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Record W4389558102 · doi:10.15376/biores.19.1.716-731

Study on bearing capacity of LVL asymmetric truss of lightweight poplar wood

2023· article· en· W4389558102 on OpenAlexaff
Zeqing Wan, Yu Zhang, Yan Liu, Xiuyuan Fang, Meng Gong

Bibliographic record

VenueBioResources · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
FundersYangzhou UniversityNational Natural Science Foundation of China
KeywordsLaminated veneer lumberTrussStructural engineeringBearing capacityLoad bearingFailure mode and effects analysisDeflection (physics)Materials scienceChord (peer-to-peer)EngineeringComposite materialVeneerComputer science

Abstract

fetched live from OpenAlex

Compared with natural wood, laminated veneer lumber has the characteristics of high strength, flexible specifications, excellent stability, and good economy. In order to study the bearing capacity of LVL trusses, the mechanical properties of LVL materials were tested. The static load test was carried out by using 3 pieces of LVL truss, and the load-deflection relationship, load-strain relationship, bearing capacity and failure mode of LVL trusses were studied. Based on the simplified joint analysis method, the metal plate connection and bolted truss were analyzed, and the bearing capacity calculation formula was developed. The results show that the upper chord instability is the main failure mode for large-span light LVL truss. A simplified formula for bearing capacity of LVL truss was proposed, and the predicted results were in good agreement with the experimental results. Finally, an application example of LVL truss engineering design and construction is briefly introduced. The research can provide technical support for the promotion and application of LVL truss of lightweight poplar wood.

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 score0.466
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.219
Teacher spread0.183 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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