A PFE/IE – SPH joint approach to model landslides from initiation to propagation

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Detalles Bibliográficos
Publicado en:Computers and Geotechnics vol. 114 (Oct 2019), p. 1
Autor principal: Lin, Chuan
Otros Autores: Pastor, Manuel, Li, Tongchun, Liu, Xiaoqing, Lin, Chaoning, Qi, Huijun, Sheng, Taozhen
Publicado:
Elsevier BV
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Acceso en línea:Citation/Abstract
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Descripción
Resumen:Landslide is a complicated natural disaster that can be divided into multiple stages such as initiation and propagation. Researchers have been attempting to reveal the characteristics of each stage by practicing various modelling methods. This paper attempts to discover whether the combination of two popular approaches, including the Partitioned Finite Elements and Interface Elements method (PFE/IE) and the depth-integrated SPH model would bear a more reliable estimation. In the first stage, the PFE/IE method is practiced to investigate the triggering of slopes. The outcome from the first stage, including the contact force and the material parameters under limit state, are then utilized as the initial condition in the analysis of the subsequent propagation stage. In the propagation stage, the depth-integrated SPH model is used to explore the landslide propagation. Consequently, the SPH model is capable of dealing with large deformation problems that occur during the landslide movement process. Two benchmark tests are performed to verify the accuracy and feasibility of applying the hybrid model to study the landslide initiation. After the justification of the PFE/IE approach, it is then combined with the SPH model by an appropriate interpolation method. The presented PFE/IE-SPH joint approach is employed to evaluate the safety margin of a practical slope, as well as the potential effect region if the landslide occurs. The simulated result from the joint approach demonstrates its capability of providing references for the mitigation and protection measures.
ISSN:0266-352X
1873-7633
DOI:10.1016/j.compgeo.2019.103153
Fuente:Computer Science Database