A practical method for calculating the corrosion rate of uniformly depassivated reinforcing bars in concrete
Bibliographic record
Abstract
Abstract The quantification of active corrosion rate of steel in concrete structures through nondestructive methods is a crucial task for scheduling maintenance/repair operations and for achieving accurate service life predictions. Measuring the polarization resistance of corroding systems and using the Stern‐Geary equation to calculate the corrosion current density of active steel is a widely‐used method for this purpose. However, these measurements are greatly influenced by environmental factors; therefore, accurate monitoring of corrosion requires integrating the instantaneous corrosion rates over time. Although advanced numerical models are helpful in research settings, they remain to be computationally expensive and complex to be adopted by general engineering community. In this paper, a practical numerical model for predicting corrosion rate of uniformly depassivated steel in concrete is developed. The model is built on Stern's earlier work that an optimum anode‐to‐cathode ratio exists for which the corrosion current on the metal surface reaches a maximum value. The developed model, which represents the corrosion rate as a function of concrete resistivity and oxygen concentration, is validated using experimental data obtained from the literature.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".