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NUMERICAL MODELLING OF REINFORCED CONCRETE FRAMES RETROFITTED WITH BUCKLING RESTRAINED BRACE INCORPORATING STEEL AND SUPERELASTIC SHAPE-MEMORY ALLOY CORES

2023· article· en· W4387821062 on OpenAlexaff
Pedro Alexandre Guimarães Rocha, Dan Palermo

Bibliographic record

VenueNED University Journal of Research · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsYork University
Fundersnot available
KeywordsShape-memory alloySMA*Structural engineeringBucklingMaterials scienceBraceFinite element methodPseudoelasticityBar (unit)Frame (networking)Core (optical fiber)Displacement (psychology)Nonlinear systemCompression (physics)Computer scienceMartensiteEngineeringComposite materialGeologyMechanical engineeringMicrostructure

Abstract

fetched live from OpenAlex

The numerical response of one-storey reinforced concrete frames designed following design standards before the enactment of modern seismic provisions was investigated in this study using the nonlinear finite element method. An unbraced frame and two frames retrofitted with a buckling restrained brace (BRB) incorporating either a stainless steel or chrome molybdenum core bar were modelled. The nonlinear static reverse cyclic loading performance of the frames was assessed. Modifications to the BRB are proposed to mitigate deficiencies identified with the original BRB and steel core bars. The modified BRB incorporating a superelastic shape-memory alloy (SE-SMA) core bar was further modelled. The results illustrate the benefits of implementing SE-SMA as a retrofit strategy to control permanent displacements. The modified BRB with a SE-SMA core exhibited improved lateral strength and displacement capacities relative to the frames retrofitted with steel cores and the control bare frame.

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)0.001
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 score0.081
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.262
Teacher spread0.220 · 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
Published2023
Admission routes1
Has abstractyes

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