In-Service Corrosion Monitoring of a Weathering Steel Bridge in Canadian Climate
Bibliographic record
Abstract
Weathering steel, widely used in bridge construction, is known for its atmospheric corrosion resistance properties. However, when exposed to harsh environmental conditions such as the de-icing salts used on the Canadian roads during winter, the protection mechanism may fail to engage properly leading to higher degradation than expected. Furthermore, the corrosion behavior often varies significantly along a single structure. In an effort to study and better understand this phenomenon, the National Research Council of Canada (NRC), in collaboration with the Ministry of Transportation of Ontario (MTO) instrumented a bridge spanning over a highway in the province of Ontario in Canada. This bridge was selected based on prior observations indicating significant variations in the corrosion behavior of its weathering steel structure. Fiber-reinforced plastic (FRP) plates with samples and sensors mounted on them were installed at 8 key areas on the bridge spanning from the piers at the intersection of the east and west directions to the abutment wall which is the farthest from the traffic. Various types of samples and sensors were prepared and installed at each area as depicted in the accompanying figure. The data from the connected sensors is sent to an FPT server allowing for continuous, real-time monitoring. The instrumentation was performed in November 2024 which means that at the time of the 247th ECS conference, 6 months of data collection with be completed. This talk will present an overview of the project as well as preliminary findings from the data gathered by the sensors. Figure 1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".