Soil-water characteristic curves of bentonites in isochoric conditions during wetting: measurement and prediction
Bibliographic record
Abstract
The determination of the soil-water characteristic curve (SWCC) of compacted bentonites in the isochoric condition is a prerequisite for unsaturated flow simulations in several geoenvironmental applications. The SWCC data are, however, not readily available for many compacted bentonites over a wide suction range due to difficulties associated with the testing. In this work, wetting SWCCs of four Indian bentonites of different plasticity were established experimentally at compaction dry densities of 1.4, 1.6, and 1.8 Mg/m 3 in isochoric conditions using two independent laboratory techniques in different suction ranges. The modified Kovács (MK) model was extended to simulate the SWCC data of the Indian bentonites. Empirical relationships between MK model parameters and compaction dry density for these bentonites were established. A theoretical procedure for the estimation of SWCCs of the compacted bentonites based on the basic bentonite properties and proposed correlations was proposed and validated on the available wetting SWCC data of compacted GaoMiaoZi (GMZ), MX80, and FEBEX bentonites from the literature studies. The proposed method predicts the wetting SWCCs of compacted powder bentonites, in volume-restrained condition, from the basic properties satisfactorily. The proposed model is an approximate method for predicting the SWCCs of bentonites in the absence of measured data and in understanding the influence of dry density and plasticity.
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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 | 0.001 |
| 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) | 0.000 | 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".