Effects of Aggregate Micro Fines (AMF), Aluminum Sulfate and Polypropylene Fiber (PPF) on Properties of Machine-Made Sand Concrete
Bibliographic record
Abstract
With the depletion and increasing demand of river sand, machine-made sand could be used more and more in concrete. In order to improve the properties of machine-made sand concrete, the effects of the aggregate micro fines (AMF) content, aluminum sulfate, and polypropylene fibers (PPF) on the slump, compressive strength, water permeability, and the chloride permeability coefficients were investigated through a single factor test method, and related mechanisms were analyzed. The results show that the optimum contents of AMF, aluminum sulfate, and the polypropylene fiber are 10 wt%, 1 wt%, and 0.6 kg/m3, respectively. The optimum content of AMF improved the compactness of concrete. The addition of aluminum sulfate promoted the initial formation of ettringite, and thereby improved the compressive strength and the permeability resistance. The polypropylene fiber can modify the pore structure distribution of concrete and reduce the porosity, thereby improving the impermeability of the concrete. The compressive strength of the machine-made sand concrete could be increased by more than 20%, and the water/chloride permeability coefficients could be decreased by more than 45%.
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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 | 0.001 |
| 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".