End-to-end deep learning method to reconstruct full-field stress distribution for ship hull structure with stress concentrations

An image-based deep learning framework is developed to reconstruct full-field stress distribution of ship hull structure from discrete measurements in this paper. The work is motivated by the lack of data to assess the strength state of vessel. The proposed deep learning framework is based on a conditional generative adversarial network (cGAN) which contains a generator and a discriminator. They are mutually trained in a supervised manner based on an adversarial mechanism. The hull structure of interest is selected to be an opening deck of a bulk carrier. A novel image encoding algorithm is also designed to fuse global stress distribution and high gradient stresses at hatch corner regions into an image. A total of 3400 stress fields are generated and simulated using finite element method (FEM) to provide a sufficiently large dataset for training and testing the network. It is shown that the proposed deep learning approach can efficiently reconstruct the full-field stress distribution of hull structure. Furtherly, the influence of measurement noise on reconstruction accuracy is discussed. The results demonstrate that the well-trained network can directly generate robust solutions to full-field stress distribution under given discrete measurements.