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Record W2008588404 · doi:10.1177/0309324713480767

An effective means for evaluating mixed-mode I/III stress intensity factors using single-edge notch beam specimen

2013· article· en· W2008588404 on OpenAlexaff
B. Ahmadi-Moghadam, Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Bibliographic record

VenueThe Journal of Strain Analysis for Engineering Design · 2013
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStress intensity factorMaterials scienceEnhanced Data Rates for GSM EvolutionStructural engineeringMode (computer interface)Intensity (physics)Finite element methodStress (linguistics)Mixed modeBeam (structure)Fracture (geology)Composite materialEngineeringComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

The main objective of this study is to produce a set of practical equations, by which one could evaluate mixed-mode I/III stress intensity factors by conducting a simple mode I single-edge notch bend specimen test as per ASTM D5054. To establish the proposed equations, a comprehensive finite element analysis is conducted to map the stress intensity factors’ distribution through the thickness of the specimen with the aim of establishing the most effective loading condition for evaluating the mode stress intensity factors. An experimental investigation is also conducted to obtain the ultimate fracture load and establish the fracture mechanism under the mixed-mode loading state. A set of correction factors is also proposed, which accounts for the influence of orientation of the initial crack and the applied load on the stress intensity factors.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.044
GPT teacher head0.277
Teacher spread0.233 · 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

Citations39
Published2013
Admission routes1
Has abstractyes

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