An adaptive artificial neural network for reliability analyses of complex engineering systems

Abstract

Reliability analyses of engineering systems require methods capable of reducing the number of function calls and response variances to calculate the failure probability. This paper is aimed to employ a combination of the Monte Carlo simulation (MCS), artificial neural network (ANN) and control variate technique (CVT) to improve the efficiency and accuracy of the reliability analyses of various engineering systems. The ANN used in this paper is of the multilayer perceptron type. The paper uses the design of experiments (DoE), importance sampling and uniform sampling methods to generate the samples. The combination of the MCS, ANN model, DoEs, and CVT is used for the reliability analyses and significantly improved the results in terms of efficiency. Various numerical examples have shown the superiority of the improved ANN model using CVT in reducing the number of calls of the limit state function and revealed that the obtained results are not sensitive to the number of hidden layer neurons. The reduced number of required simulations and computation costs has enabled the proposed method to be applicable to reliability analyses in complex engineering systems such as structures made by functionally graded materials.