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Feasibility of predictive assessment of bending performance of CLT plates of Canadian hemlock

2019· article· en· W2965198353 on OpenAlexaboutno aff
Wenbo Xie, Yao Lu, Zheng Wang, Xiwei Wang, Xiaoli Wu, Zizhen Gao

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNanjing Forestry University
KeywordsMaterials scienceBendingElastic modulusComposite materialStructural engineeringYoung's modulusShear modulusCross laminated timberEngineering

Abstract

fetched live from OpenAlex

The correlation between the bending elastic modulus of lumbers along the primary direction and that of the resultant cross-laminated timber (CLT) plates in the full size suitable for slabs or wallboards was investigated to verify the feasibility of predicting the bending performance during the manufacturing of heavy building structures of this new type of material. A batch of Canada hemlocks lumber was graded based on a vibrational test that measures longitudinal elastic modulus. The elastic modulus and shear modulus in the transverse direction were also measured using the scheme of a torsional modal analysis of a cantilever plate. CLT were fabricated using the graded lumbers in sizes suitable for slabs or wallboards. The elastic moduli of these CLT products were measured using a conventional four-point static bending test. Finally, the static measurements of the elastic moduli of the CLT were compared with their predicted values that were calculated with the aforementioned data collected from the lumber pieces. The predicted elastic modulus along the primary direction of a CLT product agreed with the measured values. Therefore, the mathematical model of the CLT plate and the equation of its elastic modulus are feasible for the bending performance prediction in industrial production of CLT.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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