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Landslides are major threats to the construction and operation of electric towers of the Sichuan–Tibet Grid Interconnection Project (STGIP). The long, narrow project corridor is vulnerable to different types of landslides, making risk management a difficult task. In this study, a kinematic-based, landslide risk management framework that includes hazard analysis, risk estimation, risk evaluation, and risk control is proposed and implemented. The spatial density of historical landslides and the spatial distribution of landslide kinematics are used to analyze landslide hazard. Kinetic energy derived from the landslide physical process model is employed to analyze the vulnerability of the towers in the risk estimation step. Then, the landslide hazard and tower vulnerability are used to comprehensively evaluate the landslide risk level of the electric towers. Electric towers with high risk are mainly situated in areas near rivers and faults, with fragile steep slopes. Fragmented rock masses and a thick regolith cover provide abundant materials for landslides, and steep slopes supply the initial conditions and energy that make them a risk. Risk-control strategies are provided based on site-specific risk. The safe construction and operation of the STGIP in the past 10 years show the effectiveness and reliability of the proposed framework. We suggest that the workflow and guidelines for landslide risk management used in this study can be adopted for large engineering projects in complex environments. • Landslide risk management in the STGIP is challenged by complex conditions. • A kinematic-based landslide risk management framework for the STGIP is proposed. • This framework has supported the safe construction and operation of the STGIP. [ABSTRACT FROM AUTHOR]

On June 24, 2017, a catastrophic rock avalanche with a volume of 5.08 × 106 m occurred in Xinmo village, Maoxian County, Sichuan Province, SW China. Several studies focusing on the triggering factors, failure mechanism and dynamic process of the Xinmo landslide have been reported in the literature. However, there are few studies with detailed three-dimensional investigations based on numerical modeling that assess the post-failure stage of the landslide. In the present study, the three-dimensional material point method is used to reproduce the kinematics of the landslide to obtain better insight into the disaster by considering the effects of fragmentation and entrainment. Based on this framework, a velocity-weakening friction law is implemented to describe the basal friction between the moving mass and the substrate, and a strain–softening Drucker–Prager constitutive model is used to describe the internal deformation of the moving mass. The entrainment processes obtained with and without the proposed model are compared, and the simulated results generated with the presented models showing the distribution characteristics of the materials in the deposit area match the observed results fairly well. The implementation of a velocity-weakening friction law and a strain-softening Drucker–Prager constitutive model in the material point method may provide valuable insight for the enforcement of physically based risk assessments of "rockslide-debris avalanches". • The 3D material point method modeling of the Xinmo landslide overcomes some limitations of other numerical methods. • The model accurately reflected the effects of fragmentation and entrainment. • Kinematic energy curves were developed to examine the post-failure behavior of the Xinmo landslide. • Topography played a crucial role in kinetic energy growth and dissipation. [ABSTRACT FROM AUTHOR]

Purely empirical and numerical methods are widely used in landslide movement prediction because they can forecast the failure time and consider influence factors, respectively. However, the combination of these two methods for prediction is rare. This paper develops an integrated landslide movement prediction model that can fully consider landslide kinematics and external influence factors using the Verhulst inverse function (VIF) and the random forest (RF) algorithm. The VIF is applied to describe the kinematic behavior of landslides using the rationale of three-stage creep deformation. The RF algorithm is to quantify the response of landslide displacement to the influence of external factors such as reservoir water level and rainfall intensities. The novelty of the VIF-RF model is illustrated by applying to a reservoir landslide, Gapa Landslide, in Southwestern China. The results show that the VIF-RF model shows significant improvement in predicting landslide movement compared with the VIF or RF model. The error analysis confirms that the root mean square error of the VIF-RF decreases by more than 20% compared with the VIF and RF models. In addition, the mean absolute percentage error of the VIF-RF models is less than 5%, a decrease by 2.3% and 10.1% compared to the VIF and RF models, respectively. The feasibility of the VIF-RF model for predicting movement of other reservoir landslides was successfully verified by the Majiagou landslide in the TGRA. The developed VIF-RF model indicates the Gapa landslide deformation is at the primary stage over the monitoring period. The displacement at the G1 and G2 monitoring locations of the Gapa landslide is projected to increase to 2719.8 and 2438.8 mm in January 2021, respectively, and the average rate in the accelerated deformation periods is projected to be 67.8 mm/month. The presented VIF-RF model provides an effective approach for predicting the long-term landslide deformation and identifying its deformation stage. • A novel prediction approach incorporating Verhulst Inverse Function (VIF) and Random Forest (RF) algorithm is proposed. • The VIF-RF model can consider landslide kinematic characteristics and external influence factors. • Predictions can be used to identify the deformation stage of a landslide. • The model overcomes the limitation of machine learning models in predicting future displacements. [ABSTRACT FROM AUTHOR]