Prediction of web crippling strength of cold-formed steel sheetings using neural networks

This study considers the use of neural networks (NNs) to predict the web crippling strength of cold-formed steel decks. Web crippling is critical for slender webs as in the case of trapezoidal sheetings which are widely used in roofing applications. The elastoplastic behaviour of web crippling is quite complex and difficult to handle. There is no well established analytical solution due to complex plastic behaviour. This leads to significant errors in various design codes. The objective of this study is to provide a fast and accurate method of predicting the web crippling strength of cold-formed steel sheetings and to introduce this in a closed-form solution which has not been obtained so far. The training and testing patterns of the proposed NN are based on well established experimental results from literature. The trained NN results are compared with the experimental results and current design codes (NAS 2001) and found to be considerably more accurate. Moreover, a trained neural network gives the results significantly more quickly than the design codes and finite element (FE) models. The web crippling strength is also introduced in closed-form solution based on the parameters of the trained NN. Extensive parametric studies are also performed and presented graphically to examine the effect of geometric and mechanical properties on web crippling strength. (c) 2006 Elsevier Ltd. All rights reserved.