Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

Abstract

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 parameters (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 revolutions 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%.

Keywords

• TBM operating Parameters
• Rock-machine mapping
• Intelligent decision-making
• Multi-constraints
• Deep learning