Effectiveness of Impressed Current Technique to Simulate Corrosion of Steel Reinforcement in Concrete
Bibliographic record
Abstract
Accelerated corrosion by means of the impressed current technique is widely used in concrete durability tests. In this study, the influence of varying the impressed current density level between 100 and 500 μA/cm2 on the actual degree of steel reinforcing bar corrosion as well as on the concrete strain behavior due to expansive corrosion products was experimentally investigated. Twelve reinforced-concrete prisms (150×250×300 mm) were used. The prisms were reinforced by two No. 10 reinforcing bars. Corrosion was induced by means of impressed current using electric power supplies. To depassify the steel reinforcement, 5% NaCl by weight of cement was added to the concrete mix. The strain response due to the expansion of corrosion products was measured at each face of the prisms. At the end of the corrosion phase, all the corroded reinforcing bars were removed, cleaned according to the ASTM G1-90 standard, and weighed to get the actual degree of mass loss. The results showed that, up to 7.27% mass loss, accelerated corrosion using the impressed current technique was effective in inducing corrosion of the steel reinforcement in concrete. With respect to Faraday's law, the use of different current densities has no effect on the percentage of mass loss. However, increasing the level of current density above 200 μA/cm2 results in a significant increase in the strain response and crack width due to corrosion of the steel reinforcement.
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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 | 0.000 |
| 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".