Concrete is a composite material that has complex mechanical properties. The mechanical properties of each of its components are different at the mesoscopic scale. Studying the relationship between the macroscopic and mesoscopic parameters of concrete can help better understand its mechanical properties at these levels. When using the discrete element method to model the macro-mesoscopic parameters of concrete, their calibration is the first challenge. This paper proposes a numerical model of concrete using the particle discrete element software particle flow code (PFC). The mesoscopic parameters required by the model need to be set within a certain range for an orthogonal experimental design. We used the proposed model to perform numerical simulations as well as response surface design and analysis. This involved fitting a set of mapping relationships between the macro–micro parameters of concrete. An optimization model was established in the MATLAB environment. The program used to calibrate the mesoscopic parameters of concrete was written using the genetic algorithm, and its macro-micro parameters were inverted. The following three conclusions can be drawn from the orthogonal test: First, the tensile strength and shear strength of the parallel bond between the particles of mortar had a significant influence on the peak compressive strength of concrete, whereas the influence of the other parameters was not significant. Second, the elastic modulus of the parallel bonding between particles of mortar, their stiffness ratio and friction coefficient, and the elastic modulus and stiffness ratio of contact bonding in the interfacial transition zone had a significant influence on the elastic modulus, whereas the influence of the other parameters was not significant. Third, the elastic modulus, stiffness ratio, and friction coefficient of the particles of mortar as well as the ratio of the contact adhesive stiffness in their interfacial transition zone had a significant influence on Poisson’s ratio, whereas the influence of the other parameters was not significant. The fitting effect of the response surface design was good.

Urea can solve the problem of concrete cracking due to temperature stress. However, its effect is affected by temperature. The influencing mechanism of temperature on urea-doped cement pastes is still unclear. This paper explores the effect of different temperatures on the hydration kinetics of urea-doped cement pastes. The isothermal calorimeter (TAM Air) was used to test hydration at three constant temperatures (20 °C, 40 °C, and 60 °C). The effects of the urea admixture and temperature on the hydration process and hydration kinetics parameters were investigated. The hydration mechanism was analyzed, and the changes in macroscopic mechanical compressive strength and porosity were tested. The results show that, as the urea content (UC) increases, the rate of hydration gradually decreases, and the increase in temperature promotes the inhibitory effect of urea. At 60 °C, UC of 8% can be reduced by 23.5% compared with the pure cement (PC) group’s hydration rate. As the temperature increases from 20 °C to 60 °C, the Krstulovic–Dabic model changes from the NG-I-D process to the NG-D process. The effect of urea on the compressive strength of the cement is mainly shown in the early stage, and its effect on later strength is not obvious. In addition, urea will increase its early porosity. The porosity will gradually decrease in the later stage. The results of the study clarify the effect of temperature on urea-doped cement pastes. The optimal content of urea in cement is about 8%, which will provide theoretical guidance for solving the cracking problem of large-volume concrete due to temperature stress.

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water–paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σ_c, tensile strength σ_t, shear strength τ_f, elastic modulus E, Poisson’s ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water–paste ratio had extremely significant effects on σ_c, σ_t, τ_f, E, ν and c. The iron powder content had a highly significant effect on σ_c, σ_t, τ_f and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σ_c, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water–paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials.