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Record W4380449621 · doi:10.52202/069179-0191

DUCTILITY OF WOOD CONNECTIONS WITH SDS SCREWS OR RING NAILS AND ANGLE BRACKETS

2023· article· en· W4380449621 on OpenAlexaff
Petr Sejkot, Asif Iqbal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
FundersStrong
KeywordsStructural engineeringThread (computing)Load bearingBearing (navigation)Failure mode and effects analysisDuctility (Earth science)Materials scienceBridging (networking)EngineeringComposite materialComputer scienceMechanical engineeringCreep

Abstract

fetched live from OpenAlex

Mechanical connections in wood structures typically consist of structural members connected with combination of cold-formed thin-walled steel angle brackets and fasteners in forms of nails or screws.Design of these steel connectors is typically based on short-term monotonic load bearing capacities.Observations from experimental testing indicate that their failure modes and resistance to a cyclic loading should be checked carefully to prevent the thread of sudden collapse of structures using these connections.This paper presents the experimental results of connections of CLT elements connected together by angle brackets and subjected to the external cyclic loading.Special focus is made on connections using heavy duty screws as fasteners.Results of experimental testing are compared to results of testing of similar connections using ring nails as fasteners.In addition, numerical simulations are made with aim to predict the failure modes and the load bearing capacities of experimentally tested connections.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.220
Teacher spread0.191 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicWood Treatment and PropertiesFrench-language works237,207