Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

Abstract

The accurate estimations of the failure probability with low-computational burden play a vital role in structural reliability analyses. Due to high-calculation cost and time-consuming Monte Carlo simulation (MCS), this paper focused on developing a novel enhanced MCS approach with an advanced machine learning method, namely hybrid enhanced MCS (HEMCS), for achieving accurate approximation of failure probability with high-efficiency computations. The failure probability is approximated by a probabilistic model using a scalar factor less than one which is multiplied on the capacity term of performance function. An adaptive input for scalar factor is proposed by the coefficient of variation of failure probability for active region in the training process of artificial neural network (ANN) with multilayer back-propagation algorithm. Four analytical optimization training approaches, active region and number of hidden nodes are discussed for accurate approximation of ANN models. The results of the HEMCS are compared with several analytical reliability methods for numerous engineering problems. Laminated composite plate and turbine bladed disk are selected to illustrate the capability of HEMCS for approximation of failure probability. The proposed method provides higher flexibility for the prediction of failure probability when compared to the enhanced MCS which also offers significantly more accurate and computationally efficient results for high-nonlinear problems.