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Record W2788232512 · doi:10.1680/jgeot.17.p.084

Pipeline–soil–water interaction modelling for submarine landslide impact on suspended offshore pipelines

2018· article· en· W2788232512 on OpenAlexafffund
Sujan Dutta, Bipul Hawlader

Bibliographic record

VenueGéotechnique · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsMemorial University of NewfoundlandRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeotechnical engineeringCentrifugeGeologyDragPore water pressureLandslideOffshore geotechnical engineeringSubmarine pipelinePipeline transportSubmarine landslideEngineering

Abstract

fetched live from OpenAlex

The submarine landslide is one of the major geohazards in deep-water oil and gas developments. The impacts of glide blocks or out-runner blocks, which carry the geotechnical properties of the parent soil mass before the landslide, on pipelines normal to the direction of slide, are investigated in this study. A computationally efficient numerical modelling technique is developed using a computational fluid dynamics approach, incorporating a strain-rate and strain-softening dependent model for the undrained shear strength of clay sediment, to simulate the lateral penetration of a pipe in a clay block. The role of water in the cavity and channel formed behind the pipe during the lateral penetration on drag force is successfully simulated. Numerical simulations for varying depths of the pipe explain the change in soil failure mechanisms in which the channel behind the pipe and berm play a significant role, especially at shallow depths. As the cavity behind the pipe may not be completely filled with soil, the limitations of smooth/rough and bonded/unbonded interface conditions, as used typically in pipe–soil interaction analysis, are discussed. Based on a comprehensive parametric study, calibrated against centrifuge test results, a set of empirical equations is proposed to calculate drag force for practical applications. The effects of inertia on drag force are examined.

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 score0.030
Threshold uncertainty score0.060

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 integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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

Citations64
Published2018
Admission routes2
Has abstractyes

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