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Record W4303647066 · doi:10.1177/00219983221132625

Modelling of creep behaviour of timber dowelled beams

2022· article· en· W4303647066 on OpenAlexaff
Lyazid Bouhala, Ahmed Makradi, Marc Oudjène

Bibliographic record

VenueJournal of Composite Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCreepMaterials scienceServiceability (structure)Uncompressed videoConstitutive equationParametric statisticsStructural engineeringBendingComposite materialFinite element methodComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

Extensive researches have been dedicated to study creep in wooden structures where good insight was gained on the phenomenological behaviour. However, long-term creep laws and practical methods to investigate the creep’s influence on safety and serviceability of wood structures during their life-cycle are still very few or don’t exist. This paper investigates the creep of wooden structures using a combined numerical and experimental study. Uni-axial tests (i.e. compression/tensile tests in different directions) were used to calibrate the constitutive law of spruce species and to identify the elastic parameters of the constitutive law. For creep parameters identification, three point bending tests were performed for compressed and uncompressed spruce wood. Subsequently, the results of the three point bending tests were used in conjunction with the numerical model in an inverse problem to obtain the viscous parameters. This technique was adopted for both compressed and uncompressed wood samples. Furthermore, a parametric study was conducted on laminated beams of uncompressed spruce boards assembled by compressed spruce dowels. The behaviour of the whole hybrid structure was studied and several interesting findings were highlighted.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.566

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)0.0010.000

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 head0.019
GPT teacher head0.201
Teacher spread0.181 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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