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The influence of the opening size on the shear performance of the cross-laminated timber (CLT) walls

2021· article· en· W3196777124 on OpenAlexaff
Daiyuan Zhang, Liming Shen, Xudong Zhu, Sujun Zhang, Meng Gong

Bibliographic record

VenueBioResources · 2021
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
FundersQinglan Project of Jiangsu Province of ChinaNational Natural Science Foundation of China
KeywordsCross laminated timberStiffnessStructural engineeringMaterials scienceShear (geology)Composite materialShear wallShear strength (soil)Direct shear testGeotechnical engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Cross-laminated timber is a wood product with excellent fire resistance and mechanical performance that is often used in tiny houses. Using the ASTM standard E564, the shear performance of cross-laminated timber wall panels, with and without openings, were investigated in this study. The specimens were made of spruce-pine-fir IIc lumber and installed on a test platform using high-strength bolts passing through them. This connection mode limited the displacements obtained in the test, primarily the shear displacements and rocking displacements. By comparing the static load test data of the three specimens with openings and the one without an opening, it was found that openings reduced the shear strength and shear stiffness. For the same sized rectangular opening, the shear stiffness of the cross-laminated timber panel was less when the wider side was horizontal (normal to the direction of the applied force). The shear stiffness of the cross-laminated timber wall panels can be effectively improved by reinforcing the areas near the openings with metal sheets. With reinforcement, the shear strength did not change drastically, but the damage to the cross-laminated timber wall panels was significantly reduced.

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 score0.386
Threshold uncertainty score0.240

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 head0.008
GPT teacher head0.194
Teacher spread0.185 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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