Amirkabir University of Technology Amirkabir Journal of Civil Engineering 2588-297X 49 2 2017 07 23 Introducing New Equation for Predicting Penetration Rate of Tunnel Boring Machine Introducing New Equation for Predicting Penetration Rate of Tunnel Boring Machine 313 322 398 10.22060/ceej.2015.398 FA M. Hosseini Department of Mining Engineering, Imam Khomeini International University, Qazvin, Iran S. J. Hosseini Department of Mining Engineering, Imam Khomeini International University, Qazvin, Iran Journal Article 2014 09 07 Tunnel Boring Machines (TBM) is among the most important machines for tunnel excavation purposes. Evaluation of the performance of these machines for excavation is of special importance due to the high cost of these machines. Prediction of the penetration rate is one of the indicators in evaluation of TBMs. There are various methods and equations for predicting the penetration rate, which are based on parameters related to the rock mass and specifications of the machine, and each of them has its own particular characteristics. Multivariable linear regressions, artificial neural networks, and adaptive neuro-fuzzy inference systems are among the highly efficient modeling and data pattern recognition methods. In this research, some equations have been proposed for predicting the penetration rate in Zagros I Tunnel by employing multivariable linear regression method and by considering the key parameters of the rock mass and the specifications of the TBM; the best equation was selected according to the results of statistical analysis. For verifying the validity of this equation, the penetration rate was calculated at certain parts of Ghomrood Tunnel. In comparison with the real values and results of other models, the outcomes of calculations indicate that predicted values for the penetration rate are of acceptable accuracy. Tunnel Boring Machines (TBM) is among the most important machines for tunnel excavation purposes. Evaluation of the performance of these machines for excavation is of special importance due to the high cost of these machines. Prediction of the penetration rate is one of the indicators in evaluation of TBMs. There are various methods and equations for predicting the penetration rate, which are based on parameters related to the rock mass and specifications of the machine, and each of them has its own particular characteristics. Multivariable linear regressions, artificial neural networks, and adaptive neuro-fuzzy inference systems are among the highly efficient modeling and data pattern recognition methods. In this research, some equations have been proposed for predicting the penetration rate in Zagros I Tunnel by employing multivariable linear regression method and by considering the key parameters of the rock mass and the specifications of the TBM; the best equation was selected according to the results of statistical analysis. For verifying the validity of this equation, the penetration rate was calculated at certain parts of Ghomrood Tunnel. In comparison with the real values and results of other models, the outcomes of calculations indicate that predicted values for the penetration rate are of acceptable accuracy. https://ceej.aut.ac.ir/article_398_b7b16ecf8ca53723593894116071700c.pdf