Prediction of failure behavior of composite hat-stiffened panels under in-plane shear using artificial neural network

Abstract

Composite hat-stiffened panels are typical composite structures that embody the concept of high strength and light weight. The large number of geometric and material parameters are involved in the design of composite hat-stiffened panels, 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 for predicting the buckling and ultimate loads of composite hat-stiffened panels under in-plane shear. First, the training, validation, and test sets of the ANN are prepared using finite element analysis. Then, an autoencoder was employed to compress the original characteristics, and a back propagation neural network was established to predict the buckling and ultimate loads. At last, the performance and generalization ability of the ANN are examined based on the test set. The results show that the trained ANN can accurately and efficiently predict the buckling and ultimate loads of composite hat-stiffened panels under in-plane shear, and will be very useful in the practical engineering design.