Fast prediction of concrete equivalent modulus based on the random aggregate model and image quadtree SBFEM

Abstract

To evaluate the mechanical property of concrete materials rapidly, a fast prediction model of the concrete equivalent modulus is proposed based on the random aggregate model and scaled boundary finite element method (SBFEM). First, a random aggregate model of meso-concrete is employed to construct the representative volume element (RVE) according to the aggregate content, gradation, shape, etc. Second, the RVE model is transformed to be a grayscale image and stored as a digital matrix. The quadtree mesh is partitioned automatically for simulation by SBFEM. There are only six types of unique subdomains, and the hanging node does not affect the simulation accuracy. The global stiffness matrix can be assembled directly according to the six subdomain stiffness matrices. Finally, the equivalent modulus is predicted by using the numerical homogenization method. Several numerical examples are employed to verify this model, and the results are compared with that of other methods. The result indicates that the proposed model can efficiently determine the equivalent modulus. Furthermore, the effect of the aggregate gradation, shape, porosity, and pore water are studied and analysed in this work. The proposed model is potential and helpful in predicting the mechanical properties of concrete or other composite materials.

Keywords

• concrete
• random aggregate model
• equivalent modulus
• image quadtree
• scaled boundary finite element method