Progressive Failure of Offshore Slopes due to Construction in Upslope Areas
Bibliographic record
Abstract
Human activities such as construction loading in upslope areas could be a potential triggering factor for many offshore landslides such as the 1979 Nice landslide. Post-slide investigations show that the existence of marine sensitive clay layers might be one of the potential causes of many large-scale submarine landslides. In this paper, a finite element (FE) modeling technique is developed to analyze the failure of a slope in undrained condition. Nonlinear strain softening behaviour of undrained shear strength of marine sensitive clays is incorporated in the FE analysis. Strain localization in narrow zones (i.e. shear bands) could be successfully simulated. The formation of shear bands and their propagation could explain some potential failure mechanisms. The FE results show that large-scale catastrophic failure of submarine slopes might have occurred due to shear band propagation through strain softening clay layers, which cannot be explained using the traditional limit equilibrium methods for slope stability analysis. Effects of different factors, such as thickness of the marine clay layer and its sensitivity, on stability of submarine slope are also examined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".