A Deep Neural Network to Predict the Residual Hull Girder Strength

La Ferlita, Alessandro (American Bureau of Shipping) | Di Nardo, Emanuel (University of Milan & University of Naples Parthenope) | Macera, Massimo (_) | Lindemann, Thomas (University of Rostock) | Ciaramella, Angelo (University of Naples Parthenope) | Koulianos, Nikolaos (American Bureau of Shipping)

OnePetro 

The main purpose of this study is to apply a Deep Neural Network (DNN) method to linear systems and to predict in a relatively short time span the ultimate vertical bending moment (VBM) for damaged ships. A Deep Neural Network approach, which is composed of multiple fully connected layers with a Rectified Linear Unit (ReLU) which is a non-linear activation function, has been applied to more than 6000 samples and validated using leave-one-out technique. The ultimate strength has been predicted for a set of completely new damage scenarios of different cross sections, enhancing that the deep neural network method can estimate the residual hull girder strength for a correlated damage index general (DIG). The predicted residual hull girder strength as well as the shift of the neutral axis are validated against Smith’s method-based results.

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