Use of the grain-size distribution for estimation of the soil-water characteristic curve
Bibliographic record
Abstract
The implementation of unsaturated soil mechanics into engineering practice is dependent, to a large extent, upon an ability to estimate unsaturated soil property functions. The soil-water characteristic curve (SWCC), along with the saturated soil properties, has proven to provide a satisfactory basis for estimating the permeability function and shear strength functions for an unsaturated soil. The volume change functions have not been totally defined nor applied in geotechnical engineering. The objective of this paper is to present a procedure for estimating the SWCC from information on the grain-size distribution and the volumemass properties of a soil. SWCCs represent a continuous water content versus soil suction relationship. The proposed method provides an approximate means of estimating the desorption curve corresponding to a soil initially slurried near the liquid limit. The effects of stress history, fabric, confining pressure, and hysteresis are not addressed. A database of published data is used to verify the proposed procedure. The database contains independent measurements of the grain-size distribution and the SWCC. The level of fit between the estimated and measured SWCCs is analyzed statistically. The proposed procedure is compared to previously proposed methods for predicting the SWCC from the grain-size distribution. The results show that the proposed procedure is somewhat superior to previous methods.Key words: soil-water characteristic curve, grain-size distribution, volume-mass properties, pedo-transfer function, unsaturated soil property functions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".