Effect of Type of Sand on the Flowability and Compressive Strength of Slag-Fly Ash Blended Geopolymer Mortar
Bibliographic record
Abstract
This paper investigates the influence of the type of fine aggregates on the properties of slag-fly ash blended geopolymer mortar.Twelve mixes were prepared with two types of sand: desert dune sand (DS) and crushed dolomitic limestone sand (CS).Different alkaline activator solution-to-binder (0.50, 0.60, and 0.65) and binder-to-sand ratios (1:2, 1:3, and 1:4) were considered to analyze their effect on the performance of the geopolymer mortar.The properties under investigation included the amount of additional water needed to maintain a flow of 150 ± 2 mm and the 7-and 14-day compressive strengths.Experimental test results showed that an increase in fine aggregates content resulted in a higher additional water demand, regardless of the type of sand used.As a result, the mortar compressive strength decreased by up to 29% compared to mixes with the lowest binder-to-sand ratios (1:2 for DS mixes and 1:3 for CS mixes).An increase in the alkaline activator solution-to-binder ratio reduced the additional water needed to satisfy the target flowability but increased the overall liquid-to-binder ratio.Meanwhile, for optimum compressive strength, DS-based mixes comprised B:S and AAS/B ratios of 1:2 and 0.60, respectively, while those of CS-based mixes were 1:3 and 0.65, respectively.Compared to mixes made with CS, those incorporating DS required the addition of more water to maintain the flowability and experienced up to 81% loss in compressive strength; still, DS-based mixes achieved 14-day compressive strengths exceeding 28 MPa.The experimental findings advocate the use of DS as fine aggregates in the production of slag-fly ash blended geopolymer mortar to be utilized in various construction applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".