Method for tunnel cross-section deformation monitoring based on distributed fiber optic sensing and neural network

The displacement of the cross section directly reflects the stress state and stability of the surrounding rock and structure, so the monitoring of it is essential during the construction and operation of the tunnel and underground engineering, particularly under the conditions of earthquake and other geological disasters. This paper introduces a new contact tunnel profile monitoring system (TPMS) in detail that uses a tilt sensor and a displacement sensor as data acquisition devices. According to the relation between the sensing physical quantity and displacement change, the displacement calculation formulas of the tunnel cross section measuring points based on the two-dimensional plane coordinate system were deduced, and in order to eliminate the actual installation and positioning deviation of the monitoring system, the method of obtaining the optimal monitoring plane and converting coordinates of the measuring points was proposed, thus establishing the theoretical basis for the application of the TPMS. With the Beishan exploration tunnel (BET) in China as the test platform, the TPMS was successfully applied for long-term monitoring. The application experience showed that the TPMS can realize continuous monitoring, automatic collection and transmission of the monitoring data, remote access whenever necessary, without affecting the transportation in the tunnel, and high accuracy, which reaches 0.01 mm. This system provides a new simple and effective method with good generality and applicability for the deformation monitoring of the tunnel and underground engineering.

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Complex Deformation Monitoring of Shield Tunnel Segment Joints using Distributed Fiber Optic Sensing Technology: Experimental Verification

IEEE Sensors Journal ◽

10.1109/jsen.2021.3139697 ◽

2021 ◽

pp. 1-1

Author(s):

Lei Zhang ◽

Yifei Cui ◽

Bin Shi

Keyword(s):

Experimental Verification ◽

Fiber Optic ◽

Deformation Monitoring ◽

Shield Tunnel ◽

Complex Deformation ◽

Sensing Technology ◽

Fiber Optic Sensing

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A real-time structural parametric identification system based on fiber optic sensing and neural network algorithms

10.1117/12.484060 ◽

2003 ◽

Cited By ~ 2

Author(s):

Zhishen Wu ◽

Bin Xu

Keyword(s):

Neural Network ◽

Real Time ◽

Fiber Optic ◽

Parametric Identification ◽

Identification System ◽

Network Algorithms ◽

Fiber Optic Sensing

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A surveillance system for urban buried pipeline subject to third-party threats based on fiber optic sensing and convolutional neural network

Structural Health Monitoring ◽

10.1177/1475921720930649 ◽

2020 ◽

pp. 147592172093064

Author(s):

Suzhen Li ◽

Renzhu Peng ◽

Zelong Liu

Keyword(s):

Neural Network ◽

False Alarm ◽

Convolutional Neural Network ◽

Surveillance System ◽

Fiber Optic ◽

Third Party ◽

The Third ◽

Time Space ◽

Fiber Optic Sensing ◽

Space Matrix

Third-party threats, such as construction activities and man-made sabotage, have become the main cause of pipeline accidents in recent years. This article proposes a surveillance system for protecting the buried municipal pipelines from third-party damage based on distributed fiber optic sensing and convolutional neural network (CNN). Due to the ability of detecting very small perturbation, the phase-sensitive optical time-domain reflectometry (φ-OTDR) is employed for distributed vibration measurements along the pipelines. A two-layer classifier based on CNN is developed: one layer is used to discriminate the third-party activities from the environmental disturbance; the other is to determine the specific type of the third-party events. Meanwhile, a time-space matrix is introduced to reduce the false alarm and correct possible errors by taking into account the continuity of the signals in time and space. Field tests are carried out to validate the effectiveness of the proposed surveillance system. The recognition results show that the CNN-based classifiers achieve the accuracy of over 97%, which is 14.8% higher than that of the traditional feature-based machine learning method using random forest (RF) algorithm. It also indicates that the time-space matrix can dramatically reduce the false alarm and enhance the recognition accuracy.

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Perimeter monitoring of urban buried pipeline subject to third-party intrusion based on fiber optic sensing and convolutional neural network

Eleventh International Conference on Information Optics and Photonics (CIOP 2019) ◽

10.1117/12.2547712 ◽

2019 ◽

Author(s):

Renzhu Peng ◽

Zelong Liu ◽

Suzhen Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Fiber Optic ◽

Third Party ◽

Buried Pipeline ◽

Fiber Optic Sensing

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Characterizing the Deep Pumping-induced Subsidence Against Metro Tunnel Using Vertically Distributed Fiber-Optic Sensing

10.21203/rs.3.rs-168411/v1 ◽

2021 ◽

Author(s):

Yunxiao Xin ◽

Xiaozhao Li ◽

Wei Zhang ◽

Qi Wang

Keyword(s):

Fiber Optic ◽

Ground Surface ◽

Deformation Monitoring ◽

Surface Settlement ◽

Curvature Radius ◽

Level Measurement ◽

Deformation Amount ◽

Metro Tunnel ◽

Fiber Optic Sensing ◽

Emergency Water Supply

Abstract Continuous pumping of groundwater will induce uneven ground settlement, which may adversely affect the nearby metro tunnels. In this paper, taking Nantong Metro Line 1 crossing Nantong Port Water Plant as an example, the surface level measurement and subsurface deformation monitoring using vertically distributed fiber-optic sensing are implemented to acquire the surface and subsurface settlement of emergency water supply conditions. The fiber optic cable vertically buried in the constant-temperature layer is used to measure the subsurface strain field and deduce the deformation amount of each stratum. The monitoring results show that, during the pumping, the deformation of the aquifer and ground surface is linearly compressed with time; after the pumping, the ground surface continues to settle linearly at a slower rate for about 50 days, followed by a slow linear rebound, and the aquifer is logarithmically rebounded. In addition, deep pumping causes the deformation of the aquifers to be much greater than the surface settlement; the surface settlement lags behind the settlement of the aquifer by 1 to 2 months; the surface rebound recovery also exhibits a similar delay. Fitting models were derived to predict the maximum settlement and curvature radius of the site, which indicates that the adverse effects against the metro tunnel are not negligible once the continuous pumping exceeds 15 days. Those insights can be referred by the practitioners for the control of urban subsidence.

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