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Record W1986203856 · doi:10.1115/1.4000360

Evaluation of Glass and Basalt Fiber Reinforcements for Polymer Composite Pressure Piping

2009· article· en· W1986203856 on OpenAlexafffund
Pierre Mertiny, Kulvinder Juss, Mohab M. El Ghareeb

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMaterials scienceComposite materialGlass fiberThermosetting polymerPipingEpoxyUltimate tensile strengthComposite numberFilament windingPressure vesselFibre-reinforced plasticLeakage (economics)Basalt fiberFiberMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Pressure piping made from fiber-reinforced polymer composites is becoming increasingly popular. This development is driven by the need for lighter and more corrosion resistant components. Compared with traditional metallic structures, composites may satisfy these requirements without compromising strength or cost-effectiveness. The field of composite materials engineering is evolving rapidly, and new analysis and processing methods, as well as material systems, are continually emerging. The present contribution focuses on fiber reinforcements and their performance in pressurized tubular structures. Recently, basalt fiber has gained in popularity and in many cases has been considered an alternative to conventional fiber materials such as E- and S-glasses for composite piping. An investigation was conducted on the performance of basalt, E-glass, and S-glass reinforcements employing uniaxial tensile test rods and tubular samples. Specimens were produced by wet filament winding using a common thermoset epoxy polymer. In addition to rod sample rupture strength, the failure behavior and strength of tube specimens were assessed for leakage and bursting under different biaxial loading conditions. Two different methodologies for the assessment of leakage failures were described and discussed. Based on the experimental findings the performance of the various fiber reinforcements was evaluated.

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)0.001
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 score0.066
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.277
Teacher spread0.259 · 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
Published2009
Admission routes2
Has abstractyes

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