Fragmentation is a common feature of rockfall that exerts a strong control on the trajectories of the generated blocks, the impact energies, and the runout. In this paper, we present a set of four real-scale rockfall tests aimed at studying the fragmentation of the rocky blocks, from the global design of the field procedure to the data analysis and the main results. A total of 124 limestone, dacite, or granite blocks ranging between 0.2 and 5 m³ were dropped from different heights (8.5 to 23.6 m) onto four slopes with different shapes (single or double bench) and slope angles (42º to 71º). The characteristics of the blocks, in particular the size, surface texture and joint condition, were measured before the drops. The trajectories of the blocks and both the initial and the impact velocities were tracked and recorded by means of three high-speed video cameras. A total of 200 block-to-ground impacts have been studied. On average, 40% of the blocks broke upon impact on the slope or on the ground, making it necessary to measure the fragments. The initial and final sizes of the blocks/fragments were measured by hand with tape, though photogrammetric techniques (UAV and terrestrial) were also used for comparison purposes. The information gathered during the field tests provides a deep insight into the fragmentation processes. On the one hand, the high-resolution slow-motion videos help to describe when and how the block breakage takes place and the spatial distribution of the pieces. On the other hand, it is possible to compute the block trajectories, the velocities, and the energy losses using videogrammetry. The results include, for instance, a block average fragmentation of 54% and 14% for the limestone and granitoids, respectively; the systematic inventory of the size fragments, which may be used for fitting the power law distributions; and after each breakage, the total angle of aperture occupied by the fragments has been measured, with values in the range 25º–145º. To figure out the different behavior of the blocks in terms of breakage/no breakage, each block-to-ground impact has been characterized with a set of parameters describing the energy level, the robustness of the substrate, and the configuration of the block contact at the impact point, among others. All these terms are combined in a function F, which is used to adjust the field data. The adjustment has been carried out, first, for the whole 200 events and later for a subset of them. The procedure and the results are described in the paper. Although the discrimination capability of F is moderately satisfactory, it is very sensitive to the test site and setup. It must be highlighted that these field tests are a unique source of data to adjust the parameters of the numerical simulation models in use for rockfall studies and risk mitigation, especially when fragmentation during the propagation is considered.

The analysis of seismic signals obtained from near‐source triaxial accelerometer recordings of two sets of single‐block rockfall experiments is presented. The tests were carried out under controlled conditions in two quarries in northeastern Spain; in the first test (Foj limestone quarry, Barcelona), 30 blocks were released with masses ranging between 475 and 11,480 kg. The second test (Ponderosa andesite quarry, Tarragona) consisted of the release of 44 blocks with masses from 466 to 13,581 kg. An accelerometer and three high‐speed video cameras were deployed, so that the trajectories, velocities, and block fragmentation could be tracked precisely. These data were used to explore the relationship between seismic energy and rockfall kinetics (the latter obtained from video analysis). We determined absolute and relative values of seismic energy and used them to estimate rockfall volumes. Finally, the seismic signature of block fragmentation was assessed in both the frequency and time domains. The ratios of seismic energy after impact to kinetic energy before impact ranged between 10⁻⁷ and 10⁻⁴. These variables were weakly correlated. The use of seismic energy relative to impacting kinetic energy was preferred for the estimation of volumes. Block fragmentation impacts were dominated by higher acceleration spectrum centroid frequencies than those of nonfragmentation impacts: 56.62 ± 2.88 and 48.46 ± 4.39 Hz at Foj and 52.84 ± 12.73 and 38.14 ± 4.73 Hz at Ponderosa.

In this paper, we present the upgraded version of RockGIS, a stochastic program for the numerical simulation of rockfalls and their fragmentation, based on a fractal model. The code has been improved to account for a range of fragmentation scenarios, depending on the impact conditions. In the simulation, the parameters of the fractal fragmentation model that define the sizes of the generated fragments were computed at each impact according to the kinematic conditions. The performance of the upgraded code was verified and validated by real-scale rockfall tests performed in a quarry. The tests consisted of the release of 21 limestone blocks. For each release, the size and spatial distribution of the fragments generated by the impacts were measured by hand and from orthophotos taken via drone flights. The trajectories of the blocks and the resulting fragments were simulated with the code and calibrated with both the volume distribution and the runout distances of the fragments. Finally, as all the relevant rockfall parameters involved were affected by strong uncertainty and spatial variability, a parametric analysis was carried out and is discussed.

An automated, fixed-location, continuous time-lapse camera system was developed to analyze the existence of rockfall precursory movements and quantify volume changes after detachments. It was implemented to monitor the basaltic formation on which Castellfollit de la Roca village is built. Due to the geometrical conditions of the area, the camera system consists of three digital cameras managed by a control unit that contains a Raspberry Pi 4 microprocessor. Images taken every day are sent to a server for processing. A workflow has been developed to work with a set of images with an irregular time interval to detect precursor movement. The first step consists of matching the images with a reference master image and filtering the vegetation to improve the process using a mask obtained by a green leaf index (GLI) index. Then, the adjusted images are used for a forward-backward correlation process carried out to detect movements. If movement is detected, a 3D model is built using structure from motion (SfM) to quantify the movements. The system has been working since September 2021. During this period, movements from 0.01 to 0.5 m and several rockfalls of a small volume have been detected.