  Timely and reasonable matching of the control parameters and geological
conditions of the rock mass in tunnel excavation is crucial for hard rock
tunnel boring machines (TBMs). Therefore, this paper proposes an intelligent
decision method for the main control parameters of the TBM based on the
multi-objective optimization of excavation efficiency and cost. The main
objectives of this method are to obtain the most important parameters of the
rock mass and machine, determine the optimization objective, and establish the
objective function. In this study, muck information was included as an
important parameter in the traditional rock mass and machine parameter
database. The rock-machine interaction model was established through an
improved neural network algorithm. Using 250 sets of data collected in the
field, the validity of the rock-machine interaction relationship model was
verified. Then, taking the cost as the optimization objective, the cost
calculation model related to tunneling and the cutter was obtained.
Subsequently, combined with rock-machine interaction model, the objective
function of control parameter optimization based on cost was established.
Finally, a tunneling test was carried out at the engineering site, and the main
TBM control parameters (thrust and torque) after the optimization decision were
used to excavate the test section. Compared with the values in the section
where the TBM operators relied on experience, the average penetration rate of
the TBM increased by 11.10%, and the average cutter life increased by 15.62%.
The results indicate that this method can play an effective role in TBM
tunneling in the test section.
