Mechanical Properties Improvement of Polyvinyl Acetate Polymer-Treated Sand with Plant Fiber Reinforcement
Bibliographic record
Abstract
Abstract Sand as a building material is pretty common in engineering constructions such as slopes and foundation pits. The great instability is generally generated because of the lack of cohesion inside and such instability is more serious during rains or when subjected to excessive external loads. This paper is aimed to study a combination of polyvinyl acetate polymer and sisal fiber as reinforcement materials. The focus of this study is to determine the effects of polymer content, fiber content, and dry sand density on the improvement in mechanical behavior of reinforced poorly graded sand. A series of direct shear, unconfined compression, and tensile tests on the reinforced sand have been conducted. The results suggest that the reinforcement effect increases with dry sand density up to 1.55 g/cm3 and then levels off. For fixed dry sand density, the strength of the improved sand enhances with the augment of polymer and plant fiber contents. At maximum contents, the reinforced sand can have 480 kPa of shear tensile strength, 1,276 kPa of unconfined compressive strength, and 240 kPa of tensile strength. The reinforcement mechanisms are revealed by Scanning Electron Microscope images. Polymer forms firm the polymer–soil matrices that enhance the fiber–sand interactions to mobilize tensile stresses. The combined use of polymer and fiber is preferred in engineering practices because of its zero negative impacts on the environment.
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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".