Prediction of optimum TBM penetration strategy with minimum energy consumption in hard rocks

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It is significant to set proper penetration in TBM projects to improve tunneling efficiency, but there is still a lack of optimized penetration strategies. TBM drivers tend to tune the operating parameters to achieve maximum penetration, leading to severe cutter wearing in hard rock tunneling. Field observations suggest that the cutter wearing is highly correlated with the tunneling specific energy (SE). This work proposes a quantitative optimum penetration prediction model based on the optimum SE principle. A series of linear cutting machine (LCM) tests and numerical simulations were conducted. The comparison between the LCM tests and the numerical simulations validates that the adopted numerical model is suitable for generating synthesized LCM test results. As a result, an optimum TBM penetration strategy dataset was established with a collection of LCM tests from the literature and numerical simulations. A regression model that correlates the optimum penetration to different rock masses is derived via the dataset and has been successfully applied to a tunnel project. The laboratory LCM tests, numerical simulations, and field observations suggest that an optimum penetration exists at a fixed cutter spacing. The proposed model can overcome the TBM drivers’ subjectivity and provide a quantitative prediction for the optimum penetration strategy in hard rocks.