Buckling load prediction of laminated composite stiffened panels subjected to in-plane shear using artificial neural networks

Stiffened panels are basic building blocks of weight sensitive structures. Design of laminated composite stiffened panels is more involved and requires the use of an optimization approach, which needs a computationally efficient analysis tool. This paper deals with the development of an analytical and computationally efficient analysis tool using artificial neural networks (ANN) for predicting the buckling load of laminated composite stiffened panels subjected to in-plane shear loading. The database for training and testing is prepared using finite element analysis. Studies are carried out by changing the panel orthotropy ratio, stiffener depth, pitch length (number of stiffeners). Using the database, key parameters are identified and a neural network is trained. The results shows that the trained neural network can predict the shear buckling load of laminated composite stiffened panels accurately and will be very useful in optimization applications where computational efficiency is paramount. (C) 2016 Elsevier Ltd. All rights reserved.