dc.contributor.author
Qian, Han
dc.contributor.author
Panagiotou, Emmanouil
dc.contributor.author
Peng, Mengyan
dc.contributor.author
Ntoutsi, Eirini
dc.contributor.author
Kang, Chongjie
dc.contributor.author
Marx, Steffen
dc.date.accessioned
2024-06-20T08:21:40Z
dc.date.available
2024-06-20T08:21:40Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/43895
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-43605
dc.description.abstract
Conceptual design is crucial for designing offshore jacket substructures because it sets the direction for the entire design process. Nevertheless, conventional simulation-based optimization methods for jacket conceptual design face challenges, such as high computational costs and restricted optimization objectives. This paper proposes a data-driven method for offshore jacket conceptual design using machine learning (ML). First, a novel dataset of completed and under-construction jackets worldwide was established as the cornerstone of ML. The dataset comprised “in-action” data capturing key structural parameters of jackets and information on design boundary conditions. Subsequently, different features were comprehensively selected to identify and visualize their correlations for an interpretable data-driven design, ensuring the effectiveness of the dataset for training the ML models. Finally, random forest and eXtreme gradient boosting models were trained on the data from the selected feature subsets and then employed to predict individual jacket structural parameters. The predictive performance of the models indicates that the dataset and feature selection can capture the fundamental and shared characteristics of well-designed jackets, thereby improving the accuracy and efficiency of the conceptual design process. This study suggests the potential of a data-driven conceptual design for offshore jacket substructures.
en
dc.format.extent
18 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Offshore jacket substructure
en
dc.subject
Conceptual design
en
dc.subject
Data -driven method
en
dc.subject
Machine learning
en
dc.subject
Feature selection
en
dc.subject.ddc
000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
dc.title
A novel dataset and feature selection for data-driven conceptual design of offshore jacket substructures
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
117679
dcterms.bibliographicCitation.doi
10.1016/j.oceaneng.2024.117679
dcterms.bibliographicCitation.journaltitle
Ocean Engineering
dcterms.bibliographicCitation.volume
303
dcterms.bibliographicCitation.url
https://doi.org/10.1016/j.oceaneng.2024.117679
refubium.affiliation
Mathematik und Informatik
refubium.affiliation.other
Institut für Informatik
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refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
1873-5258
refubium.resourceType.provider
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