Flutter Derivatives Identification and Aerodynamic Performance of an Optimized Multibox Bridge Deck
Bibliographic record
Abstract
The bridge deck sections used for long-span suspension bridges have evolved through the years, from the compact box deck girders geometrical configurations to twin-box and three-box bridge decks sections. The latest generation of split and multiple-box bridge decks proved to have better aerodynamic behavior; thus further optimization methods are sought for such geometrical configurations. A new type of multibox bridge deck, consisting of four aerodynamically shaped deck boxes, two side decks for the traffic lanes and two middle decks for the railway traffic, connected between them by stabilizing beams, was tested in the wind tunnel for identifying the flutter derivatives and to verify the aerodynamic performance of the proposed multibox deck. Aerodynamic static force coefficients were measured for the multibox bridge deck model, scaled 1 : 80, for Reynolds numbers up to 5.1 × 10 5 , under angles of attack between −8° and 8°. Iterative Least Squares (ILS) method was employed for identifying the flutter derivatives of the multibox bridge deck model, based on the results obtained from the free vibration tests and based on the frequency analysis the critical flutter wind speed for the corresponding prototype of the multibox bridge was estimated at 188 m/s.
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.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".