H. Mohammadi - MSc Student, Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran
M. A. Ebrahimi Farsangi - Assistant Professor of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
R. Rahmannejad - Assistant Professor of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
H. Nezamabadi Poor - Assistant Professor of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

In tunneling, selection of a Tunnel Boring Machine (TBM) is based on the interaction between the rock characteristics and the features of the machine, which is being selected. TBM Advance Rate (TAR) against a particular rock as an economic factor plays a very important role on the selection of a TBM machine. Many factors relevant to the properties of rock, technical specification of TBM and working condition affect on the TBM advance rate. Many work carried out to predict TAR. In this paper an Artificial Neural network (ANN) modeling was adopted. The network used was a RBF, which showed promising results.

TBM, Advance Rate, ANN, RBF