Deep learning method for predicting the strengths of microcracked brittle materials

The strengths of brittle or quasi-brittle materials strongly depend on the interaction of distributed microcracks. Traditional micromechanics methods are difficult to exactly predict the strengths of materials containing a large number of microcracks. In this paper, a micromechanics-based deep learning method is proposed to predict the strengths of two-dimensional microcracked brittle materials. Utilizing a numerical method based on Kachanov’s theory of microcrack interaction, we generate a data set containing a large number of images of two-dimensional microcracked specimens and their load-bearing capacity under various in-plane loading. A deep neural network is formulated based on this data set to establish the implicit mapping between the load-bearing capacity of the specimens and the spatial distribution of microcracks. Numerical experiments demonstrate that the trained deep neural network can accurately and efficiently predict the load-bearing capacity of microcracked brittle materials.