Flow through and Volume Change Behavior of a Compacted Expansive Soil Amended with Natural Biopolymers
Bibliographic record
Abstract
Natural biopolymers offer a sustainable alternative for improving soil behavior due to their inert nature, small dosage requirement, and applicability under ambient temperatures. This research evaluates the efficacy of natural biopolymers for ameliorating an expansive soil by using a 0.5% dosage of cationic chitosan, charge-neutral guar gum, and anionic xanthan gum during compaction. The results of laboratory investigations indicate that the flow through and volume change properties of the expansive soil were affected variably. The dual porosity, characterized by low air entry due to inter-aggregate pores (AEV1 of 4 kPa) and high air entry due to the clay matrix (AEV2 of 200 kPa) of the soil, was healed using chitosan and guar gum (AEV of 200 kPa) but was enhanced by the xanthan gum (AEV1 of 100 kPa and AEV2 of 200 kPa). The s-shaped swell–shrink path of the soil comprised structural (e from 1.23 to 1.11), normal (e from 1.11 to 0.6), and residual stages (e ranged from 0.6–0.43). This shape was converted into a j-shaped path through amendment using chitosan and guar gum, showing no structural volume change, with e from about 1.25 to 0.5, but was reverted to a more pronounced form by xanthan gum, with e from 1.5 to 1.32, 1.32 to 0.49, and 0.49 to 0.34 in the three stages, respectively. The consolidation behavior of the soil was largely unaffected by the addition of biopolymers such that the saturated hydraulic conductivity decreased from 10−9 m/s to 10−12 m/s over a void ratio decrease from 1.1 to 0.6. At a seating stress of 5 kPa, the swelling potential (7.8%) of the soil slightly decreased to 6.9% due to the addition of chitosan but increased to 9.4% and 12.2% with guar gum and xanthan gum, respectively. The use of chitosan and guar gum will allow the compaction of the investigated expansive soil on the dry side of optimum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".