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Record W2331032405 · doi:10.1139/cgj-2012-0185

Modelling the embedment process during offshore pipe-laying on fine-grained soils

2012· article· en· W2331032405 on OpenAlexaffvenue
Z.J. Westgate, David White, Mark Randolph

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsEmbedmentCentrifugeSubseaGeotechnical engineeringSubmarine pipelineGeologyPipeline transportPore water pressureDisplacement (psychology)SiltEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Subsea pipelines are becoming an increasingly significant element of offshore hydrocarbon developments as exploration moves into deep-water environments further from shore. During the lay process, pipelines are subject to small amplitude vertical and horizontal oscillations, driven by the sea state and lay vessel motions. Centrifuge model tests have been used to simulate these small-amplitude lay effects, with varying degrees of idealization relative to the real lay process. In the soft soils found in deep water, pipe embedment can exceed a diameter or more, thus significantly affecting the lateral pipe–soil interaction, axial resistance, and thermal insulation. In this paper, results from centrifuge model tests are used to calibrate a model for calculating the dynamic embedment of a subsea pipeline. The model uses elements of plasticity theory to capture the effects of combined vertical and horizontal loading, and incorporates the softening of the surrounding soil as it is remoulded due to the pipeline motions. Influences from the lay rate, lay geometry, and sea state are included in the calculation process. The model is compared with observed as-laid pipeline embedment data from field surveys at three different offshore sites. Using site-specific soil parameters obtained from in situ testing and idealized pipe loads and motions to represent the load and displacement patterns during offshore pipe-laying, respectively, the model is shown to capture well the final as-laid embedment measured in the field surveys.

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 score0.104
Threshold uncertainty score0.963

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 integrity0.0000.002
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations35
Published2012
Admission routes2
Has abstractyes

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