Fatigue assessment comparison between a ship motion-based data-driven model and a direct fatigue calculation method
Journal article, 2023

Ocean-crossing ship structures continuously suffer from wave-induced loads when sailing at sea. The encountered wave loads cause significant variations in ship structural stresses, leading to accumulated fatigue damage. Where large inherent uncertainties still exist, it is now common to use spectral methods for direct fatigue calculation when evaluating ship fatigue. This paper investigates the use of a machine learning technique to establish a model for 2800TEU container vessel fatigue assessment. Measurement data from 3 years of cross-Atlantic sailing demonstrated and validated the machine learning model. In this investigation, the ship’s motions were used as inputs to build a machine learning model. The fatigue damage amounts predicted using a machine learning model were compared with those obtained from full-scale measurements and direct fatigue calculation. The pros and cons of the methods are compared in terms of their capability, robustness, and prediction accuracy.

full-scale measurement

direct calculation

ship motion

ship fatigue

machine learning

Author

Xiao Lang

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Da wu

Wuhan University of Technology

Wuliu Tian

Beibu Gulf University

Chi Zhang

Wuhan University of Technology

Jonas Ringsberg

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Journal of Marine Science and Engineering

20771312 (eISSN)

Vol. 11 12 1-16 2269

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Materials Engineering

Vehicle Engineering

Probability Theory and Statistics

Areas of Advance

Information and Communication Technology

Transport

Materials Science

Driving Forces

Sustainable development

Innovation and entrepreneurship

Roots

Basic sciences

DOI

10.3390/jmse11122269

More information

Latest update

1/8/2024 7