Real-Time Aeroelastic Hybrid Simulation Method for Bridge Deck Section Models
Bibliographic record
Abstract
A section model test effectively assesses the aeroelastic behavior of long-span bridges in a wind-resistant design. The conventional approach uses springs to support a mass-calibrated physical section model scaled to the similarity principle. Modal damping can also be modeled using sticks soaked in oils. Although this procedure has been successfully applied to most bridges, it involves physical limitations in selecting the model scale and vibration frequencies. Also, certain degrees of time and effort are required for the calibration and modification of dynamic properties in order to achieve precision. This study proposes a new real-time aeroelastic hybrid simulation (RTAHS) approach that eliminates the potential drawbacks of the conventional spring-supported section model test. With this new approach, the aeroelastic force on the physical section model is directly measured using supporting load cells. The equation of motion is solved numerically, and linear electric motors impose the expected movement of the model in a real-time fashion. The hardware of the RTAHS consists of linear electric motors, motor drivers, sensors, and an Ethernet for Control Automation Technology (EtherCAT) based real-time motion controller. The hardware is controlled with three control loops, i.e., numerical integration, time-delay compensation, and PID control of the position. For this study, a series of comparative wind tunnel tests was used to demonstrate the validity of the proposed RTAHS concept.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".