## 3 Dataset Establishment  
### Data Acquisition  
Based on the three gradation-controlling parameters (\(\alpha\), \(\beta\), \(d_{\text{max}}\)) in Equation (2) and porosity (\(n\)) as variables, a total of 256 sets of CGS specimens were designed, with each variable set at four levels. Specifically, the parameter (\(\alpha\)) was assigned values of 100, 200, 300, and 400; \(\beta\) took values of 2, 4, 6, and 8 \(\times\) 10\({}^{-4}\); \(d_{\mathrm{max}}\) was set to 10, 20, 30, and 40 mm; and \(n\) varied as 0.1, 0.15, 0.20, and 0.25. The CFD-DEM simulations through PFC 6.0 (3D) were conducted to model permeability tests, determining the permeability coefficients for each specimen [39; 40]. The primary procedures of the numerical simulation are as follows (see Figure 1):  
1. Numerical model construction and CGS specimen generation  
The first step involves defining the geometric dimensions of the test specimen, which is typically set as a cylindrical specimen with a diameter of 300 mm and a height of 600 mm. This configuration ensures sufficient representativeness while minimizing boundary effects. Either rigid or periodic boundaries can be applied to simulate realistic particle movement under seepage flow. Subsequently, CGS specimens are generated based on the prescribed gradation characteristics, either by employing an exponential continuous gradation equation or by directly importing experimental gradation data to ensure that the particle distribution adheres to the target gradation curve. The specimen is generated using the deposition method, where particles progressively fill the designated test region. After particle generation, gravitational settling is applied, followed by equivalent compaction, to achieve adequate particle contact and establish a stable pore structure. The initial porosity is then adjusted to approximate the target value, providing a reasonable initial state for subsequent seepage simulations. 2. CFD fluid domain definition and computational grid generation  
The computational fluid dynamics (CFD) domain is first defined to align with the geometric boundaries of the CGS specimen. Suitable fluid boundary conditions are then imposed, such as applying a constant hydraulic head at the top and specifying either a fixed pressure or a free drainage boundary at the bottom to replicate actual seepage conditions. Next, the fluid domain is discretized using a computational grid, typically employing either a uniform cubic mesh or an adaptive meshing strategy. The grid size is selected to be compatible with particle dimensions, usually matching the smallest particle diameter to capture fluid-solid interface characteristics accurately. Once the meshing process is completed, the porosity of each grid element is calculated, and the fluid-solid coupling relationship is established to ensure the accuracy of the CFD computation. The fluid properties, including dynamic viscosity and density, are then defined. Fluid motion within  
Figure 1: Modeling using the CFD-DEM coupling method. (**a**) Cylindrical space generation; (**b**) Sample particle generation; (**c**) Seepage field application. the CGS medium is described using the Navier-Stokes equations to capture seepage flow behavior. The detailed meso-mechanical parameters in the numerical seepage simulations are presented in Table 1.  
3. Seepage test simulation and data analysis  
The seepage boundary conditions are initially imposed in the simulation of the seepage test. A constant hydraulic head or a constant flow rate is applied at the top, allowing fluid to infiltrate the CGS medium, while the bottom boundary is subjected to either a fixed pressure or a free drainage condition to ensure a steady seepage state. Once steady-state seepage is established, the pressure gradient and local flow velocity distribution within the specimen are monitored. The overall permeability coefficient is then computed based on [PERSON]'s law, providing quantitative insight into the seepage behavior of the CGS material.