Effect of Recycled Fine Aggregates on the Performance of Slag-Fly Ash Blended Geopolymer Masonry Mortar
Bibliographic record
Abstract
This paper aims to investigate the performance of slag-fly ash blended geopolymer masonry mortar (GMM) made with recycled fine aggregates (RFA).The effect of replacing natural fine aggregates (NFA) with RFA at 0 and 100% replacement rates was examined through three sets of GMM mixes comprising a binder-to-aggregates ratio of 1:2-1:3, a fly ash-to-slag ratio of 2:1-4:1, and a solution-to-binder ratio of 0.5-0.7.The precursor binder was activated using sodium silicate and sodium hydroxide at a mass ratio of 1.5.GMM mixes were evaluated for fresh and hardened properties.Test results showed that the flow was reduced by up to 12% upon 100% RFA replacement, with higher loss in mixes made with a higher fly ash-to-slag ratio.Meanwhile, RFA mixes had faster initial setting times than the control mix made with NFA, especially with a low binder-to-aggregates ratio of 1:3.For similar RFA replacement, the 28-day compressive strength decreased by up to 73%.The highest strength loss was noted for the mix made with a fly ash-to-slag ratio of 4:1.Yet, despite the deficit in performance due to RFA incorporation in GMM, all mixes complied with international standards for masonry applications.Such research findings provide evidence of the viability of utilizing RFA in cement-free masonry mortar, thereby contributing to enhancing the sustainability of the construction industry by conserving non-renewable natural resources and recycling wastes.
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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".