Machine Learning Techniques for Predicting the Quantity of ANFO Used in Blasting a Bench in an Open Pit Mine
Bibliographic record
Abstract
The objective of this study is to predict the quantity of ANFO required for bench blasting in an open pit mine in Peru, through the application of advanced machine learning techniques. Six models were selected: Artificial Neural Networks (ANNMLP), Random Forests (RF), Support Vector Machines for Regression (SVR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (BR), due to their ability to handle complex multidimensional data and their success in similar applications, such as rock fragmentation prediction. The methodology included the collection of data from 208 drill holes, which were divided into training (70%), validation (15%), and testing (15%) sets. The models were evaluated using RMSE, MSE, MAE, and R2. The KNN model showed the best performance, with an R2 of 0.84, RMSE of 2.37, MSE of 5.60, and MAE of 1.35, standing out in predictive accuracy. This study contributes to the accurate prediction of the ANFO quantity required for bench blasting in open-pit mining, providing a useful tool for improving explosives management based on the specific characteristics of the terrain and operational conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".