The Influence of Strength Variability in the Analysis of Slope Failure Risk
Bibliographic record
Abstract
Probabilistic analysis of failure problems in geomechanics is often directed towards assessing the mean and variance of design quantities (e.g. Factor of Safety, bearing capacity, limiting earth pressure) as a function of the mean and variance of input quantities (e.g. shear strength parameters). When spatial correlation length is also included as an input parameter, an additional complexity is introduced in that this parameter directly impacts the locally averaged shear strength along a failure surface. A key advantage of the Random Finite Element Method (RFEM) over conventional methods is that no a priori assumptions are made about the shape or location of the critical failure mechanism. The RFEM enables the mechanism to "seek out" the critical route leading to the minimum factor of safety. By forcing the mechanism to be circular (say), traditional approaches are inevitably "upper bound" and can lead to unconservative conclusions regarding slope failure risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".