Reliability analysis of structures using artificial neural network based genetic algorithms

A new class of artificial neural network based genetic algorithms (ANN-GA) has been developed for reliability analysis of structures. The methods involve the selection of training datasets for establishing an ANN model by the uniform design method, approximation of the limit state function by the trained ANN model and estimation of the failure probability using the genetic algorithms. By effectively integrating the uniform design method with the artificial neural network based genetic algorithms (ANN-GA), the inherent inaccuracy of the selection of the training datasets for developing an ANN model in conventional ANN-GA has been eliminated while keeping the good features of the ANN-GA. Due to a small number of training datasets required for developing an ANN model, the proposed methods are very effective, particularly when a structural response evaluation entails costly finite element analysis or when a problem has a extremely small value of failure probability. Three numerical examples involving both structural and non-structural problems illustrate the application and effectiveness of the methods developed, which indicate that the proposed methods can provide accurate and computationally efficient estimates of probability of failure.