Rock mass strength variability for probabilistic open-pit slope stability analysis
Bibliographic record
Abstract
As part of reliability-based design acceptance criteria, probabilistic slope stability analysis is increasingly being used for open-pit slope design. This analysis evaluates the mean factor of safety, probability of failure, and coefficient of variation for the resulting probability density function of factor of safety values. Estimating rock mass strength variability is crucial. Hoek–Brown criteria are commonly used strength parameters, as are equivalent Mohr–Coulomb parameters (calculated from Hoek–Brown), particularly for probabilistic slope stability analysis. This article describes these two strength criteria when considering univariate and bivariate distributions of the unconfined compressive strength and rock material constant. Results demonstrate differences in the variability of the equivalent Mohr–Coulomb parameters under different dependence considerations than the Hoek–Brown parameters, potentially affecting the calculated probability of failure and factor of safety results. Furthermore, they highlight an inherent correlation between Mohr–Coulomb parameters that derives from the algorithm used to calculate them from Hoek–Brown criteria. This inherent correlation is important to obtain mean factors of safety, probabilities of failure, and coefficients of variation that are consistent with the variability in Hoek–Brown parameters estimated by the practitioner and are, therefore, key to informed implementation of reliability-based design acceptance criteria.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".