Sensitivity and Calibration of Three‐Dimensional SPH Formulations in Large‐Scale Landslide Modeling. Issue 2 (28th January 2023) - Li, Shuai
Abstract: Numerical prediction of landslide runout and deposition is important for estimating landslide risk and developing mitigation plans. The choice of a suitable model and its parameters and a confident calibration strategy are crucial for numerical simulations. Here, we evaluated two constitutive models with a three‐dimensional smoothed particle hydrodynamics (SPH) method by simulating the catastrophic 11 October 2018 Baige landslide. The results indicate that both the soil mechanic and fluid models can capture the dynamic runout and deposition morphology while using different values of input parameters. A point‐wise comparison of deposit elevation can minimize the calibration error. Numerical models were constrained accurately by utilizing both the static observation data and dynamic seismic signals. The effects of friction on deep‐seated landslides motion and deposition are more significant than cohesion. The 3D model includes the effects of shear stresses and velocities inside the material body, resulting in a reduced friction coefficient compared to the 2D model (e.g., depth‐averaged model). Our study highlights the potential of the 3D SPH method for modeling large‐scale complex landslides. Plain Language Summary: Landslides belong to a type of earth surface process recognized by their high damage potential. Computer models can simulate the landslides' movement to predict speed, forces, and deposition, which can help to delineate areas at risk and to design mitigation measures. We simulated the 2018 Baige landslide in China with a method known as smoothed particle hydrodynamics (SPH), to test whether it is suitable for landslide simulation and to investigate protocols for model calibration when including seismic data in addition to information on the landslide's deposit. In this method, the landslide body is represented by millions of small, moving, interacting particles, which offers advantages over established models in terms of computation time and details in the simulation. We find that SPH is suitable for modeling large‐scale natural landslides. The seismic data are more valuable in the model calibration than landslide deposit observations. The new approach yields landslide simulations that deliver more details on particle velocities within the landslide body, and their spatial and temporal distribution. These details can be used to infer the properties of landslides during the sliding process, which helps to better understand landslides in general and to set up models for events where little data are available. Key Points: A three‐dimensional mesh‐less graphics processing unit‐accelerated smoothed particle hydrodynamics formulation is suitable for modeling large‐scale natural landslides The selection of an appropriate constitutive model depends on the landslide features and materials Including dynamic seismic signals into numerical model calibration routines in addition to topographic changes improves calibration