Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model

The study of mooring forces is an important issue in marine engineering and offshore structures. Although being widely applied in mooring system, numerical simulations suffer from difficulties in their multivariate and nonlinear modeling. Data-driven model is employed in this paper to predict the mooring forces in different lines, which is a new attempt to study the mooring forces. The height and period of regular wave, length of berth, ship load, draft and rolling period are considered as potential influencing factors. Input variables are determined using mutual information (MI) and principal component analysis (PCA), and imported to an artificial neural network (NN) model for prediction. With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology, MI is found to be more appropriate to provide effective input variables than PCA. Although the three factors regarding ship characteristics are highly correlated, it is recommended to input all of them to the NN model. The accuracy of predicting aft spring line force attains as high as 91.2%. The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors.