Optimal Design of Structures for Earthquake Loading by Self Organizing Radial Basis Function Neural Networks

Abstract

In order to efficiently find the optimal design of structures subjected to earthquake loading two strategies are adopted. In the first strategy, a neural system consisting of self organizing map (SOM) and radial basis function (RBF) neural networks is employed to predict the time history responses of structures. The neural system is termed as self organizing radial basis function (SORBF) networks. To train SORBF, the input-output samples are classified by employing SOM clustering, and then an RBF neural network is trained for each cluster by using the data located. In the second strategy, an improved genetic algorithm, the so-called virtual subpopulation (VSP), is employed to find the optimum design. To improve the performance generality of the SORBF, a VSP based optimal approach is employed. Two structures are designed for optimal weight using exact and approximate time history analyses. The numerical results demonstrate the efficiency and computational advantages of the proposed methodology.