Large-deformation finite-element modelling of earthquake-induced landslides considering strain-softening behaviour of sensitive clay
Bibliographic record
Abstract
Large-scale landslides in sensitive clays cannot be explained properly using the traditional limit equilibrium or Lagrangian-based finite-element (FE) methods. In the present study, dynamic FE analysis of sensitive clay slope failures triggered by an earthquake is performed using a large-deformation FE modelling technique. A model for post-peak degradation of undrained shear strength as a function of accumulated plastic shear strain (strain-softening) is implemented in FE analysis. The progressive development of “shear bands” (the zone of high plastic shear strains) that causes the failure of a number of soil blocks is simulated successfully. Failure of a slope could occur during an earthquake and also at the post-quake stage until the failed soil masses come to a new static equilibrium. Upslope retrogression and downslope runout of the failed soil blocks are examined for varying geometries and soil properties. The present FE simulations can explain some of the conditions required for different types of seismic slope failure (e.g., spread, flowslide or monolithic slides) to be triggered, as observed in the field.
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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 | 0.001 | 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".