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Record W4411936696 · doi:10.1139/cjce-2024-0540

Evaluation and improvement of the Canadian seismic provisions for moderately ductile steel concentrically braced frames with chevron and split-X bracing

2025· article· en· W4411936696 on OpenAlexafffundvenueabout
Bardia Mahmoudi, Ali Imanpour

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChevron (anatomy)BracingStructural engineeringBraced frameBraceSteel frameEngineeringDuctility (Earth science)GeologyGeotechnical engineeringMaterials scienceComposite materialFrame (networking)Mechanical engineering

Abstract

fetched live from OpenAlex

This paper aims to (1) examine the influence of key design parameters on the seismic behaviour of steel chevron and split-X braced frames, with emphasis on damage concentration; (2) evaluate Canadian design provisions for such braced frames; and (3) propose improvements for more accurately estimating their seismic demands. By varying different design parameters, 21 frames are designed according to the Canadian steel design standard. A fibre-based numerical model is then developed to examine the seismic behaviour of the braced frames by performing nonlinear response history analysis. The analysis results are used to interrogate key design parameters, including brace force, column moment, beam unbalanced force, and deflection. A new parameter, called drift concentration ratio, is introduced to quantify damage concentration in braced frames. Furthermore, the adequacy of Canadian seismic provisions in predicting the seismic demands of steel chevron and split-X braced frames is examined. Finally, recommendations are made to estimate design demands on their columns and beams more accurately.

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 score0.576
Threshold uncertainty score0.984

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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