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Record W2072664665 · doi:10.1002/pc.22150

Long‐term hygrothermal response of perforated GFRP plates with/without application of constant external loading

2012· article· en· W2072664665 on OpenAlexaff
Shiva Eslami, Fathollah Taheri‐Behrooz, Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Bibliographic record

VenuePolymer Composites · 2012
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFibre-reinforced plasticMaterials scienceComposite materialStiffnessGlass fiberComposite numberMoistureDegradation (telecommunications)Computer science

Abstract

fetched live from OpenAlex

Abstract The use of glass fiber‐reinforced polymer (GFRP) composites is increasingly being considered in various applications where the composite is subjected to harsh hot and humid conditions. Although information on the performance of GFRP under hot and humid conditions is available, the characteristics and the response of perforated GFRP under such conditions have not been fully explored. In this article, the response of perforated GFRP plates subject to hot and humid environment is examined. The applicability and accuracy of Fick's model for establishing the amount of moisture absorption by such composites is examined, and an improved model is proposed. The article also demonstrates the influence of constant external loading on such perforated GFRP while undergoing conditioning in a hot and humid environment. Moreover, since the strength and stiffness of composites can be significantly affected by harsh environments, the degradation in the strength and stiffness of the perforated GFRP as a function of time is established. A new model is proposed wherein degradation of the strength of such perforated composites may be established as a function of time and geometric entities. The model can also account for the influence of the applied loading. POLYM. COMPOS. 2012. © 2012 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 score0.203
Threshold uncertainty score0.811

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 communication0.0000.000
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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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