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Record W4242007507 · doi:10.24124/2020/59098

Performance of glued-in-rod connections in cross-laminated timber

2020· dissertation· en· W4242007507 on OpenAlexaff
Ayansola Gbenga

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCross laminated timberStiffnessStructural engineeringRodConnection (principal bundle)Tension (geology)EngineeringMathematicsComposite materialMaterials scienceCompression (physics)

Abstract

fetched live from OpenAlex

The widespread availability of Cross-laminated timber (CLT) provides opportunities to extend the use of wood beyond traditional low-rise residential construction. Glued-in rods (GiR) are an interesting technical solution for numerous structural applications in timber engineering. Although GiR connections have the potential to be used in combination with CLT, research on this application is scarce. In this thesis, an experimental investigation on the performance of GiR in CLT is presented. Two different 5-ply CLT panel thicknesses (139 and 175 mm), two steel rod diameters (d = 12.7 and 19.1 mm) and five different anchorage lengths (la = 6d, 10d, 12d, 14d and 18d) were considered in single and multiple rod connections. A total of 260 specimens were fabricated and subsequently tested under uni-axial quasi-static monotonic tension. The results were assessed in terms of load-carrying capacity, stiffness and failure modes and demonstrated that GiR in CLT offer an alternative high capacity timber connection.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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