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Record W2122383369 · doi:10.1115/1.4024448

Plastic Response Estimation in Repeated Elastic Analyses for Strain Hardening Material Model

2013· article· en· W2122383369 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFinite element methodLimit loadStrain hardening exponentHardening (computing)Structural engineeringElastic modulusMaterials scienceFinite strain theoryStrain energyLinear elasticityContext (archaeology)Composite materialEngineering

Abstract

fetched live from OpenAlex

Simplified limit analysis techniques have already been employed for limit load estimation on the basis of linear elastic finite element analysis (FEA) assuming elastic-perfectly-plastic material model. Due to strain hardening, a component or a structure can store supplementary strain energy and hence carries additional load. In this paper, an iterative elastic modulus adjustment scheme is developed in context of strain hardening material model utilizing the “strain energy density” theory. The proposed algorithm is then programmed into repeated elastic FEA and results from the numerical examples are compared with inelastic FEA results.

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.

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)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.638
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.365
Teacher spread0.285 · 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