Microporous Membrane Technology for Measurement of Soil-Water Characteristic Curve
Bibliographic record
Abstract
Abstract High air entry ceramic disks are commonly used for the control of matric suction in triaxial and direct shear tests and in tests for obtaining the soil-water characteristic curve. Geotechnical engineers have made increasing use of high air entry ceramic disks in order to measure or control matric suctions during unsaturated soil testing. One of the limitations associated with the use of high air entry ceramic disks has been the time required in order for equilibrium conditions to be established across the disk. Improving the efficiency associated with establishing suction equilibrium is of considerable interest to geotechnical engineers. Pressure membrane technology involving the use of microporous membranes is one possible technology that could result in improved performance for the measurement or control of matric suctions. If microporous membrane technology can be used, the end result could provide considerable time and cost savings, particularly in the measurement of soil-water characteristic curves (SWCCs). In this study, a new apparatus was developed to make use of a microporous membrane for the measurement of the SWCC with matric suction of up to 25 kPa. The maximum AEV (i.e., air-entry value) of the membrane is 250 kPa. This paper presents the results of laboratory SWCC measurements on several soil types using pressure membrane technology. Comparisons are made between the soil-water characteristic curves measured using a conventional pressure plate apparatus and those obtained from the new micro membrane apparatus.
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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".