Prediction of deck grillages lifting deformation using a data-driven Def-GAN network

Abstract

The thin-walled deck grillages are prone to deformation in lifting process of ship blocks construction due to huge rope tension loads. Optimizing hoisting scheme with the aid of CAE analysis techniques before implementing is conductive to controlling structure deformation. A deformation prediction method based on generative adversarial network (Def-GAN) is developed in this paper to perform quick-response and precise deformation predictions under a given hoisting scheme of deck grillages. This artificial intelligence network model has an adversarial architecture and comprises a pair of submodels, namely generator and discriminator. The datasets including lifting conditions and deformation distributions of deck grillages are set up to train and test the Def-GAN model. The deformation distributions in datasets are obtained from a series of FEM analyses. An image coding and mapping algorithm is employed to deal with input data of lifting conditions and output data of deformation distributions respectively. The results show that the predictive model achieved an R-squared accuracy of 0.997 and a mean squared error of 0.005 compared to numerical results for the test dataset. In addition to predicting deformation of grillages lifting, the Def-GAN model is a promising candidate tool for hoisting scheme optimization.