Effect of temperature distribution and thermal gradient on the thermal response of steel–concrete composite girders
Bibliographic record
Abstract
Thermal loads significantly influence bridge performance, particularly in extreme climates. This study identified a significant scope for investigating how changes in constituent material strength influence the overall temperature field and affect stress distribution in composite bridges. Also, this study focuses on investigating the thermal effects on steel–concrete composite bridges, an area that hasn't been extensively explored compared to concrete or steel bridges. The objective is to develop an efficient finite element (FE) model to simulate temperature variations and their impact on composite bridge girders considering varying concrete strengths. It involves developing an experimentally validated FE model of a dimensionally reduced steel–concrete composite bridge girder to assess the structural effects of a 24 h simulated temperature field through a sequentially coupled thermo-mechanical analysis technique in Abaqus. The findings indicate that under similar thermal boundary conditions, composite bridge girder specimens with varying concrete strengths exhibit differences in stress values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".