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Publications
Sadovský, Z. and Guedes Soares, C. (2009), “Artificial neural network in probabilistic assessment of strength of thin imperfect plates”, Reliability, Risk and Safety - Theory and Applications, Bris, R., Guedes Soares, C., & Martorell, S. (Eds.), Taylor & Francis Group, London, U.K., Vol. 2, pp. 1373-1376
Probabilistic assessment of post-buckling strength of thin plate is a formidable 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 the paper, employing an artificial neural network (ANN), an approximate description of plate strength as a function of geometrical imperfection is considered. 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 measured imperfections of ship plating and FEM strength.
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