Investigation of slope failure mode evolution during large deformation in spatially variable soils by random limit equilibrium and material point methods

Liu, Xin; Wang, Yu; Li, Dianqing

doi:10.1016/j.compgeo.2019.03.022

Cited by 103 publications

(23 citation statements)

References 43 publications

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“…Similar to the random material point method (RMPM, see Wang et al 2016a;Liu et al 2019;Remmerswaal et al 2021) initially proposed by Fenton and Vanmarcke (1990) to generate RFs for a finite element mesh (RFEM), we combined this approach with the codes proposed by Räss et al (2019b) to generate an isotropic peak cohesion field to demonstrate its influence on the mechanical behaviour.…”

Section: Appendix C: Volumetric Locking and Damping Correctionsmentioning

confidence: 99%

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)

Wyser

Alkhimenkov

Jaboyedoff

et al. 2021

Preprint

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Abstract. We propose an explicit GPU-based solver within the material point method (MPM) framework on a single graphics pro- cessing unit (GPU) to resolve elastoplastic problems under two- and three-dimensional conﬁgurations (i.e., granular collapses and slumping mechanics). Modern GPU architectures, including Ampere, Turing and Volta, provide a computational framework that is well suited to the locality of the material point method in view of high-performance computing. For intense and nonlocal computational aspects (i.e., the back-and-forth mapping between the nodes of the background mesh and the material points), we use straightforward atomic operations (the scattering paradigm). We select the generalized interpolation material point method (GIMPM) to resolve the cell-crossing error, which typically arises in the original MPM, because of the C0 continuity of the linear basis function. We validate our GPU-based in-house solver by comparing numerical results for granular collapses with the available experimental data sets. Good agreement is found between the numerical results and experimental results for the free surface and failure surface. We further evaluate the performance of our GPU-based implementation for the three-dimensional elastoplastic slumping mechanics problem. We report i) a maximum performance gain of x200 between a CPU- and GPU-based implementation, provided that ii) the hardware limit (i.e., the peak memory bandwidth) of the device is reached. We ﬁnally showcase an application to slumping mechanics and demonstrate the importance of a three-dimensional conﬁguration coupled with heterogeneous properties to resolve complex material behaviour.

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Section: Appendix C: Volumetric Locking and Damping Correctionsmentioning

confidence: 99%

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)

Wyser

Alkhimenkov

Jaboyedoff

et al. 2021

Preprint

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show abstract

“…Accurate estimates of soil and rock properties only contribute to the accuracy of predicted response of geotechnical structures, e.g., the predicted settlement of foundations. On the other hand, the spatial variability of ground properties exists long before the project and affects significantly the actual response of geotechnical structures (e.g., Fenton and Griffiths 2002;Wang et al 2011;Liu et al 2019). The knowledge uncertainties and spatial variability, therefore, should be differentiated in probabilistic analyses.…”

Section: Uncertainty Propagation During Ground Property Characterizationmentioning

confidence: 99%

“…For example, side resistance of a pile foundation is a summation of unit pile side resistance along a pre-specified surface (i.e., the interface between the pile shaft and soils). However, the SRV model may not perform well when variation of spatial coordinates of the ground properties is important, such as slope stability analysis (e.g., Griffiths and Fenton 2004;Liu et al 2019) where a search for the minimum resistance path is needed from a large number of potential slip surfaces. In these cases, RF modeling of spatial variability, with explicit consideration of spatial coordinates, is preferred.…”

Section: Physical Meaning Of Single Random Variable Modelingmentioning

confidence: 99%

Bayesian Perspective on Ground Property Variability for Geotechnical Practice

Wang¹,

Zhao²,

Cao³

2019

Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

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Soils and rocks are natural heterogeneous geo-materials, and their properties exhibit site-specific spatial variability as an outcome of the previous geological processes that the soils and rocks in the site have undergone. Spatial variability of ground properties and other geotechnical uncertainties may be modelled probabilistically using random variables or random field. Some questions are frequently raised by practicing geotechnical engineers when they consider using probabilistic methods. For example, what is the physical meaning of failure probability and random variable or random field modeling? Is a large amount of data necessary for using probabilistic methods? This paper aims at providing answers to these questions from a Bayesian perspective. Bayesian methods and tools are also presented that were recently developed for characterization of ground property variability from sparse site investigation data.

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“…The continuous methods describe material behavior using macroscopic constitutive equations of mechanical, hydraulic, and thermal-mechanical behaviors. In addition to FEM, smoothed particle hydrodynamics (SPH) [16,17], the material point method (MPM) [18,19], and computational fluid dynamics (CFD) [20][21][22] are popular continuous methods used in landslide simulation. As for the discontinuous methods, the discrete element method (DEM) [23][24][25][26][27] and discontinuous deformation analysis (DDA) [28,29] are most used.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study

Peng

Song

et al. 2019

Applied Sciences

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Forecasting the occurrence potential of landslides is important but challenging. We aimed to forecast the failure potential of the Helong landslide, which is temporarily stable but has clearly deformed in recent years. To achieve the goal, we used reconnaissance, remote sensing, drilling, laboratory tests, topographical analysis, and electrical resistivity tomography (ERT). The factor of safety (FOS) of the slope was first calculated using a limit equilibrium method. The results show the FOS of the slope was 1.856 under natural conditions, 1.506 under the earthquake conditions, 1.318 under light rainfall, 0.986 under heavy rainfall, 1.075 under light rainfall and earthquake, and 0.832 under simultaneous heavy rainfall and earthquake. When the FOS is less than 1.35, the slope is considered metastable according to the Technical Code for Building Slope Engineering (GB50330-2013) published by the Chinese Ministry of Housing and Urban-Rural Development. Based on the drilling data and digital elevation data, a three-dimensional discrete element method (DEM) model was used to simulate potential landslides. The simulation was used to examine catastrophic slope failure under heavy rainfall conditions within a range of friction coefficients and the corresponding affected areas were determined. Then, we analyzed a typical run-out process. The dynamic information of the run-out behavior, including velocity, run-out distance, and depth, were obtained, which is useful for decision support and future landslide hazard assessment.

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Investigation of slope failure mode evolution during large deformation in spatially variable soils by random limit equilibrium and material point methods

Cited by 103 publications

References 43 publications

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)

Bayesian Perspective on Ground Property Variability for Geotechnical Practice

Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study

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