Corrosion performance of reinforcing steel in concrete containing supplementary cementing materials
Bibliographic record
Abstract
This paper summarizes the corrosion results of a 5.3 year study on the corrosion performance of reinforcing steel in concrete slabs containing supplementary cementitious materials and exposed to chlorides. Chloride ions were introduced into the reinforced concrete slabs through a natural migration process, i.e. a ponding solution of 3.4% sodium chloride on the top slab surface of the concrete slab. The concrete mix was designed with a 0.32 water-tocementitious materials ratio (w/cm), containing plain Portland cement and additions of fly ash Class C, fly ash Class F, silica fume and blast furnace slag. Concrete slabs with Portlandcement only and different w/c ratios (0.32, 0.43 and 0.55) were also tested. The thickness of concrete cover to the steel reinforcing bars ranged from 13 mm to 76 mm. Corrosion of the reinforcing steel bars was evaluated using the half-cell potential, linear polarization and AC impedance techniques. The test results indicated that the concrete incorporating fly ash Class C had the best performance with respect to chloride induced corrosion followed by the concrete containing silica fume and the control concrete with w/c of 0.32. In these concrete slabs the corrosion rate of the steel bars was relatively low, even with 13 mm concrete cover. The concretes made with Class F fly ash and blast furnace slag performed better than thePortland cement concretes with w/c of 0.43 and 0.55.
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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".