A Time-Varying Reliability Analysis Method Based on BP Neural Network with Adaptive Optimization Strategy

The traditional Monte Carlo simulation (MCS) method for time-varying reliability analysis (TRA) typically demands a large number of samples to achieve accurate reliability assessment results, and the total number of samples increases as the nonlinearity of the performance function increases. This paper proposes an adaptive surrogate model framework for TRA based on the back propagation (BP) neural network to reduce computational costs and enhance prediction performance. First, an improved multi-objective dwarf mongoose optimization algorithm (IMODMOA) is proposed to optimize BP neural network parameters, which can effectively improve model stability. Second, aiming to enhance model generalization and prediction accuracy, an adaptive sample selection strategy is designed to address the feature learning requirements of the BP neural network in TRA. Finally, an adaptive optimal BP surrogate model with high robustness is established by integrating IMODMOA parameters optimization, adaptive sample selection and dynamic training strategies, providing an efficient solution for TRA. Compared with conventional methods, the proposed method is validated through two numerical examples and one engineering case to confirm its superiority. The results demonstrate that the presented approach achieves superior performance in terms of prediction stability and accuracy compared with the existing methods.