Influence of Probability Distribution of Shear Strength Parameters on Reliability-Based Rock Slope Analysis
Bibliographic record
Abstract
This paper presents a study on the influence of the probability distribution curve of shear strength parameters on the reliability analysis of a rock slope. The findings of this investigation is applied a rock slope in Luoyang, China. The probability distribution of shear strength parameters (c and φ) may not follow the normal distribution assumed in most reliability-based analyses. There is a negative correlation between c and φ, which will affect significantly the results of the reliability analyses. In order to study the influence of a probability distribution curve, four different types of distribution curves are used in this study. It is concluded that the failure probability of a rock slope is sensitive to the probability distribution of the shear strength parameters. Based on this study, the failure probability can vary about ten times between two extreme cases where two different distribution curves are used. It is also found that the failure probability increases while the correlation coefficient between c and φ decreases. A sensitivity analysis of the probability distribution curve is suggested for future reliability analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".