Resistance to Chloride Ion Permeability of Concrete Mixed with Fly Ash, Slag Powder, and Silica Fume
Bibliographic record
Abstract
This paper attempts to study the resistance to chloride ion permeability of concrete mixed with different admixtures such as fly ash (FA), slag powder (SP), and silica fume (SF).For this, taking the C50 concrete with the 1:1 proportion of FA and SP, the test method for rapid chloride ion migration (RCM) coefficients was used to study the resistance to chloride ion permeability and economic benefits of the concrete under the conditions of different total amount of admixtures, SF content, and air content.The results show that the increase in the total amount of the admixture can improve the concrete resistance to chloride ion penetration, but in a gradually weakening trend; the increase in SF content can increase the concrete resistance to chloride ion penetration, but the enhancement effect is gradually decreased; the SF has more significant effect on improving the 56d concrete resistance to chloride ion penetration; the increase of air content greatly increases the chloride ion migration coefficient of concrete at 28d, and the chloride ion migration coefficient increases linearly at 56d; the air content within 5% has an insignificant effect on the resistance to the chloride ion permeability; the NH40-2 was chosen to be the optimal mix ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".