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.

隧道开挖中岩体的控制参数与地质条件的及时合理匹配对于硬岩隧道掘进机（TBMs）至关重要。因此，本文提出了一种基于开挖效率和成本多目标优化的TBM主要控制参数智能决策方法。该方法的主要目标是获取岩体和机器最重要的参数，确定优化目标，建立目标函数。在本研究中，渣土信息作为重要参数被纳入传统的岩体和机器参数数据库中。岩机交互模型是通过改进的神经网络算法建立的。使用现场收集的 250 组数据，验证了岩机相互作用关系模型的有效性。然后，以成本为优化目标，得到掘进与刀具相关的成本计算模型。随后，结合岩机相互作用模型，建立了基于成本的控制参数优化目标函数。最后在工程现场进行掘进试验，利用优化决策后的主要TBM控制参数（推力和扭矩）对试验段进行开挖。与TBM操作人员依靠经验值相比，TBM平均穿透率提高了11.10%，刀具平均寿命提高了15.62%。结果表明，该方法能在TBM试验段掘进中发挥有效作用。