Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reconstructing the Global Stress of Marine Structures Based on Artificial Intelligence

Tao Zhang
ORCID logo ,
Jiajun Hu
* ,
Erkan Oterkus
,
Selda Oterkus
,
Xueliang Wang
,
Zhentao Jiang
,
Guocai Chen
Version 1 : Received: 30 May 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (08:23:51 CEST)
Version 2 : Received: 7 June 2023 / Approved: 8 June 2023 / Online: 8 June 2023 (10:52:36 CEST)

A peer-reviewed article of this Preprint also exists.

Zhang, T.; Hu, J.; Oterkus, E.; Oterkus, S.; Wang, X.; Jiang, Z.; Chen, G. Reconstructing the Global Stress of Marine Structures Based on Artificial-Intelligence-Generated Content. Appl. Sci. 2023, 13, 8196. Zhang, T.; Hu, J.; Oterkus, E.; Oterkus, S.; Wang, X.; Jiang, Z.; Chen, G. Reconstructing the Global Stress of Marine Structures Based on Artificial-Intelligence-Generated Content. Appl. Sci. 2023, 13, 8196.

Abstract

This paper proposes an AI-based approach to overcome the limitations of the SHM system in measuring global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An ANN is used to establish the solution relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize accuracy by considering factors like the number of sensors, neural network framework, and convergence criteria. The accuracy of the ANN is verified through a real-scale model test, demonstrating 93.6% accuracy. This technology represents a significant advancement, enhancing the practicality of the structural health monitoring (SHM) system from “point monitoring" to “field monitoring".

Keywords

n/a; ship and Ocean Engineering; Marine Structures; Global Stress; Artificial Intelligence; structure health monitoring (SHM)

Subject

Engineering, Marine Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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