MétaCan
Menu
Back to cohort
Record W2078161498 · doi:10.1139/l07-081

Inelastic performance of screw-connected cold-formed steel strap-braced walls

2008· article· en· W2078161498 on OpenAlexaffvenue
M. Al-Kharat, Colin A. Rogers

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsMcGill University
FundersU.S. Army Corps of Engineers
KeywordsBraceStructural engineeringBraced frameTension (geology)Ductility (Earth science)Materials scienceFracture (geology)Shear wallCold-formed steelComposite materialEngineeringUltimate tensile strengthCreepFinite element methodFrame (networking)Mechanical engineering

Abstract

fetched live from OpenAlex

Cold-formed steel, screw-connected strap-braced walls (15 specimens, 2.44 m × 2.44 m each), designed following a capacity-based approach, were tested to evaluate their performance in the inelastic range of behaviour. Gross cross-section yielding of the tension braces was the specified failure mode in the design procedure. Extended tracks and additional shear anchors were installed, such that inelastic deformations would be limited to tension yielding of the braces. Walls without extended tracks were able to reach their yield level; however, damage to other frame elements occurred, thus reducing the ductility level that was attained. Cyclically loaded walls (0.5 Hz) with track extensions showed that wall performance is also dependent on the strain rate experienced by the braces. An Fu / Fy ratio greater than 1.2 is necessary to limit the possibility of brace fracture under seismic loading. Preliminary force modification factors, Rd = 2.0 and Ro = 1.3, are recommended for walls designed and detailed to achieve ductile performance.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.162
Teacher spread0.154 · 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

Citations33
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicStructural Load-Bearing AnalysisFrench-language works237,207