# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 5 Experimental Results and Analysis
### Evaluation
#### 5.2.2 Rock Mass Parameter Characterization Evaluation  
The study area was selected from the DK325+000 to DK327+100 section of the Huayingshan Tunnel. Three rock samples, labeled TS-1, TS-2, and TS-3, were collected at DK326+000. Data from the field, combined with deep learning and laboratory rock mechanics tests, helped systematically identify fissures, extract parameters, and estimate mechanical properties. This provided a scientific basis for the tunnel project's design and construction. The detailed process of the study is outlined below. The sampling process and the location of sampling are shown in Figures 14 and 15.  
Using the geometric features of the fissures extracted by FraSegNet, parameters such as length, width, dip, density, and area were calculated. The results, shown in the table, display the fracture characteristics of each rock sample in the study area. These results provide the needed inputs for estimating the mechanical parameters of the rock mass. Nodal extraction process and results by FraSegNet are shown in Figure 16. Table 11 shows the joint parameters.  
Figure 16: Nodal extraction process and results by FraSegNet.  
Figure 14: Specific flow chart for extracting rock parameters.  
Figure 15: Schematic diagram of rock sample location.  
To assess the effectiveness of deep learning in engineering applications, the mechanical parameters of surrounding rock were analyzed and compared using the DK326+010 mileage section as a case study. The rock parameters extracted using deep learning and BartonBandis were compared with those obtained from indoor tests and converted using the Hoek-Brown criterion. The results showed minimal errors, indicating high consistency. Deviations in key parameters such as density, cohesion, friction angle, and modulus of elasticity were within the acceptable engineering range. This validation confirms that the deep learning-based method for extracting nodal parameters and estimating mechanical properties is highly accurate, reliable, and efficient, effectively replacing traditional manual methods.  
The technical methods and data analysis in this study confirm that the optimized FraSegNet model identifies joints more accurately. This provides reliable technical support and serves as a valuable reference for future research on joint identification in complex engineering projects. Table 15 lists the data comparison.  
In the comparison between the computer model predictions and the actual values, the following differences were observed for the various rock parameters:  
Smallest Difference: The smallest difference observed across all parameters was in Poisson's ratio, where the difference between the Barton-Bandis and Hoek-Brown models for each sample was consistently 0 (no difference), with values of 0.21 for TS-1 and 0.2 for TS-2 and TS-3. This indicates that both models gave identical results for this parameter.  
Largest Difference: The largest difference observed was in elastic modulus for TS-2, with a difference of 0.04 GPa between the Barton-Bandis and Hoek-Brown models. This suggests that the models yielded slightly different results in terms of rock deformation capacity under axial stress, especially for sample TS-2. These differences illustrate the model's performance across various mechanical properties, with certain parameters showing near-identical results, while others exhibit minor discrepancies, highlighting areas where further refinement of the models may be beneficial.  
After data processing, the relative error, standard deviation, maximum error, and average error of the rock mass parameters obtained using the Barton-Bandis method and the Hoek-Brown criterion for the joints identified by FraSegNet were calculated. The average relative error between the two methods was 1.5%, well within the acceptable error range of 10%, ensuring the reliability and applicability of the results [34]. This shows that the joints identified by the FraSegNet model are highly accurate and suitable for engineering applications.