IABSE TG3.1 final analysis: Comparison between full-scale measurements and numerical simulations of a long-span bridge during a non-synoptic event
Bibliographic record
Abstract
Over the past few years, the frequency and intensity of extreme climate events such as thunderstorms and downbursts have increased. These non-synoptic events involve large fluctuations in wind speed and angle of attack, potentially inducing critical conditions for bridge decks. Technological advancements in full-scale monitoring have not only confirmed these characteristics but have also enabled deeper investigations of long-span bridges aeroelastic behavior. These circumstances have proven to be a valuable opportunity for the IABSE TG3.1, which has been involved for the past 6 years in benchmarking software to compute bridge responses to turbulent winds, to extend the validation of numerical methods to extreme weather phenomena. To achieve this goal, it was decided to compare the results of numerical simulations obtained by the participants with the measured full-scale response of the Hardanger Bridge during the storm “Tor”, which struck the bridge in 2016. To provide a suitable input for the simulations, wind tunnel tests were carried out in the Politecnico di Milano wind tunnel on a deck sectional model of the bridge. This paper presents the first numerical results and compares them with the monitored bridge response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".