Study of TBM Performance Prediction Using Rock Mass Classification

"Rock mass classification systems are often applied in many empirical design practices in rock engineering, some contrasting with the original intent and applications of these classification systems, for example, estimation of TBM performance in various ground conditions. While accurate estimation of machine performance is significantly impacted by rock mass properties, parameters used in many of the existing classifications are more related to ground support design and not rock mass boreablity. The results of many investigations on this issue have shown a weak correlation between TBM rate of penetration and rock mass classification. This limitation can be overcome by fine tuning the rock mass classification input parameters to represent influence of rock mass properties on TBM performance as has been the objective of RME or QTBM. This paper will offer an overview of the impact of rock mass on TBM performance and introduces an empirical equation for predicting performance of rock TBMs based on rock mass classification. The results of the preliminary analysis of a new model has revealed that the use of the proposed system can offer a reasonable accuracy for estimation of TBM performance in various rock masses. 1. INTRODUCTION Hard rock tunnel boring has become more or less the standard method of tunneling for tunnels of various sizes with lengths over 1.5–2 km. Estimating the performance of TBM is a vital phase in tunnel design, and for the choice of the most appropriate excavation machine. During the past three decades, numerous TBM performance prediction models for evaluation of TBM rate of penetration and advance been proposed. In brief, all the TBM performance prediction models can be divided into two distinguished approaches, namely theoretical and empirical ones (Rostami et al., 1996). The most common and recent works on this topic are summarized in Table 1. Currently, two models including Colorado School of Mines or CSM (Rostami and Ozdemir, 1993; Rostami, 1997) and Norwegian University of Science and Technology or NTNU (Blindheim, 1979; Bruland, 1998) models are the most recognized TBM performance prediction and prognosis models in use around the world."