# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 3 Stability Analyses
### Rockfall Susceptibility Analysis
On the contrary, the spatial distribution of the examined rock blocks (both the already failed and the potentially unstable) seems to follow certain directions across the face of each slope. This could easily be understood with the predetermined optimal failure models for each sub-region, as the formation of these rock blocks followed the directions of the discontinuity set(s) which contributed to any of the potential failures. Specifically, for sub-region U1, the main potentially unstable block volumes that were identified, across the slope's profile, ranged from small to moderate with values between 0.5 m\({}^{3}\) and 1.6 m\({}^{3}\). In sub-region U2, the potentially unstable block's volume distribution mainly fluctuated between 0.6 m\({}^{3}\) and 1.3 m\({}^{3}\). In the third sub-region (U3), the volume distribution mode displayed its highest range for any of the investigated slopes, with values mainly varying from 1.2 m\({}^{3}\) up to 5.2 m\({}^{3}\). Of course, as mentioned before, some of those volume excesses (>5.1 m\({}^{3}\)) comprised isolated cases and they were not considered as  
Figure 16: _Cont._  
Figure 15: Volume distribution for both the fallen (blue) and the potentially unstable (red) blocks for sub-region U1. Figure 16: Volume distribution for both the fallen (blue) and the potentially unstable (red) blocks for sub-region U2. Figure 17: Cont. Figure 17: Volume distribution for both the fallen (blue) and the potentially unstable (red) blocks for sub-region U3. Figure 18: _Cont._  
After the identification of the population, the magnitude and the spatial distribution of the potentially unstable rock blocks with a 3D approach, a rockfall analysis based on trajectory simulations was executed via RocFall software v.8.0.12 (RocScience Inc., Toronto, ON, Canada), in order to predict the potential rockfall hazards in the area. RocFall software can be characterized as a 2D statistical analysis program designed to assist with the assessment of slopes at risk for rockfalls. Energy, velocity and \"bounce height\" were determined by using the software for each sub-region. Distributions and comprehensive statistics of these parameters were automatically calculated along each slope's profile and projected as graphs. This software can also assist in determining remedial measures. Information about the kinetic energy and location of impact on a barrier can help determine the effectiveness (capacity, size and location) of barriers [71]. The barriers can be custom, user-defined or predefined from the software's library. As a result, the extracted information is vital for the estimation of each sub-region's hazard level, based on the proposed Rockfall Hazard and Risk Rating System. Figure 18: Volume distribution for both the fallen (blue) and the potentially unstable (red) blocks for sub-region U4. In general, a detached block descends very rapidly through the air by falling, bouncing and rolling [72], while its trajectory mainly depends on the geometry of the slope. In order to model the trajectory of the falling rocks and design the rockfall barriers, it was necessary to determine some parameters for all four (4) sub-regions. These were the normal and tangential coefficients of restitution (R\({}_{\rm n}\) and R\({}_{\rm t}\), respectively) of the bed rock and each slope's most critical profile. The values of two coefficients of restitution (R\({}_{\rm n}\) and R\({}_{\rm t}\)) for the limestone were based on previous publications, for limestone rocks found in similar geological settings with similar properties as the one in the investigated site, estimated as 0.5 and 0.5, respectively [73]. The critical slope profile for each of the four (4) sub-regions was extracted from the georeferenced 3D point-cloud via CloudCompareTM software v.2.11.1 (Anoia). Specifically, a polyline was constructed for each profile followed the morphology.