Modeling of large-deformation behaviour of marine sensitive clays and its application to submarine slope stability analysis
Bibliographic record
Abstract
Post-slide investigations suggest that many large-scale submarine landslides occur through marine sensitive clay layers. A nonlinear mathematical model for post-peak degradation of undrained shear strength of sensitive clay is proposed based on experimental results. A method for estimation of model parameters is presented. Incorporating the model, an analytical solution is developed to examine possible mechanisms of large-scale submarine landslides. Analyses are performed for mild infinite slopes where the failure initiates from a “fully weakened zone” of soil having undrained shear strength lower than the shear stress acting parallel to the slope. The driving force, in excess of resistance, generated from the fully weakened zone is then transferred to the surrounding soil elements resulting in shear band formation due to strain-softening behaviour of sensitive clays. When the length of the fully weakened zone is greater than a critical length, catastrophic shear band propagation (self-driven without any additional external force) occurs, which could result in large-scale offshore landslides. A simple design chart is developed to calculate the critical length. Compared with a 2005 study by Puzrin and Germanovich based on a linear post-peak shear strength degradation model, the present study gives a conservative estimation of critical length for catastrophic shear band propagation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".