Experimental Study of Cationic-Modified Biopolymer for Increasing the Shear Strength of Sand
Bibliographic record
Abstract
The application of biopolymers as a more environmentally friendly alternative to cement has emerged as an interesting research subject.The purpose is to enhance the shear strength of sandy soils. In this article, the selected biopolymer is cationic-modified starch. It is expected that cationic starch will have less water absorption properties since modified starch has cationic groups in place of the OH- groups found in the normal starch. This cationic-modified starch namely Amylofax. Five types of samples are created for this testing, including Sample A is prepared with the composition of (silica sand + 2% Amylofax T1100 (w/w) + 20% water (w/w))., Sample B consists of (silica sand + 2% Amylofax T2200 (w/w) + 20% water (w/w))., Sample C is comprised of (silica sand + 2% Amylofax T1100 (w/w) + 2% Glucomannan (w/w) + 20% water (w/w)), Sample D consists of (silica sand + 2% Amylofax T2200 (w/w) + 2% Glucomannan (w/w) + 20% water (w/w)), and Sample E includes (Ottawa sand + 2% cement (w/w) + 20% water (w/w)). The samples were tested using a direct shear test apparatus to determine the soil shear strength parameters (c) cohesion and (Ø) internal friction angle. After conducting the tests on the sand samples with the addition of modified starch biopolymer (cationic starch), it was found that the cohesion value was 961kPa, and the internal friction angle was 63°. These results indicate higher shear strength values compared to sand mixed with natural starch.
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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 | 0.001 |
| 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".