Soil-Water Characteristic Curves of Stabilized Expansive Soils
Bibliographic record
Abstract
The engineering properties of expansive soils are conventionally improved through the use of additives such as fly ash, lime, and chemical additives. Such soils are often referred to as stabilized or modified or treated expansive soils. The soil-water characteristic curves (SWCC) of two expansive soils from Texas were measured both in natural and stabilized conditions using the pressure plate apparatus in the suction range of 0-1,000 kPa. The SWCC results are used to interpret the expansive soil behavior due to stabilizer treatment. In addition, relationships were developed between the basic soil and stabilizer properties such as water content, dry density, liquid limit, plastic limit, and stabilizer dosages and the model constants of the SWCC formulation of Fredlund and Xing via multiple linear regression analysis. The analysis showed that higher coefficients of correlations can be achieved by using six independent soil properties. The comparisons between the predicted and measured volumetric water contents are within ±20% for ash-treated expansive soils, and within ±15% for combined ash- and fiber-treated expansive soils. The research data and interpretation analysis presented here can be extended to understand volume change behaviors of other stabilized expansive soils using the SWCC test data.
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 | 0.000 | 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) | 0.001 | 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".