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Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Author

Listed:
  • Liulei Bao

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Guangcheng Zhang

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Xinli Hu

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Shuangshuang Wu

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Xiangdong Liu

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

Abstract

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day 2 , is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

Suggested Citation

  • Liulei Bao & Guangcheng Zhang & Xinli Hu & Shuangshuang Wu & Xiangdong Liu, 2021. "Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function," Energies, MDPI, vol. 14(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1091-:d:502105
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    References listed on IDEAS

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    1. Chuang, Chin-Wei & Lin, Chao-Yuan & Chien, Chang-Hai & Chou, Wen-Chieh, 2011. "Application of Markov-chain model for vegetation restoration assessment at landslide areas caused by a catastrophic earthquake in Central Taiwan," Ecological Modelling, Elsevier, vol. 222(3), pages 835-845.
    2. Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," 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. 61(2), pages 635-646, March.
    3. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," 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. 30(3), pages 383-398, November.
    4. Tommaso Carlà & Emanuele Intrieri & Federico Traglia & Nicola Casagli, 2016. "A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series," 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. 84(1), pages 669-683, October.
    5. Kyungjin An & Suyeon Kim & Taebyeong Chae & Daeryong Park, 2018. "Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources," Sustainability, MDPI, vol. 10(2), pages 1-13, January.
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