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Record W1990855692 · doi:10.1139/l06-036

Light-gauge steel-frame wood structural panel shear wall design method

2006· article· en· W1990855692 on OpenAlexfundvenueaboutno aff
A E Branston, F A Boudreault, C Y Chen, Colin A. Rogers

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
FundersIndustry CanadaMcGill University
KeywordsStructural engineeringShear wallFastenerStiffnessSeismic analysisShear (geology)Ductility (Earth science)EngineeringStructural systemShear strength (soil)Geotechnical engineeringGeologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Design guidelines for laterally loaded (wind and seismic) light-gauge steel-frame – wood structural panel shear walls are currently unavailable in Canadian standards and codes. A research project was initiated at McGill University in 2001 with the objective of developing a shear wall design method that could be used in conjunction with the 2005 National Building Code of Canada (NBCC). An extensive program of tests was first carried out to establish a database of shear wall information. The equivalent energy elastic–plastic (EEEP) analysis approach was then chosen to derive key design parameters for the shear walls, including nominal shear strength, elastic stiffness, overstrength, and ductility. This paper presents the development of the proposed design method, the resulting nominal strength and unit elastic stiffness values according to typical perimeter fastener schedules and sheathing type, and the calibration of a resistance factor to the 2005 NBCC wind loads. Overstrength values used for a capacity-based seismic design approach and factors of safety for wind loading are also provided.Key words: shear wall, light-gauge steel, wood structural panel, earthquake, wind, design.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.016
GPT teacher head0.198
Teacher spread0.183 · 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

Citations37
Published2006
Admission routes3
Has abstractyes

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