Abstract
The analysis of perforated steel members is a 3D problem in nature, there still exist many difficulties for the traditional analytical expressions to be used in the perforated steel member design. The proliferation of industrial “Big-Data†has created many new opportunities for those working in science, engineering and business. Computational intelligence technology from industrial big data can provide a more effective help for decision-making of enterprises’ innovative design. This paper describes work that aims to use neural network technology to establish an intelligent design model for prediction of the ultimate load of thin-walled steel perforated sections. Compared with those of the traditional analytical model, the intelligent design model for the solving the hard problem of complex steel perforated sections is very promising.
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