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Record W4411778355 · doi:10.1007/s00107-025-02278-0

Evaluation of in-plane shear performance of CLT using the asymmetric four-point bending test method and detailed examination of the method

2025· article· en· W4411778355 on OpenAlexaff
Kaito Yamagata, Takuro Mori, Mohammed Mestar, Ryo Inoue

Bibliographic record

VenueEuropean Journal of Wood and Wood Products · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsRoyal Military College of Canada
FundersHiroshima University
KeywordsShear (geology)BendingCross laminated timberStructural engineeringPlane (geometry)Point (geometry)Test (biology)Test methodMathematicsMaterials scienceComposite materialGeologyEngineeringGeometryStatistics

Abstract

fetched live from OpenAlex

Abstract The use of cross-laminated timber (CLT) and laminated veneer lumber (LVL) in wooden structures including non-residential, tall and large buildings is increasing, even though they might be subjected to important horizontal forces. The asymmetric four-point bending test method, originally used for lumber and glulam, can be used to evaluate the in-plane shear performance of CLT. This test may have led to bending failure in the case of the CLT specimens. In this study, asymmetric four-point bending tests were conducted to evaluate appropriate shear strength and modulus. Span ratio of 0.5–1.0 is recommended for shear failure to occur as the latter is less affected by compression force. The shear strength exhibited a positive correlation with the ratio of the perpendicular layer, suggesting that the shear strength can be easily estimated. The diagonal measurement of shear deformation is a convenient method because it has less effect on deformation due to the direction of the grain.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.273
Teacher spread0.241 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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