Geotechnical engineering behaviors of gellan gum biopolymer treated sand
Bibliographic record
Abstract
Biological approaches have recently been explored as environmentally friendly alternatives to engineered soil methods in geotechnical engineering practices. The use of microbial induced calcite precipitation, reactive enzymes, and microbial polymers, such as biopolymers, in soil improvement has been studied by researchers around the world. In the present study, gellan gum, a microbial polysaccharide generally used in the food industry due to its hydrogel rheology, was used to strengthen sand. The effects of gellan gum on the geotechnical behaviors of cohesionless sand were evaluated through a series of experimental programs including an unconfined compression test, direct shear test, falling head permeability test, and scanning electron microscopy. The geotechnical properties (friction angle, cohesion, and unconfined compressive strength) of gellan gum–treated sands were determined based on varying moisture conditions: initial, dried, and re-submerged. Gellan gum has a distinct strengthening effect on cohesionless sands through artificial cohesion that varies with the moisture conditions. The strengthening effect of gellan gum on sand appears to be a result of the combination of enhanced bonding between unreactive sand particles and the agglomeration of sand particles through hydrogel condensation, in which the agglomerated sand particles behave as enlarged aggregates in soil.
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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) | 0.001 | 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".