MétaCan
Menu
Back to cohort
Record W3084988918 · doi:10.2749/newyork.2019.2644

Super-long span bridge aerodynamics: on-going results of the TG3.1 benchmark test – Step 1.2

2019· article· en· W3084988918 on OpenAlexaff
Giorgio Diana, Stoyan Stoyanoff, Andrew Allsop, Luca Amerio, Tommaso Argentini, Miguel Cid Montoya, Santiago Hernández, José Ángel Jurado, Igor Kavrakov, Guy L. Larose, Allan Larsen, Guido Morgenthal, Simone Omarini, Daniele Rocchi, Martin N. Svendsen

Bibliographic record

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsAeroelasticityAerodynamicsBenchmark (surveying)Bridge (graph theory)Wind tunnelStructural engineeringSpan (engineering)Stability (learning theory)Computer scienceTurbulenceEngineeringAerospace engineeringPhysicsGeologyMeteorologyMedicineMachine learning

Abstract

fetched live from OpenAlex

This paper is part of a series of publications aimed at the divulgation of the results of the 3-step benchmark proposed by the IABSE Task Group 3.1 to define reference results for the validation of the software that simulate the aeroelastic stability and the response to the turbulent wind of super-long span bridges. Step 1 is a numerical comparison of different numerical models both a sectional model (Step 1.1) and a full bridge (Step 1.2) are studied. Step 2 will be the comparison of predicted results and experimental tests in wind tunnel. Step 3 will be a comparison against full scale measurements. The results of Step 1.1 related to the response of a sectional model were presented to the last IABSE Symposium in Nantes 2018. In this paper, the results of Step 1.2 related to the response long-span full bridge are presented in this paper both in terms of aeroelastic stability and buffeting response, comparing the results coming from several TG members.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueReportSame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207