Numerical investigations of the failure mechanism of spreading landslides
Bibliographic record
Abstract
Spreading landslides are geohazards that often occur in sensitive clay areas. A unique characteristic of the spreads is that an initial small local slope failure may cause a catastrophic landslide above the horizontal failure surface, forming horsts and grabens. Although many hypotheses were proposed to explain the failure mechanism of spreads, some topography of the landslides observed from field investigations cannot be well explained by the existing hypotheses. This study revisits the 2010 Saint-Jude spreading landslide by using the coupled Eulerian–Lagrangian approach within ABAQUS to reveal the failure mechanism of spreading landslides. Two types of cross-sections (static and dynamic cross-sections) were selected to monitor the total horizontal force of the sliding mass during the process of migration. It was found that there are two spreading failure mechanisms to form horsts and grabens. The first one is the static spread failure mechanism that global failure occurs accompanied by spreading failure. The other one is the dynamic spreading failure mechanism that after the global failure, the sliding masses break into horsts and grabens during the forward movement under the pushing of the sliding mass at the back and the blocking action of the soil mass in the front. These two failure mechanisms of spreads can well explain various geomorphologic shapes found in the Saint-Jude landslide.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".