A neural dynamics model for structural optimization—Application to plastic design of structures

Abstract

In a companion paper we presented a neural dynamics model for optimization of structures by integrating penalty function method, Lyapunov stability theorem, Kuhn-Tucker condition, and the neural dynamics concept. In this paper, we apply the model to optimum plastic design of low-rise steel frames. The objective and constraint functions are scaled to improve the efficiency and numerical conditioning of the algorithm. As demonstrated in the convergence histories for the four examples presented, the neural dynamics model yields stable results no matter how the starting point is selected. Since the neural dynamics model lends itself to concurrent processing effectively, development of a concurrent neural dynamics model for optimization of large structures appears a very promising approach, which is currently under investigation by the authors.