Counterpropagation Neural Networks in Structural Engineering

Abstract

Neural network computing has recently been applied to structural engineering problems. Most of the published research is based on a back-propagation neural network (BPN), primarily due to its simplicity. The back-propagation algorithm, however, has a slow rate of learning and is therefore impractical for learning of complicated problems requiring large networks. In this paper, we present application of counterpropagation neural network (CPN) with competition and interpolation layers in structural analysis and design. To circumvent the arbitrary trial-and-error selection of the learning coefficients encountered in the counterpropagation algorithm, a simple formula is proposed as a function of the iteration number and excellent convergence is reported. The CPN is compared with the BPN using two structural engineering examples reported in recent literature. We found superior convergence property and a substantial decrease in the central processing unit (CPU) time for the CPN. In addition, CPN was applied to two new examples in the area of steel design requiring large networks with thousands of links. It is shown that CPN can learn complicated structural design problems within a reasonable CPU time.