MétaCan
Menu
Back to cohort
Record W2082335554 · doi:10.1139/l06-097

Seismic force modification factors for light-gauge steel-frame - wood structural panel shear walls

2007· article· en· W2082335554 on OpenAlexfundvenueaboutno aff
F A Boudreault, Caroline Blais, Colin A. Rogers

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsShear wallStructural engineeringShear (geology)StiffnessSeismic analysisDuctility (Earth science)Gauge (firearms)EngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Design guidelines for laterally loaded light-gauge steel-frame – wood structural panel shear walls are not available in Canadian codes. A design method for the calculation of shear stiffness and strength has been developed for use with the 2005 National Building Code of Canada (NBCC), however. This method was based on the analysis, using an equivalent energy elastic–plastic (EEEP) approach, of over 180 single-storey shear wall tests of various configurations. Ductility-related (R d ) and overstrength-related (R o ) force modification factors also need to be defined to calculate equivalent static seismic forces following the 2005 NBCC. This paper describes the development of these two factors based on the EEEP analysis of the shear wall test results. To verify the "test-based" R d and R o values, nonlinear time-history dynamic analyses of two representative buildings were carried out using a suite of 10 earthquake records scaled to the 2% in 50 year uniform hazard spectrum (UHS) for Vancouver, British Columbia. Preliminary values have been determined for the force modification factors, namely R d = 2.5 and R o = 1.7.Key words: shear wall, light-gauge steel, wood structural panel, seismic, R value.

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 score0.340
Threshold uncertainty score0.795

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 head0.020
GPT teacher head0.198
Teacher spread0.178 · 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

Citations38
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicWood Treatment and PropertiesFrench-language works237,207