Examining Feasibility of Developing a Rock Mass Classification for Hard Rock TBM Application Using Non-linear Regression, Regression Tree and Generic Programming

Abstract

Geotechnical and geological parameters have the greatest impact on the performance of hard rock tunnel boring machines (TBMs). This includes the rock and rock mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use rock mass classification systems for improving the accuracy of the machine performance prediction. Rock mass classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), Classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new rock mass classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a rock mass classification system that can be used for TBM applications.