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Record W3203483722 · doi:10.2749/ghent.2021.1982

Super-long span bridge aerodynamics benchmark: additional results for TG3.1 Step 1.2

2021· article· en· W3203483722 on OpenAlexaff
Giorgio Diana, Luca Amerio, Vincent De Ville, Santiago Hernández, Guy L. Larose, Simone Omarini, Stoyan Stoyanoff, Tommaso Argentini, Michael Andersen, José Ángel Jurado, Allan Larsen, Daniele Rocchi, Andrew Allsop, Miguel Cid Montoya, Teng Wu, Igor Kavrakov, Guido Morgenthal, Martin N. Svendsen

Bibliographic record

VenueReport · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsAeroelasticityBenchmark (surveying)AerodynamicsBridge (graph theory)Stability (learning theory)Span (engineering)Computer scienceTask (project management)Structural engineeringWind tunnelEngineeringAerospace engineeringMachine learningSystems engineeringMedicineGeology

Abstract

fetched live from OpenAlex

<p>This paper presents the ongoing benchmark results of IABSE Task Group 3.1. The task of this working group is to create benchmark results for the validation of methodologies and software programs developed to assess the stability and the buffeting response of long span bridges. Indeed, accurate estimations of structural stability and response to strong winds are critical for the successful design of long-span bridges. While the first results of the benchmark, dealing with a section approach, have been already published, in this paper the ongoing activity and results of the task group are presented. The topic of these results is the numerical response of a full-bridge model under the actions of a multi-correlated wind field both in terms of aeroelastic stability and buffeting response.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.235
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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