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A coupled discrete element and depth-averaged model for dynamic simulation of flow-like landslides

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posted on 2022-01-18, 13:26 authored by Xiaoli Su, Xilin Xia, Qiuhua LiangQiuhua Liang, Jingming Hou
Flow-like landslides commonly happen in mountainous areas and may threaten people's lives, damage their properties, and create negative impact on the environment. Computer modelling has become an effective tool to support landslide risk assessment and management. Models based on discrete element method (DEM) can capture micro-mechanical behaviour of soils, simulate large deformation and have been widely used for landslide simulations. However, these models are computationally too demanding for large-scale applications. On the other hand, depth-averaged models (DAM) have been well reported for simulation of flow-like landslides over large spatial domains due to its relatively high computational efficiency. To combine the advantages of both types of modelling approaches, this paper develops a novel landslide model by coupling a DEM model with a DAM for landslide simulation, in which the DEM component is employed to better simulate the complex landslide dynamics in the source area and the DAM is adopted to predict the predominantly convective movement in the runout and deposition zone. Finally, the new coupled landslide model is validated against several test cases, including a field-scale event. Satisfactory results have been obtained, demonstrating that the coupled model is able to reproduce the dynamic process of flow-like landslides.

Funding

China Scholarship Council (No. 201706300057)

WeACT project (NE/S005919/1) funded by the UK Natural Environment Research Council (NERC) through the SHEAR programme.

History

School

  • Architecture, Building and Civil Engineering

Published in

Computers and Geotechnics

Volume

141

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Computers and Geotechnics and the definitive published version is available at https://doi.org/10.1016/j.compgeo.2021.104537

Acceptance date

2021-10-27

Publication date

2021-11-18

Copyright date

2022

ISSN

0266-352X

eISSN

1873-7633

Language

  • en

Depositor

Prof Qiuhua Liang . Deposit date: 17 January 2022

Article number

104537

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