On the use of the KozenyCarman equation to predict the hydraulic conductivity of soils
Bibliographic record
Abstract
The saturated hydraulic conductivity of a soil can be predicted using empirical relationships, capillary models, statistical models, and hydraulic radius theories. A well-known relationship between permeability and the properties of pores was proposed by Kozeny and later modified by Carman. The resulting equation is largely known as the KozenyCarman (KC) equation, although the two authors never published together. In the geotechnical literature, there is a large consensus that the KC equation applies to sands but not to clays. This view, however, is supported only by partial demonstration. This paper evaluates the background and the validity of the KC equation using laboratory permeability tests. Test results were taken from publications that provided all of the information needed to make a prediction: void ratio, and, either the measured specific surface for cohesive soils, or the gradation curve for noncohesive soils. The paper shows how to estimate the specific surface of a noncohesive soil from its gradation curve. The results presented here show that, as a general rule, the KC equation predicts fairly well the saturated hydraulic conductivity of most soils. Many of the observed discrepancies can be related to either practical reasons (e.g., inaccurate specific surface value; steady flow not reached; unsaturated specimens, etc.) or theoretical reasons (some water is motionless; hydraulic conductivity of soils is anisotropic). These issues are discussed in relation to the predictive capabilities of the KC equation.Key words: permeability, prediction, gradation curve, specific surface.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".