Predicting the Young’s Modulus of Concrete Using a Particle-Based Movable Cellular Automata Method

The elastic modulus is one of the fundamental parameters controlling the mechanical behaviour of concrete. In this study, the Movable Cellular Automata (MCA) method is applied to predict the Young’s modulus of concrete based on the properties of its components. Each automaton represents one component: cement paste, fine aggregate, or coarse aggregate. A parametric sensitivity analysis was performed using Grey System Theory (GST) on hypothetical concrete modeled with the MCA method. The analysis showed that the coarse aggregate type, coarse aggregate-to-total aggregate ratio, and water-to-cement ratio have the greatest impact on the Young’s modulus. To test the effectiveness of the MCA method in modelling concrete, results of numerical simulations were compared with experimental data available in the literature. The first numerical simulations were conducted for mortars containing cement paste and sand as well as for concretes produced by adding granite to them. Two approaches were used to perform the simulations; in the first approach, a sample contained automata representing cement paste, sand, and granite, while in the second the automata represented mortar and granite. High consistency was achieved, with results from both approaches differing by only 0.6%. Subsequent simulations focused on concretes with different water-to-cement ratios (0.45, 0.55, and 0.65), the origin of the basaltic aggregate, and various aggregate contents (60%, 54%, 48%, and 42%). Results showed high agreement between simulations and experimental data, confirmed by a high coefficient of determination R2 of 0.84 and mean squared error of 2.43 GPa2. Finally, simulations were performed for lightweight expanded clay aggregate concrete, resulting in an R2 of 0.86 and mean squared error of 0.81 GPa2, which demonstrates the effectiveness of the MCA method in predicting the static elastic modulus of concrete.