Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization
This work addresses the challenge of safe and efficient TBM construction for tunneling engineers, representing an incremental improvement by integrating existing methods.
The paper tackled the problem of optimizing Tunnel Boring Machine (TBM) operating parameters by developing a dual-driven method combining physical rules and data mining, which increased the average penetration rate by 11.3% and reduced total cost by 10.0% in a field verification.
The decision-making of TBM operating parameters has an important guiding significance for TBM safe and efficient construction, and it has been one of the research hotpots in the field of TBM tunneling. For this purpose, this paper introduces rock-breaking rules into machine learning method, and a rock-machine mapping dual-driven by physical-rule and data-mining is established with high accuracy. This dual-driven mappings are subsequently used as objective function and constraints to build a decision-making method for TBM operating parameters. By searching the revolution per minute and penetration corresponding to the extremum of the objective function subject to the constraints, the optimal operating parameters can be obtained. This method is verified in the field of the Second Water Source Channel of Hangzhou, China, resulting in the average penetration rate increased by 11.3%, and the total cost decreased by 10.0%, which proves the practicability and effectiveness of the developed decision-making model.