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Record W2947990554 · doi:10.1139/cjce-2018-0642

Effect of nonstructural components on the dynamic characteristics of light-frame wood buildings

2019· article· en· W2947990554 on OpenAlexaffvenue
Ghazanfarah Hafeez, Ghasan Doudak, Ghyslaine McClure

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsStructural engineeringStiffeningModalShear wallStiffnessStructural loadModal analysisVibrationComposite numberTuned mass damperEngineeringMaterials scienceComposite materialDamperFinite element method

Abstract

fetched live from OpenAlex

This research deals with ambient vibration measurements (AVM) in 16 multi-storey light-frame wood buildings with wood-based shear walls as the main lateral load resisting system. Its primary objective is to evaluate the effect of nonstructural sheathing panels and the connectivity between firewall-separated buildings, on the modal properties of the tested buildings. Lower natural frequencies, corresponding mode shapes and equivalent structural damping were extracted from the AVM records. The study confirmed that the increase in stiffness of the finished buildings was dominant over their increase in mass when sway mode periods of finished buildings and bare structural frameworks are compared. The stiffening effect of gypsum wallboard was deemed significant and should be accounted for in seismic design. The study also indicates that even when the building section were only nominally connected to the firewall, composite action in the lateral response was taking place.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.003
GPT teacher head0.169
Teacher spread0.167 · 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 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

Citations6
Published2019
Admission routes2
Has abstractyes

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