Constructing Microstructural Evolution System for Cement Hydration From Observed Data Using Deep Learning

Jifeng Guo, C. L. Philip Chen, Lin Wang, Bo Yang, Tong Zhang, Liangliang Zhang

Abstract: Cement has been widely used in civil engineering directly and plays a critical role in cement-based materials, e.g., concrete. As the microstructural evolution of cement hydration predominates the final physical properties, an accurate simulation of hydration is highly required to enable scientists to evaluate the performance and help design new cementitious materials. However, despite significant effort and progress, a satisfactory model to realistically and accurately simulate the evolution of three-dimensional (3-D) microstructure has not yet to be constructed, mainly because cement hydration is one of the most complex phenomena in material science. In this work, a novel near-realistic microstructural model is proposed to simulate the cement hydration system using deep learning and cellular automata. It is designed to break through the bottleneck of fidelity to real microstructural evolution. The dynamical system is constructed based on a 3-D cellular automaton, in which behavior is controlled by deep neural networks distilled from microstructural images. In addition, a dynamic stratified sampling method with variable capacity is proposed to ensure the representativeness of samples for reducing the computation cost of training. Experiments manifest that the simulated hydration is in accordance with the actual development in different aspects, such as near-realistic microstructure and approximate process. Furthermore, the constructed system also demonstrates promising generalization capability even under various conditions.