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Numerical Modeling of Kinetic Features and Stability Analysis of Jinpingzi Landslide

Author

Listed:
  • Jiaxuan Huang

    (Chinese-German Institute of Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Weichao Du

    (32023 Troops, Dalian 116023, China)

  • Mowen Xie

    (Department of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

The kinetic features of a slow-moving landslide situated above the Wudongde hydropower station were analyzed using particle flow code 3D (PFC3D) software. This research was based on geological investigations, remote sensing interpretation, and digital elevation models to build the structure of the Jinpingzi landslide. Finite element analysis (FEM) was used to determine the sliding surface. Strength reduction theory (SRT) and particle flow code coupling were used to invert the macro-strength parameters into micro-strength parameters. Finally, the slope failure process was simulated. Meanwhile, the displacement vector angle (DVA) and velocity were used for stability analysis. The simulation results of the kinetic features of slow-moving landslides show that the initial stage begins with accelerated movement, followed by constant-velocity movement and instability failure. The larger the reduction coefficient is, the shorter the duration of each stage is. A two-parameter instability criterion is proposed based on velocity, DVA, and reduction coefficient. Using this criterion, the critical velocity was 200 mm/s, and the critical DVA was 28.15°. The analysis results agree with the actual field monitoring results and motion process. This work confirms that the PFC3D modeling method is suitable for simulating the motion features of landslides.

Suggested Citation

  • Jiaxuan Huang & Weichao Du & Mowen Xie, 2023. "Numerical Modeling of Kinetic Features and Stability Analysis of Jinpingzi Landslide," Land, MDPI, vol. 12(3), pages 1-17, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:679-:d:1096814
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    References listed on IDEAS

    as
    1. S. Wan & T. Lei & T. Chou, 2010. "A novel data mining technique of analysis and classification for landslide problems," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(1), pages 211-230, January.
    2. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1663-1689, January.
    3. Zhuo Chen & Danqing Song, 2021. "Numerical investigation of the recent Chenhecun landslide (Gansu, China) using the discrete element method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 717-733, January.
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