Neurobiological computational models in structural analysis and design

Abstract

This paper examines the role of neural computing strategies in structural analysis and design. A principal focus of the work resides in the use of neural networks to represent the force-displacement relationship in static structural analysis. Such models provide computationally efficient capabilities for reanalysis, and appear to be well suited for application in numerical optimum design. The paper presents an overview of the neutral computing approach, with special emphasis on supervised learning techniques adopted in the present work. Special features of such learning strategies which have a direct bearing on numerically accuracy and efficiency, are examined in the context of representative structural optimization problems.