New challenges in the IABSE TG3.1 benchmark on super long span bridge aerodynamics
Bibliographic record
Abstract
In the last years, extreme climate events as thunderstorm and downburst are becoming increasingly frequent and widespread. These phenomena could significantly impact on the dynamic response of super long-span bridges since they are typically characterized by a sudden variations of the mean wind speed combined with large vertical angles of attack. This contingency is considered an interesting opportunity for the IABSE Task group 3.1, involved for the last 5 years in the benchmark of the software for the computation of the bridge response to the turbulent wind, to extend the applicability of the consolidated numerical procedures to a case of study characterized by a non-synoptic wind. To reach this purpose, taking as a target the full-scale data measured on the Gjemnessund Bridge during two different incoming wind conditions, a comparison with numerical results is proposed. Specifically, the working group has defined two steps of increasing complexity. The first, given the same input data to the participants, consists of a preliminary numerical benchmark while, the second, concerns the comparison between the outcomes and the dynamic response of the real bridge. In this paper, the results of the wind tunnel tests, performed to measure all the aerodynamic coefficients required for numerically simulating the bridge response, are reported. Finally, the first step is presented and some preliminary outcomes are shown.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".