Evaluation of Hard Rock Tunnel Boring Machine (TBM) Performance Using Stochastic Modeling
Jafarshirzad P. Ghasemi E. Yagiz S. Kadkhodaei M.H.
August 2023Springer Science and Business Media Deutschland GmbH
Geotechnical and Geological Engineering
2023#41Issue 63513 - 3529 pp.
Penetration rate is one of the most critical parameters affecting the performance of TBM machines and it is complicated to predict due to uncertainty in excavating operations and geological parameters. In this paper, using Monte Carlo simulation, the penetration rate is predicted, and the sensitivity of the parameters affecting the penetration rate in the TBM machine is evaluated. For this purpose, a database containing in-situ rock properties, rock mass characteristics, and mechanical parameters related to TBM in the Queens Water transfer tunnel has been used. A mathematical model has been developed using principal component analysis to simulate. The results of evaluating the performance of the developed model showed that the model has acceptable performance (VAF = 84.4%, RMSE = 0.03, NSE = 0.82, and R2 = 0.84) and can be used in the development of Monte Carlo simulations. The simulation results showed that during the excavating route, the probability of achieving a penetration rate higher than 2.4 m/h is 90%. Also, the results of the effect of effective parameters on the penetration rate showed that with increasing parameters such as brittleness index (The most effective with a correlation coefficient of 0.528), angle between weakness plane, and tunnel axis, cutter head power, and cutter head torque, the penetration rate increases. Conversely, increasing parameters such as distance between plane of weakness, specific energy, and thrust force (The lowest effect with a correlation coefficient of − 0.13) of the machine has a negative effect on the penetration rate.
Monte Carlo simulation , Principal component analysis , Queens water transfer tunnel , TBM penetration rate
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Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran
School of Mining and Geosciences, Nazarbayev University, Nur-Sultan City, 01000, Kazakhstan
Department of Mining Engineering
School of Mining and Geosciences
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