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Experimental Investigation and Numerical Modeling of Tongue-and-Groove Plank Wood Decking under the Effects of Concentrated Loads

2014· article· en· W1963634967 on OpenAlexafffundabout
K. Rocchi, Ghasan Doudak

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2014
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringPlankDeflection (physics)StiffnessFinite element methodEngineeringRoof

Abstract

fetched live from OpenAlex

Tongue-and-groove plank wood decking is a product that is commonly used in post and beam timber construction to transfer gravity loads on roofs and floors. In 2010, the National Building Code of Canada changed the application area of the specified concentrated roof live loads from 750×750 mm to 200×200 mm. Preliminary analysis showed that the change in the application area of concentrated loads would have a significant impact on the design of decking systems. An experimental program was undertaken at the University of Ottawa’s structural laboratory to better understand the stiffness characteristics of plank decking under concentrated loads. The experimental test program was complimented with a detailed finite-element model in order to predict the behavior of a plank decking system, especially the force transfer between decks through the tongue and groove joint. The study found that under concentrated loads, the stiffness of the decking system increased significantly as more boards were added. The number of boards found to be representative of a system was eight boards. A deflection coefficient of γ=0.4 was found to be appropriate to calculate the deflection for the simple span, two-span continuous, and controlled random layup, under concentrated load on an area of 200 by 200 mm. A finite-element model was created and compared with the experimental results, and it was found to predict the behavior with reasonable accuracy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.299

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 head0.009
GPT teacher head0.187
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 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

Citations2
Published2014
Admission routes3
Has abstractyes

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