Stability assessment of slopes with cracks using limit analysis
Bibliographic record
Abstract
Cracks are a common occurrence in soil slopes, and a method is described for including the presence of cracks in stability assessment based on the kinematic approach of limit analysis. While many cracks may be present in a slope, the failure mechanism typically involves one crack, whose location has the most adverse influence on stability. A translational mechanism, typical of rock slope failures, is demonstrated to illustrate the method, followed by a rotation collapse analysis that is more appropriate for soils. Pre-existing (open) cracks are considered, as well as the cracks that form as part of the slope collapse mechanism. The maximum crack depth is determined by stability of the vertical crack boundary. This maximum crack depth may be reduced significantly by seepage forces in the slope. The most adverse location of the crack is determined from an optimization procedure where the minimum of the slope critical height is sought. The presence of water is included in the analysis, and stability charts are developed. The influence of the presence of cracks on stability of gentle slopes was not found to be significant, but the effect on the outcome of the analysis increases with an increase in inclination angle and the presence of pore-water pressure. The difference in the critical height of a 60° slope with an open crack and without one can be as much as 50%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".