A new model for predicting the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions

  • Mohammad Hossein Arbabsiar Ph.D. candidate of Mining engineering, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran https://orcid.org/0000-0002-4369-5426
  • Mohammad Ali Ebrahimi Farsangi Associate Professor, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran https://orcid.org/0000-0002-7930-6955
  • Hamid Mansouri Associate Professor, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
Keywords: Advance rate, Regression models, TBM geotechnical risk, Rock engineering systems, Hard rock TBM, Zagros long tunnel

Abstract

The prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most impor- tant concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnical risks (representing media characteristics) in rock TBM tunnelling. Fifteen main parameters that influence the geotechnical hazards were used in the modelling. In establishing an interaction matrix and also a parameter rating, the views of five experts were taken into account. The Vulnerability Index (VI) (geotechnical risk levels) for 2058 datasets out of 2168 sets of data from 53 geological zones in 11 km of the Zagros long tunnel was obtained. In the second step, based on the machine operating parameters such as torque, cutter head rotation per minute, cutter normal force and media characteristics (represented by VIs), which were used as input parameters and advance rate was used as an output parameter, while using 2058 datasets, linear and non-linear multiple regression analyses were carried out. 110 datasets (out of 2168 datasets), which were not used in the modelling, were ap- plied to evaluate the performance of regression models and other models in literature and the results were compared. The obtained results showed that the new linear model proposed with R2=0.83 and RMSE=0.12 has a better performance than the other models.

Published
2020-03-13
How to Cite
Arbabsiar, M. H., Ebrahimi Farsangi, M. A., & Mansouri, H. (2020). A new model for predicting the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions. Rudarsko-geološko-Naftni Zbornik, 35(2). Retrieved from https://hrcak.srce.hr/ojs/index.php/rgn/article/view/10289
Section
Mining