3D slope stability analysis considering strength anisotropy by a micro-structure tensor enhanced elasto-plastic finite element method
Bibliographic record
Abstract
This article presents a micro-structure tensor enhanced elasto-plastic finite element (FE) method to address strength anisotropy in three-dimensional (3D) soil slope stability analysis. The gravity increase method (GIM) is employed to analyze the stability of 3D anisotropic soil slopes. The accuracy of the proposed method is first verified against the data in the literature. We then simulate the 3D soil slope with a straight slope surface and the convex and concave slope surfaces with a 90° turning corner to study the 3D effect on slope stability and the failure mechanism under anisotropy conditions. Based on our numerical results, the end effect significantly impacts the failure mechanism and safety factor. Anisotropy degree notably affects the safety factor, with higher degrees leading to deeper landslides. For concave slopes, they can be approximated by straight slopes with suitable boundary conditions to assess their stability. Furthermore, a case study of the Saint-Alban test embankment A in Quebec, Canada, is provided to demonstrate the applicability of the proposed FE model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".