Modifying the Properties of Granulated Blast Furnace Slag Mortar to Adhere to International Standards
Bibliographic record
Abstract
The need for environmentally friendly binders is driven by the high carbon footprint of Portland cement.Alkali-activated slag mortar is a promising alternative, but its application is limited by how quickly it sets.The ASTM specifications for mechanical strength and setting time were met by optimizing the granulated blast furnace slag mortar developed in this study.The mortar was activated using different SS/SH ratios (1.5 to 2.5) and sodium hydroxide molarities (8M and 10M), in addition to the two superplasticizers (PMS and PCE).The optimal formulation (SS/SH = 1.5, 8M NaOH, with PCE) had a flexural strength of 6.3 MPa, compressive strength of 42.5 MPa, and initial and final setting times of 45 and 130 min, respectively, after 28 d.In comparison to PMS, the PCE-enhanced mixes exhibited improved mechanical performance and a 23% decrease in shrinkage.This study demonstrates that optimizing the SS/SH ratio and NaOH molarity offers a more straightforward, scalable, and economical method for producing alkali-activated slag mortars that satisfy international standards while improving durability and workability, in contrast to more sophisticated approaches such as encapsulated activators or CO₂-modified slag.
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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 | 0.001 | 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".