Real-Time Aeroelastic Hybrid Simulation Method for a Flexible Bridge Deck Section Model
Bibliographic record
Abstract
To address the challenges in predicting the aeroelastic phenomenon and the resulting wind-induced forces on slender bridges, a real-time aeroelastic hybrid simulation (RTAHS) system was developed. The RTAHS system directly measures the aerodynamic and aeroelastic forces through load cells. It controls the next step’s position of the deck section model with linear motors by solving the governing equations of motion in real time. Given the complex shape of a bridge deck section geometry, load cells are chosen for force measurement instead of as pressure sensors. In the previous RTAHS system proposed by the authors, the inertial forces of a rectangular section model were eliminated from the measured forces under the assumption of the model’s rigid-body motion. However, when conducting RTAHS experiments with a realistic bridge deck section model, increasing the mass ratio between the mass of the model and the target mass input to the hybrid system results in unstable vibrations. This instability is primarily attributed to forces generated by the model’s flexibility. This study developed an improved RTAHS system, which took into account the inertial forces arising from the nonrigid motion of the flexible bridge deck section model. An accelerometer was additionally installed at the midpoint of the model, and the inertial forces caused by the nonrigid behavior were compensated using a calibration factor derived from impact hammer tests. This approach was validated by comparing the spring-supported experiments conducted on a realistic bridge deck section model.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".