A Novel Predictive Model for Post-Buckling Behavior of Stiffened Conical Shells under Geometric Imperfections using Artificial Neural Networks

In recent years, the conical shells under external pressure are widely used in construction of the under-water pressure hulls, covers of aero-engines and storage tanks. Strength of thin-walled shells under external pressure are usually influenced by the buckling phenomenon. So, its study is in high degree of importance. The buckling analysis of thin conical shells based on theoretical and experimental methods is accompanied by shortcomings such as time consuming and complexity. In this paper, an efficient method based on Artificial Neural Network (ANN) is presented for prediction of buckling and post-buckling behavior of conical shells. Primarily, the linear and non-linear buckling loads of the truncated cones with various thickness and stiffener dimensions are obtained by using the Finite Element (FE) analysis. Then, these obtained results are submitted to the Neural Network for training. In order to verify the solution procedure, the predicted results of ANN are compared with those of extracted from FE analysis. It is shown, that the predictive model benefits from high convergence and accuracy. Finally, some predicted results of buckling and post-buckling analysis of conical shells is figured.