Explainable AI-based generation of offshore substructure designs

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

Historically the design of Offshore Wind Turbines (OWT) depends on various influence factors which are set by engineers. Typically, most parameters are determined during the initial design phase and have less consideration on the influences of the entire life-cycle of a structure, leading to the over-exploitation of a single design, like monopiles. In this work, we define the design process as a multi-objective optimization problem and use Artificial Intelligence (AI) to discover multiple optimal solutions, while providing feedback, in the form of feature importance explanations for generated structures. Our approach results in efficient designs, while explanations can improve engineers' understanding of alternative design possibilities.

Details

OriginalspracheEnglisch
TitelProceedings of the 33rd International Ocean and Polar Engineering Conference, 2023
Redakteure/-innenJin S. Chung, Decheng Wan, Satoru Yamaguchi, Shiqiang Yan, Igor Buzin, Hiroyasu Kawai, Hua Liu, Ivana Kubat, Bor-Feng Peng, Ali Reza, Venkatachalam Sriram, Suak Ho Van
Herausgeber (Verlag)International Society of Offshore and Polar Engineers
Seiten286-292
Seitenumfang7
ISBN (Print)9781880653807
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Publikationsreihe

ReiheProceedings of the International Offshore and Polar Engineering Conference
ISSN1098-6189

Konferenz

Titel33rd International Ocean and Polar Engineering Conference, ISOPE 2023
Dauer19 - 23 Juni 2023
StadtOttawa
LandKanada

Externe IDs

ORCID /0000-0001-8735-1345/work/160479751
ORCID /0000-0002-3578-3098/work/160479856

Schlagworte

Schlagwörter

  • Artificial Intelligence, Evolutionary Algorithm, Industrial Design, Machine Learning, Multi-objective Optimization, Offshore Wind Turbines