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Record W3098259138 · doi:10.15376/biores.16.1.354-371

Racking performance of poplar laminated veneer lumber frames and frame-shear hybrid walls

2020· article· en· W3098259138 on OpenAlexafffund
Yan Liu, Zizhen Gao, Hongwei Ma, Meng Gong, Honghe Wang

Bibliographic record

VenueBioResources · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
FundersMinistry of Science and Technology of the People's Republic of ChinaNew Brunswick Innovation Foundation
KeywordsLaminated veneer lumberShear wallStructural engineeringVeneerMaterials scienceStiffnessShear (geology)Oriented strand boardComposite materialBeam (structure)EmbedmentEngineering

Abstract

fetched live from OpenAlex

This study examined the racking performance of poplar laminated veneer lumber (LVL) frames using bolted steel filling plates to connect beam-column joints, poplar LVL frames using the embedment bars to connect beam-column joints, and frame-shear hybrid walls made of poplar LVL studs and oriented strand board (OSB) sheathing panels. A new design load spreader beam was used on the side of the top of a specimen to apply monotonic and cyclic loadings. It was found that the lateral force resistance, stiffness, and ultimate loads of poplar LVL pure frames with bolted steel filling plate connections and closed rod connections were much lower than those of the poplar LVL frame-shear wall hybrid structure. The highest initial stiffness of the poplar LVL hybrid frame-shear wall was 1.77 kN/mm, which was 24% and 22% lower than that of the conventional shear wall made with spruce-pine-fir studs and OSB or plywood sheathing panels, respectively. The poplar LVL frame-shear wall hybrid structure showed lower degradation in stiffness than the conventional shear wall. The hybrid frame-shear wall structures made of poplar LVL could meet the requirements of Chinese standard; however, diagonal braces were required in use of poplar LVL pure frames.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.410

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 head0.011
GPT teacher head0.173
Teacher spread0.162 · 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 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

Citations9
Published2020
Admission routes2
Has abstractyes

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