A deep neural network-based method to predict J-integral for surface cracked plates under biaxial loading

As surface cracks may cause potential failure for engineering structures, it is of vital significance to carry out the fracture assessment for cracked structures. The crack driving force in the form of J -integral is an important input parameter in fracture assessment. This paper focuses on the determination of J -integral for surface cracked plates under biaxial loading. A method for predicting the J -integral based on the deep neural network (DNN) is proposed to estimate J -integral efficiently and accurately. Firstly, extensive three-dimensional (3D) elastic - plastic finite element (FE) analysis is conducted to compute the J -integral along the crack front for developing a FE J - integral datasets containing 1600 cases. Various crack aspect ratios, crack depth ratios, strain hardening exponents, ratios of the loading perpendicular to the crack plane to yield stress, and biaxial ratios, are strategically considered. Secondly, DNN models for predicting the J -integral are constructed and trained according to the obtained datasets by FE analysis. Then, the evaluation criteria for DNN models and the grid search method are utilized to determine the optimal DNN model structure. Finally, test cases are employed to verify the feasibility and prediction accuracy of the determined DNN model. Based on validation results, the determined DNN model exhibits sufficiently accurate prediction performance. This research provides an idea for engineering applications, through building software based on continuously optimized DNN models, the J - integral for surface cracked plates under biaxial loading can be accurately and efficiently predicted for performing fracture assessments.