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Record W2995073657 · doi:10.1139/cgj-2019-0410

Experimental measurement of monotonic and cyclic lateral resistance of risers and pipelines in Gulf of Mexico clays

2019· article· en· W2995073657 on OpenAlexvenueno aff
Husham A. Al-Janabi, Jinbo Chen, Meng Luo

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersShell
KeywordsCatenaryTrenchGeotechnical engineeringLateral movementTouchdownSeabedStiffnessGeologyAmplitudeConsolidation (business)Pipeline transportStructural engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The location near the touchdown zone of a steel catenary riser at the seabed is a primary “hot spot” for fatigue assessment, with seabed stiffness having a major influence on the predicted fatigue life. This paper presents the results of laboratory model tests in the lateral direction with the motivation to appropriately capture the fundamental mechanism of soil interaction with the pipeline or riser in the lateral direction. The objectives of this study are to evaluate (i) the fundamental mechanism of soil interaction with the pipeline or riser in the lateral direction subjected to monotonic and cyclic loading, (ii) the evolution of lateral resistance with different (small to large) displacement amplitudes, (iii) the degradation of lateral resistance while increasing the number of cycles, and (iv) the recovery of the soil strength with time. The primary findings from the tests are that (i) the lateral resistance on the riser–pipeline drops sharply after trench formation, (ii) the lateral resistance across the trench approaches zero and reaches a steady state at a large number of cycles, (iii) the shape of trenches depends on the lateral displacement amplitude and the initial penetration depth, and (iv) some regain in strength occurs after a period for consolidation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.498

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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