Large deformation dynamic analysis of progressive failure in layered clayey slopes under seismic loading using the particle finite element method
Bibliographic record
Abstract
Abstract This paper presents the failure analysis of layered clayey slopes with emphasis on the combined effect of the clay’s weakening behavior and the seismic loading using the particle finite element method (PFEM). Diverse failure mechanisms have been disclosed via the PFEM modelling when the strain-weakening behavior of clay is concerned. In contrast to a single layered slope exhibiting either a shallow or a deep failure mode, a layered slope may undergo both failure modes with a time interval in between. Seismic loadings also enlarge the scale of slope failure in clays with weakening behavior. The failure of a real layered slope (i.e. the 1988 Saint-Adelphe landslide, Canada) triggered by the Saguenay earthquake is also studied in this paper. The simulation results reveal that the choice of the strain-softening value controls the slip surface of the landslide and the amplification effect is important in the triggering of the landslide.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".