Selecting Minimum Factors of Safety for 3D Slope Stability Analyses
Bibliographic record
Abstract
Geotechnical engineers understand there is uncertainty and risk in the input parameters for slope stability analyses and within the analysis methodologies themselves. Decades of research and inverse analyses of slope failures have resulted in widespread acceptance of certain factors of safety (FS) in typical situations, e.g., a static two-dimensional (2D) factor of safety of 1.3 is often used for temporary or low risk slopes and 1.5 for permanent slopes. However, these FSs are not appropriate for use with three-dimensional (3D) analyses because 3D analyses account for additional shear resistance that is generated along the sides of the slide mass. The contribution of the additional shear resistance can be significant in shallow slide masses or for translational slide masses with a width to height ratio less than six, resulting in calculated values of 3D FS that are greater than the calculated 2D FS. To achieve the same level of safety or risk as a static 2D FS of 1.3 or 1.5, the user must use a greater minimum FS for 3D analyses. This paper presents methods for calculating a suitable minimum 3D FS to achieve a similar level of safety or risk as a minimum 2D FS, such as 1.3 or 1.5, would afford.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".