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Record W3112341822 · doi:10.1139/cgj-2020-0219

Impact of scour on lateral resistance of wind turbine monopiles: an experimental study

2020· article· en· W3112341822 on OpenAlexvenueno aff
Qiang Li, Amin Askarinejad, Kenneth Gavin

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersChina Scholarship CouncilTechnische Universiteit Delft
KeywordsPileGeotechnical engineeringCentrifugeOffshore wind powerStiffnessTurbineGeologyDisplacement (psychology)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

The majority of offshore wind structures are supported on large-diameter, rigid monopile foundations. These piles may be subjected to scour due to the waves and currents that causes a loss of soil support and consequently decreases the pile capacity and system stiffness. The results of numerical models suggest that the shape of the scour hole affects the magnitude of pile capacity loss; however, there is a dearth of experimental test data that quantify this effect. This paper presents a series of centrifuge model tests on an instrumented model pile that investigates the effects of scour-hole geometry on the response of a laterally loaded pile embedded in sand. The pile instrumentation allowed load–displacement and p–y (soil reaction – displacement) curves to be derived. Three scour geometries (global, local wide, and local narrow) and three scour depths (1D, 1.5D, and 2D; where D is pile diameter) were modelled. For all three scour types, pile moment capacity decreased almost linearly with increase of scour depth. Simple empirical relations were proposed to evaluate the detrimental influence of scour on the pile moment capacity. A new method has been developed to allow designers to quantify the effect of scour-hole shape and severity of scour on the pile response.

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 score0.113
Threshold uncertainty score0.794

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

Explore more

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