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Record W2486478391 · doi:10.1139/cgj-2015-0597

Development of a force–resultant model for spudcan footings on loose sand under combined loads

2016· article· en· W2486478391 on OpenAlexvenueno aff
Ning Cheng, Mark Cassidy

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersLloyd's Register
KeywordsCentrifugeGeotechnical engineeringPlasticityGeologySubmarine pipelineYield surfaceHardening (computing)EngineeringStructural engineeringFinite element methodConstitutive equationMaterials science

Abstract

fetched live from OpenAlex

Spudcans are typical foundations used in shallow to moderate-depth water oil and gas fields to support jack-up drilling units. Understanding the behaviour of spudcans under combined loadings is crucial to the overall response of the jack-up structure. This paper presents the development of a strain-hardening plasticity model for a spudcan footing on loose sand. Most of the model components are developed from direct centrifuge observations. The centrifuge tests were performed at an acceleration of 100 times that of the Earth’s gravity on a model spudcan footing subjected to combined vertical, horizontal, and moment loads. All the experiments have been designed and conducted to allow the results to be interpreted with a strain-hardening plasticity framework. Combined loads were applied by using a novel apparatus, which enables independent vertical, horizontal, and rotational movements of the footing. Test results also revealed the existence of a three dimensional sliding surface that intersects with the conventional yield surface. This additional surface has been defined analytically. Retrospective simulation of the experimental data using the plasticity model confirms the model’s capability for use in predicting the behaviour of larger spudcan applications offshore.

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: none
Teacher disagreement score0.884
Threshold uncertainty score0.759

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 head0.013
GPT teacher head0.204
Teacher spread0.192 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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