The Prediction of Buckling Load of Laminated Composite Hat-Stiffened Panels Under Compressive Loading by Using of Neural Networks

Background:

Stiffened panels are being used as a lightweight structure in aerospace, marine engineering and retrofitting of building and bridge structure. In this paper, two efficient analytical computational tools, namely, Finite Element Analysis (FEA) and Artificial Neural Network (ANN) are used to analyze and compare the results of the laminated composite 75⁰-hat-stiffened panels.

Objective:

Finite Element (FE) is an efficient and versatile method for the analysis of a complex problem. FE models have been used to generate data set of four different parameters. The four parameters are extensional stiffness ratio of skin in the longitudinal direction to the transverse direction, orthotropy ratio of the panel, the ratio of twisting stiffness to transverse flexural stiffness and smeared extensional stiffness ratio of stiffeners to that of the plate.

Results and Conclusion:

For training of ANN, multilayer feedforward back-propagation has been used as a network function with two-hidden layers in the neural network. The good network architecture is achieved after several iterations to predict the buckling load of the stiffened panel. ANN prediction for unknown new data set is in good agreement with FEA results of different cases, which show that ANN tool can be used for the design of complex structural problems in civil engineering and optimization of the laminated composite stiffened panel.

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