Cardu, M.; Catanzaro, E.; Farinetti, A.; Martinelli, D.; Todaro, C. Performance Analysis of Tunnel Boring Machines for Rock Excavation. Appl. Sci.2021, 11, 2794.
Cardu, M.; Catanzaro, E.; Farinetti, A.; Martinelli, D.; Todaro, C. Performance Analysis of Tunnel Boring Machines for Rock Excavation. Appl. Sci. 2021, 11, 2794.
Cardu, M.; Catanzaro, E.; Farinetti, A.; Martinelli, D.; Todaro, C. Performance Analysis of Tunnel Boring Machines for Rock Excavation. Appl. Sci.2021, 11, 2794.
Cardu, M.; Catanzaro, E.; Farinetti, A.; Martinelli, D.; Todaro, C. Performance Analysis of Tunnel Boring Machines for Rock Excavation. Appl. Sci. 2021, 11, 2794.
Abstract
The study takes into account different classes of tunnel boring machines (TBMs), with the aim of identifying correlation models which are meant to estimate, at a preliminary design phase, the construction time of a tunnel and to evaluate the mechanical and operational parameters of the TBMs, starting from the knowledge of the tunnel length and/or the excavation diameter. To achieve this goal, first of all a database was created, thanks to the collection of the most meaningful technical parameters from a large number of tunnels; afterward, it was statistically analysed through Microsoft Excel. In a first phase, forecasting models were identified for the three types of machines investigated, separately for compact rocks (open TBM) and fractured rocks (single and double shield TBM). Then, the mechanical parameters collected through the database were analysed, with the aim of obtaining models that take into account, in addition to the type of TBM, the geological aspect, and the type of rock characterising the rock mass. Finally, the validation of the study was proposed in a real case, represented by the Moncenisio base tunnel, a work included in the new Turin–Lyon connection line. The estimated values were compared with the real ones, in order to verify the accuracy of the experimental models identified.
Keywords
Tunnel boring machine (TBM); correlation models; mechanical and operational parameters; performance prediction; model validation
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
