A computational toy model for shallow landslides: Molecular Dynamics approach

TL;DR

This paper introduces a 2D Molecular Dynamics-inspired computational model to simulate shallow landslides triggered by rainfall, capturing particle trajectories, failure conditions, and power-law distributions similar to real landslides and earthquakes.

Contribution

It presents a novel MD-based algorithm for modeling landslide initiation and propagation, incorporating static friction decrease and particle interactions with Lennard-Jones potential.

Findings

Simulation reproduces power-law distributions of landslide events

Model captures velocity patterns similar to real landslides

Potential for failure time prediction using inverse velocity method

Abstract

The aim of this paper is to propose a 2D computational algorithm for modeling of the trigger and the propagation of shallow landslides caused by rainfall. We used a Molecular Dynamics (MD) inspired model, similar to discrete element method (DEM), that is suitable to model granular material and to observe the trajectory of single particle, so to identify its dynamical properties. We consider that the triggering of shallow landslides is caused by the decrease of the static friction along the sliding surface due to water infiltration by rainfall. Thence the triggering is caused by two following conditions: (a) a threshold speed of the particles and (b) a condition on the static friction, between particles and slope surface, based on the Mohr-Coulomb failure criterion. The latter static condition is used in the geotechnical model to estimate the possibility of landslide triggering. Finally the interaction force between particles is defined trough a potential that, in the absence of experimental data, we have modeled as the Lennard-Jones 2-1 potential. In the model the viscosity is also introduced and for a large range of values of the model's parameters, we observe a characteristic velocity pattern, with acceleration increments, typical of real landslides. The results of simulations are quite promising: the energy and the time triggering distributions of local avalanches shows a power law distribution, analogous to the observed Gutenberg-Richter and Omori power law distributions for earthquakes. Finally it is possible to apply the method of the inverse surface displacement velocity [Fukuzono 1985] for predicting the failure time.