Numerical implementation and modeling of earthquake induced landslides for slopes with soft and sensitive clay layers
Bibliographic record
Abstract
Earthquake induced landslides pose a significant threat to many communities, environment and infrastructure. The potential damages could be severe in sensitive clay slope failures because the post-peak softening behaviour could cause retrogressive failure of soil blocks resulting in large-scale landslides. The failed soil blocks generally displace over a large distance during earthquake and post-quake stages. Therefore, upslope retrogression and downslope runout are two important phenomena need to be studied for better understanding of risks associated with landslides in sensitive clays. The traditional limit equilibrium methods, commonly used in slope stability analysis, cannot model retrogressive failure or deformation of slopes. The present study concentrates on development of large deformation finite element (FE) models using a Coupled Eulerian- Lagrangian (CEL) approach to simulate the failure of soft and sensitive clay slopes triggered by earthquakes. Analyses are performed for pseudostatic and dynamic loading conditions modeling the undrained behaviour of clay as elasto-plastic material with and without post-peak degradation of shear strength. A nonlinear post-peak strength degradation model as a function of accumulated plastic shear strain is implemented in FE analysis. In addition to CEL, FE analyses are performed using Lagrangian-based FE techniques to show the advantages of CEL to simulate large landslides. The CEL approach can successfully simulate the formation of shear bands (zone of accumulated shear strains), type of failure commonly observed after earthquake, upslope retrogression and downslope runout for varying geometry and soil properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".