Cement and Superplasticizer Combinations: Compatibility and Robustness
Bibliographic record
Abstract
Abstract This study highlights the notion of robustness of combinations of cements and superplasticizers. Tests done with various cements and different families of superplasticizers showed that although a combination of a cement and a superplasticizer could be compatible, it is not necessarily robust. Sometimes a little variation in the dosage of the admixture could lead to detrimental side effects, such as segregation, excessive set retardation, or excess air content in the concrete. Results showed that the chemical composition of the cement is critical to ensure good compatibility and adequate robustness of various combinations of cements and superplasticizers. The chemical nature of the superplasticizer also plays a role in the behavior of such combinations. The C3A content, the soluble (alkali) sulphate content, and the fineness of the cement, which influence the adsorption rate of the superplasticizers on the cement particles, are among the key factors that control the compatibility and the robustness of cement-superplasticizer combinations, especially for polysulfonated admixtures. Based on the results of this study, a rough prediction of the compatibility and robustness of cements and superplasticizers could be made by analyzing the chemical composition of the cement and the chemical nature of the superplasticizer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".