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Record W2811301502 · doi:10.1080/19386362.2018.1483812

A finite-discrete element approach for modelling polyethylene pipes subjected to axial ground movement

2018· article· en· W2811301502 on OpenAlexafffund
Masood Meidani, Mohamed A. Meguid, Luc Chouinard

Bibliographic record

VenueInternational Journal of Geotechnical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsFinite element methodPolyethyleneDistortion (music)Geotechnical engineeringDiscrete element methodMaterials scienceGranular materialSoil structure interactionStructural engineeringEngineeringMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

The response of medium-density polyethylene (MDPE) pipes subjected to ground movement is often investigated using soil-pipe interaction models that were originally developed for steel pipes. In this study, the behaviour of MDPE pipes buried in dense sand under pull-out force is investigated using a coupled finite-discrete element framework. The pipe is modelled using finite elements whereas the granular soil is modelled using discrete elements. The model is validated using experimental data and then used to investigate the response of the pipe and the surrounding soil. The response of the MDPE pipe-soil system to axial loading is found to differ significantly from that of steel pipes due to the elongation and distortion that develop in the MDPE pipes, which affect the mobilized friction forces along the pipe. This study demonstrates that caution must be exercised when using current methods in the analysis of MDPE 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2018
Admission routes2
Has abstractyes

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