Influence of Mesh Size, Number of Slices, and Number of Simulations in Probabilistic Analysis of Slopes Considering 2D Spatial Variability of Soil Properties
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Bibliographic record
Abstract
The random limit equilibrium method (RLEM) is a relatively new method of probabilistic slope stability analysis which uses a combination of 2D random field theory, limit equilibrium methods, and Monte Carlo simulation. The random finite element method (RFEM) uses a combination of 2D random field theory, finite element method of analysis, strength reduction method, and Monte Carlo simulation. In this paper, the effects of mesh size, number of slices, and number of Monte Carlo simulations on computed probability of failure are investigated using both approaches. Computation times using both methods to solve the same slope problem are also compared. Recommendations for mesh size, number of slices, and number of Monte Carlo simulations, with respect to the spatial correlation length, using RLEM are presented.
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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 | 0.000 | 0.001 |
| 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