Predicting geometry factors and normalized T‐stress of centrally cracked Brazilian disk specimens using deep learning method

Abstract

AbstractIn this paper, the geometry factors of mode I and mode II (YI and YII) and the normalized T‐stress (T*) of the centrally cracked Brazilian disk specimen are predicted using a deep learning approach. Three deep neural networks are developed to model the relationship between the crack angle (α) and the ratio of half the crack length to the radius (a/R) as input variables and each of YI, YII, and T* as output variables. Three independent databases consisting of 174, 174, and 117 data points are prepared for YI, YII, and T*, respectively, from the previous works to train, generalize, and validate the deep neural networks. Finally, sensitivity analysis of YI, YII, and T* to α and a/R is conducted, and mathematical models are obtained using the partial dependence plots, which can be used in the optimization of stress intensity factors and T‐stress of the cracked Brazilian disk specimens with different dimensions.