## 2 Evaluation Model  
The discrete element method (DEM) is a numerical technique used to simulate the behavior of materials composed of discrete particles. In geotechnical engineering, DEM is particularly suitable for simulating the mechanical behavior of particulate materials (such as soil and rock) under various loading conditions. Genetic algorithms (GA) are optimization algorithms that simulate the process of natural selection in biological evolution [39; 40] and are known for their powerful optimization capabilities. They exhibit strong global search capabilities, along with high robustness and adaptability. This evaluation model combines genetic algorithms with the discrete element method, utilizing field monitoring data to intelligently validate the DEM model, thus obtaining the most accurate representation of the actual engineering state. This approach is then applied to assess the stability of the rock slope anchoring system. The implementation steps of this evaluation method are as follows:  
(1) Collect the basic data of the rock slope anchoring system and construct a discrete element method (DEM) numerical analysis model. Field investigations of the rock slope anchoring system are conducted to collect landslide morphological data, including the elevation of the anchored slope, landslide boundaries, the shape and distribution characteristics of structural surfaces, and slope deformation characteristics. Deformation data of the anchored slope and stress data of the anchoring structures are collected using on-site monitoring instruments. For example, displacement data of the slope surface are collected via GPS monitoring points, and variations in anchoring force are measured using load cells attached to the anchor cables. Drawing on the collected data, a numerical analysis model of the rock slope anchoring system is constructed in the 3 DEC discrete element software. Secondary factors are disregarded due to their limited impact on the overall results, and major factors are considered to improve the computational efficiency of the software. (2) Conduct field or laboratory tests to determine the range of values for the constitutive model and related parameters. The most commonly used creep constitutive models in geotechnical engineering are divided into two categories: empirical models based on experimental data and data fitting, and component models derived from theoretical analysis. However, empirical models lack a theoretical basis and merely fit experimental data. By contrast, component models can more accurately reflect the mechanical properties of the soil and rock mass, while being simple and easy to use, making them more widely applied in practical engineering. Based on laboratory or field creep tests, appropriate constitutive relationships and the corresponding range of parameter values are determined. (3) Collect monitoring data of the rock slope anchoring system and define the objective function. The difference between the actual monitoring data and the corresponding response data in the numerical model is used as the objective function of the genetic algorithm. (4) Perform parameter optimization using a genetic algorithm. The optimal parameters of the creep model are determined by combining the response characteristics of the genetic algorithm and the numerical analysis model with the field monitoring data. A genetic algorithm is implemented using Python 3.6 to combine the simulated response of the anchored slope model in 3 DEC 7.0 software with the objective function of the genetic algorithm. Simultaneously, field monitoring data of the rock slope anchoring system are used for intelligent optimization to determine the optimal parameters of the numerical analysis model. (5) Determine the optimal DEM numerical analysis model. The constitutive relationships and optimal parameters determined in Step (2) and Step (4) are assigned to the analysis model in 3 DEC software. (6) Set the analysis time and perform the numerical model calculation.