Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

A novel ship fatigue damage’s prediction model based on the artificial neural network approach
Qiang Wang, Pengyao Yu*, Xin Chang, Gaojie Fan, Guangzhao Li

College of Naval Architecture and Ocean Engineering, Dalian Maritime University Dalian, China

ABSTRACTexperienced by ships. Even though it’s not easy to obtain the stress ofall the hot spots, it’s easy to obtain the ship motion from the monitoring
system. The emphasis of this study is to establish a novel ship fatigue
damage’s prediction model based on the ANN approach. The
relationship between ship motion and fatigue stress response isestablished, and the fatigue hot spot damage can be obtained throughthe directly measured motion data.
Some previous studies have introduced artificial neural networks into
the ship field, such as the Moreira (2020) and Chung (2020). Moreiracarried out the neural network model for estimation of hull bendingmoment and shear force of ships in waves. Chung developed adetection approach of a damaged mooring line in tension leg platform(TLP) based on deep neural networks (DNN). Up to now, there is noresearch that establish the relationship between ship motion and fatigue
damage, and can directly predict the fatigue damage from ship motion.
In our work, based on the spectral analysis, the ship motion response isobtained as the input database and the hot spot fatigue damage isobtained as the output database. The databases are obtained from thefatigue calculation of a container ship. For the network training theback-propagation (BP) is employed, which is a common method oftraining ANNs how to perform a given task. Through the training ofneural network, a satisfactory prediction results are obtained.

A novel ship fatigue damage’s prediction model based on the artificial
neural network (ANN) approach is proposed in this paper. The ship
motion response is obtained as the input database and the hot spot
fatigue damage is obtained as the output database. The relationshipbetween ship motion response and hot spot fatigue damage isestablished by the ANN. By comparing the predicted data with thereference data, it can be concluded that the model proposed in thispaper is able to accurately predict fatigue damage according to the ship
motion.

KEY WORDS: Fatigue damage; motion response; artificial neuralnetwork (ANN).

INTRODUCTION
Fatigue is one of the most significant failure modes for ship structures.
Early studies focused on calculation of fatigue cumulative damage
based on theoretical method, such as Zhen (2012) and Kukkanen(2004). Zhen (2012) used the simplified procedures by ChinaClassification Society and the spectral analysis method to make thefatigue strength assessment of the typical structural details of theSWATH. Kukkanen (2004) carried out the fatigue analyses using aspectral approach to predict the fatigue life considering various sea
areas, speeds and three different models. In general, we only focuses on
the prediction of fatigue life in the design stage. In fact, for ships of thesame design, different operating experiences can lead to different
fatigue damage. Therefore, it’s worthy to study the prediction approachof fatigue damage considering the sea states experienced by the ship.
Yue (2021) proposed a hybrid frequency-time domain method of hull
fatigue damage combined with the linear damage cumulative rule. Kim(2019) carried out a fatigue damage assessment for an ice-going shipnavigating through broken ice fields using a numerical model. Thestress history was obtained by a numerical model and thecorresponding fatigue analysis. Even though many scholars (such as
Yue and Kim) have used the fatigue stress response history as the input
data for fatigue damage calculation, the fatigue stress response can not
be obtained easily. Ship motion can directly reflect the sea states

Input and output data for the ANN model
Since the reasonable and abundant data are the important premise forthe establishment of neural network, the input data of ship motion
response and the output data of ship fatigue damage are obtained by thespectral analysis method in the present study.
Generation of the input data
The three-dimensional linear potential flow theory is used to calculate
the ship motion. The regular wave with unit amplitude is used tocalculate the ship motion response transfer function. A container ship istaken as the analysis model, the main principal of the container ship islisted in the Table 1, and the hydrodynamic mesh is shown in Fig.1.
The analysis is performed for 7 wave headings (0° to 180° in 30°increments) due to the symmetrical characteristic and 20 frequencies(0.1-2.0 rad/s in 0.1 rad/s increments). The speed used in fatigue
analysis is 14 knots, which is the navigation speed. Since the wave is3145