Surface altering optimisation in slope stability analysis with non-circular failure for random limit equilibrium method
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Bibliographic record
Abstract
In limit equilibrium slope stability analysis, surface altering optimisation (SAO) is a novel approach to minimise the factor of safety for a given slip surface using spline curves in 2D. It is a local search algorithm that when combined with a global search method, can form a powerful hybrid optimisation technique used in slope stability analysis. Probabilistic analysis of a slope with spatial variability is a computationally intensive example that would demonstrate the accuracy and speed of optimisation techniques. In this paper, the probabilistic analysis results of three different slopes with both complicated and straightforward geometries are presented, and the application of SAO in spatial variability analysis using random limit equilibrium method (RLEM) is demonstrated. It was found that SAO combined with a global search method provides fairly accurate results and yields curtailed computational effort.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it