Granular matter is ubiquitous in nature and is present in diverse forms in important
engineering, industrial and natural processes. Particle-based computational modelling has …

Advances in numerical methods have provided useful tools for investigating the complex
behaviour of landslides, which can be a valuable support for landslide hazard assessment …

Abstract Machine learning approaches have found immense potential to revolutionise the
constitutive modelling of granular materials. However, data scarcity poses a significant …

Microbiologically induced calcium carbonate precipitation (MICP), as a promising
reinforcement technique, has been investigated extensively through element and model …

The coupled thermo-hydro-mechanical (THM) response of liquid-infiltrated porous media
underpins the safe operation and maintenance of key engineering infrastructure …

Physics-informed neural networks (PINNs) have recently prevailed as differentiable solvers
that unify forward and inverse analysis in the same formulation. However, PINNs have quite …

Handling contact problems in the Material Point Method (MPM) has long been a challenge.
Traditional grid-based contact approaches often face issues with mesh dependency, while …

In this paper, a new formulation of material point method (MPM) to model coupled soil
deformation and pore fluid flow problems is presented within the framework of the theory of …

Abstract In this study, Material Point Method (MPM) is improved to simulate coseismic slope
stability and liquefaction-induced embankment failure under earthquake loading. First, by …

We present a novel multiscale framework that integrates the single-point multiphase material
point method (MPM) and the discrete element method (DEM) to model the complex freeze …