Performance of Hybrid Glass Fiber-Reinforced Slag-Fly ash Blended Geopolymer Concrete
Bibliographic record
Abstract
This study evaluates the effect of different combinations and volume fractions of hybrid glass fibers (GF) addition on the properties of slag-fly ash blended geopolymer concrete.Two types of GF (A and B) with different lengths (24 and 43 mm) were considered.GF were incorporated solely or in a hybrid combination.Three combinations of hybrid GF were used with A:B ratios of 3:1, 1:1, and 1:3 at a fixed volume fraction of 1%.Three volume fractions (0.5, 1.0, and 1.5%) were utilized with a GF hybrid combination having A:B ratio of 1:1.The performance was characterized by the workability, 1-and 7-day compressive strength, and 7-day splitting tensile strength.The experimental test results showed that the addition of GF had an adverse effect on the geopolymer concrete workability.Yet, mixes with hybrid GF were more workable than counterparts made with a single type of GF.Furthermore, the addition of hybrid GF combinations increased the compressive and splitting tensile strength by up to 26 and 59%, respectively, compared to the plain control mix.Increasing the hybrid GF volume fractions up to 1% enhanced the strengths.Superlative strengths were noted upon incorporating more long GF in the hybrid GF combination, i.e., the mix having A:B ratio of 1:3.Findings highlight the ability to improve the hardened properties of slag-fly ash blended geopolymer concrete using hybrid GF while maintaining adequate workability.
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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) | 0.001 | 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".