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Record W3000070998 · doi:10.1139/cjce-2019-0366

An experimental and numerical investigation of pullout behavior of ductile iron water pipes buried in sand

2020· article· en· W3000070998 on OpenAlexafffundvenue
Parththeeban Murugathasan, Ashutosh Sutra Dhar, Bipul Hawlader

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandMitacs
KeywordsGeotechnical engineeringStiffnessPipeline transportFinite element methodMaterials scienceGeologySoil structure interactionCanalisationCylinder stressStructural engineeringPipingEngineeringComposite material

Abstract

fetched live from OpenAlex

Buried pipelines, used for transporting liquid and gas, are often subjected to axial pulling forces due to seasonal temperature changes and (or) relative ground movements. The axial pullout force is exerted through soil–pipe interaction, which depends on the pipe material, surrounding soil and pipe–soil interface properties. In this research, axial pullout force on ductile iron pipe buried in sand is first experimentally investigated through development of a new laboratory facility. Based on the test results, simplified methods for predicting the axial forces are discussed. Finite element modelling is then used to investigate the mechanism of soil–pipe interaction and identify the key parameters contributing to the axial pullout force, which could not be measured during the laboratory tests. The constrained dilation of sand near the pipe–soil interface and arching effects due to a different stiffness of the pipe with respect to the soil are found to influence the mobilized axial forces on 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 score0.332
Threshold uncertainty score0.479

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations15
Published2020
Admission routes3
Has abstractyes

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