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Record W3082144786 · doi:10.3390/app10175983

Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels

2020· article· en· W3082144786 on OpenAlexaff
Yannian Zhang, Moncef L. Nehdi, Xiaohan Gao, Lei V. Zhang

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsWestern University
Fundersnot available
KeywordsFlexural strengthStructural engineeringComposite numberNail (fastener)Ultimate tensile strengthSandwich-structured compositeBearing capacityEngineeringComposite materialMaterials science

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) is an innovative wood panel composite that has been attracting growing interest worldwide. Apart from its economic benefits, CLT takes full advantage of both the tensile strength parallel to the wood grain and its compressive strength perpendicular to the grain, which enhances the load bearing capacity of the composite. However, traditional CLT panels are made with glue, which can expire and lose effectiveness over time, compromising the CLT panel mechanical strength. To mitigate such shortcomings of conventional CLT panels, we pioneer herein nail-cross-laminated timber (NCLT) panels with more reliable connection system. This study investigates the flexural performance of NCLT panels made with different types of nails and explores the effects of key design parameters including the nail incidence angle, nail type, total number of nails, and number of layers. Results show that NCLT panels have better flexural performance than traditional CLT panels. The failure mode of NCLT panels depends on the nail angle, nail type, and quantity of nails. A modified formula for predicting the flexural bearing capacity of NCLT panels was proposed and proven accurate. The findings could blaze the trail for potential applications of NCLT panels as a sustainable and resilient construction composite for lightweight structures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.295

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 head0.040
GPT teacher head0.235
Teacher spread0.195 · 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
Published2020
Admission routes1
Has abstractyes

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