Optimization of Concrete Mesoscopic Parameters Based on Deep Learning and Energy Dissipation Theory

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Abstract
Optimizing the mesoscopic structure of concrete based on energy dissipation theory is an effective approach to enhancing its performance. However, related works are limited, and the mathematical relationship between the mesoscopic structure and the energy dissipation mechanism remains unclear. In this study, image processing technology and MATLAB were employed to quantitatively characterize the mesoscopic structure of concrete. A mesoscopic model of concrete was constructed using PFC2D to study the influence of mesoscopic parameters on energy dissipation, and a mathematical model of energy dissipation incorporating mesoscopic parameters was fitted using a deep neural network. In addition, the genetic algorithm with associated individuals (GA‐AIs) was applied to optimize the mesoscopic parameters. The main findings are as follows: (1) Based on the entropy‐containing random aggregate method, Fourier shape reconstruction analysis, and density‐damping random field method, the aggregate spatial arrangement, shape characteristics, and mortar matrix characteristic property were quantified separately. (2) Under uniaxial compression, the three types of mesoscopic parameters showed positive correlations to varying degrees with the energy dissipation capacity of concrete. By adjusting these parameters, the energy dissipation capacity can be effectively modulated. (3) The GA‐AIs efficiently optimized the mesoscopic parameters, enabling effective control of the energy dissipation capacity of concrete. Based on the optimization results under a specific working condition, the algorithm can infer mesoscopic parameter values to meet different performance requirements for this condition.
Funding Information
  • National Natural Science Foundation of China (51778633)

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