Reconstruction of the meso-scale concrete model using a deep convolutional generative adversarial network (DCGAN)

Abstract

The deep learning has been widely used in engineering mechanics, composite materials and other fields due to its powerful information analysis ability. A Deep Convolutional Generative Adversarial Network (DCGAN) is trained in this paper and used to generate a large number of meso-scale concrete models. The CT scanning technology is employed to obtain the 2D slice images of concrete, which are transformed into binary images through a series of preprocessing operations and used as the training dataset of neural network to train the DCGAN. The pre-processing of input dataset, such as the CT image preprocessing and the data enhancement, and some training strategies of the DCGAN are provided in detail. The meso-scale concrete model generated by the trained DCGAN is similar with the training samples and cannot be identified with the naked eye. The reliability of the generated concrete samples is further verified by two indexes (i.e. the aggregate area fraction and the aggregate fractal dimension), and the results show that the generated samples have high consistency with the training samples on the boxplot and box-counting D (the errors are within around 10% and 2%, respectively). In addition, the similar stress–strain curves and the damage evolution processes of the training and generated samples are obtained by the uniaxial compression tests. Compared with the traditional modeling method of meso-scale concrete, the proposed method is more convenient on generating the mass of meso-scale concrete samples and resolves the conflict between computational efficiency and sample quantity. It can be used to study the relationship between the macroscopic and mesoscopic behaviors of concrete from the statistical perspective.