Abstract
In the context of tunnel excavation with Tunnel Boring Machines (TBM), the possibility of quickly determining, on site and with low-cost tools, both the grain size curve and the volume of the excavated muck, can support the correct setting of the machine and the possible reuse of the material itself for applications such as structural concrete for tunnel linings, road embankments and fillings and so on. For this purpose, unconventional data acquisition methodologies were investigated, such as photogrammetry and LiDAR sensors mounted on smartphones, since they are time-effective from a processing point of view and can be easily adapted and modulated with relatively low budgets. In particular, for the determination of the grain size curve, methods already present in the state-of-the-art were tested, such as BASEGRAIN (expeditiously studied for river pebbles with rounded shapes and not with sharped edges like those of this study), and artificial intelligence techniques for automatic segmentation of rocks from images (Mask R-CNN). For the Volumetric estimation tests were conducted both on the entire sample and on individual grains in order to correctly estimate the volume of the whole excavated sample. The results showed how it is possible to obtain with these techniques on one hand a proper approximation of the granulometric curve, fully comparable with laboratory data, and on the other hand cumulative curves.