Active learning-based KNN-Monte Carlo simulation on the probabilistic fracture assessment of cracked structures

Abstract

The probability of fracture (POF) assessment of complex cracked structures is a difficult task in the reliability assessment of engineering structures. Due to the complexity of structure, balancing efficiency and accuracy is the top concern in POF calculation. In this study, a novel probabilistic solution method called AKNN-MCS (Active learning-based K-Nearest Neighbors-Monte Carlo Simulation) is proposed. Combining the active learning strategy and the KNN algorithm, this method could get accurate POF results using a few samples. In detail, POF calculation is treated as a classification problem. A learning function is proposed to select sample points near the limit state surface. Then the selected sample points are added into training data set T. A convergence criterion is defined to decide when to stop the enrichment of T. Thanks to the above active learning strategy, the trained KNN model could have a great generalization ability with only a few training samples required. The proposed method is validated by POF assessment of a finite thickness plate containing a surface semi-elliptical crack and POF assessment of the CT specimen. Results show that AKNN-MCS is three or four orders of magnitude more efficient than MCS for almost identical POF results.