A soft computing based approach for the prediction of ultimate strength of metal plates in compression

This paper presents two plate strength formulations applicable to metals with nonlinear stress–strain curves, such as aluminum and stainless steel alloys, obtained by soft computing techniques, namely Neural Networks (NN) and Genetic Programming (GP). The proposed soft computing formulations are based on well-defined FE results available in the literature. The proposed formulations enable determination of the buckling strength of rectangular plates in terms of Ramberg–Osgood parameters. The strength curves obtained by the proposed soft computing formulations show perfect agreement with FE results. The formulations are later compared with related codes and results are found to be quite satisfactory.