Modelling the post-failure stage of rainfall-induced landslides of the flow type
Bibliographic record
Abstract
The geomechanical modelling of failure and post-failure stages of rainfall-induced shallow landslides represents a fundamental issue to the proper assessment of failure conditions and recognizes the potential for long travel distances of the failed soil masses. Considering that these phenomena are among the most catastrophic natural hazards, as a contribution to the topic this paper discusses the potential of a hydromechanical coupled finite element model (FEM) to analyze the post-failure stage using an advanced constitutive model. In particular, simple undrained triaxial tests and experimental evidence of centrifuge tests are reproduced first, for both loose and dense soils. Then, two slope scale benchmarks are analyzed in the cases of vertical downward or horizontal water seepage and for both loose and dense soils. Compared with results obtained through standard limit equilibrium analyses, the coupled FEM provides a new comprehensive framework for failure and post-failure scenarios that includes a significant reduction of mean effective stresses, also in the case of a loose soil slope subjected to vertical downward water seepage. The obtained results are particularly encouraging because they outline the possibility to analyse both the failure and post-failure stages in a unique framework. Moreover, the numerical analyses indicate that the post-failure mechanisms are intimately tied to specific predisposing factors and boundary conditions, rather than to a single mechanical or state parameter of soil, such as, for instance, the soil relative density.
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 | 0.000 |
| 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 | 0.000 | 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".