Abstract In this study, in view of the problems existing in environmental monitoring of urban tunnels, such as im? perfect monitoring schemes, large amount of monitoring data and many factors affecting the data, a collaborative fusion method for tunnel environmental monitoring data based on multi-sensor is proposed to improve the accuracy of environmental monitoring. Firstly, the box plot method is used to detect the abnormal values in the environmental data, and the abnormal values are corrected with the mean substitution method, improving the accuracy of the data;Secondly, data fusion of homogeneous sensors is realized with the adaptive weighted average algorithm, reducing the system redundancy effectively; Finally, data fusion of heterogeneous sensors is realized based on the PSO-BP neural network algorithm. The results show that the accuracy of the BP neural network fusion model optimized by the PSO algorithm in judging the environmental grade of a tunnel is more than 80%, which is better than the traditional BP neural network. It is confirmed that the overall environmental quality of the tunnel can be evaluated more reliably with this method, which provides important decision information for the intelligent control of electromechanical facilities in the tunnel.
Abstract：
In this study, in view of the problems existing in environmental monitoring of urban tunnels, such as im? perfect monitoring schemes, large amount of monitoring data and many factors affecting the data, a collaborative fusion method for tunnel environmental monitoring data based on multi-sensor is proposed to improve the accuracy of environmental monitoring. Firstly, the box plot method is used to detect the abnormal values in the environmental data, and the abnormal values are corrected with the mean substitution method, improving the accuracy of the data;Secondly, data fusion of homogeneous sensors is realized with the adaptive weighted average algorithm, reducing the system redundancy effectively; Finally, data fusion of heterogeneous sensors is realized based on the PSO-BP neural network algorithm. The results show that the accuracy of the BP neural network fusion model optimized by the PSO algorithm in judging the environmental grade of a tunnel is more than 80%, which is better than the traditional BP neural network. It is confirmed that the overall environmental quality of the tunnel can be evaluated more reliably with this method, which provides important decision information for the intelligent control of electromechanical facilities in the tunnel.
MI Chun LI Siying MOU Jiayi YUAN Xiaolong LI Tao
.Study on Collaborative Fusion Method for Urban Tunnel Environmental Monitoring Data Based on Multi-sensor[J] MODERN TUNNELLING TECHNOLOGY, 2023,V60(5): 177-185
2023, Vol. 60 