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Record W2271956366

Wood I-Joist Model Sensitivity to Oriented Strandboard Web Mechanical Properties

2010· article· en· W2271956366 on OpenAlexfundno aff
Jean-Frédéric Grandmont, Alain Cloutier, Guy Gendron, Richard Desjardins

Bibliographic record

VenueCorpus Université Laval (Université Laval) · 2010
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsJoistStructural engineeringStiffnessFinite element methodOriented strand boardMaterials scienceYoung's modulusDeflection (physics)Laminated veneer lumberShear (geology)Composite materialEngineering
DOInot available

Abstract

fetched live from OpenAlex

Research on wood I-joist design has often used laboratory testing, but simulation using the finite
\nelement method (FEM) offers advantages, including the possibility to separately study different joist
\ncomponents. The objective of this project was to perform a sensitivity analysis using FEM to determine
\nwhich oriented strandboard (OSB) properties have higher impact on I-joist shear strain and deflection. OSB
\nmechanical properties were changed from 50 to 200% of the reference value to determine their impact on
\nweb shear strain and I-joist deflection. The model was primarily sensitive to in-plane web shear stiffness,
\nwhich changed I-joist deflection up to 23%. The model was also sensitive to the web tensile modulus of
\nelasticity parallel and perpendicular to joist length and, to a lesser extent, to web shear stiffness. These
\nproperties changed I-joist deflection up to 2 and 1%, respectively. These findings will be used to plan future
\nwork to experimentally determine sensitive OSB web properties required to develop a finite element model
\nof the mechanical behavior of wood I-joists.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.175
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 teacher head, not a consensus.

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

Citations7
Published2010
Admission routes1
Has abstractyes

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