Numerical modeling of combined effects of upward and downward propagation of shear bands on stability of slopes with sensitive clay
Bibliographic record
Abstract
Summary Modeling of progressive development of zones of large inelastic shear deformation (shear band) that results from strain‐softening behavior of sensitive clays could explain the failure mechanisms of large landslides. Because of toe erosion, a shear band can be initiated and propagated upward (inward) from the river bank. On the other hand, upslope surcharge loading could generate shear bands that might propagate down towards the river bank. In the present study, upward and downward propagation of shear bands and failure of sensitive clay slopes are modeled using the Coupled Eulerian Lagrangian approach in Abaqus finite element (FE) software. It is shown that the formation and propagation of shear bands are significantly influenced by kinematic constraints that change with displacements of the soil masses, and therefore the propagation of an existing shear band might be stopped and new shear bands could be formed. The main advantages of the present FE modeling are: (i) extremely large strains in the shear bands can be successfully simulated without numerical issues, (ii) a priori definition of shearing zones is not required to tackle severe strains; instead, the FE program automatically identifies the critical locations for shear band formation and propagation. Toe erosion could significantly increase the slope failure potential because of upslope surcharge loading. FE analyses with a thick and thin sensitive clay layers show that the global failure could occur at lower surcharge loads in the former as compared to the latter cases. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".