Non-destructive corrosion monitoring of steel reinforcement in concrete
Bibliographic record
Abstract
The inadequate and inaccurate application of corrosion monitoring techniques for testing reinforced concrete structures had led to considerable early deterioration of these facilities and has resulted in high restoration and replacement costs. The prospect and diligent use of readily available early damage detection tools for use in regular maintenance of these structures could permit timely interventions for restoration and upkeep. This research program was aimed at determining the effectiveness of selected nondestructive testing methods as reliable indicators of early onset corrosion in steel-reinforced concrete. The methodology analyzed the onset of corrosion in a set of steel reinforcing rebars at a specific depth from the concrete surface, and used the resulting data as a predictor of corrosion activity in other reinforcing bars at different depths. Two series of tests were conducted: the first series included nine individually reinforced concrete samples with varying cover thicknesses; the second series consisted of nine reinforcing steel bars, distributed equally in concrete specimens at different depths from the concrete cover. All specimens were subjected to accelerated corrosion using methods developed at McGill University to reproduce corrosion of steel rebars in a naturally aggressive environment as closely as possible. The research results concur with the findings of other research programs, affirming that these monitoring tools are adequate predictors of corrosion. Nonetheless, they lack precision and are unable to track the time to corrosion initiation in structural concrete elements.
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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".