A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Design of Automatic Monitoring System for Subway Tunnel Based on Deep Neural Network
The stability of the subway structure plays an important role in ensuring the safe passage of the subway, and it is difficult in both the construction process and the subsequent operation stage of the subway tunnel. In order to ensure the safety and durability of subway engineering structures in long-term service, it is necessary to research, develop and apply the structural intelligent health monitoring system, and monitor the safety status during its service in real time in order to avoid major accidents. Taking the automatic monitoring and deformation prediction of tunnel engineering as the research object, this paper puts forward an automatic monitoring algorithm of subway tunnel based on deep convolution neural network (CNN), which preprocesses the automatic monitoring data and analyzes the deformation prediction in the process of crossing the tunnel. The results show that this algorithm is more accurate for the feature recognition of subway tunnel monitoring information, which is 25.88% higher than the traditional algorithm. Constructors can use this model to guide the design and construction of underpass process, which provides guarantee for the smooth construction of subway and the safe operation of high-speed rail, and also provides reference for the optimization of similar projects in the design stage, refined construction management in the construction stage and safety guarantee in the operation stage in the future.
Design of Automatic Monitoring System for Subway Tunnel Based on Deep Neural Network
The stability of the subway structure plays an important role in ensuring the safe passage of the subway, and it is difficult in both the construction process and the subsequent operation stage of the subway tunnel. In order to ensure the safety and durability of subway engineering structures in long-term service, it is necessary to research, develop and apply the structural intelligent health monitoring system, and monitor the safety status during its service in real time in order to avoid major accidents. Taking the automatic monitoring and deformation prediction of tunnel engineering as the research object, this paper puts forward an automatic monitoring algorithm of subway tunnel based on deep convolution neural network (CNN), which preprocesses the automatic monitoring data and analyzes the deformation prediction in the process of crossing the tunnel. The results show that this algorithm is more accurate for the feature recognition of subway tunnel monitoring information, which is 25.88% higher than the traditional algorithm. Constructors can use this model to guide the design and construction of underpass process, which provides guarantee for the smooth construction of subway and the safe operation of high-speed rail, and also provides reference for the optimization of similar projects in the design stage, refined construction management in the construction stage and safety guarantee in the operation stage in the future.
Design of Automatic Monitoring System for Subway Tunnel Based on Deep Neural Network
Zhu, Li (author) / Song, Limin (author) / Kong, Siyang (author)
2023-08-11
832216 byte
Conference paper
Electronic Resource
English
3D deformation monitoring of subway tunnel
| British Library Conference Proceedings | 2009