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Record W4318957074 · doi:10.1139/cgj-2022-0103

A hybrid model to simulate the trench effect on the fatigue analysis of steel catenary risers in the touchdown zone

2023· article· en· W4318957074 on OpenAlexafffundvenue
Hossein Janbazi, Hodjat Shiri

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsTrenchTouchdownCatenaryStiffnessSeabedNonlinear systemStructural engineeringGeotechnical engineeringGeologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

The gradual trench formation of steel catenary risers (SCRs) in the touchdown zone is known to significantly affect the SCR’s fatigue life. However, there is still no coherent agreement among researchers on the beneficial or detrimental effects of the trench on fatigue. Recent studies have shown that a potential source of contradictory fatigue results could be the methodology to incorporate the trench in the numerical simulations. Since the predefined mathematical trench profiles create non-realistic contact pressure hot spots in the seabed, and the nonlinear hysteretic seabed interaction models may cause premature trench stabilization, both methods distort the damage distribution. To resolve these problems, a new model called the Hybrid Trench Model (HTM) has been developed in this study by combining the linear soil stiffness and nonlinear hysteretic seabed interaction model. This hybrid model provides an equivalent stiffness distribution in the touchdown zone to simulate the trench profile obtained from a nonlinear riser–seabed interaction model. HTM’s capability in developing deep trenches, e.g., 5D, was examined along with perfect compatibility with the natural catenary shape of the riser, exhibiting the reliability of this method to incorporate the trench effect into the fatigue analysis.

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)0.001
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.247
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

Citations5
Published2023
Admission routes3
Has abstractyes

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