The Performance of corrosion inhibiting systems in concrete bridge barrier walls - 5 years of field data
Bibliographic record
Abstract
Repairs to corrosion-damaged reinforced concrete structures are inconvenient and expensive to the users and owners. Estimates place their cost in the billions of dollars, not including the environmental toll of repeated repairs. Corrosion inhibiting systems have long been considered one of the best solutions to the corrosion problem in steel reinforcement, but limited information is available on their actualperformance and effectiveness in the field. The NRC's Institute for Research in Construction has been working with the Ministry of Transportation of Quebec and seven product manufacturers to study the field performance of corrosion-inhibiting systems on the Vachon Bridge near Montreal. Different corrosion-inhibiting systems were applied to eight consecutive spans on one of the bridge's reconstructed barrier walls in 1996. They included concrete admixtures, reinforcing steel coatings, and concrete surface coatings/sealers. Two remaining spans of the barrier wall served as control sections. To monitor the effectiveness of the corrosion inhibiting systems, each span was equipped with embedded temperature and humidity sensors, and reference electrodes. On site corrosion surveys, including half-cell potential and corrosion rate measurements, along with concrete coring were conducted yearly. Although thecorrosion rates measured on the barrier wall reinforcement were still relatively small after 5 years, the results indicate that the concrete admixtures, especially the nitrite-based type, performed better than the other systems in reducing or delaying the corrosion of reinforcement in the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".