Chloride transport and the resulting corrosion of steel bars in alkali activated slag concretes
Bibliographic record
Abstract
As the relative performance of alkali activated slag (AAS) concretes in comparison to portland cement (PC) counterparts for chloride transport and resulting corrosion of steel bars is not clear, an investigation was carried out and the results are reported in this paper. The effect of alkali concentration and modulus of sodium silicate solution used in AAS was studied. Chloride transport and corrosion properties were assessed with the help of electrical resistivity, non-steady state chloride diffusivity, onset of corrosion, rate of corrosion and pore solution chemistry. It was found that: (i) although chloride content at surface was higher for the AAS concretes, they had lower chloride diffusivity than PC concrete; (ii) pore structure, ionic exchange and interaction effect of hydrates strongly influenced the chloride transport in the AAS concretes; (iii) steel corrosion resistance of the AAS concretes was comparable to that of PC concrete under intermittent chloride ponding regime, with the exception of 6 % Na 2 O and Ms of 1.5; (iv) the corrosion behaviour of the AAS concretes was significantly influenced by ionic exchange, carbonation and sulphide concentration; (v) the increase of alkali concentration of the activator generally increased the resistance of AAS concretes to chloride transport and reduced its resulting corrosion, and a value of 1.5 was found to be an optimum modulus for the activator for improving the chloride transport and the corrosion resistance.
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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) | 0.000 | 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".