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Longitudinal Bending and Failure of GFRP Pipes Buried in Dense Sand under Relative Ground Movement

2012· article· en· W2014632091 on OpenAlexaff
Mohamed Almahakeri, Amir Fam, Ian D. Moore

Bibliographic record

VenueJournal of Composites for Construction · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
Fundersnot available
KeywordsFibre-reinforced plasticMaterials sciencePipeline transportBendingComposite materialBeam (structure)Glass fiberCorrosionGeotechnical engineeringStructural engineeringGeologyEnvironmental science

Abstract

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Pipelines extend thousands of kilometers for transport and distribution of oil, gas, and other chemical products. With the ever persistent challenges often faced with corrosion, relative rigidity, and other issues characteristic to steel pipes, the need to explore the use of new pipeline materials, such as glass fiber–reinforced polymers (GFRP), increases. The pipe-soil interaction and the longitudinal behavior of such pipes resulting from relative ground movements is poorly understood. In this study, a series of GFRP pipe bending experiments have been conducted on 115-mm-diameter and 1,830-mm-long GFRP pipes buried in dense sand. The pipe ends were pulled by two parallel cables attached to a spreader beam outside the test region, which was pulled by a hydraulic actuator. The study investigated the effect of laminate structure of pipe, including a cross-ply and angle-ply laminates, on the strength, deflections and failure modes, at different burial depth-to-diameter (H/D) ratios of 3, 5, and 7. Results were also compared with steel control pipes of comparable dimensions and pressure rating. The peak load was shown to increase as burial depth increases, and was generally associated with soil failure, except for the angle-ply pipe at H/D=7 that experienced a structural failure. At peak loads, the net deflections of GFRP pipes were 4–7.5 times those of the equivalent steel pipes, with the cross-ply pipes being stiffer than angle-ply pipes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.415

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

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