Existing rock mass classification systems, such as rock mass rating (RMR) are often used in many empirical design practices in rock engineering contrasting with their original application, i.e. estimation of TBM performance in various ground conditions. However, the use of RMR or similar classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters. The results of many investigations on this topic have shown a weak correlation between TBM penetration rate and RMR. This limitation can be addressed by performing regression tree analysis which revises the weights assigned to input parameters to better represent influence of rock mass properties on TBM performance. This paper offers an overview of the impact of rock mass classification on TBM performance and introduces a new model based on regression tree using the input parameters of RMR system to predict the performance of hard rock TBMs. The results of the preliminary analysis show that the use of the proposed model can improve the accuracy of TBM performance estimates in various rock masses. This is based on the comparison between the estimated and actual rate of penetration of TBMs in two tunneling projects in igneous and sedimentary rocks. This study shows the potential of regression tree approach to offer more suitable rating of input parameters for this application, if sufficiently diverse database of machine performance is used in the analysis.
Hard rock tunnel boring has become the preferred method of tunneling for tunnels of various sizes with length over 1.5–2 km due to achievement of higher speed and lower cost. Estimating the performance of TBM is a vital part of tunnel design and selection of the most appropriate machine type and specification. During the past three decades, numerous TBM performance prediction models have been proposed which can be divided into two distinguished approaches, namely theoretical and empirical methods [1]. Currently three different models including Colorado School of Mines or CSM [2] and Norwegian University of Science and Technology or NTNU [3], and field penetration index (FPI) [4] models are the most recognized TBM performance prediction and prognosis models in use around the world.
The CSM model allows calculation of the cutting forces on disc cutters to reach certain penetration into the rock surface with given physical and mechanical properties. This method offers the advantages of being able to consider the detail geometry of the cutterhead (disc diameter, tip width, and spacing). However, the original CSM model does not take the natural discontinues of the rock mass into consideration. Obviously, rock mass characteristics play a great role in determining the cutting speed of the TBM. To address this shortcoming, Yagiz [5] and Ramezanzadeh [6] have offered modifications to the original CSM model to incorporate rock mass properties as input parameters into the model. On the other hand, Bruland [3] updated and improved the NTNU models based on field data originally collected from Norwegian tunnels, and later expanded to other tunneling projects around the world. The NTNU model uses specialized rock drillability/boreability indices such as Drilling Rate Index (DRI) and rock mass properties including joint spacing and orientation to estimate TBM performance. The indices used by NTNU model originated from the drilling experiments and the related tests are only available in specialized rock mechanics laboratories and not very common.
