A New Stochastic Response Surface Method in Spatial Variability Slope Stability Analysis
Bibliographic record
Abstract
Spatial variability is becoming more and more common in limit equilibrium slope stability analysis.With the recent advances in the abilities for limit equilibrium slope stability analysis to handle complicated models, it is more important than ever to have a fast and accurate method to perform slope stability analysis in spatially varying soils.The use of response surfaces is widely adopted in the literature to deliver efficient stochastic analyses.However, their implementation is usually deeply correlated with the choice of a field generation algorithm.This makes the response surface method inflexible since it sometimes requires the field generation algorithm to be completely rewritten.Finely discretized spatially varying random fields can have many random variables which make the response surface difficult to determine.To solve this, the authors have proposed a modified response surface guided approach to slope reliability analysis in spatially varying soils which is independent of the choice of field generation algorithm.This approach was evaluated using a two-dimensional problem, and validated by comparing its accuracy and speed against the traditional Monte Carlo simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".