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Record W2462702129 · doi:10.1139/cgj-2016-0071

Pipe–soil interaction model for current-induced pipeline instability on a sloping sandy seabed

2016· article· en· W2462702129 on OpenAlexvenueno aff
Fu‐Ping Gao, Ning Wang, Jinhui Li, Xiting Han

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaMajor State Basic Research Development Program of ChinaNational Natural Science Foundation of China
KeywordsSeabedEmbedmentGeotechnical engineeringSubmarine pipelineCurrent (fluid)GeologyLateral earth pressureParametric statisticsPore water pressurePipeline transportEnvironmental science

Abstract

fetched live from OpenAlex

As offshore exploitation moves to deeper waters, ocean currents become the prevailing hydrodynamic loads on pipelines, and at the same time a sloping seabed is always encountered. The prediction of lateral soil resistance is vital in evaluating pipeline on-bottom stability. Unlike previous pipe–soil interaction models used mainly for horizontal seabed conditions, a pipe–soil interaction model for current-induced downslope and upslope instabilities is proposed by using the limit equilibrium approach. The Coulomb’s theory of passive earth pressure for the sloping seabed is incorporated in the derivation. The model verification with existing full-scale tests shows good agreement between the experimental results and predicted ones. Parametric study indicates that the effect of slope angle on pipeline lateral soil resistance is significant in the examined range of slope angle from –15° to 15°. The critical pipeline embedment and corresponding passive pressure decrease approximately linearly with increasing slope angle.

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)0.000
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.249
Teacher spread0.224 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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