Probabilistic Design of Slopes in Normally Consolidated Clays
Bibliographic record
Abstract
Natural soils always exhibit spatial variability with the properties varying from point to point. The majority of probabilistic slope stability analyses model strength parameters as stationary random fields, i.e. the mean and standard deviation of strength are constant everywhere. Due to the influence of the effective overburden pressure however, normally consolidated soils regularly display an increasing undrained strength trend with depth. Using recently published deterministic solutions as a benchmark, this paper uses random finite element method (RFEM) to investigate the influence of spatial variability on the undrained stability of non-stationary random slopes, where the mean strength increases linearly with depth while the coefficient of variation remains constant. Results are presented in the form of charts that give the mean and standard deviation of the dimensionless stability number. By reading the charts presented in this paper, engineers can obtain a preliminarily assessment of the probability of slope failure for normally consolidated clay slopes.
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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 | 0.000 |
| 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".