Advancing the Prediction and Evaluation of Blast-Induced Ground Vibration Using Deep Ensemble Learning with Uncertainty Assessment
Bibliographic record
Abstract
Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-induced ground vibration are imperative for environmental protection and sustainable development. The empirical approaches give inaccurate results, as evident in the literature. Hence, numerous researchers have started to use fast-growing soft computing approaches that are satisfying in prediction performance. However, achieving high-prediction performance and detecting prediction uncertainty is crucial, especially in blasting operations. This study aims to propose a deep ensemble model to predict the blast-induced ground vibration and quantify the prediction uncertainty, which is usually not addressed. This study used 200 published data from ten granite quarry sites in Ibadan and Abeokuta areas, Nigeria. The empirical equation (United States Bureau of Mines-based approach) was applied for comparison. The comparison of the models demonstrated that the proposed deep ensemble model achieved superior performance, offering more accurate predictions and more reliable uncertainty quantification. Specifically, it exhibited the lowest root mean square error (22.674), negative log-likelihood (4.44), and mean prediction interval width (1.769), alongside the highest R2 value (0.77) and prediction interval coverage probability (0.95). The deep ensemble model reached the desired coverage of 95%, demonstrating that uncertainty was not underestimated or overestimated.
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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".