Influence of Magnetic Water on Self-compacting Concrete Using Sulphate Resisting Cement
Bibliographic record
Abstract
This paper aims to design a green and high-performance magnetic water concrete from normal materials used for manufacutring concrete.First, magnetic water was prepared by placing magnets into a beaker containing water of 985 Gauss magnetic field.Then, self-compacting concretes (SCCs) were produced with the optimal mix ratio of sulphate resisting cement (SRC), metakaolin, fine aggregate, coarse aggregate, micro steel fibers, super plasticizer and water.Next, a contrastive experiment was carried out between the SCC added with magnetic water, and that added with general water.The results show that the SCC prepared with magnetic water achieved a moderately better workability, 10% higher compressive strength, 5% higher tensile strength, and 6.74% lower price than the SCC prepared with general water.The research provides an innovative technique to build structures using high-performance material with low environment impact at a moderate price.
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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".