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.

Highlights

  • The curved plates used in ship construction must be able to withstand repeated axial and complex loads; its stability is confirmed by estimating the ultimate strength

  • In this study, based on a deep neural network, the ultimate strength of the curved plate was predicted over a wider range under a transverse load in various scenarios. These results indicate the ability of the deep neural network to efficiently estimate the ultimate strength to acquire the optimal design of the ship and cost reduction in the initial design stage

  • Prior to constructing a neural network model to generate data for use in deep neural network learning, a case used for hull construction in a shipyard and a curved plate showing nonlinear behavior were selected and studied using ANSYS, which is a universal analysis program

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Summary

Introduction

In recent years, owing to the reduction in the weight of ships, extremely thin curved plates have become vulnerable to buckling and corrosion [1]. It is important to analyze buckling and collapse of structural elements such as curved plates. The transverse compressive force is generated by the difference between the hydrostatic pressure of the ship and the pressure due to the weight of the cargo. During the transverse and combined loading states, a physical behavior occurs which is different from that of the longitudinal loading. This acts as a factor influencing the ultimate strength, which is an essential part of this research.

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