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Record W2532779620 · doi:10.1139/cgj-2016-0090

Offshore pipelines and ice gouge geohazards: comparative performance assessment of decoupled structural and coupled continuum models

2016· article· en· W2532779620 on OpenAlexaffvenue
Kenton Pike, Shawn Kenny

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsCarleton UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsFinite element methodSubmarine pipelineGeotechnical engineeringNonlinear systemStructural engineeringEngineeringGeologyCivil engineeringComputer scienceMarine engineering

Abstract

fetched live from OpenAlex

Offshore pipelines in ice environments may be subjected to unique geohazards such as seabed ice gouging. These events involve nonlinear processes including large deformations and strains, contact mechanics, and failure mechanisms. Current pipeline engineering design practice employs decoupled, structural finite element modelling procedures to assess system demand and capacity. The inherent error and uncertainty within this approach drives conservative engineered solutions. Physical modelling and continuum numerical simulation tools complement this engineering framework to improve confidence in predicted outcomes. The relative performance of engineering models, used in current practice, and numerical simulation tools, including structural and continuum finite element modelling procedures, to predict the deformation and strain response of a buried pipeline subjected to an ice gouge event is examined. Refinements to the numerical modeling procedures and establishment of a consistent and compatible reference framework for the performance evaluation differentiate this study from others, which are subsets of the current investigation. For the parameter analysis conducted, within an equivalent reference framework, the outcomes demonstrate key factors, including superposition error and directional load decoupling, that influence model error that may not be as significant as previously considered. The scope and extent of this outcome is not fully understood and requires further investigations to delineate the significance across a wider parameter range.

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 score0.045
Threshold uncertainty score0.731

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 integrity0.0000.000
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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