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Experimental study of the uniaxial stress-strain relationships of parallel strand bamboo in the longitudinal direction

2019· article· en· W2981737307 on OpenAlexaff
Baolu Sheng, Yuling Bian, Yanyan Liu, Ying Hei Chui, Aiping Zhou

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of Alberta
FundersDoctorate Fellowship Foundation of Nanjing Forestry UniversityPriority Academic Program Development of Jiangsu Higher Education InstitutionsNanjing Forestry University
KeywordsBambooMaterials scienceComposite materialBucklingTension (geology)Compression (physics)Structural engineeringBrittlenessUltimate tensile strengthShearing (physics)Stress (linguistics)StiffnessMaterial failure theoryFinite element methodEngineering

Abstract

fetched live from OpenAlex

Parallel strand bamboo has extensive potential applications as a structural material for construction. Studying longitudinal stress-strain relationships is essential as a means to build a constitutive law for parallel strand bamboo composites and to conduct an inelastic analysis for structural members constructed by this material. For this reason, failure modes and the damage mechanisms were investigated for tension and compression in parallel strand bamboo composites in the longitudinal direction. An analytical stress-strain formula for the parallel strand bamboo composites was developed for tension and compression related calculations. Tensile failure was caused by the damage of the longitudinal fibers and showed brittle characteristics. The compressive failure resulted from the buckling of the fibers near the damage area. In addition, three types of failure modes were observed; longitudinal buckling failure, compressive-shearing failure, and longitudinal crush failure. The stress-strain relationship in the longitudinal direction of parallel strand bamboo composites exhibited linear behaviour for tension. However, the stress-strain relationship for compression remained linear within the proportional limit, while becoming nonlinear, which can be simulated by a quadratic polynomial, once the stress exceeded the limit. The experimental data agreed well with the model predictions, showing that the present model had high prediction accuracy.

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 score0.087
Threshold uncertainty score0.117

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 head0.047
GPT teacher head0.233
Teacher spread0.187 · 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
Published2019
Admission routes1
Has abstractyes

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