Data-based assessment of rock strengths and cuttability using the monitored parameters while drilling, tunneling, and mining
Bibliographic record
Abstract
Mechanized rock excavation is a commonly used method in underground projects in mining and tunneling. The performance of mechanized rock breaking depends on the rock’s cuttability, which is traditionally analyzed through extensive field experiments, significantly affecting work efficiency. Therefore, establishing a method to quickly and accurately assess the rock mass characteristics and cuttability based on drilling, tunneling, and mining parameters is of great importance in practice. In this study, three databases were developed for drilling parameters, tunneling parameters, and mining parameters from field and experimental measurements. The hiking optimization algorithm (HOA) and Ivy algorithm (IVYA) were introduced to optimize the gradient boosting decision tree (GBDT) model. For each database, two hybrid ensemble models were developed to predict uniaxial compressive strength (UCS) and identifying cuttability levels. The training and testing dataset division ratio for all models is 4:1, with cross-validation applied to prevent overfitting. The results indicate that the developed models can be directly applied to real-time analysis of rock strength characteristics and cuttability based on drilling, tunneling, and mining parameters, facilitating the real-time adjustment of cutting equipment operation parameters and improving rock-breaking efficiency. Finally, a user-friendly graphical user interface (GUI) was developed for easy use by non-algorithm operators on site.
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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 | 0.000 | 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".