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Record W2754315051 · doi:10.1002/cepa.339

11.06: Application of hybrid simulation for the evaluation of the buckling response of steel braced frame columns

2017· article· en· W2754315051 on OpenAlexaff
Ali Imanpour, Martin Leclerc, Romain Siguier, Robert Tremblay

Bibliographic record

Venuece/papers · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsPolytechnique MontréalUniversity of Alberta
Fundersnot available
KeywordsStructural engineeringBucklingBraced frameFrame (networking)Steel frameEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Hybrid simulation is an economical structural testing technique in which the critical part of the structure expected to respond in the inelastic range is tested physically whereas the rest of the structure is modelled numerically using a finite element analysis program. This paper describes the application of hybrid simulation for steel I‐shaped columns in Multi‐Tiered Braced Frame (MT‐BFs) structures. A full‐scale W250×101 column part of a two‐tiered concentrically braced frame was physically tested while the rest of the frame was numerically analysed using the OpenSees finite element program. The development and main challenges of the hybrid simulation are first described and the main findings of the hybrid simulation are then presented. The test results show that the testing technique is an effective experimental tool to generate reliable data on column stability limit states. Furthermore, the results from the hybrid simulation confirmed the findings of previous numerical simulations to assess the seismic response of columns in steel braced frames.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.302
Teacher spread0.268 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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