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The decision-making method of tunnel boring machine (TBM) operating parameters has a significant
guiding significance for TBM safe and efficient construction, and it has been one of the TBM tunneling
research hotspots. For this purpose, this paper introduces an intelligent decision-making method of TBM
operating parameters based on multiple constraints and objective optimization. First, linear cutting tests
and numerical simulations are used to investigate the physical rules between different cutting param
eters (penetration, cutter spacing, etc.) and rock compressive strength. Second, a dual-driven mapping of
rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking
is established with high accuracy by combining rock-breaking rules and deep neural networks (DNNs).
The decision-making method is established by dual-driven mapping, using the effective rock-breaking
capacity and the rated value of mechanical parameters as constraints and the total excavation cost as
the optimization objective. The best operational parameters can be obtained by searching for the rev
olutions per minute and penetration that correspond to the extremum of the constrained objective
function. The practicability and effectiveness of the developed decision-making model is verified in the
Second Water Source Channel of Hangzhou, China, resulting in the average penetration rate increasing by
11.3% and the total cost decreasing by 10%.
2. Intelligent decision-making method for TBM operating
parameters
2.1. Dual-driven rock-machine mapping
In this section, we adapt DNN to simulate the rock-machine
mapping model based on physical rules and data mining. TBMs
are typically equipped with hundreds of sensors to monitor the
status of the TBM system and the surrounding environment.