Influence of mixture composition on the properties of SCC incorporating metakaolin
Bibliographic record
Abstract
This paper studies and evaluates the properties of self-consolidating concrete (SCC) containing different percentages of metakaolin (MK) by varying the mixture components and mixture proportions. In total, 32 mixtures with varied percentages of MK and mixture compositions are investigated for the effects on compressive strength, flowability, passing ability and high-range water-reducer admixture (HRWRA) demand. The percentage of MK, coarse-to-fine aggregate (C/F) ratio, coarse aggregate size, binder content and percentage of air entrained in the mixture are varied to study the influence of these variables on the fresh properties of SCC containing MK. SCC mixtures containing silica fume and SCC containing slag are also tested for comparison. The results show that increasing the percentage of MK up to 20% in SCC increases the compressive strength, viscosity, passing ability and HRWRA demand, but decreases the flowability of the mixture. In addition, the flowability of SCC mixtures improves with larger aggregate size, higher binder content and higher percentage of entrained air. The passing ability of SCC mixtures also improves with lower C/F ratio, larger aggregate size, higher binder content and the inclusion of entrained air in the mixture. The results also indicate that increasing the binder content or increasing the percentage of the entrained air has the most significant effect on improving the fresh properties of SCC mixtures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".