The Importance of Cross-Correlation in Probabilistic Analyses of Rock Slopes Using Generalized Hoek-Brown Criterion
Bibliographic record
Abstract
The generalized Hoek-Brown failure criterion is widely used in rock mechanics to predict the failure of rock masses. It was introduced to the industry in its entirety in 2002. With the help of this criterion, the difficult to obtain rock mass material constants mb, s, and a (herein termed the “derived parameters”) can be estimated with the use of the more easily obtained field parameters, GSI, mi, and D (herein termed the “primary parameters”). As a result, the primary parameters and the generalized Hoek-Brown criterion have gained wide acceptance in the industry. At the same time, probabilistic analysis has become more popular in recent years. This paper considers the probabilistic analysis of a rock slope. Two cases are considered: (1) the random variables are assigned to the primary parameters; and (2) the random variables are assigned to the equivalent derived parameters. It was found that converting from primary to derived parameters resulted in a very significant difference in results if the correlation of the derived parameters is not considered. The importance of considering cross-correlation coefficients when performing a probabilistic analysis with derived parameters is highlighted.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".