Porous stones in permeability measurement: drawbacks and solution
Bibliographic record
Abstract
Porous stones are commonly used in geotechnical laboratory testing to provide solid support at the two ends of the tested specimen. Although porous stones can have several advantages, they can induce a hydraulic impedance that might alter the flowrate results. Numerous suggestions and recommendations were issued to avoid some of the problems encountered when using porous stones, such as clogging and stones' low permeability coefficient ( Kp). However, it is proven in this technical note that the existence of a soil–porous stones interaction prevents the elimination of porous stones' influence, leading to unreliable permeability results. A practical solution based on manufacturing unique porous stones (MS) is suggested in an attempt to eliminate such an influence. The porous stones' influence is highlighted by running excess pore pressure dissipation tests under triaxial conditions with calibrated beads. The results show a soil–porous stones interaction and a flowrate increase up to 700% due to MS. This interaction is addressed through permeability measurements of three soil types in a flexible-wall permeameter using standard porous stones (SS) and the newly manufactured stones (MS). At least one order of magnitude of difference in the soil permeability measurement and a decrease in the dissipation test time are observed with MS. This technical note also validates the limitation of the superposition approach for correcting the soil's permeability ( Ksoil). It is shown that porous stones can induce a signature directly related to the soil type and applied pressure.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".