This study aims to evaluate the feasibility and effectiveness of a modified adaptive genetic algorithm (AGA) with Universal Distinct Element Code (UDEC) simulation in analyzing fracture surface feature points of an anticline rocky slope. Using coordinate data from 30 fracture surface feature points, the traditional GA and modified AGA methods were compared, with the mean value of the normalized Mahalanobis distance indicating the reliability of the results. The study found that the modified AGA approach with UDEC had a significantly smaller mean value of normalized Mahalanobis distance than the traditional GA approach, demonstrating its higher accuracy and reliability in analyzing the fracture surface feature points of the rocky slope. Additionally, the research found that the location of the fracture surface of the anticline rocky slope is closely related to the inhomogeneous bulk density caused by weathering. These findings contribute to sustainability efforts by improving our understanding of the behavior of rocky slopes, informing better land management and infrastructure planning, and reducing uncertainties in predicting the behavior of rocky slopes for more sustainable infrastructure development and land management practices.

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.