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Record W4364361616 · doi:10.1680/jgein.22.00351

Strain assessment of polyethylene pipes in dense sand subjected to axial displacement

2023· article· en· W4364361616 on OpenAlexaff
Auchib Reza, Ashutosh Sutra Dhar, M. Rahman

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGeotechnical engineeringMaterials sciencePolyethyleneDisplacement (psychology)Shear stressStress (linguistics)GeogridGeosyntheticsMechanicsHigh-density polyethyleneComposite materialStructural engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Buried polyethylene pipes used in gas distribution systems can experience excessive wall strain when exposed to ground movement that can affect the performance of the pipes in service. This paper presents full-scale laboratory tests performed to investigate the responses of medium-density polyethylene (MDPE) gas-distribution pipes in dense sand when subjected to axial ground movement. Pipes buried in sand in a large test box were pulled at the rates of 0.5 mm/min, 1 mm/min, and 2 mm/min to simulate relative ground movement in the longitudinal direction. The test facility was instrumented to measure pulling force, pipe wall strain, and soil stress. The measured pullout force was significantly higher than predicted using the equations recommended in current design guidelines, which is attributed to the increase of normal stress on the pipe wall by shear-induced dilation of interface soil. The cavity expansion theory was successfully applied to calculate the normal stress increase. The distribution of measured strains was nonlinear along the pipe length. Assuming a parabolic distribution of the strains, simplified equations were developed to calculate pullout resistance and pipe wall strain from the relative ground displacement. The developed method reasonably predicted the pipe strain measured during the tests.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.459

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 head0.262
Teacher spread0.254 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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