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NUMERICAL MODELING AND FULL-SCALE EXPERIMENTS OF GLUED WOODEN STRUCTURES JOINT DESTRUCTION ON CARBON-FIBER DOWEL PINS

2020· article· en· W3043037935 on OpenAlexaff
Mikhail A. Vodiannikov, Galina G. Kashevarova, Danil Starobogatov

Bibliographic record

VenueInternational Journal for Computational Civil and Structural Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsDurham College
Fundersnot available
KeywordsDowelFinite element methodStructural engineeringJoint (building)Materials scienceDeformation (meteorology)Ultimate failureComposite materialUltimate tensile strengthEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of numerical modeling and full-scale experiments of the failure process of a glued laminated timber beam with rigid joint in the middle. All the connecting parts are made of carbon fiber. The structural analysis is done with the finite element method (ANSYS software). The nonlinear problem was solved. The contact interaction of the structural elements in the process of deformation and fracture, as well as orthotropy of the wood, the transversely isotropic properties of the plates, and the real diagrams of the deformation of carbon fiber dowel pins were taken into account. The influence of the structural parameters of the joint on the position of the most loaded dowel pin in the joint and the bearing capacity of the general structure are investigated. To verify the structural analysis results, field tests were carried out before destruction by a stepwise increasing load on a personally designed stand. The destruction of the structure occurred according to the forecast of the numerical model as a result of the mutual slip of the glued wood layers and the destruction of the polymer matrix of the glued dowel pins with the beginning of the formation of plastic joints and the formation of cracks in the wood at the junction.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.239
Teacher spread0.217 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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