Numerical Study on the Effect of Climate Parameters on the Extreme Thermal Gradients in Concrete Box Girders
Bibliographic record
Abstract
Bridge codes tend to provide general guidance on the thermal gradients acting on bridge decks based on data from historical extreme events that have occurred within a country, without considering the location of the bridge itself. However, the thermal gradient is a function of the climate conditions that occur locally, in the vicinity of the bridge. Thus, a significant number of bridge decks are designed for climate conditions that might not be representative of their locations. The aim of this research is to optimize current guidelines to ensure that thermal gradients are derived based on bridge location. This objective is achieved through the investigation of the relationship that occurs between climate parameters and the resulting thermal extremes. An advanced finite-element platform was used to model the thermal performance of a concrete box girder. Several sets of meteorological data from 18 locations across Canada (representative of different Canadian climate types) were used as input in the thermal models to simulate the temperature distribution within the bridge deck. Upon analysis of the results, it was determined that a correlation exists between the direct normal irradiance (DNI) at a certain location and the resulting thermal differential that occurs between the top and the interior of the cross section. Four categories were defined, with each category representing a range of DNI values and a resulting range of thermal differentials between the top and the interior. To demonstrate the applicability of the established relationship, a case study was performed in which the maximum limit provided by the Canadian Highway Bridge Design Code was investigated across several provinces in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".