Evaluating residual dyke resistance using the Random Material Point Method

Remmerswaal, Guido; Hicks, Michael

doi:10.1016/j.compgeo.2021.104034

Cited by 13 publications

(5 citation statements)

References 22 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: Discussionmentioning

confidence: 99%

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

et al. 2021

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Abstract. We propose an explicit GPU-based solver within the material point method (MPM) framework using graphics processing units (GPUs) 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 non-local 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 200-fold performance gain between a CPU- and a single-GPU-based implementation, provided that (ii) the hardware limit (i.e. the peak memory bandwidth) of the device is reached. Furthermore, our multi-GPU implementation can resolve models with nearly a billion material points. 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: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…In the case of quasi ‐static analysis the acceleration of the body is assumed to be zero, which reduces (1) to that of the AMPLE framework 29 . The surface traction term in (1) is neglected as the focus of this article is on the stability of the MPM rather than the imposition of surface tractions within the method, which is a research question in its own right (for example, see Bing et al 31 or Remmerswaal 32 ). We also restrict the Dirichlet boundary conditions to coincide with the background grid as the imposition of general Dirichlet constraints is another active area of research 31,33,34 …”

Section: Materials Point Methodsmentioning

confidence: 99%

Ghost stabilisation of the material point method for stable quasi‐static and dynamic analysis of large deformation problems

Coombs

2023

Numerical Meth Engineering

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The unstable nature of the material point method (MPM) is widely documented and is a barrier to the method being used for routine engineering analyses of large deformation problems. The vast majority of articles concerning this issue are focused on the instabilities that manifest when a material point crosses between background grid cells. However, there are other issues related to the stability of MPMs. This article focuses on the issue of the conditioning of the global system of equations caused by the arbitrary nature of the position of the physical domain relative to the background computational grid. The issue is remedied here via the use of a ghost stabilisation technique that penalises variations in the gradient of the solution field near the boundaries of the physical domain. This technique transforms the stability of the MPM, providing a robust computational framework for large deformation explicit dynamic and implicit quasi‐static analysis.

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“…Among different methods, the commonly used first-order reliability method (FORM) [2] and Second-order reliability method (SORM) [3] are only applicable for relatively simple engineering problems. Monte Carlo simulation (MCS)-based methods [4][5][6] can provide an unbiased estimation of the failure probability (P f ) and are not limited by the implications or complexity of performance function. However, MCS methods often require extensive computational cost for the performance function (usually more than 10 5 times [7]).…”

Section: Introductionmentioning

confidence: 99%

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Yuan

et al. 2023

Applied Sciences

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In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

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Evaluating residual dyke resistance using the Random Material Point Method

Cited by 13 publications

References 22 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)

Ghost stabilisation of the material point method for stable quasi‐static and dynamic analysis of large deformation problems

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

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