The discrete element method (DEM) was used to establish the slump model and J-Ring model of concrete to describe the flow behavior in the slump test and J-Ring test. Then, the contact parameters of particle-particle and particle-geometry for the concrete DEM model, including restitution coefficient, rolling friction coefficient, static friction coefficient, and surface energy, were measured. In order to avoid the influence of the shape and size of the aggregate, this paper used high-precision glass spheres as the aggregate of the concrete for meso-calibration test, slump test, and J-Ring test. Comparing the simulation results of DEM model with slump test result, a very high agreement between the initial stage, the rapid flow stage, and the slow flow stage of the slump flow–time curve can be found as well as the final slump and slump flow. Moreover, similar to the slump DEM model, the DEM models of J-Ring test, V-funnel test, and U-channel test were established to study the passing ability and filling ability of concrete with outstanding accuracy. Therefore, the concrete DEM model with contact parameters and JKR model can be adopted to study the flow behavior of the fresh concrete.

The fact that high-strength concrete is easily to crack has a significant negative impact on its durability and strength. This paper gives an optimum design method of high-strength concrete for improving crack resistance based on orthogonal test artificial neural networks (ANN) and genetic algorithm. First, orthogonal test is operated to determine the influence of the concrete mix proportion to the slump, compressive strength, tensile strength, and elastic modulus, followed by calculating and predicting the concrete performance using ANN. Based on results from orthogonal test and ANN, a functional relationship among slump, compressive strength, tensile strength, elastic modulus, and mix proportion has been built. On this basis, using the widely used shrinkage and creep models, the functional relationship between the concrete cracking risk coefficient and the mix proportion is derived, and finally genetic algorithm is used to optimize the concrete mix proportion to improve its crack resistance. The research results showed that, compared with the control concrete, the cracking risk coefficient of the optimized concrete was reduced by 25%, and its crack resistance was significantly improved.

In this paper, the mesocalibration test was used to measure the contact parameters (restitution coefficient, rolling friction coefficient, static friction coefficient, and surface energy) between coarse aggregate particles and mortar particles in Discrete Element Method (DEM) model of concrete. Then, the DEM model of concrete slump was established according to the coarse aggregate gradation to study the flow behavior of coarse aggregate in fresh concrete. The slump test result was compared with the output of the slump DEM model with high consistent, indicating the promising reliability of the mesocalibration test. Finally, based on the mesocalibration test results, the DEM model of pumping concrete was established. It was obtained that the pumping pressure calculated by the numerical model was similar to that of the pumping test with satisfactory accuracy better than the concrete pumping pressure calculated according to the rheological test results of concrete and lubricating layer. On this basis, the movement trajectory of coarse aggregate in the pump pipe was analyzed and the influence of coarse aggregate on the pumping performance of concrete was revealed.