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Experimental testing and comparison with analytical methods for glued-in rods in cross-laminated timber

2025· article· en· W7109140690 on OpenAlexafffund

Bibliographic record

VenueEngineering Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsQueen's UniversityUniversity of British Columbia, Okanagan CampusUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRodExperimental dataMaterials testingCross laminated timber

Abstract

fetched live from OpenAlex

With the advent of engineered wood products, timber has evolved into a sustainable material for modern innovative structural systems. This resurgence in the application of timber has also spurred the development of innovative connection systems. Among various timber connection methods, glued-in rods (GiRs) have emerged as a modern solution, offering superior load-carrying capacity, aesthetic integration, and suitability for concealed applications. In this study, sixteen GiR connections in cross-laminated timber (CLT) are experimentally tested to evaluate failure modes, load–displacement behavior, and strain distribution along the rod embedment length. These experimental results, combined with data from previously published studies, are used to assess the predictive accuracy and reliability of existing analytical and empirical design equations developed for the GiR connection design. The study highlights the importance of considering both the embedment length and the grain orientation when designing GiR connections in CLT. While most existing design formulations provide reasonable strength estimates, their accuracy decreases at higher embedment lengths. Moreover, variability in failure behavior across studies underscores the complexity of GiR connections. In summary, the findings of this study provide insight into the performance of current design models and contribute to the advancement of reliable methodologies for GiR connections in CLT structures.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.584

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 head0.018
GPT teacher head0.337
Teacher spread0.319 · 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 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

Citations7
Published2025
Admission routes2
Has abstractyes

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