Modeling method of concrete material at mesoscale with refined aggregate shapes based on image recognition

Abstract Numerical simulation of concrete materials has become widely accepted, and modeling the aggregate based on the actual material is critical. However, the particle shape control still needs to be improved. In this paper, a refined aggregate modeling method is proposed using statistical rules based on image recognition. The geometrical data of 4407 real particles are extracted from the specimen cross-sections, and the joint probability distributions of particle size and aspect ratio are investigated. The statistical rules are remarkable and stable. The recommended distribution functions for geometrical parameters are provided to guide the simulation. The aggregate model is refined to a three-level framework based on the particle size, aspect ratio, and surface texture, and a corresponding concrete modeling method is also proposed. To investigate the method effect, numerical experiments on 800 models are conducted. The results show that it is necessary to precisely control the particle aspect ratio for accurate simulation.

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