How to Improve the Quality of Laboratory Permeability Tests in Rigid-Wall Permeameters: A Review
Bibliographic record
Abstract
Abstract ASTM D2434, Standard Test Method for Permeability of Granular Soils (Constant Head) (Withdrawn 2015), and ASTM D5856, Standard Test Method for Measurement of Hydraulic Conductivity of Porous Material Using a Rigid-Wall, Compaction-Mold Permeameter, are used to measure the saturated hydraulic conductivity, Ksat, of soil specimens in rigid-wall permeameters (RWPs). Several laboratory conditions and settings explain why the tests do not give Ksat values but unsaturated hydraulic conductivity, K(Sr), values for a degree of saturation, Sr, that is often in the 80–85 % range. It is suggested to improve ASTM D2434 and ASTM D5856 by adding two requirements: (1) use a watertight-and-airtight RWP (a control method is provided), and (2) use a mass-and-volume method to obtain the true Sr value of the tested specimen. To illustrate potential detrimental impacts of current standards, the article describes a case where sand was planned to be used as a filter layer for a solid waste project. Large quantities of sand had been delivered at the construction site. The Ksat value of the sand, as compacted, had to exceed 10−4 m/s to satisfy a bylaw. To prove this, two laboratories followed ASTM D2434 for their tests but found K values of 5−8 × 10−5 m/s. The project engineers asked the authors to make verifications. The prior tests were redone and yielded similar K values. However, it was found that the real Sr value was close to 80 % instead of being assumed to be 100 %. Other tests were performed after using vacuum and deaired water in a watertight-and-airtight permeameter: the specimens reached Sr = 100 % and gave Ksat values of about 2 × 10−4 m/s, 3–4 times higher than initial tests. As a result, the already delivered sand satisfied the bylaw condition and there was no need to return large quantities of sand already delivered, to purchase a new type of sand after having done laboratory tests, and to have a time delay in construction, all these items having a high economic impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".