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Record W2911725290 · doi:10.1139/cgj-2018-0836

Effects of reconsolidation time on holding capacity of deepwater dynamically installed anchors

2019· article· en· W2911725290 on OpenAlexvenueno aff
Yong Fu, Yong Liu, Jian Yu

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeClosure (psychology)Term (time)WorkoverSimple (philosophy)Dimensionless quantityComputer scienceGeotechnical engineeringEngineeringPetroleum engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

A dynamically installed anchor (DIA) is a practical and promising type of anchor for deepwater offshore engineering. Currently, very few research studies have reported the effects of reconsolidation time on the holding capacity of DIAs. Moreover, there is no well-established, simple, and practical method for determining the time-dependent holding capacity of a DIA. To solve this problem, a series of centrifuge model tests was conducted by varying the reconsolidation time. In the intermediate-term reconsolidation scenario, the normalized holding capacity increased with the dimensionless time in an approximately log-linear manner. Based on this finding and a conventional American Petroleum Institute (API) method put forth in 2007 as well as the cylindrical cavity expansion theory, a simple three-term framework was developed to estimate the time-dependent holding capacity of DIAs. In this framework, cavity closure and skin friction were quantitatively correlated with the reconsolidation time. To validate the feasibility of the proposed framework, the predicted results were compared with the experimental results of cases in this study and other researchers’ work. As such, it was verified whether this framework could be used to estimate the time-dependent holding capacity of DIAs rationally.

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 score0.043
Threshold uncertainty score0.631

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 integrity0.0000.001
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 head0.003
GPT teacher head0.163
Teacher spread0.160 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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