Robust equivalent tunnelling Mohr–Coulomb strength parameters for generalised Hoek–Brown media
Bibliographic record
Abstract
A novel robust method for estimating equivalent tunnelling Mohr–Coulomb strength parameters in elastic–plastic or elasto–brittle–plastic media satisfying Hoek–Brown failure criterion is proposed. Based on the best uniform approximation technique, this method consists of explicit and closed-form formulae in terms of elementary functions, making the estimation of equivalent parameters more convenient than other methods. A simple code is provided and a series of benchmark test cases are performed for validation. When the tunnel support is accurately known, the performance estimates of this method are better than estimates of best fitting in an artificial stress range and best fitting in the existing range methods, but not as accurate as the performance of equating model responses method and that proposed by Jimenez et al. However, when the estimation of support pressure is poor, this method is the most robust among all methods. In elasto–brittle–plastic rock mass, the advantage of robustness of this method is even more obvious. Considering both accuracy and robustness, this method can be employed as a preferable alternative.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".