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Record W1988441113 · doi:10.1002/pen.21610

Cyclic interaction between normal and shear stresses of an epoxy polymer—Experiments and model predictions

2009· article· en· W1988441113 on OpenAlexafffund
Zihui Xia, Xinghe Shen, Fernand Ellyin

Bibliographic record

VenuePolymer Engineering and Science · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCreepComposite materialShear (geology)EpoxyUltimate tensile strengthPure shearShear stressSimple shear

Abstract

fetched live from OpenAlex

Abstract This investigation focuses on the axial‐torsional loading interaction of an epoxy polymer, Epon 826/Epi‐Cure Curing Agent 9551. Thin‐walled tubular specimens were subjected to combined constant tensile (or shear) stress and cyclic shear (or tension) loading schemes. Pure tensile creep and shear creep tests were also performed to compare the creep deformation to that with superimposed cyclic shear or cyclic tension. Test data clearly showed that cyclic shear (or cyclic tension) have a readily discernible effect on the tensile (or shear) creep deformation. Similarly, a superimposed constant tensile (or shear) load affects the hysteresis responses in cyclic shear (or cyclic tension). A nonlinear constitutive model developed by the authors was used to simulate the observed normal‐shear stress interaction. Due to the inclusion of an effective stress parameter in its nonlinear function, this model was able to account for the normal‐shear coupling effect. However, the incorporation of a general loading/unloading rule led to inaccurate simulation of the observed oscillatory creep response. A modification of the general rule was proposed and better predictions on both the cyclic and the creep responses could be obtained. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.408
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 teacher head, 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

Citations4
Published2009
Admission routes2
Has abstractyes

Explore more

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