4.7 Article

Dynamic process analysis of the Baige landslide by the combination of DEM and long-period seismic waves

期刊

LANDSLIDES
卷 18, 期 5, 页码 1625-1639

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-020-01595-0

关键词

Landslide; Dynamic process; Discrete element method; Seismic waves; Frictional velocity-weakening; Parameter sensitivity

资金

  1. NSFC [41520104002, 42022054]
  2. Strategic Priority Research Program of CAS [XDA23090303]
  3. National Key Research and Development Program of China [2017YFC1501000]
  4. Youth Innovation Promotion Association

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This study establishes an adapted Hertz-Mindlin contact model and frictional velocity-weakening law, validates the simulation results by combining long-period seismic waves and numerical modeling, and analyzes the dynamic process of two landslides, demonstrating the feasibility of this combination procedure for predicting and identifying areas susceptible to large landslides.
In this study, based on the frictional velocity-weakening law used in continuum modeling, an adapted Hertz-Mindlin contact model between particles and the ground surface is established to accurately simulate the landslide dynamics process. Moreover, the long-period seismic waves indicating the dynamic characteristics of large landslides are adopted to determine the computational parameters for the discrete element method (DEM). By combining the force-time functions obtained from the long-period seismic waves and numerical modeling, the dynamic parameters and reasonableness of the simulation results of the first Baige landslide on October 11, 2018, in eastern Tibet, China, have been validated. The friction coefficient obtained in this study is also in agreement with the relationship between the volume and friction coefficients reported in the literature. Taking the same model and parameters, the dynamic process leading to the second Baige landslide on November 3, 2018, is analyzed. The modeled profile and position of the maximum thickness of the deposits arising from the two landslides both match the observations well. It is demonstrated that this combination procedure is feasible for the prediction and identification of areas susceptible to large landslides.

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