Effect of Different Additives on the Compressive Strength of Very High-Volume Fly Ash Cement Composites
Bibliographic record
Abstract
Abstract The cement industry is responsible for about 5–7% of global greenhouse gas emissions and with the rapid rise in global warming, it is imperative to produce an ecofriendly alternative to Portland cement. Fly ash (FA) is an abundantly available and least utilized industrial byproduct with good pozzolanic properties that can help reduce the carbon footprint of cement composites. We investigated replacing 80% of the cement content with different blends of FA, nanosilica (NS) and silica fume (SF). Hydrated lime and a set accelerator were used to increase the pozzolanic reactivity of the blended cement composites. The portlandite released with 20% cement content was insufficient for the pozzolanic reaction of the blended cement composites containing FA and SF, requiring externally added hydrated lime. The addition of a set accelerator significantly increased the pozzolanic reaction and the resultant compressive strength, and these increased with the increasing content of the set accelerator. The replacement of SF with NS led to a remarkable increase in the pozzolanic reaction. The corresponding compressive strength of FA mixed with cement composites increased with increasing percentage composition of NS.
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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) | 0.003 | 0.001 |
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".