Numerical modelling of submarine landslides with sensitive clay layers
Bibliographic record
Abstract
A large-deformation finite-element (FE) modelling technique is presented to model submarine landslides. A strain softening model for undrained shear strength of marine clay is incorporated in the FE modelling. The development of large plastic shear strain concentrated zones (shear bands) and their propagation with displacement of soil mass are simulated. FE simulations show that the existence of a weak layer might result in the initiation and propagation of shear bands leading to large-scale progressive landslides. Such progressive development of failure planes cannot be simulated using the limit equilibrium method of slope stability analysis. Depending upon the geometry and soil properties, a number of failure patterns are identified which are comparable with morphologic features seen in field observations. Based on this type of FE analysis and compared with seabed morphology, the developed failure planes could be identified where the shear strengths are expected to be lower because of pre-shearing than the shear strengths of the soil outside these zones, which could then be implemented in the modelling of seabed for offshore development projects in the areas where failure occurred in the past.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".