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Record W2323542640 · doi:10.1061/41000(315)16

Hybrid Simulation of the Gravity Load Collapse of Reinforced Concrete Frames

2008· article· en· W2323542640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStructures Congress 2008 · 2008
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOpenSeesSubstructureStructural engineeringEarthquake shaking tableFrame (networking)Computer scienceNonlinear systemHybrid systemComputer simulationGravity damActuatorReinforced concreteEngineeringFinite element methodSimulation

Abstract

fetched live from OpenAlex

A hybrid simulation test setup is developed to investigate and validate the application of hybrid simulation to the gravity load collapse of reinforced concrete frames. The OpenFresco software framework for hybrid simulation is used in combination with an event-driven real-time predictor/corrector ensuring continuous hybrid testing. A shear-critical reinforced concrete column loaded through three dynamic actuators constitutes the physical substructure while a nonlinear ductile reinforced concrete frame makes up the numerical substructure within the OpenSees environment. This paper presents a nonlinear transformation method designed to allow for an accurate application of the loading on the specimen and a new predictor corrector scheme to eliminate force feedback oscillations. Validation of this hybrid simulation setup is achieved through a comparison with a shaking table test of the same reinforced concrete frame by performing two hybrid tests. Based on the results from the hybrid tests, procedures which are expected to improve the results of the hybrid test are presented.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.420

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)0.0000.000

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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