Structural optimization of single-layer domes using surrogate-based physics-informed neural networks

This study aims at generation of a novel artificial bee colony algorithm using surrogate finite element method with neural network technique. In this paper, theory of surrogate finite element method with physics-informed neural networks (PINNs) are generated and applied to deal with the geometrically nonlinear optimization problem of size, shape and topology for single-layer domes. In the artificial bee colony algorithm, the feedforward neural network is used to surrogate finite element analyses. Three numerical examples of 10-bar truss, Lamella dome, and Kiewit dome are carried out to verify feasibility and accuracy of the proposed method. Results of the present study are in good agreement with ones from literature. It is indicated that optimization processes can be considerably accelerated using the modified algorithm. That is, using the neural network surrogate-based models could significantly increase computational efficiency of structural optimum design for single-layer domes.