Wireless sensing and prediction method for convergence deformation of mountain tunnels

WANG Tian-ning; WANG Li-ning; XUE Ya-dong; ZHANG Yue; ZHANG Dong-ming; HUANG Hong-wei

doi:10.11779/CJGE2020S1044

Volume 42 Issue S1

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Chinese Journal of Geotechnical Engineering > 2020 > 42(S1): 224-228. > DOI: 10.11779/CJGE2020S1044

WANG Tian-ning, WANG Li-ning, XUE Ya-dong, ZHANG Yue, ZHANG Dong-ming, HUANG Hong-wei. Wireless sensing and prediction method for convergence deformation of mountain tunnels[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(S1): 224-228. DOI: 10.11779/CJGE2020S1044

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Wireless sensing and prediction method for convergence deformation of mountain tunnels

1.
College of Civil Engineering, Tongji University, Shanghai 200092, China
2.
China Construction Communications Engineering Group Corp. Ltd., Beijing 100142, China

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Received Date: June 01, 2020
Available Online: December 07, 2022

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Abstract

Abstract

Mastering the deformation laws of the surrounding rock during tunnelling is the key to the safety of construction. At present, the deformation detection of the tunnel section in the drilling and blasting method is mostly based on the total stations. Nevertheless, the amount of monitoring data applied in the detection was difficult to complete the fine analysis of convergence deformation. The wireless wensors network (WSN) is employed to realize the long-term continuous monitoring of key regions of the rock tunnel. Meanwhile, by setting up WSN monitoring equipment based on micro-electrical mechanical system sensors, a dynamic risk management and control system platform during tunnel construction based on Web is developed in Yingpanshan tunnel, Yunnan, China. Thus, the continuous monitoring of the deformation at the key region of the tunnel is realized. In addition, in accordance with the time series of deformation, the convergence deformation value of tunnel section is then predicted more accurately by the long short-term memory network. In summary, the performance of practical engineering application proveds the effectiveness of the proposed method.
- mountain tunnel,
- convergence deformation,
- wireless sensor network,
- long short-term memory network

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