Probabilistic slope stability analysis for practice
Bibliographic record
Abstract
The impact of uncertainty on the reliability of slope design and performance assessment is often significant. Conventional slope practice based on the factor of safety cannot explicitly address uncertainty, thus compromising the adequacy of projections. Probabilistic techniques are rational means to quantify and incorporate uncertainty into slope analysis and design. A spreadsheet approach for probabilistic slope stability analysis is developed. The methodology is based on Monte Carlo simulation using the familiar and readily available software, Microsoft® Excel 97 and @Risk. The analysis accounts for the spatial variability of the input variables, the statistical uncertainty due to limited data, and biases in the empirical factors and correlations used. The approach is simple and can be applied in practice with little effort beyond that needed in a conventional analysis. The methodology is illustrated by a probabilistic slope analysis of the dykes of the James Bay hydroelectric project. The results are compared with those obtained using the first-order second-moment method, and the practical insights gained through the analysis are highlighted. The deficiencies of a simpler probabilistic analysis are illustrated. Key words: probabilistic analysis, slope stability, Monte Carlo simulation, spatial variability.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".