首页|School of Traffic and Transportation Researchers Update Knowledge of Robotics (3 D Monitoring Model for Real-Time Displacement of Metro Tunnel under 'Dual Carbon ' Background)
School of Traffic and Transportation Researchers Update Knowledge of Robotics (3 D Monitoring Model for Real-Time Displacement of Metro Tunnel under 'Dual Carbon ' Background)
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Researchers detail new data in robotic s. According to news reporting from the School of Traffic and Transportation by NewsRx journalists, research stated, "Real-time automatic displacement monitorin g of metro tunnels is vital for ensuring operational safety and contributes to c arbon reduction goals by improving system efficiency." Funders for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from School of Traffic a nd Transportation: "This study focuses on key monitoring elements such as displa cement, settlement, convergence, and cracking. Through the analysis of continuou s monitoring data, a real-time displacement monitoring model for metro tunnels b ased on robotic total stations is proposed. This model can timely identify poten tial risks, thereby ensuring the safe operation of tunnels and reducing carbon e missions from unnecessary maintenance operations, thereby reducing the carbon fo otprint of metro operations. This article takes the Jinan Metro Tunnel Displacem ent Real-time Monitoring Project in China as a case study and constructs a compr ehensive monitoring framework using robotic total stations, intelligent automate d deformation monitoring data collectors, and cloud servers. The implementation details of the project, displacement monitoring principles, monitoring system co nstruction, and data analysis processes are elaborated in detail. Taking the mon itoring data of Jinan Metro Line 2 from April 1, 2022, to May 31, 2023, as an ex ample, the results show that the tunnel displacement is within the safe range, v erifying the practical application value of the method proposed in this paper."
School of Traffic and TransportationEm erging TechnologiesMachine LearningRoboticsRobots
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