Sazlidere Bridge Wind Tunnel Testing Assisted Design
Bibliographic record
Abstract
In the context of the Northern Marmara Motorway Section 8 Project, a new cable-stayed bridge, the Sazlidere Bridge, is being designed and works have already started on site. To support this fast-paced project, RWDI is collaborating with the design team and providing wind engineering services. The activities employ wind tunnel experiments and are focused on two main aspects: aerodynamic stability of the bridge and the risk of wind-induced overturning of large lorries crossing the bridge. With respect to aerodynamic stability, limited tests on a sectional model of the bridge deck were performed in one of RWDI’s wind tunnels using a state-of-the-art sectional model test rig. Testing of a full bridge aeroelastic model with relevant topography was also conducted allowing for the simulation of atmospheric turbulence from any wind direction. This testing was used primarily to verify the aerodynamic stability of the structure for various wind directions and to measure the buffeting response of the bridge to turbulent winds. Further wind tunnel testing on a small-scale rigid transport lorry model was completed to measure the aerodynamic force and moment coefficients of the vehicle as a function of wind direction. These data were combined with a numerical model for the roll-over stability of high-sided vehicles to calculate critical wind speeds versus wind direction for lorries driving along the central span of the bridge.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".