Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations
Bibliographic record
Abstract
This study introduces an innovative approach to predict and mitigate blast-induced vibrations by optimizing blast patterns. By combining a statistical model with the frog algorithm, the method achieves enhanced accuracy and efficiency. Addressing a notable gap in blast engineering, this research uniquely integrates statistical models and optimization algorithms for vibration control. Data from 58 blasting events at Golgohar Iron Ore Mine No. 1 were utilized, with 40 datasets used for model training and 18 reserved for independent evaluation. In the prediction phase, four statistical and four AI-based models were developed to estimate peak particle velocity (PPV). Classical evaluation metrics, including R, R², RMSE, MAPE, MAD, and MSE, were applied to identify the best model. The multivariable linear regression model demonstrated superior accuracy, achieving R = 0.94, R² = 0.925, and low error metrics. Following this, the optimization phase employed the multivariable linear regression model as the objective function, integrated with the frog algorithm, to minimize PPV. Several models were developed to assess the influence of algorithmic parameters under the specific conditions of the mine. The results provide a reliable and practical methodology for predicting PPV and optimizing blast patterns, effectively reducing ground vibrations. This straightforward approach offers significant utility for pre-blasting planning and contributes to the advancement of sustainable and efficient blasting practices.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".