Artificial neural network model of the strength of thin rectangular plates with weld induced initial imperfections

Probabilistic assessment of post-buckling strength of thin plate is a difficult problem because of computational effort needed to evaluate single collapse load. The difficulties arise from the nonlinear behaviour of an in-plane loaded plate showing up multiple equilibrium states with possible bifurcations, snap-through or smooth transitions of states. The plate strength depends heavily on the shape of geometrical imperfection of the plate mid-surface. In this paper, an artificial neural network (ANN) is employed to approximate the collapse strength of plates as a function of the geometrical imperfections. For the training set, mainly theoretical imperfections with the corresponding collapse loads of plate calculated by FEM are considered. The ANN validation is based on the measured imperfections of ship plating and FEM strength.