A Computational Framework for the Aerodynamic Shape Optimization of Long-Span Bridge Decks
Bibliographic record
Abstract
A computational framework for automated shape modification of long-span bridge decks is proposed. The proposed technique involves the use of computational fluid dynamics (CFD) for aerodynamic analysis, surrogate models for response function approximation and numerical optimization routines for iterative selection of optimal shapes. A brief review of aerodynamic shape modification measures for decks of long-span bridges is presented. The framework is applied for aerodynamic fairing design of a typical plate-girder stiffened deck with the objective of reducing the lateral wind load and improving aerodynamic stability under smooth flow. Numerically evaluated optimal fairing shapes are compared with that of Bronx Whitestone Bridge and Deer Isle Bridge. It is shown that sharper triangular fairings are effective to reduce wind induced drag, but shorter fairings with height around 60% to 70% of deck depth are effective to improve the aerodynamic stability of elongated H-shaped decks. Furthermore, asymmetric triangular fairings are found to be effective to improve the aerodynamic performance of asymmetric decks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".