Effect of Sand to Aggregate Ratio on the Properties of Self-Compacting High-Performance Concrete
Bibliographic record
Abstract
Self-compacting high-performance concrete (SCHPC) combines the properties and advantages of self-compacting concrete and high-performance concrete in both fresh and hardened states. For the SCHPC mix design, sand to aggregate ratio is a crucial parameter and plays an important role in governing the properties of SCHPC mix. This paper presents the results of an experimental investigation on the flowability, passing ability and mechanical properties of SCHPC mixes for various sand to total aggregate (S/A) ratio and water to cementitious material (w/cm) ratio. Tests were conducted on specimens using four (w/cm) ratios: 0.26, 0.30, 0.35 and 0.40 and two (S/A) ratios: 48% and 53%. All the mixtures were tested using slump flow test, J-Ring test, and L-box test in the fresh state as well as compressive strength, splitting tensile strength, and unit weight in the hardened state. The test results revealed that a lower S/A ratio (0.48) enhanced the flowability whereas the higher S/A ratio (0.53) enhanced the passing ability. The lower S/A ratio (0.48), containing greater proportion of coarse aggregate, generally improved the mechanical properties of SCHPC compared to the mixes with the higher S/A ratio (0.53).
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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 | 0.000 | 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".