Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

Abstract

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris’ law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris’ law parameters.