# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 6 Conclusions  
This paper presents an innovative nodal extraction method utilizing FraSegNet, integrated with the Hoek-Brown criterion, to effectively convert rock body parameters into rock  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **TS-1** & **TS-1** & **TS-2** & **TS-2** & **TS-3** & **TS-3** \\  & **Barton–Bandis** & **Hoek–Brown** & **Barton–Bandis** & **Hoek–Brown** & **Barton–Bandis** & **Hoek–Brown** \\ \hline Density & 2451 & 2445 & 2482 & 2478 & 2467 & 2462 \\ Cohesion & 8.96 & 8.8 & 4.05 & 4.03 & 6.9 & 6.88 \\ Friction Angle & 39.66 & 39.43 & 32.42 & 32.35 & 31.56 & 31.79 \\ Elastic Modulus & 27.29 & 27.32 & 12.46 & 12.42 & 21.37 & 21.3 \\ Shear Modulus & 10.46 & 10.51 & 4.83 & 4.78 & 8.26 & 8.19 \\ Bulk Modulus & 18.23 & 18.21 & 8.33 & 8.28 & 14.26 & 14.2 \\ Tensile Strength & 1.27 & 1.2 & 0.67 & 0.66 & 0.67 & 0.64 \\ Poisson’s Ratio & 0.21 & 0.21 & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Data comparison.  
mass parameters. The experimental results demonstrate that the method accurately extracts nodal information and efficiently transforms rock parameters, showcasing its substantial potential for broader applications. Future research could focus on improving the FraSegNet network's performance in complex geological environments and refining the parameter transformation model to achieve more accurate mechanical analyses of rock bodies:  
By incorporating the ResNet101 backbone, the SE attention mechanism, and a dynamic learning rate strategy, the optimized FraSegNet network enhances its feature extraction capabilities. This improvement is particularly beneficial for handling complex backgrounds and fine-grained targets. The experimental results show that the network achieves a training accuracy of 0.9711 and a minimum loss rate of 0.0717, with validation accuracy reaching 0.9348 and a minimum loss rate of 0.1642. The IoU and Dice coefficients for the automatic segmentation of nodal fissures stabilize at 0.94 and 0.98, respectively, demonstrating a notable enhancement in capturing fissure edge details compared to traditional segmentation methods. Additionally, the model excels in recognizing joints in tunnel surrounding rocks, with the confusion matrix and Grad-CAM visualization further validating its superior classification performance and ability to accurately assess dense joint areas.  
By combining the Barton-Bandis fracture parameter mapping with the Hoek-Brown strength criterion, this study establishes a clear relationship between the nodal features extracted by FraSegNet and the mechanical parameters of rock bodies. This integration provides reliable data support for future engineering applications, offering improvements in joint segmentation accuracy and a more precise reflection of rock body mechanical properties compared to traditional methods.  
The proposed method was successfully tested in the Huayingshan Tunnel, validating its applicability and efficiency under complex geological conditions. It enhances the accuracy of rock joint and fissure identification, providing a reliable foundation for rock engineering design, stability analysis, and safety assessments. This approach holds significant value for both theoretical research and practical engineering applications.  
Data curation: Y.Z.; Formal analysis: Y.Z.; Funding acquisition: Y.Z.; Investigation: G.Z.; Project administration: G.Z.; Resources: G.Z.; Software: G.Z.; Supervision: G.Z.; Validation: Q.L.; Weiting-original draft: Q.L.; Visualization: X.Y.; Methodology: X.Y.; Writing-review&editing: H.Z. All authors have read and agreed to the published version of the manuscript.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
We would like to thank Hebei Provincial Laboratory of Mining Industry Development with Safe Technology Priority for data support.  
The authors declare no conflicts of interest.