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Record W2185589973 · doi:10.1139/cgj-2012-0357

Uplift soil–pipe interaction in granular soil

2013· article· en· W2185589973 on OpenAlexvenueno aff
Jai K. Jung, Thomas D. O’Rourke, Nathaniel A. Olson

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersDivision of Civil, Mechanical and Manufacturing Innovation
KeywordsGeotechnical engineeringVoid ratioDisplacement (psychology)GeologySofteningModulusDeformation (meteorology)Finite element methodBilinear interpolationMechanicsStructural engineeringMaterials scienceEngineeringMathematicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Soil–pipeline interaction for uplift in granular soil is evaluated by means of a two-dimensional, finite element (FE) continuum model with a Mohr–Coulomb (MC) yield surface for peak strength, a strain-softening relationship tied to critical void conditions, and an equivalent modulus that is consistent with soil deformation at maximum uplift resistance. The model accounts for soil migration beneath the pipe through FE mesh adjustment coordinated with upward pipe displacement. A systematic comparison of model results with multiple full-scale test measurements of pipe in dry sand show excellent agreement both with respect to maximum force and force–displacement relationships, including post-peak performance. The relationship between peak upward force and pipe depth is developed for various sand densities, all of which show maximum force at a depth-to-diameter ratio of 30. Hyperbolic and bilinear models for vertical upward force versus displacement are presented. The analytical approach described in this paper benefits from its adaptation to MC strength selection available in many commercial software packages.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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 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

Citations57
Published2013
Admission routes1
Has abstractyes

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