Structural reliability analysis of aircraft wing rib fatigue cracking using surrogate dynamic Bayesian network

Abstract

Dynamic Bayesian networks (DBNs) are crucial for evaluating time‐sensitive structural risks in aircraft, yet their inherent complexity can create substantial computational demands. This research introduces a surrogate model, specifically a two‐layer artificial neural network (ANN), to replace the most computationally intensive node in the DBN, typically associated with an ordinary differential equation solver. The surrogate model was chosen after comparing 24 regression machine learning models and optimizing hyperparameters, leading to a significant reduction in computational time and an improvement over traditional methods such as Monte Carlo simulations. The surrogate model demonstrates practical significance with its conservative risk estimations and successful application to a real‐world structural fatigue issue in an F‐4E aircraft intermediate rib. The unique aspect of this research lies in the strategic application of ANN to mitigate computational challenges, thereby enhancing the performance of DBN in fatigue risk analysis.