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Record W2180158877 · doi:10.15376/biores.11.1.626-633

Evaluation of Modulus of Elasticity of Laminated Strand Lumber by Non-Destructive Evaluation Technique

2015· article· en· W2180158877 on OpenAlexaff
Zhiqiang Wang, Jianhui Zhou, Meng Gong, Ying Hei Chui, Xiaoning Lu

Bibliographic record

VenueBioResources · 2015
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceComposite materialElasticity (physics)Young's modulusStructural engineeringElastic modulusEngineering

Abstract

fetched live from OpenAlex

Non-destructive evaluation (NDE) techniques have been shown to be effective for evaluating mechanical properties of wood-based materials. In this study, transverse vibration of a beam under the two ends free boundary condition (FBV) was employed to measure the modulus of elasticity (MOE) in different strength directions of laminated strand lumber (LSL). It was found that the MOE values measured by the FBV were slightly higher than those measured using the three-point bending test (TPB), and the correlation coefficients between the MOE values along the major and minor strength directions measured by the two methods were 0.845 and 0.938, respectively. The effect of the length/depth ratio on measured MOE values by the FBV method was also investigated. With the decrease in the length/depth ratio of LSL, the measured MOE in the major and minor strength directions decreased. The results of the ANOVA test showed that the length/depth ratios had a significant effect on the measured MOE. The experimental values of MOE of LSL in the diagonal strength direction matched well with the theoretical values calculated by the Hankinson-type formula.

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)0.001
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 score0.044
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.252
Teacher spread0.216 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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