Wind tunnel testing and frequency domain buffeting analysis of a 5000 m suspension bridge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Buffeting’s importance is highlighted in the case of super-long bridges. It remains unclear how buffeting will perform and whether the traditional methods still apply when bridge’s span is approaching its upper limitation. To figure out this, a full aeroelastic model testing has been carried out with the span arrangement 2000+5000+2000 m. A comparison with the existing bridges reveals the similarities and differences. It shows vertical and torsional buffetings are important for a 5000 m bridge while lateral response is almost static. Fundamental modes contribute most kinetic energy. With the increasing of wind speed, the aerodynamic coupling effect becomes prominent while the structural coupling effect keeps limited. Afterwards, four analytical methods, namely SRSS and CQC, with and without self-induced forces, are used for the buffeting displacements. The calculation indicates the multi-mode (CQC) analysis is still applicable on a 5000 m bridge but the mode-by-mode (SRSS) method is risky since the neglection of aerodynamic coupling effect. Parameter analysis shows aerodynamic admittance function and force coherence are decisive while the cross terms are of secondary importance. Since all data is experimentally measured, the reliability of analytical results can be ensured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it