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Record W2513243739 · doi:10.1139/cjce-2015-0399

Seismic design and response of steel multi-tiered concentrically braced frames in Canada

2016· article· en· W2513243739 on OpenAlexafffundvenueabout
Ali Imanpour, Robert Tremblay

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringBraced frameBraceSeismic analysisDuctility (Earth science)Flexural strengthTension (geology)Frame (networking)BendingEngineeringSteel frameGeologyCompression (physics)Materials scienceCreepMechanical engineering

Abstract

fetched live from OpenAlex

This article investigates the seismic design and response of steel multi-tiered concentrically braced frames (MT-BFs) in which braces meet at columns between diaphragms. The seismic design provisions of CSA S16-14 are described and illustrated for three-tiered Type MD (moderately ductile) and five-tiered Type LD (limited ductile) braced frames. Analysis methods are proposed to evaluate the in-plane flexural demand on columns. The seismic response of the frames is examined through nonlinear response history analysis. As assumed in design, inelastic deformations tend to concentrate in one tier over the frame height, causing non-uniform drift demands and in-plane bending moments in the columns. CSA S16 provisions predicted well the frame in-plane flexural response and result in acceptable ductility demands on the braces. An extended seismic analysis and design approach that accounts for vertical distribution of brace tension yielding along the frame height is proposed for frames that exceed the limits prescribed in CSA S16.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.173
Teacher spread0.165 · 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

Citations20
Published2016
Admission routes4
Has abstractyes

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