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Record W4416878513 · doi:10.22320/s0718221x/2025.41

Load-carrying capacity of housings in solid timber beams

2025· article· en· W4416878513 on OpenAlexaffabout
Rafid Shams Huq, Tyler W. Heal, Thomas Tannert

Bibliographic record

VenueMaderas Ciencia y tecnología · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMoistureSolid woodReinforcementBeam (structure)EurocodeJoint (building)Water contentCompression (physics)

Abstract

fetched live from OpenAlex

Timber framing has traditionally relied on metal fasteners, with have a high carbon footprint and often limited aesthetic appeal. These challenges can be addressed by using traditional joints such as timber housings. However, there are no design guidelines available that account for the joint geometric parameters, mechanical reinforcements, or the wood moisture content during fabrication and when loaded. In this research, the influence of joint geometry, wood moisture and reinforcement on the load-carrying capacity of solid timber beams with housings was investigated. A total of 150 Pseudotsuga menziesii (Douglas fir) beams were tested according standard, considering different moisture condition (wet or dry) at the time of cutting and at the time of testing. The tests confirmed that greater bearing depth and the use of self-tapping screws as reinforcement lead to increased load-carrying capacity. However, moisture condition significant affected only the double housings, not the single housings. In addition, 198 small-scale specimens were tested for shear, tension and compression to evaluate the impact of small clear specimen material strength on the beam load-carrying capacity. The results showed that these properties were weak predictors of housing performance. Finally, a design approach based on existing Canadian code provisions is suggested.

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 score0.294
Threshold uncertainty score0.532

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

Explore more

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