New rock toughness index based on drilling and tensile strength by using entropy of decision tree and geotechnical analysis
Bibliographic record
Abstract
The term ‘toughness’ refers to a rock's resistance to excavation and indentation by boring or cutting tools. It is evaluated in relation to the ductile properties of rocks, which show visible plastic deformation. Various indices exist for evaluating toughness, which is the opposite of brittleness. In this study, 24 samples from three types of rocks (igneous, metamorphic and sedimentary) were selected to evaluate the toughness indices. The toughness indices were assessed based on existing methods such as strain, energy and strength, and specialized tests such as the brittleness value (S 20 ), Sievers’ J value (S J ), punch penetration test (BI), and porosity (n). The best toughness index found by using entropy and Gini split from the Iterative Dichotomiser 3 algorithm decision tree was S J = 18. By using the Sievers’ J value and rock tensile strength a new rock toughness index is proposed, which is related to specific energy. A new toughness index is presented as the S J drillability coefficient toughness (T SDC ). The value of T SDC = 5 N mm –3 was found to be a good threshold for indicating the energy consumed during drilling and rock excavation. The results showed that the energy required to excavate tough rocks is greater than for brittle rocks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".