such as geotechnical engineering, explosives engineering, and geology. Despite the importance of controlling ground vibrations, the mining industry commonly uses empirical methodologies based on concepts related to the nuclear tests era (charge weight scaling relationships). Over the past decade, attempts have been made to popularize more elaborate methodologies. These methods range from basic waveform superposition to analytical and numerical methods. Such methodologies are difficult to utilize daily because of their complexity, sometimes low accuracy (despite their sophistication), requirement for difficult to obtain parameters, and time needed to reach solutions using complex algorithms. In this paper, a different methodology to control and predict ground vibrations from blasting is presented. This methodology uses a semi-empirical approach, where the generation and propagation of ground vibration waves and the ground vibration characteristics at a point of interest are considered through the collection of a signature waveform. The methodology allows assessment of the optimum delay between charges to minimize and control ground vibration levels. A case study is included to demonstrate the benefits and the methodology in detail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".