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Record W2312580428 · doi:10.1139/tcsme-2011-0020

ADAPTIVE FINITE ELEMENT METHOD TO DETERMINE K<sub>I</sub> AND K<sub>II</sub> OF CRACK PLATE WITH DIFFERENT E<sub>INCLUSION</sub>/E<sub>PLATE</sub> RATIO

2011· article· en· W2312580428 on OpenAlexvenueno aff
Wiroj Limtrakarn, Pramote Dechaumphai

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersCommission on Higher Education
KeywordsFinite element methodEnhanced Data Rates for GSM EvolutionStress intensity factorMaterials scienceInverseReflection (computer programming)Stress (linguistics)Fracture mechanicsMathematical analysisGeometryMathematicsStructural engineeringComposite materialComputer scienceEngineering

Abstract

fetched live from OpenAlex

An adaptive finite element method is presented to determine the KI and KII stress intensity factors of crack plate with different inclusions. The paper starts from describing two-dimensional fracture mechanics theory, an adaptive finite element formulation and the reflection photoelastic technique. An adaptive finite element method is evaluated by analyzing two examples. A single edge cracked plate made from polycarbonate. The second example is the slant edge 45° cracked plate subjected to a uniform uniaxial tensile stress. The KI and KII results are found to be function of the crack length per width and the inverse function of E ratio. These examples demonstrate the efficiency of the adaptive finite element method to provide accurate solutions as compared to those from the reflection photoelastic technique.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.191
Teacher spread0.178 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207