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Record W2006748353 · doi:10.1093/qjmam/hbi017

Fracture mechanics of specially orthotropic shells containing a crack

2005· article· en· W2006748353 on OpenAlexaff
Rong Liu, Tie Zhang, Xijia Wu, Chunhui Wang

Bibliographic record

VenueThe Quarterly Journal of Mechanics and Applied Mathematics · 2005
Typearticle
Languageen
FieldEngineering
TopicElasticity and Wave Propagation
Canadian institutionsCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsOrthotropic materialStress intensity factorClosure (psychology)Crack closureShell (structure)Materials scienceMechanicsStructural engineeringStress (linguistics)Crack growth resistance curveStress concentrationIntensity (physics)Fracture mechanicsComposite materialFinite element methodPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

This article presents a theoretical analysis of specially-orthotropic shells containing a crack in terms of a crack-closure theory. The formulation of Delale and Erdogan for crack problems in shells is extended to include the effect of crack-face closure. The influence of material orthotropy and shell curvatures on the closure behaviour and consequently on the stress intensity factor are studied. It is demonstrated that crack-face closure has a significant impact on the stress intensity factor and it tends to reduce the maximum stress intensity factor. The crack-face closure effect on the stress intensity factor increases with the shell radii. In flat plates, as a special case of shells when the shell radii become infinitely large, the difference of the closure stress intensity factor between the closure case and non-closure case has a maximum. The influence of material orthotropy on the closure behaviour varies with the ratio of the two shell curvatures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

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