Mapping the chloride-induced corrosion damage risks for bridge decks under climate change
Bibliographic record
Abstract
Climate change is expected to alter the environmental factors that are known to influence the corrosion process, creating additional uncertainties in the long-term performance of reinforced concrete (RC) decks. With due consideration of site-specific exposure and environmental conditions, this study aims to investigate the degree to which projected climate change may impact corrosion-induced damage for RC bridges. A hierarchical two-tier framework was developed incorporating the material deterioration process simulation at the local element level, and a component level prediction of the corrosion-induced damage severity and extent over the bridge deck domain. The predictive accuracy of this framework was validated against the historical bridge inspection data. Case studies were performed for decks located in Toronto and Victoria to investigate the influence of climate data resolution and climate projection models on deck deterioration status. At last, ANN (artificial neural network) and SVM (support vector machine) approaches were used to generate a series of cartographic expressions to reveal how the corrosion-induced deck deterioration risk varies with the region due to the difference in the environmental conditions. These maps can serve as visual tools to express the corrosion damage risks for different bridge locations and to formulate region-based durability design requirements.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".