Effects of Compound Rust Inhibitor on the Performance of Concrete with a Large Amount of Mineral Admixtures
Bibliographic record
Abstract
The corrosion of rebars in concrete is a major cause of the falling bearing capacity of concrete structures.The durability of concrete could be effectively enhanced by adding mineral admixtures.Many studies have shown that rust inhibitors can effectively prevent rebar corrosion.Taking commonly used slag, fly ash, and silica fume as mineral admixtures, this paper explores how an alcohol-amine compound rust inhibitor affects the compressive strength, chloride penetration resistance, carbonation resistance, and rebar corrosion resistance of concrete with a large amount of mineral admixtures.The research results show that the addition of rust inhibitor hinders the early hydration of cementitious materials in concrete; the chloride diffusivity and carbonation depth of concrete were minimized, when the ratio of N,N-dimethylethanolamine (DMEA) to sodium monofluorophosphate (MFP) in the compound rust inhibitor stands at 15:1; the addition of rust inhibitor clearly suppressed the corrosion loss of the rebars in concrete, resulting in a low rebar corrosion rate.The research results provide experimental evidence for reducing rebar corrosion in reinforced concrete.
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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) | 0.001 | 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".