Large deformation finite-element modelling of progressive failure leading to spread in sensitive clay slopes
Bibliographic record
Abstract
The occurrence of large landslides in sensitive clays, such as spreads, can be modelled by progressive development of large inelastic shear deformation zones (shear bands). The main objective of the present study is to perform large deformation finite-element modelling of sensitive clay slopes to simulate progressive failure and dislocation of failed soil mass using the coupled Eulerian Lagrangian (CEL) approach available in Abaqus finite-element software. The degradation of undrained shear strength with plastic shear strain (strain-softening) is implemented in Abaqus CEL, which is then used to simulate the initiation and propagation of shear bands due to river bank erosion. The formation of horsts and grabens and dislocation of soil masses that lead to large-scale landslides are simulated. This finite-element model explains the displacements of different blocks in the failed soil mass and also the remoulding of soil around the shear bands. The main advantages of the present finite-element model over other numerical models available in the literature are that it can simulate the whole process of progressive failure leading to spread. The finite-element results are consistent with previous conceptual models proposed from field observations. The parametric study shows that, depending upon geometry and soil properties, toe erosion could cause three types of shear band formation: (a) only a horizontal shear band without any global failure; (b) global failure of only one block of soil; (c) global failure of multiple blocks of soil in the form of horsts and grabens.
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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 | 0.000 |
| 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".