RELIABILITY BASED OPTIMIZATION USING NEURAL NETWORKS M. Papadrakakis and N. D. Lagaros

In this paper a robust and efficient methodology is presented for treating large-scale reliability-based, structural optimization problems. The optimization part is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method incorporating the importance sampling technique for the reduction of the sample size. The elasto-plastic analysis phase, required by the MCS, is replaced by a neural network predictor in order to predict the necessary data for the MCS procedure. The use of neural networks is motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required by MCS. A training algorithm is implemented for training the NN utilizing available information generated from selected elasto-plastic analyses.