Predictive Capabilities of 2D and 3D Block Propagation Models Integrating Block Shape Assessed from Field Experiments

Block propagation models have been used for years for rockfall hazard assessment. However, the calibration of model parameters that allow the simulations to accurately predict rockfall trajectories for a given study site remains a key issue. This research aims at investigating the predictive capabilities of block propagation models after a preliminary calibration phase. It is focused on models integrating the shape of blocks since, despite their sound physical bases, they remain less used than lumped-mass approaches due to their more recent popularisation. Benefiting from both a recently built model integrating block shape, usable in 2D and 3D, and from recent experimental results at the slope scale, we first performed a calibration based on the use of the 2D model, and then we evaluated the predictive capabilities of the calibrated model in 2D and in 3D using the remaining part of the experimental results. The calibrated model simulations predict the main characteristics of the propagation, that is the preferential deposit zones and the ranges of velocities at specific locations. Good matches between simulations and experimental results in both the calibration and validation phases emphasizes the wide applicability of the model: after a calibration phase on a sufficient number of different soil types, the model may be used in a predictive manner. The good match between 2D and 3D simulations also highlights the ease-of-use of the model for field applications, as the 2D model produces sufficiently accurate results while also being easier and faster to calibrate. As classically observed for block propagation models, the model is not sufficient to predict the details of the velocity and stopping points but provides accurate predictions of the global ranges of these quantities, in particular of the extreme values. To lift these limitations in terms of predictive capabilities, more advanced calibration procedures based on optimization techniques constitute a promising path forward.