Propagation velocity of landslide-induced liquefaction and entrainment of overridden loose, saturated sediments
Bibliographic record
Abstract
Loose saturated granular materials are particularly susceptible to instability, resulting in deviatoric strain softening, and static liquefaction. When instability occurs in the context of a landslide, the consequences in terms of the mobility of the debris and risk to life and property can be catastrophic. Physical model landslides initiated in a geotechnical centrifuge under rising groundwater conditions were used to trigger instability and static liquefaction. Four experiments with a loose contractile soil and two experiments with a dense dilative soil were performed. The velocity of propagation of the liquefaction front within the loose granular soils at the base of a landslide was quantified using a dense sensor network of pore water pressure sensors and high-speed imaging. On triggering of a landslide, a localized toe failure was observed to shear and liquefy the soil at the base of the landslide. However, the velocity of this initial failure (0.5 m/s) was an order of magnitude slower than the subsequent 4.2 m/s propagation velocity of the liquefaction front. These experiments demonstrated and quantified how a localized failure onto a liquefiable deposit may propagate liquefaction much farther than simply the runout of the localized failure, and highlight the potential implications and consequences of such an occurrence.
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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 | 0.000 |
| 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) | 0.001 | 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".