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Record W4406962987 · doi:10.3390/buildings15030440

Biaxial Resistance of Pre-Engineered Beam Hangers in Glulam

2025· article· en· W4406962987 on OpenAlexafffund
Houman Ganjali, Fei Tong, Thomas Tannert

Bibliographic record

VenueBuildings · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsUniversity of Northern British Columbia
KeywordsStructural engineeringCable glandShear (geology)Beam (structure)Ductility (Earth science)Shear wallEngineeringStructural systemMaterials scienceMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

In timber construction, Glulam post-and-beam systems are commonly used to transfer vertical loads to the foundation. In such systems, the connections play a critical role in structural performance. Pre-engineered connectors, which facilitate fast and efficient assembly, are typically designed to resist only vertical shear loads. However, during seismic and wind events, post-and-beam systems deform horizontally, and axial forces develop at the connections. In this research, the performance of RICON and MEGANT pre-engineered connectors was studied under biaxial loading involving concurrent shear and axial forces. A total of 12 full-scale tests on Glulam frame segments were conducted. Neither type of connector experienced any resistance loss under concurrent shear loads equal to the factored shear resistance and axial loads equal to 5% of the factored shear resistance. The axial load-carrying capacity of the RICON and MEGANT connectors was up to 124% and 97% of their factored shear resistance, respectively. The global failure of all the studied connectors demonstrated both ductility and residual deformation capacity. These results provide valuable information for engineers designing Glulam post-and-beam systems in seismic regions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.356

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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