Effect of Climate Change on Thermal Loads in Concrete Box Girders
Bibliographic record
Abstract
Throughout their service life, bridges are exposed to ambient actions and environmental influences such as wind, thermal, and snow loads. Bridge design for environmental actions is currently based on observed, historical climate data. However, the effects of climate change have put these guidelines into question due to the ongoing and projected change in climate conditions. Bridge engineers are adapting current guidelines and design provisions to incorporate climate change. The main challenges encountered in this endeavor are the nonavailability of future climate data in the required format and the ability of bridge engineers to access and use these data as needed. The focus of this study is to investigate the effect of climate change on thermal load. The objective is achieved through the development of a methodology that can be used to model future hourly climate data; these may be input as boundary conditions in a thermal finite-element model to determine the thermal load acting on a bridge. To demonstrate the methodology, future climate conditions are projected for two locations across Canada (Toronto and Whitehorse), whereas the resulting thermal loads acting on the bridge are determined during heat waves, cold waves, and periods of high daily temperature variation. The results show that climate change could lead to a significant increase in the magnitude of thermal loads on bridges. It is also shown that the effects of climate change on the thermal load vary significantly depending on the general circulation model used, the emission scenario, and the location.
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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) | 0.002 | 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".