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Record W1980934233 · doi:10.1520/jte20120269

A Numerical Study on Effective Modulus of Elasticity in Crack Length Evaluation for Single-Edge Bending Specimens

2013· article· en· W1980934233 on OpenAlexaff
Enyang Wang, Wenxing Zhou, Guowu Shen

Bibliographic record

VenueJournal of Testing and Evaluation · 2013
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsMaterials scienceFinite element methodPlane stressBendingStructural engineeringModulusComposite materialElasticity (physics)Young's modulusEnhanced Data Rates for GSM EvolutionElastic modulusStress (linguistics)Displacement (psychology)Stress intensity factorFracture mechanicsEngineering

Abstract

fetched live from OpenAlex

Abstract The actual state of stress in the remaining ligament of a single-edge bending (SE(B)) specimen, which is neither plane stress nor plane strain, is critical in accurately predicting the crack length in the specimen involved in the unloading compliance method. Three-dimensional finite element analyses were carried out in this study to evaluate the effective modulus of elasticity that reflects the actual state of stress corresponding to the crack mouth opening displacement (CMOD) compliance of the SE(B) specimen. Both plane-sided and side-grooved specimens with a wide range of geometric configurations and crack lengths were included in the study. A practical approach is further proposed to quickly evaluate the effective modulus from the CMOD compliance determined based on the test data. The proposed approach was verified using experimental data and numerical analysis results. The results of the verification suggest that replacing the elastic modulus corresponding to the plane stress or plane strain condition with the effective modulus in commonly used a/W–CMOD compliance equations can markedly improve the accuracy of the predicted crack length.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.305
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.075
GPT teacher head0.313
Teacher spread0.238 · 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 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

Citations4
Published2013
Admission routes1
Has abstractyes

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