The design of tunnels must be conducted based on the knowledge of the territory. The longer the structure, the larger the area to be investigated, and the greater the number of surveys and tests to be performed in order to thoroughly examine all the relevant features. Therefore, optimization of the investigation process is strongly required to obtain complete and reliable data for the design of the infrastructure. The fast development of remote sensing technologies and the affordability of their products have contributed to proving their benefits as supports for investigation, encouraging the spreading of automatic or semi-automatic methods for regional scale surveys. Similarly, considering the scale of the rock outcrop, photogrammetric and laser scanner techniques are well-established techniques for representing geometrical features of rock masses, and the benefits of non-contact surveys in terms of safety and time consumption are acknowledged. Unfortunately, in most cases, data obtained at different scales of investigations are only partially integrated or compared, probably due to the missing exchange of knowledge among experts of different fields (e.g. geologists and geotechnical engineers). The authors, after experiencing such a lack of connection among the results of different surveys concerning tunnels, propose a multiscale approach for the optimization of the investigation process, starting from the regional scale, to obtain the data that can be useful not only for planning more detailed surveys in a preliminary phase, but also for making previsions on the discontinuity sets that are present in the rock masses subjected to excavations. A methodological process is proposed and illustrated by means of a case study. Preliminary results are discussed to highlight the potentiality of this method and its limitations.
Umili, G., Bonetto, S., & Ferrero, A. M. (2018). An integrated multiscale approach for characterization of rock masses subjected to tunnel excavation. Journal of Rock Mechanics and Geotechnical Engineering, 10(3), 513–522. https://doi.org/10.1016/j.jrmge.2018.01.007