Simplified probabilistic slope stability design charts for cohesive and cohesive-frictional (<i>c</i>-ϕ) soils
Bibliographic record
Abstract
Design charts to estimate the factor of safety for simple slopes with cohesive-frictional (c-[Formula: see text]) soils are now available in the literature; however, the factor of safety is an imperfect measure for quantifying the margin of safety of a slope because nominal identical slopes with the same factor of safety can have different probabilities of failure due to variability in soil properties. In this study, simple circular slip slope stability charts for [Formula: see text] = 0 soils by Taylor in 1937 and c-[Formula: see text] soils published by Steward et al. in 2011 are extended to match estimates of factors of safety to corresponding probabilities of failure. A series of new charts are provided that consider a practical range of coefficient of variation for cohesive and frictional strength parameters of the soil. The data to generate the new charts were produced using conventional probabilistic concepts together with closed-form solutions for cohesive soil cases, and Monte Carlo simulation in combination with conventional limit equilibrium-based circular slip analyses using the SVSlope program for c-[Formula: see text] soil cases. The charts are a useful tool for geotechnical engineers when making a preliminary estimate of the probability of failure of a simple slope without running Monte Carlo simulations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.008 | 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".