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Record W2043934232 · doi:10.1115/pvp2007-26774

Damage Behavior of Joined Fiber-Reinforced Polymer Pipe Under Monotonic and Cyclic Loading

2007· article· en· W2043934232 on OpenAlexaff
Pierre Mertiny, Kerstin Ursinus

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialAdhesiveComposite numberJoint (building)CorrosionFibre-reinforced plasticStructural engineeringPolymerFiberDamage toleranceLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

Fiber-reinforced polymer composite pipe may provide superior performance in terms of weight and corrosion resistance compared to structures made from conventional engineering materials. Using advanced winding techniques and adhesive bonding, composite tubes of high quality can be manufactured and joined in a cost effective manner. However, the current understanding of the damage mechanisms and long-term behavior of the tubes and joints is limited, thus the prediction of composite pipe performance is currently inadequate. Prior to developing modeling and failure prediction methodologies, it is imperative to identify and describe the types and evolution of damage associated with these structures. The present study is concerned with the monotonic and cyclic damage behavior of adhesively bonded glass-fiber reinforced epoxy polymer tubes. Experimental analyses on small-scale and large-scale specimens were conducted, and results revealed characteristic damage modes for the pipe body and the joint area. It was observed that damage modes and their severity depended on the applied biaxial pipe stress ratio and the type of loading (monotonic or cyclic).

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 score0.015
Threshold uncertainty score0.832

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)0.0010.000

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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