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Record W3042999289 · doi:10.1080/13287982.2020.1786988

Cyclic performance and fracture of wide flanged concentrically steel braced frames

2020· article· en· W3042999289 on OpenAlexaff
Madhar Haddad, Nigel Shrive

Bibliographic record

VenueAustralian Journal of Structural Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFlangeBraceStructural engineeringBraced frameBeam (structure)EngineeringWeldingFracture (geology)Finite element methodColumn (typography)Geotechnical engineeringFrame (networking)Mechanical engineeringConnection (principal bundle)

Abstract

fetched live from OpenAlex

Concentrically braced steel frames (CBFs) are often used in buildings to resist lateral loads induced by wind and seismic excitations. Large-size wide-flange (WF) steel brace members could be selected and used in these frames. In the current study, a finite element model (FEM) with a strain-based fracture rule is presented to investigate the performance of a large-size WF brace member in single-storey CBF with different sizes of beams and columns when subjected to reversed axial displacements. The effects of restraining the far ends of the beam bottom-flange, increasing the thickness of tab-plates, increasing the number of bolts used to connect the tab-plates to the web of the beam, and the location of the work-point along the brace with respect the beam-column assembly, on the hysteresis response of the frame are investigated. Results suggest that it may be advantageous to reduce the number of bolts and to increase the bolt size to allow for an increase in the thickness of the tab-plates to resist the extreme rotational demand in high seismic regions, thereby preventing fracture of the weld between the tab-plates and the column flange.

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)0.000
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 score0.373
Threshold uncertainty score0.593

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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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