Centrifuge modelling of static liquefaction in submarine slopes: scaling law dilemma
Bibliographic record
Abstract
The scaling laws for the centrifuge modelling of the initiation and propagation of static liquefaction in submerged slopes are investigated in this paper. A theoretical model is developed to analytically determine the scaling factor of fluid viscosity in simulating the onset of static liquefaction by detailed analysis of the hydromechanical processes at the grain scale. Based on this, a fluid with a viscosity of [Formula: see text]-times that of water ([Formula: see text]-fluid) is suggested, where N is the geometrical scaling factor in centrifuge modelling. A fluid with a viscosity of N-times that of water (N-fluid) was adopted for simulating dynamic events in the centrifuge; N-fluid is used and suggested by previous researchers. Centrifuge tests were designed to examine and verify the scaling factors for pore fluid viscosity in simulating the onset of static liquefaction and the post-liquefaction behaviour of subaqueous landslides. These tests were performed at 10g, 30g, and 50g conditions, with [Formula: see text]-fluid, N-fluid or water, where g is the Earth’s gravitational acceleration. Results confirm that the correct scaling factors (prototype/model) for pore fluid viscosity are [Formula: see text] and 1/N for investigating the onset of static liquefaction and the post-failure behaviour of the submarine slopes, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".