Determination of the 1st Buckling and Collapse Loads for Integrally Stiffened Panels by Artificial Neural Network and Design of Experiment Methodology

Abstract

Buckling is a structural instability that load carrying capacity of a structural element may suddenly decrease. This sudden change in the load carrying capacity may cause catastrophic failures. Therefore, determination of the first buckling and collapse loads of structural elements is essential. FE analyses and structural testing are used to determine buckling characteristics of a structural element. However, in early design stages, FE analyses are time consuming and structural testing is costly. In this paper, artificial neural network tool is used to reduce computational effort to determine buckling loads of integrally stiffened structural panels in early design stages. Moreover, Latin Hypercube Sampling (LHS) methodology is used to reduce the number of required FE analyses to generate database that artificial neural network is based on. Mean errors and fit performance model results are compared to determine accuracy of the neural network results.