Probabilistic Fatigue Crack Growth Prediction With Improved Particle Filter and Multiple Models

Abstract

Predicting fatigue crack growth (FCG) is critical for structural safety and reliability, but it is severely challenged by inherent material uncertainties and the limitations of any single physical model. This study proposes a probabilistic FCG prediction method that integrates an improved particle filter (PF) and a Bayesian fusion for multiple FCG models. To alleviate post-resampling particle impoverishment, a component-wise modified Metropolis–Hastings (MMH) move algorithm is introduced as a particle rejuvenation step after resampling, which is designed to preserve particle diversity more effectively than the conventional resampling strategies. Furthermore, the Bayesian fusion dynamically weighs multiple FCG models (Paris–Erdogan, Kujawski, Forman, Nasgro), enabling mitigation of single-model bias. The proposed method was applied on the FCG prediction of a group of compact-tension (CT) specimens that have undergone experimental testing. Comparisons between the predicted and experimental results demonstrate that the proposed method provides more accurate FCG prediction in most of the CT specimens and the corresponding 95% confidence intervals (CIs) can encompass the measured crack paths.