MétaCan
Menu
Back to cohort

Numerical Analysis of Pullout Capacity of Composite Suction Caisson Foundation Used For Large Scale Underwater Compressed Gas Energy Storage

2024· article· en· W4405537188 on OpenAlexaff
Hu Wang, Wei Xiong, Zecheng Zhao, Tonio Sant, Rupp Carriveau, David S.‐K. Ting, Zhiwen Wang

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCaissonSuctionUnderwaterGeotechnical engineeringFoundation (evidence)Composite numberScale (ratio)GeologyEnergy storageEnvironmental scienceEngineeringMarine engineeringMaterials scienceMechanical engineeringComposite materialPhysicsPower (physics)Oceanography

Abstract

fetched live from OpenAlex

Abstract The suction caisson foundation, characterized by its unique installation methodology and excellent bearing capabilities, has emerged as a crucial supporting structure for marine constructions. However, the distinctive alternating cyclic high load patterns exhibited by underwater compressed gas energy storage systems significantly challenge the vertical pullout bearing capacity of regular suction caisson foundations. In this study, a novel composite suction caisson foundation is proposed to enhance the vertical pullout capacity. The pullout capacity under perfectly drained conditions is investigated by finite element analysis with ABAQUS/Standard 2020. A comparative analysis of the pullout capacity between regular suction caisson (RSC) and composite suction caisson (CSC) is conducted, focusing on aspect ratios of 0.5, 1, and 1.5. The results show that the pullout load of the proposed CSC, with the same aspect ratio, increased by a range of 12.8% to 100% compared to that of the RSC. As the aspect ratio increases, the trend of increasing pullout load decreases. The variation of displacement for the caisson at its ultimate pullout capability is relatively small, between 0.01D and 0.027D.

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 score0.643
Threshold uncertainty score0.411

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207