Development of Carbonation-Cured Alkali-Activated Slag Masonry Units
Bibliographic record
Abstract
This paper investigates the impact of early accelerated carbonation curing on the performance of alkali-activated slag concrete masonry blocks.The study employed crushed dolomitic limestone aggregates, with a binder-to-aggregate ratio of 1:6.A sodiumbased alkaline solution, prepared with a sodium silicate to sodium hydroxide ratio of 1.5, was incorporated into the concrete batch at a solution-to-binder ratio of 0.6.The influence of carbonation curing parameters on the performance of concrete masonry blocks was evaluated, including initial ambient curing duration (0, 2, and 4 hours), carbonation duration (4 and 24 hours), and carbonation pressure (1 and 5 bars).These parameters were analyzed in relation to carbon uptake, compressive strength at 1, 7, and 28 days, and water absorption of the concrete masonry blocks.Additionally, a comparative assessment of the global warming indices associated with the production of cementitious and carbonation-cured alkali-activated slag masonry units was developed.The results reveal that carbon uptake and water absorption capacity increased with longer initial and carbonation curing durations and higher carbonation pressures.The compressive strength was either maintained or decreased due to the decalcification of calcium-rich gel produced during the activation reaction by carbonation curing.However, all mixes demonstrated suitability as non-load-bearing blocks, achieving strengths higher than 4.1 MPa after 1 day of batching.The environmental impact analysis indicator a reduction of 79.2% in the production of 1 m 3 of carbonation-cured alkali-activated slag concrete in comparison to cement-based counterparts.
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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".