## 4 Discussion  
In this study, a novel stability evaluation method for rock slope anchorage systems was developed by integrating the discrete element method (DEM) with genetic the algorithm (GA). The proposed approach leverages DEM to obtain comprehensive multi-field data, including displacement fields, stress distributions, and the stress-strain behavior of anchorage structures. Simultaneously, GA is employed to optimize numerical model parameters by fitting them to actual monitoring data, thus ensuring the model accurately reflects in situ conditions. The results demonstrate that this combined methodology is both feasible and reliable, offering a precise representation of the deformation and stress evolution within the rock slope anchorage system. Moreover, the method provides a clear visualization of the system's stability evolution, allowing for an intuitive and dynamic evaluation of slope stability. The effectiveness of the proposed method arises from the complementary strengths of DEM and GA. DEM is well-suited for modeling large displacements, contact surface sliding, and separation in discontinuous rock masses, capturing the internal deformation and stress redistribution across structural planes and jointed rock masses with high fidelity. Meanwhile, GA contributes robust global optimization capabilities, with strong adaptability and resilience, enabling efficient calibration of model parameters. Compared to traditional rigid limit equilibrium methods, which fail to account for the interactive behavior between the rock slope and anchorage system or the progressive evolution of stability, this approach offers a more integrated and dynamic assessment framework. Additionally, by incorporating actual monitoring data into the optimization process, the method addresses the limitations of conventional DEM analyses that often rely solely on laboratory-derived parameters, which may not accurately represent field conditions. Figure 12: Numerical simulation results for creep time of 90,000 hours. (**a**) Slope displacement monitoring curves for TPZQX-4-3 and TPZQX-4-; (**b**) horizontal displacement contour map. However, certain limitations remain. The method is highly dependent on the quality and availability of monitoring data, which directly influences the accuracy of model calibration and, consequently, the reliability of stability evaluations. In future research, we aim to enhance the robustness of the approach by integrating multi-dimensional monitoring datasets into the objective function. This will allow cross-validation among different data sources, thereby mitigating the impact of low-quality measurements and improving the precision of GA-based parameter optimization. Currently, the stability assessment focuses primarily on horizontal displacement, as it serves as a sensitive and critical indicator of the overall stability state of the rock slope anchorage system. Horizontal displacement tends to exhibit pronounced changes during instability events. Nonetheless, considering displacement in three dimensions--including vertical and lateral movements--would provide a more comprehensive understanding of system behavior. However, such an extension would significantly increase computational demands, making it less practical for real-time engineering applications given the limitations of current optimization and DEM computation speeds. Future developments will focus on algorithmic improvements, including parallel computing strategies, to enable efficient multi-dimensional analysis for more holistic stability assessments. Additionally, the study employs the Kelvin model, which represents linear viscoelastic behavior through a parallel configuration of ideal springs and dashpots. While suitable for describing steady-state creep in rock slopes, the Kelvin model is inadequate for capturing non-linear damage accumulation associated with brittle failure or accelerated creep phenomena.