A Heuristic Search Method for Critical Slip Surfaces with Weak Layers
Bibliographic record
Abstract
In slope stability, weak layers are thin layers of low-strength materials which can potentially form part of the critical sliding surface. The search for the critical slip surface in slope stability is a complicated optimization problem that minimizes the factor of safety by altering the parameters corresponding to the geometry of a slip surface. While searching for the critical slip surface, it is important to fully consider the weak layers in a model. Traditionally, slope stability programs have handled weak layers by clipping the slip surfaces, which are generated during the automatic searching stage, to the weak layers. If multiple weak layers touch a slip surface, there are multiple permutations of clippings that can be performed on the slip surface by the weak layers using any, all, or none of those weak layers. As such, it can become very difficult to identify which combination of the weak layers would produce the lowest factor of safety. In this paper, a new method is proposed which efficiently considers the contribution of each weak layer via an additional optimization parameter. The proposed approach is demonstrated via an example and is shown to be relatively faster in speed against leading industry methods, while maintaining comparable accuracy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".