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Collision of Shaped Boulders With Sand Substrate Investigated by Experimental, Stochastic, and Discrete Approaches
Author(s) -
Garcia B.,
Richefeu V.,
Baroth J.,
Daudon D.,
Villard P.
Publication year - 2020
Publication title -
journal of geophysical research: earth surface
Language(s) - English
Resource type - Journals
eISSN - 2169-9011
pISSN - 2169-9003
DOI - 10.1029/2019jf005500
Subject(s) - collision , monte carlo method , kinematics , discrete element method , predictability , rockfall , work (physics) , computer science , mathematics , geology , mechanics , geotechnical engineering , landslide , engineering , statistics , physics , classical mechanics , mechanical engineering , computer security
This paper focuses on experimental and numerical studies aiming to better characterize the rebound kinematics between a natural soil and rock boulders using single‐block collision models. The variability of the dissipative parameters needed to reproduce the experimental results was studied considering two different approaches: stochastic transfer matrix and discrete element model (DEM). An experimental process taking into account all possible variables related to the collision was setup for small‐scale tests involving cylindrical and triangular boulders that introduces natural variability during impact. The objective of the experimental tests is to identify the relationship between incident and reflected velocities (before and after impact, respectively), in order to improve the predictability of the two numerical proposed approaches. The experimental results help to quantify the amount of energy losses during impacts and thus expands the knowledge to reproduce more realistic rockfall events. This work is divided in three main axes: (i) statistical analysis of experimental trajectories based on experimental tests; (ii) stochastic approach predicting in a simple way from initial velocities the kinematic of blocks after rebounds; and (iii) calibration of a discrete element model including probabilistic analysis based on Monte Carlo approach. This way, the two collision approaches presented in this work are able to predict rebounds in terms of averages, standard deviations, and distributions.

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