A Practical Method to Solve Slump Loss Problem in PNS Superplasticized High-Performance Concrete
Bibliographic record
Abstract
Abstract Using the mini-slump and slump tests, three low-alkali Portland cements were found to be incompatible with a PNS superplasticizer in terms of fluidity loss in cement pastes and con-cretes having low W/C ratios (0.30 to 0.35). The addition of a small amount of sodium sulfate appeared to be a practical method to solve the slump loss problem of these superplasticized cement pastes and concretes. The addition of sodium sulfate between 0.2 to 0.8% (or at a Na2O eq. of 0.10 to 0.35%) significantly reduces the rate of the slump loss and maintains a high slump value over 90 min after mixing of the superplasticized concrete mixes having a W/C ratio of 0.30. Moreover, the addition of sodium sulfate was not found to affect the air content of the fresh concretes. The setting of the concretes are retarded or accelerated by no more than 2 h depending on the amount of sodium sulfate added. Furthermore, the addition of sodium sulfate up to 0.5% to the superplasticized concretes increases not only the 1-day compressive strength but also the 28-day compressive strength. The addition of sodium sulfate over 0.5% was found to increase the 1-day compressive strength but to decrease the 28-day compressive strength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".