A critical examination of corrosion rate measurement techniques applied to reinforcing steel in concrete
Bibliographic record
Abstract
This paper presents an investigation of five corrosion‐monitoring techniques for reinforced concrete. The techniques studied are the potentiodynamic, galvanostatic, and coulostatic direct‐current techniques as well as electrochemical impedance spectroscopy (EIS), and the connectionless electrical pulse response analysis (CEPRA) technique. The study included monitoring corrosion rates on reinforced concrete specimens with a range of admixed chloride percentages, cover depths, and rebar diameters for 8 months. After this period, the rebars were extracted for mass loss measurements to determine the average corrosion rates. EIS was found to provide accurate measurements of active and passive corrosion rates with a simplified spectrum‐analysis procedure. Galvanostatic and potentiodynamic techniques were able to measure the corrosion rates for actively corroding reinforcements accurately, while the coulostatic technique overestimated it. For passive reinforcements, the coulostatic technique provided reliable corrosion rate estimates, while the potentiodynamic technique provided a minor overestimation, due to the fast scan rate used, and the galvanostatic technique failed in detecting passivity, due to the short measurement duration and confinement failure. Finally, the CEPRA technique provided accurate corrosion rate predictions except for passive rebars with small diameters embedded in saturated 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".