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• W2974186908 endingPage "102928" @default.
• W2974186908 startingPage "102928" @default.
• W2974186908 abstract "Abstract Establishing the balance between slurry supporting pressure and expected water-earth pressure is an important criterion to ensure excavating face stability in shield tunneling. To overcome the inaccuracy and hysteresis of manual operations, this paper presents a model predictive control (MPC) system for the slurry pressure balance during construction through effectively regulating the slurry circulation and air pressure holding systems according to geological conditions. The MPC structure consists of a diagonal recurrent neural network (DRNN) that approximates the complex relationship between slurry pressure and tunneling parameters, an optimizer which produces the optimal air pressure and slurry level based on the multi-step ahead predictions, and an evolved particle swarm optimization (EPSO) algorithm. The proposed EPSO can update the structure and weights of DRNN concurrently to better cater to the changeable stratum. The optimizer can excellently compensate the time delays in slurry pressure regulation by incorporating the logical control sequence of actuator systems into the EPSO procedure. The simulation results demonstrated that the presented approach can accurately track the desired water-earth pressure and significantly enhance the robustness of slurry supporting system in tunneling, and the novel EPSO also performed higher convergence speed and precision than the classic algorithms used for comparison." @default.
• W2974186908 created "2019-09-26" @default.
• W2974186908 creator A5021263205 @default.
• W2974186908 creator A5025270532 @default.
• W2974186908 date "2019-11-01" @default.
• W2974186908 modified "2023-10-17" @default.
• W2974186908 title "Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization" @default.
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• W2974186908 doi "https://doi.org/10.1016/j.autcon.2019.102928" @default.
• W2974186908 hasPublicationYear "2019" @default.
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