Speaker: Marius Huber

Date: Thursday 18th of March 2021, 1:20 pm.


Deep seated gravitational slope deformations (DSGSDs) are a commonly observed type of slope instability process found in mountain ranges all over the world. Their rates of movement differ fundamentally from more superficial rapid mass movements known in high relief terrain, with displacements characterized by long periods of dubious activity or time spans of inactivity. If some DSGSDs do not induce catastrophic failures but instead cease activity, others might be in a preparatory stage before a large volume catastrophic landslide. This last evolutionary stage could be favored by geological and fracturing conditions, as well as topographical, climatic, or seismo-tectonic conditions. However, due to the scarcity of very large rockslides, this possible catastrophic evolution and the reasons for it are still poorly documented and understood.

In order to explore the conditions that lead or not to an unfavorable catastrophic failure, we propose to use discrete element models to investigate DSGSDs from a mechanical viewpoint. After validating our modeling approach based on comparisons with Limit Equilibrium Analysis of simple slope geometries, we explore preconditions and mechanisms acting towards failure considering the influence of mechanical rock properties, slope topography, and pre-existing structures. Furthermore, we can implement our method on real existing case studies by shaping our simulations with digital terrain models (DTMs).

Comparison of our modeling results with those DSGSDs for which long-term and recent activity has been documented (e.g. La Clapière case in the South Western Alps of France) provides insight into their prevailing patterns of deformation (continuous or stepwise over time) and the conditions (geological, fracturing or topographic) for a catastrophic landslide or progressive stabilization. This may lead to better understanding and prediction of behavior for large scale deep seated deformation in mountainous areas, especially relevant for estimating natural hazards.