Long-term field test of temperature gradients on the composite girder of a long-span cable-stayed bridge
Bibliographic record
Abstract
Bridges are inevitably affected by daily, seasonal and annual air temperature and solar radiation. The thermal effects on bridges, especially for long-span cable-stayed bridges with composite girders, are significant and complicated. The evaluation of temperature distribution has been a primary concern to bridge engineers and researchers. This article presents a more than one-year temperature database of a long-span cable-stayed bridge with a small cantilever length-to-web depth ratio in the composite girder. Uniform temperature, linear temperature difference, thermal curvature, and self-equilibrated thermal stress are considered to imply the characteristics of the temperature distribution in such composite girders. Two profiles (profile 1 and profile 2) for positive vertical temperature gradient and one profile (profile 3) for negative vertical temperature gradient are proposed. The extreme temperature differences with a 100-year return period are determined for each profile with extreme value analysis. Among the three profiles, profile 2 is unique for composite girders with a small cantilever length-to-web depth ratio. Based on parametric studies, profile 2 is revised with the cantilever length-to-web depth ratio for wide applications. Finally, comparisons of vertical temperature gradients are made between the investigated composite girder and the recommendations in Chinese Specification.
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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 | 0.000 | 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) | 0.000 | 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".