# Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study
## 4 Results and Discussions
### Post-Failure Analysis  
The post-failure behavior of the sliding mass movement was simulated using EDEM 2.7 software (DEM solution Ltd., Edinburgh, Scotland), which was developed by the University of Edinburgh. Before the simulation, a 3D slope model was constructed. We used a DEM with an accuracy of 10 m in 2018 to create the model. Point clouds that contained the terrain position and elevation information were obtained from the digital elevation model, which was then imported into computer automated design (CAD) software Rhino 5.0 (Robert McNeel & Associates Inc, Seattle, Washington). A solid  
Figure 8: Factor of safety (FOS) results of stability analysis. model of the slope that could be used in the numerical simulation was constructed, and the potential sliding mass was extracted based on a field investigation and drilling data. In the DEM simulation, the topography, except the potential sliding mass, was set to be walls to improve the computation efficiency, whereas the sliding mass was set as an aggregate of balls. The bonded particle model (BPM) was used for the potential sliding mass. BPM is conceptually based on bonding together a packed group of spheres to form a breakable body. The bond can transfer force and moment simultaneously, and it ruptures when the tensile stress or shear stress exceeds the tensile or shear strengths. Thus, the BPM is suitable for the simulation of rock and soil behavior [23; 24; 25]. In the BPM, there are several micro-parameters, including the parameters \(\mu\) (friction coefficient), \(E\) (Young's modulus), and \(\
u\) (Poisson's ratio) between particles; and the bonding parameters \(\overline{\sigma}_{c}\) (tensile strength of bonding), \(\overline{\tau}_{c}\) (shear strength of bonding), \(\overline{k}_{n}\) (normal bond stiffness of bonding), and \(\overline{k}_{s}\) (tangential bond stiffness of bonding). The microscopic parameters cannot be measured easily, and a theory is not available to determine the magnitude of the relationship between macroscopic and microscopic parameters. Therefore, in the DEM, parameter calibration experiments are usually performed. A series of numerical simulations are conducted for comparison with laboratory tests. If the outcomes are consistent, then the micro-parameters can be considered appropriate. Soil aggregate parameters were adopted from a numerical shear test, which was calibrated using the real values (Figure 9). The contact parameters between the balls are shown in Table 2. After the 3D slope model was finished (Figure 10), the run-out process was started by reducing the wall friction coefficient below the sliding mass [24]. Figure 9: Parameter calibration of the DEM: (**a**) flow chart, (**b**) numerical direct shear test, and (**c**) comparison of the shear test between the numerical model and the laboratory experiment. Potential failure of the slope may occur under natural dry conditions or with a rainfall. Therefore, the friction coefficient between the soil and slip surface may differ. According to Table 1, the value of the friction coefficient was 0.36 (20\({}^{\circ}\)) under normal conditions and 0.26 (15\({}^{\circ}\)) under saturated conditions. Here, we set four groups of friction coefficients for the slip surface: 0.35, 0.30, 0.25, and 0.2. The results showed that with a decreasing friction coefficient, the extent of the potential failure area increased (Figure 11), and the road, as well as some buildings, will be affected when the friction coefficient is lower than 0.3. When the friction coefficient of the slip surface was 0.35, the source basically remained intact, only moving a little downward (Figure 11a). With an increase in humidity, the friction coefficient decreased, and the failure potential area was 170 m long and 90 m wide, with an average depth of 7 m when the source was completely saturated (Figure 11c). The friction coefficient on the slip surface was frequently lower than the internal friction [32; 33]; thus, a lower friction coefficient value was used in the simulation (Figure 11d).