A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression

Abstract

The evaluation of ship strength during the hull structure design stage is essential for structural safety. Additionally, plate is one of the basic structural members of the hull, it is important to analyze and predict the ultimate strength. The curved plate used for shipbuilding must be able to withstand repeated axial loads and complex loads, and stability needs to be confirmed through ultimate strength analysis. In general situations, the magnitude of the transverse compression is smaller than that of the longitudinal and combined loads, but transverse compression causes different physical behaviors from the longitudinal load state, which affects the ultimate strength, so study on the ultimate strength of the curved-plate under transverse compression is essential. Therefore, in this paper, a curved plate under transverse compressive load was selected as a subject, and the ultimate strength of the curved plate under the corresponding compression condition was predicted using a deep learning model. To obtain the training data for the deep-learning model, 4050 cases were selected and analyzed using the ANSYS. The accuracy of the model was verified by comparing the results predicted by the model and empirical equations with those of the FEM analysis. The study shows that it is possible to consider the ultimate strength more efficiently in the initial design stage of the ship.