Predicting the saturated hydraulic conductivity of sand and gravel using effective diameter and void ratio
Bibliographic record
Abstract
This paper assesses methods to predict the saturated hydraulic conductivity, k, of clean sand and gravel. Currently, in engineering, the most widely used predictive methods are those of Hazen and the Naval Facilities Engineering Command (NAVFAC). This paper shows how the Hazen equation, which is valid only for loose packing when the porosity, n, is close to its maximum value, can be extended to any value of n the soil can take when its maximum value of n is known. The resulting extended Hazen equation is compared with the single equation that summarizes the NAVFAC chart. The predictive capacity of the two equations is assessed using published laboratory data for homogenized sand and gravel specimens, with an effective diameter d 10 between 0.13 and 1.98 mm and a void ratio e between 0.4 and 1.5. A new equation is proposed, based on a best fit equation in a graph of the logarithm of measured k versus the logarithm of d 10 2 e 3 /(1 + e). The distribution curves of the differences log(measured k) log(predicted k) have mean values of 0.07, 0.21, and 0.00 for the extended Hazen, NAVFAC, and new equations, respectively, with standard deviations of 0.23, 0.36, and 0.10, respectively. Using the values of d 10 and e, the new equation predicts a k value usually between 0.5 and 2.0 times the measured k value for the considered data. It is shown that the predictive capacity of this new equation may be extended to natural nonplastic silty soils, but not to crushed soils or plastic silty soils. The paper discusses several factors affecting the inaccuracy of predictions and laboratory test results.Key words: permeability, sand, prediction, porosity, gradation curve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".