Mechanical Performance and Flow of Sustainable Alkali-Activated Slag Concrete
Bibliographic record
Abstract
This paper evaluates the effects of different parameters on the compressive strength and flow against time for alkali-activated blast furnace slag concrete.Coarse aggregate proportions, fineness of precursor, water dosage, superplasticizer dosage and nature of alkaline activator were investigated.This study shows that within the different tested parameters, the nature of the alkaline activator and the added water dosage have the most critical effect on strength and flow development.Sodium silicate combined with sodium hydroxide enables curing at 23°C with strength values exceeding 30 MPa whereas concretes activated with sodium hydroxide need heating to achieve similar values.Na2SiO3/NaOH 8M ratios of 0.5, 0.75 and 1.0 resulted in compressive strength values of 30.6, 45.1 and 47.0 MPa at 3 days, respectively, for specimens kept at 38°C for the first 24 hours.Increasing the water added to the mixture triggers a decrease in strength; however, the use of a naphthalenebased superplasticizer allows a reduction of water added to the mixture and results in increasing compressive strength.Increasing both the water content and the naphthalene-based superplasticizer dosage allows to maintain flowable concrete conditions over an extended period of time.The rate of flow loss against time for concretes activated with sodium silicate combined to sodium hydroxide was lower than the rate of flow loss of concretes using sodium hydroxide only.An optimal alkali-activated slag concrete mix design with added water dosage of 72 kg/m 3 , no superplasticizer, a Na2SiO3/NaOH 8M ratio of 0.5, a sand content of 670 kg/m 3 , a coarse aggregate content of 1040 kg/m 3 (40 % 14-20 mm, 30 % 10-14 mm and 30 % 5-10 mm) and a total binder content of 400 kg/m 3 gave flow values of 155 mm after 30 minutes, 100 mm after 60 minutes and strength values of 38.7 MPa (3 days) and 41.9 MPa (7 days).
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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) | 0.002 | 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".