Characteristic triaxial strength of intact rock for LSD
Bibliographic record
Abstract
Determination of characteristic values may be a fundamental step in the design process when reliability-based design (RBD), limit states design (LSD) and load and resistance factor design (LRFD) approaches are applied. However, there seems to be no recognised approach for obtaining characteristic values of triaxial rock strength. This paper compares the use of non-linear regression and non-linear quantile regression models for obtaining estimates of characteristic triaxial strength of intact rock by fitting the non-linear Hoek–Brown empirical strength criterion to published datasets of triaxial rock strength. It is shown that the form of results obtained from a quantile regression model (i.e. a ‘characteristic criterion’) may be more useful to practising engineers than those produced by a non-linear regression model. For an extensive dataset, the methods give similar results. However, with small datasets of a size generally encountered in geotechnical engineering, both methods may be unreliable. This suggests that objective techniques that augment the limited test data should be developed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".