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Record W1997703001 · doi:10.1139/l06-035

Making use of brace overstrength to improve the seismic response of multistorey split-X concentrically braced steel frames

2006· article· en· W1997703001 on OpenAlexfundvenueaboutno aff
Martin Lacerte, Robert Tremblay

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBracingBraceStructural engineeringBraced frameEngineeringSeismic analysisBucklingInstabilitySteel frameGeotechnical engineeringFrame (networking)Mechanical engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

This paper presents a study on the inelastic seismic response of multistorey split-X concentrically braced steel frames. Structures with different plan areas and heights are examined for Victoria, British Columbia, and Québec City, Quebec. Structures designed according to current code provisions are found to develop a concentration of inelastic demand over their height, which can lead to dynamic instability. This behaviour was more pronounced for the taller and larger structures located in western Canada. A design method is proposed to mitigate the concentration of inelastic deformations by a proper selection of the bracing members. P–Δ effects also need to be included in the design. Guidelines are also proposed for the accumulation of brace-induced axial loads for column design. Structures up to 12 storeys designed according to the proposed method are found to exhibit a stable inelastic response and storey drifts within acceptable limits.Key words: bracing member, braced steel frame, buckling, instability, seismic, storey drift.

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 score0.083
Threshold uncertainty score0.622

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 head0.009
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

Citations23
Published2006
Admission routes3
Has abstractyes

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