A New Apparatus for Seepage and Internal Erosion Soil Column Tests in Geotechnical Centrifuge
Bibliographic record
Abstract
ABSTRACT Understanding the hypergravity effect on seepage and internal erosion is the essential precondition for dam hydraulic disaster modeling using geotechnical centrifuges. Soil column testing is useful to bridge this knowledge gap, but previous attempts did not provide adequate functionality in centrifuge environments. This study develops a centrifuge-available apparatus for seepage and internal erosion soil column tests (CASIE). CASIE ensures a consistent and stable circulating water supply with no less than 34,000 ml/min at 80 g via double-bowl upstream and downstream water tanks and a vertical, multistage centrifugal pump. The hydraulic gradient can be controlled by adjusting the elevation of the upstream water tank using a servo lifting system with a vertical displacement range of 1.2 m and a maximum vertical speed of 155 mm/min. A rigid-wall permeameter is developed for multiple applications in soil column tests for seepage and internal erosion. The flowrate through the specimen can be measured using four parallel-installed oval gear flowmeters with a large measurement range of 10–10,000 ml/min. To validate the capabilities of CASIE, two suffusion (one form of internal erosion) tests were conducted at 1 g and 30 g. The results reveal that the scaling factor for the critical hydraulic gradient of 30 g to 1 g is 1/10. It is much less than the predicted value of 1, indicating that suffusion failure is more readily triggered in the hypergravity environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".