## 11 Discussion  
The practical value of the RQD method can be significantly enhanced through its integration with advanced geotechnical testing techniques, addressing the limitations of traditional approaches. Below, we explore various methods and their contributions:  
**Seismic Techniques:** Seismic methods, particularly those analyzing P-wave velocities, enable RQD assessment over larger areas without extensive drilling. This approach evaluates rock mass quality by correlating seismic wave velocities with fracture density and rock elasticity. For example, the RQD can be derived using the ratio of in situ seismic velocities (V\({}_{\text{PF}}\)) to laboratory-measured velocities (V\({}_{\text{p0}}\)), as shown in Equation (30). These techniques reduce environmental impacts and provide continuous data for large-scale characterization. Integrating seismic-derived RQD values with borehole data enhances the reliability of geotechnical models, particularly in projects involving anisotropic or fractured rock masses. **Discrete Fracture Network (DFN) Modeling:** DFN models offer a detailed representation of fracture systems by integrating data from multiple sources, such as boreholes and rock exposures. These models improve RQD calculations by accounting for fracture orientation, intensity, and spacing. As discussed, the DFN approach not only mitigates RQD's directional dependency but also facilitates the more accurate connectivity modeling of fracture networks. For instance, integrating DFN-derived volumetric fracture intensity (lvv) with traditional RQD measures addresses scale and anisotropy issues, providing a more representative understanding of rock mass behavior. **Machine Learning and Automated Analysis:** Advances in deep learning and AI-based image analysis have made automated RQD calculations more efficient. As recent studies have shown, automated methods can analyze joint spacing, fracture patterns, and block sizes with high precision, reducing the subjectivity inherent in manual logging. Incorporating these techniques into RQD analysis enables the rapid processing of large datasets, offering consistent and repeatable results in both field and laboratory settings. **Hybrid Approaches:** Combining RQD with other rock mass classification systems, such as the Geological Strength Index (GSI) or Rock Mass Rating (RMR), improves its practical applicability. For example, GSI charts use RQD-derived parameters to estimate structural and surface conditions, highlighting the interdependence of these classification methods. Additionally, integrating seismic and DFN-derived fracture data with traditional RQD values enhances the resolution of rock mass characterization, especially in highly fractured zones. **Modified RQD Techniques:** Several modifications to traditional RQD, such as weighted joint density (RQD\({}_{\text{wjd}}\)), corrected RQD (RQD\({}_{\text{c}}\)), and improved RQD (RQD\({}_{\text{i}}\)), incorporate advanced parameters like fracture density, orientation, and weak zones. These enhancements provide more accurate and reliable assessments in complex geological conditions, such as in highly jointed or weak rock masses. For example, Equations (5) and (11) demonstrate how these modifications address the limitations of the original RQD method, improving its alignment with real-world observations. By integrating these advanced techniques and modifications, the RQD method evolves into a more comprehensive and versatile tool for geotechnical and rock engineering applications. This approach not only addresses the method's inherent limitations but also aligns with contemporary demands for precision and scalability in rock mass characterization. The analysis of various methods for calculating rock quality designation (RQD) highlighted the inherent limitations and provided insights into the effectiveness of the proposed modifications.