Physical, mechanical, and durability properties of gypsumPortland cementnatural pozzolan blends
Bibliographic record
Abstract
This paper deals with the effect of gypsumPortland cement and gypsumPortland cementnatural pozzolan ratios on the physical, mechanical, and durability properties of gypsumPortland cementnatural pozzolan blends. The results indicate that the setting time of these paste decreases with the increase of gypsum content in the mixture, ranging from 8 to 11 min. The addition of superplasticizer increases the setting time from approximately 11 to 35 min. This increase is greatly dependent on the plasticizer admixture dosage. These blends show a kinetic of capillary water absorption very similar to that of the Portland cement binder. Sorptivity is strongly influenced by the type of binder, binder composition and waterbinder ratio. Porosity of blended gypsum binders ranges from 12% to 37%. Their water absorption is high, reaching 27% in the blends with a greater proportion of gypsum. The gypsumPortland cement blends themselves possess good water resistance, which is further enhanced by the addition of natural pozzolan and superplasticizer. The water-cured blends with the composition of 41:41:18 (gypsum : Portland cement : natural pozzolan) and 41:41:18S1 (gypsum : Portland cement : natural pozzolan : 1% superplasticizer) offer a compressive strength of approximately 20 MPa at room temperature. These blends give excellent properties retention after aging in water at 20°C for 95 days. Their good resistance to water decreases as the gypsum content in the mixture is raised. However, the strength loss for the gypsumPortland cementnatural pozzolan blends is generally less than that observed for the gypsum binder.Key words: gypsum, Portland cement, natural pozzolan, physical, mechanical, durability.
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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 | 0.001 | 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".