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Record W2012127087 · doi:10.1115/pvp2010-25277

Incorporation of Strain Hardening Effect Into Limit Load Analysis

2010· article· en· W2012127087 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

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLimit loadHardening (computing)TangentLimit analysisStrain hardening exponentMaterials scienceLimit (mathematics)Bilinear interpolationStructural engineeringComposite materialMechanicsComputer scienceFinite element methodMathematicsMathematical analysisGeometryPhysicsEngineering

Abstract

fetched live from OpenAlex

In this paper a novel method for finding out limit loads in the components or structures by incorporating strain hardening effects is presented. This has been done by including certain amount of the strain hardening into limit load analysis, which normally idealizes the material to be perfectly plastic. The typical strain hardening curves including bilinear hardening and Ramberg-Osgood material models are investigated. The paper also concentrates on plastic reference volume correction concept to find the active volume participating in plastic collapse. The reference volume concept in combination with mα-Tangent method is used to estimate the lower bound limit load of different components.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.328
Teacher spread0.292 · 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