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Record W4281476763 · doi:10.3390/f13060826

Shear Property and Uniform Vertical Load Capacity of Bamboo I-Beams

2022· article· en· W4281476763 on OpenAlexaboutno aff
Xiaomeng Yang, C Sun, Faren Huo, Yong Gong, Yuhui Sun

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsBambooFlangeBearing capacityShear (geology)Materials scienceStiffnessStructural engineeringLoad bearingBeam (structure)Composite materialShear forceEngineering

Abstract

fetched live from OpenAlex

Bamboo oriented strand boards (BOSB) are very suitable for application in construction structures because of their excellent mechanical properties. This research investigated the shear performance of bamboo I-beams composed of BOSB to verify the structural performance of I-beams. Short beam shear tests and uniform vertical load capacity tests were performed to investigate the effects of various factors on the properties of bamboo I-beams. The results showed that shear bearing capacity and uniform vertical load capacity of bamboo I-beams exceeded the requirements for performance-rated I-Joists in APA PRI-400-2021. The shear bearing capacity, stiffness, and failure types of bamboo I-beams were determined by the web materials, flange–web joint type, and beam depth. Increasing the bamboo I-beam depth without changing the flange dimensions had no significant effect on the shear bearing capacity and stiffness of bamboo I-beams. The shear bearing capacity and stiffness of wooden orientated strand board webbed I-beams were almost half of those of bamboo I-beams with the same depth. The shear bearing capacities of specimens calculated based on the shear bearing capacity calculation formula of I-beams recommended in the Canadian standard were reasonably close to the experimental results. The uniform vertical load capacity of bamboo I-beams gradually decreased as the depth of the bamboo I-beam increased from 300 mm to 500 mm.

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 score0.529
Threshold uncertainty score0.478

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 head0.029
GPT teacher head0.204
Teacher spread0.176 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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