Neural Network Model for Optimization of Cold-Formed Steel Beams

Abstract

An important advantage of cold-formed steel is the greater flexibility of cross-sectional shapes and sizes available to the structural steel designer. However, the lack of standard optimized shapes makes the selection of the most economical shape very difficult if not impossible. This task is further complicated by the complex and highly nonlinear nature of the rules that govern their design. A general mathematical formulation and computational model is presented for optimization of cold-formed steel beams. The nonlinear optimization problem is solved by adapting the robust neural dynamics model of Adeli and Park, patented recently at the U.S. Patent Office. The basis of the design can be American Iron and Steel Institute (AISI) allowable stress design (ASD) or load and resistance factor design (LRFD) specifications. The computational model has been applied to three different commonly used types of cross-sectional shapes: hat-, I-, and Z-shapes. The robustness and generality of the approach have been demonstrated by application to three different examples. This research lays the mathematical foundation for automated optimum design of structures made of cold-formed shapes. The result would be more economical use of cold-formed steel.