Measurements of Shear Wave Velocity for Collapsible Soil
Bibliographic record
Abstract
This paper examines the effects of collapsible soil structure on shear wave velocity. The study attempts to simulate hydraulic fill sand deposits, which represent a natural soil deposition process that can result in a collapsible soil structure. A series of resonant column tests and bender element tests on Ottawa sand was conducted on sand specimens and prepared by dry pluviation and simulated hydraulic fill methods subjected to various confining pressures. Shear wave velocities measured from both methods of deposition are compared and discussed. Results from this study show that for soil specimens with the same void ratio, samples prepared by simulated hydraulic fill have a lower shear modulus and shear wave velocity than the specimens prepared by dry pluviation, and the differences are more pronounced at higher confining pressures. The resonant column test results performed in this study were consistent with results from the discrete element analysis, full-scale testing, and centrifuge testing. The discrete element analysis suggests that soil fabric and number of particle contacts are the key factors affecting the shear wave velocity. These factors are dependent on the methods of deposition. Results from this study examining hydraulic fill collapsible structure shear wave velocity provide a step forward toward a better correlation between soil dynamic properties measured in field and laboratory tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".