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Record W3204112333 · doi:10.2749/ghent.2021.1415

Investigation of hybrid multi-core buckling-restrained brace components

2021· article· en· W3204112333 on OpenAlexaff
Pierre Thibault, Charles-Darwin Annan, Pampa Dey

Bibliographic record

VenueReport · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStructural engineeringBraceBucklingStiffnessYield (engineering)Core (optical fiber)Strain hardening exponentInduced seismicityCompression (physics)Tension (geology)Computer scienceMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Contemporary seismic-resistant design of steel braced frames is based on dissipating seismic energy through significant inelastic axial deformation in brace components. Buckling-restrained braced (BRB) frames are a type of concentrically braced frame (CBF) characterised by braces that yield both in tension and in compression. These braces therefore exhibit superior cyclic performance compared with traditional CBFs. However, buckling-restrained braces commonly display a low post- yield stiffness, causing substantial interstory drifts and large residual drifts after seismic events. Moreover, yielding of the core is often only tied to a single performance objective, thus making its performance at other levels of seismicity largely unknown. One promising solution is the use of a hybrid BRB, where multiple cores made from different metals are connected in parallel to work together and complement each other. This research is geared towards first evaluating the potential of different combinations of core materials, followed by the design of a hybrid BRB system that can accommodate multiple core plates. Results show that the post-yield behaviour of hybrid BRBs is improved by employing a combination of 350WT carbon steel and another metal with low-yield and high strain-hardening behaviour, such as stainless steels, aluminium alloys, or other grades of carbon steels. Finally, a detailed overview of one hybrid BRB solution is proposed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.249
Teacher spread0.207 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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