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Record W2797769523 · doi:10.1520/jte20170271

Behavior and Law of Crack Propagation in the Dynamic-Static Superimposed Stress Field

2018· article· en· W2797769523 on OpenAlexaff
Renshu Yang, Chenxi Ding, Liyun Yang, Yufei Zhang, Peng Xu

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsStress (linguistics)Materials scienceStructural engineeringField (mathematics)MechanicsComposite materialLawEngineeringPhysicsMathematicsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract The aim of this study is to investigate the behavior and the law of crack propagation, particularly under the dynamic-static superimposed stress field in the poly(methyl methacrylate) specimens. We introduce a novel system of digital laser dynamic caustics experiments with a self-designed dynamic-static loading device. These specimens are divided into three groups and subjected to the static stress field, the dynamic stress field, and the stress superposition field, respectively. The conclusion can be obtained by comparing the crack length and the dynamic stress intensity factor of the main cracks during propagation under the three different stress fields. The static stress field significantly reduces the arrest toughness of the main cracks, which is the main reason for longer crack propagation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.098
GPT teacher head0.363
Teacher spread0.265 · 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 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

Citations12
Published2018
Admission routes1
Has abstractyes

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