A rain induced landslide 3D model based on molecular dynamics with fractal and fractional water diffusion

Gianluca Martelloni; Franco Bagnoli; Alessio Guarino

doi:10.1016/j.cnsns.2017.03.014

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arXiv · 1611.06167

A rain induced landslide 3D model based on molecular dynamics with fractal and fractional water diffusion

Gianluca Martelloni·Franco Bagnoli·Alessio Guarino

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Abstract

We present a three-dimensional model, based on cohesive spherical particles, of rain-induced landslides. The rainwater infiltration into the soil follow the either the fractional or the fractal diffusion equations. We solve analytically the fractal diffusion partial differential equation (PDE) with particular boundary conditions to simulate a rainfall event. Then, for the PDE, we developed a numerical integration scheme that we integrate with MD (Molecular Dynamics) algorithm for the triggering and propagation of the simulated landslide. Therefore we test the numerical integration scheme of fractal diffusion equation with the analytical solution. We adopt the fractal diffusion equation in term of gravimetric water content that we use as input of triggering scheme based on Mohr-Coulomb limit-equilibrium criterion, adapted to particle level. Moreover, taking into account an interacting force Lennard-Jones inspired, we use a standard MD algorithm to update particle positions and velocities. Then we present results for homogeneous and heterogeneous systems (i.e. composed by particles with same or different radius respectively). Interestingly, in the heterogeneous case, we observe segregation effects due to the different volume of the particles. Finally we show the parameter sensibility analysis both for triggering and propagation phase. Our simulations confirm the results of our previous two-dimensional model and therefore the feasible applicability to real cases.

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Gianluca Martelloni, Franco Bagnoli, Alessio Guarino. 2016-11-18. A rain induced landslide 3D model based on molecular dynamics with fractal and fractional water diffusion. https://doi.org/10.1016/j.cnsns.2017.03.014

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