Application of artificial neural networks to evaluation of ultimate strength of steel panels

Structural design of ships and offshore structures has been moving towards limit state design or reliability-based design. Improving the accuracy and efficiency of predicting the ultimate strength of structural components, such as unstiffened panels and stiffened panels, has a significant impact on our daily structural design. Empirical formulations have been widely used because of their simplicity and reasonable accuracy. In the past, empirical formulations were generally developed by using regression analysis. The model uncertainties of good empirical formulations are around 10%–15% in terms of coefficients of variation. In this paper, artificial neural networks (ANN) methodology is applied to predict the ultimate strength of unstiffened plates under uni-axial compression. The proposed ANN models are trained and cross-validated using the existing experimental data. Different ways to construct ANN models are also explored. It is found that ANN models can produce a more accurate prediction of the ultimate strength of panels than the existing empirical formulae. The ANN model with five (original) input variables has slightly better accuracy than the model with three input variables. This demonstrates the capacity of the ANN method to establish successfully a functional relationship between input and output parameters.