Intelligent decision method for main control parameters of tunnel boring machine based on multi-objective optimization of excavation efficiency and cost

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