Multiscale insights into Sliding Surface Liquefaction through DEM simulations
Bibliographic record
Abstract
Recognizing the mechanisms that trigger liquefaction is critical for developing reliable models to prevent landslides. The tendency for liquefaction to occur generally decreases with increasing soil density. However, when grain fragmentation occurs, the material becomes more contractive, making liquefaction possible even in relatively dense samples. This phenomenon was first recognized and named Sliding Surface Liquefaction (SSL) by Kyoji Sassa’s research group ( Soils Found , a=Vol 36, 1996, pp.53-64 ), who reported comprehensive laboratory studies on the topic. Yet, the mechanisms at the grain scale remain poorly understood. To advance in the understanding of SSL and support the development of predictive models, we investigate the links between micro- and macromechanical behavior in crushable granular materials subjected to constant volume shearing. We perform two-dimensional simulations using the Contact Dynamics Discrete Element Method, focusing on the effects of particle fragmentation strength and grading evolution during undrained shearing until liquefaction. The results reveal that higher densities and particle strength delay the onset of liquefaction. At high densities, regardless of the strength of the particles, grading during crushing asymptotically approaches an ultimate distribution, which depends on the initial density and is not associated with the occurrence of liquefaction. Although the amount of grain fragmentation is lower in looser samples, liquefaction occurs in earlier stages than in denser cases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".