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.
Bibliographical noteFunding Information:
Acknowledgemenf-This material is based upon work supported by the National Science Foundation under grant no. MSS-9222114, American Iron and Steel Institute and American Institute of Steel Construction, which is gratefully acknowledged.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Modelling and Simulation
- Materials Science(all)
- Mechanical Engineering
- Computer Science Applications