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Record W4380449567 · doi:10.52202/069179-0450

EXPERIMENTAL ANALYSIS OF A SCREW-GLUED SHEAR CONNECTION FOR USE IN MASS TIMBER COMPOSITE PANELS

2023· article· en· W4380449567 on OpenAlexaff
Tyler Hull, Daniel Lacroix

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStructural engineeringDeflection (physics)Composite numberStiffnessAdhesiveMaterials scienceShear (geology)VibrationComposite materialCross laminated timberConnection (principal bundle)Engineering

Abstract

fetched live from OpenAlex

Ribbed or box section mass timber composite (MTC) panels offer a potential solution to the floor span limitations of conventional mass timber panels due to vibration.Glued connections have shown to be a viable shear connection in MTCs due to their high strength and stiffness.Investigated herein is the behaviour of a screw-glued connection of commercially available construction adhesive and self-tapping screws, to assess its viability to adequately transfer forces between the flanges and webs of MTCs.Shear tests on four 400mm long dimensional lumber T-sections yielded an average slip modulus of 17.63N/mm per mm 2 of glue, with an average yield stress of 4.59MPa and 3.47MPa from two bilinear data fit models.The connection was used to fabricate six 1.8m long dimensional lumber T-beams which were tested in flexure and used to validate a numerical model capable of capturing the behaviour.The results agreed well, with the average experimental midspan deflection being 0.98 of the average numerical midspan deflection.Overall, when used on a 10m HMT panel, analytical results using the γ-method indicated a nearly fully composite shear connection was achieved, and a vibration-controlled span of 10.4m was possible for the 455mm deep section.

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 score0.008

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.257
Teacher spread0.211 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same topicWood Treatment and PropertiesFrench-language works237,207