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Record W2145038419 · doi:10.1061/9780784413692.080

Numerical Analysis of Submerged Soil Behavior in Pipeline Installations Crossing Rivers

2014· article· en· W2145038419 on OpenAlexaff
Jinsung Cho, Jason S. Lueke, Samuel T. Ariaratnam

Bibliographic record

VenuePipelines 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsInstitute of Health Economics
Fundersnot available
Keywordsvon Mises yield criterionFinite element methodGeotechnical engineeringTrenchOverburdenPipeline (software)Trenchless technologyStress (linguistics)GeologyEnvironmental sciencePipeline transportEngineeringStructural engineeringMaterials science

Abstract

fetched live from OpenAlex

The paper investigates the pattern of soil stress around a pipeline installed under the river using a nonlinear three-dimensional finite element method (FEM). This study compares von Mises soil stresses occurring in native soil adjacent to river crossing pipeline between traditional open trench (OT) and horizontal directional drilling (HDD) pipe installation methods. The Mohr-Coulomb theory is utilized to describe soil behavior in the finite element models. The entire model is assumed to be elastoplastic. The whole research considers saturated native soil one month after construction. Additionally, design parameters (i.e., depth of cover and annular space) in HDD method are examined to understand their influence on soil stress occurring around original soil. The paper investigates how critical design parameters (i.e., density and diameter) in the annular space affect the pattern of maximum von Mises soil stresses occurring in native soil adjacent to pipeline installed under the river. Finally this study found that when the OT method is used for pipeline crossing under the river, stress occurring in the soil cover is greater than when the HDD method is used. In addition, the diameter of the annular space in HDD method could impact on total soil stress occurring in the soil overburden.

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 score0.281
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.229
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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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