Incorporating the Effects of Climate Change into Bridge Deterioration Modeling: The Case of Slab-on-Girder Highway Bridge Deck Designs across Canada
Bibliographic record
Abstract
Climate change is expected to impact both the operational and structural performance of infrastructure such as buildings, roads, and bridges. However, infrastructure design guides widely rely on historical climate data, if any, for informing design requirements. The goal of this research was to explore a methodology for modeling bridge deck design against corrosion attack in a changing climate. Three deterioration stages were simulated to understand the time to deck failure. Corrosion initiation of reinforcing steel was considered by utilizing a deterministic diffusion-based model predicting the time to reinforcement corrosion initiation. Crack initiation and crack growth were also simulated using mechanistic approaches to illustrate the sensitivity of bridge deck deterioration and design service life to changes in bridge deck design and a changing climate across major cities in Canada. The findings indicate that a changing climate has the potential to significantly alter the service life of a bridge deck, but the effect is strongly dependent on the durability design of the bridge deck. It is recommended that bridge designers strive to utilize mechanistic-empirical models that incorporate high-resolution climate data as inputs for better understanding changes in deterioration as a consequence of a nonstationary climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".