Evaluation of sandy soil stabilized with Tragacanth gum biopolymer for geotechnical applications
Bibliographic record
Abstract
• TG stabilized sand as a sustainable construction material is evaluated. • The influence factors include TG content, density and time. • UCS improvement ranged from 177 % to 259 %, depending on the dry unit weight. • Cohesion increased up to 562 %, while internal friction angle boosted up to 51 %. • SEM images show formation of biopolymer network, reducing void spaces. Environmentally friendly soil improvement approaches are recently a global interest due to their lower environmental impact compared to traditional stabilizers. The traditional stabilizers, such as cement, contribute significantly to greenhouse gas emissions and soil degradation. Tragacanth Gum (TG), a carbohydrate polymer, is an eco-friendly additive, which offers a more sustainable and less polluting alternative. Only a few studies on the strength behavior of TG stabilized soils have been conducted. This study investigates the effect of TG addition in improving low-strength sandy soil. Unconfined compressive strength (UCS), direct shear, and scanning electron microscopy (SEM) tests were carried out on stabilized sandy soil with TG at 0.5 %, 1 %, and 2 % by weight after curing times of 1, 3, and 7 days to evaluate their short-term performance. UCS values increased from 20 kPa to 72 kPa, depending on the dry unit weight of sandy soil. The TG stabilization improved cohesion value from 8 kPa to 53 kPa and internal friction angle from 30.88° to 46.64°, for dry unit weights of 16 and 17 kN/m 3 , respectively. This study shows the prospect of using TG as a greener additive in geotechnical and pavement applications.
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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) | 0.001 | 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) | 0.001 | 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".