## 3 Results and Discussion
### Model and Parameters  
#### 3.2.1 Numerical Simulation Model Generation  
To study the mechanical properties of the Xiyu conglomerate, a numerical model was generated based on the porosity statistics and real CT reconstruction images in the previous section. The size of the model was the same as the actual size of the sample. To generate an accurate numerical simulation model, the first step is to import the previously generated DXF file into PFC. Next, according to the geometry, different properties of particles are  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm/Metric** & **Label** & **IoU(\%)** & **Dice(\%)** & **F1(\%)** \\ \hline UNet & gravel & 81.78 & 89.97 & 89.97 \\  & pore & 56.87 & 72.51 & 72.51 \\  & matrix & 45.51 & 62.56 & 62.56 \\ HRNet & gravel & 82.47 & 90.39 & 90.39 \\  & pore & 62.3 & 76.77 & 76.77 \\  & matrix & 55.98 & 71.78 & 71.78 \\ SegFormer & gravel & 83.67 & 91.11 & 91.11 \\  & pore & 72.98 & 84.38 & 84.38 \\  & matrix & 59.2 & 74.37 & 74.37 \\ ModSegFormer & gravel & 86.24 & 92.61 & 92.61 \\  & pore & 77.81 & 87.52 & 87.52 \\  & matrix & 67.99 & 80.94 & 80.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation accuracy of the four methods.  
Figure 10: Segmentation effect of the improved SegFormer. (**a**) Original image, (**b**) image after segmentation.  
filled into the model. Finally, the model is servoed. The specific steps to generate the model are as follows:  
1. Based on the porosity statistics in the previous section, we set the porosity to 0.2 and set the maximum-to-minimum particle size ratio Rmax/Rmin to 1.66. The file containing the coordinate points of the polygons generated earlier was imported into the PFC. The balls were automatically filled by approximating the medial surface of the geometry outlines.
2. To simulate the different properties of various parts of the Xiyu conglomerate, we classified the contacts of the Xiyu conglomerate model into three categories. By judging the group to which the two ends of the contact belong, it can be divided into gravel contacts, matrix contacts, and gravel-matrix contacts.  
The generated model is then subjected to servo control to achieve an ideal state of contact between the particle systems as quickly as possible, and a loading analysis is performed based on this state. This process is essential for using the discrete element method for mechanical analysis. For biaxial compression tests on the Xiyu conglomerate, the loading plates around the rock sample need to be adjusted to complete the servo control and stabilize its internal structure. This can be done by adjusting the servo speed in horizontal and vertical directions continuously. Meanwhile, the number of contacts between the particles and boundary walls is counted, and the servo parameter G for the next time step is calculated. The wall normal velocity \(u_{n}^{w}\) is obtained again, and servo control for the next time step is started. This cycle is repeated until the average contact stress on all the boundary walls reaches the specified requirements. Assuming that \(N_{c}\) is the number of particles in contact with the boundaries, \(k_{n}\) is the average contact stiffness, \(\delta_{t}\) is the time step length, \(A\) is the wall area, \(\alpha\) is the stress release factor, and \(\sigma^{measured}\) and \(\sigma^{required}\) are the measured stress and target stress on the loading plates, respectively. The servo adjustment coefficient and loading speed can be expressed, respectively, as follows:  
\[G=\frac{\alpha A}{k_{n}N_{c}\delta_{t}} \tag{16}\]  
\[u_{n}^{w}=G*abs\Big{(}\sigma^{measured}-\sigma^{required}\Big{)} \tag{17}\]