{'fraction_non_alphanumeric': 0.02188183807439825, 'fraction_numerical': 0.00437636761487965, 'mean_word_length': 4.941558441558442, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Deep Neural Network to Predict the Residual Hull Girder Strength

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.