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Record W2899334006 · doi:10.4043/29092-ms

Pile Foundation System for Offshore Protection Structure

2018· article· en· W2899334006 on OpenAlexaboutno aff
Richard F. Phillips, Gerry Piercey, J. Barrett

Bibliographic record

VenueOTC Arctic Technology Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsSubseaPileSubmarine pipelineSeabedCentrifugeFoundation (evidence)Marine engineeringGeotechnical engineeringIcebergMooringGeologyEngineeringSea iceOceanography

Abstract

fetched live from OpenAlex

Abstract The hydrocarbon production facilities offshore eastern Canada must contend with icebergs. Subsea infrastructure is typically protected by placement in excavated drill centers (EDCs). EDCs are large excavations where subsea infrastructure is installed below the depth of potential iceberg penetration. However, EDCs are expensive for marginal field developments and therefore alternative concrete Subsea Ice Protection Structures (SIPS) are being evaluated. A concept 8 m high circular concrete SIPS of 45m and 25m outside and inside diameter, respectively, would be placed directly on the seabed. Global loads from contact with an iceberg are estimated to be less than 100 MN without ice management. Pipe piles are proposed to resist the lateral forces. Such large diameter pipe piles are commonly used on the Grand Banks to anchor mooring lines. Movement of the iceberg onto the SIPS may also generate greater vertical load onto the structure, increasing the sliding resistance. This paper presents an evaluation of the ultimate lateral load capacity of the piled foundation at large displacements. Centrifuge model tests were compared to numerical modelling results and to reduce the uncertainty with extrapolating the P-y method of pile analysis beyond its typical limits. The load sharing behavior between the piles and the SIPS foundation base was also evaluated.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.674

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

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