On Preferred Saturation Methods for Geotechnical Flow Tests
Bibliographic record
Abstract
Abstract Laboratory flow tests performed on granular materials in rigid-wall permeameters are used to determine the hydraulic conductivity of saturated materials or to study particle migration. For such tests, specimen preparation is important, particularly the saturation process. The presence of gas bubbles in the tested specimen leads to smaller hydraulic conductivity values and may affect particle migration. Therefore, measures must be taken to obtain fully saturated specimens. In order to improve saturation, two elements have to be taken into consideration: first, the presence of gas in the permeant water; second, the presence of gas in the pores of the specimen. Several flow tests are described in ASTM standards. These standards involve different conditioning methods for permeant water (e.g., heating, seepage through a sand filter, cavitation under vacuum), and different specimen saturation methods (e.g., saturation with an upward flow under vacuum, following carbon dioxide flushing). In this article, water deaeration and specimen saturation methods were evaluated and compared using a mass-volume method. In the first phase, the importance of using deaerated permeant water was examined by comparing different conditioning methods for permeant water. Saturation using water in equilibrium with atmospheric pressure was compared with saturation with water heated at 40°C, water deaerated using a sand filter, and water deaerated by cavitation under vacuum. In the second phase, the influence of purging the gas contained in the soil specimen before saturation using carbon dioxide or a vacuum was compared. In both cases, the permeant water was deaerated by cavitation under vacuum. These tests showed that combining a purge of the specimen with carbon dioxide with saturation using an upward flow of water deaerated by cavitation under vacuum yielded the highest degree of saturation for granular specimens in a rigid-wall permeameter. Another advantage of this combination is that it accelerates specimen saturation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".