A real-time interaction platform for settlement control during shield tunnelling construction

Xie, Xuansong; Wang, Qiang; Shahrour, Isam; Li, Jun; Zhou, Biao

doi:10.1016/j.autcon.2018.06.012

Cited by 37 publications

(15 citation statements)

References 43 publications

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“…In this study, data gathered from Bai-Cang-Ling Station to the Railway Station (BR section, shown in Figure 7) of Metro Line 1 in Nanning, China, is used [2]. e section consists of 806 rings in total, 1.209 km in length, and is excavated using a Herrenknecht SPB with a diameter of 6.28 m. e excavation was performed from December 2014 to June 2015. e SPB shield machine was designed for the round gravel condition, which is suitable for settlement control in the urban areas.…”

Section: Case Study Of Nanning Metromentioning

confidence: 99%

“…As for the geology, both the spring line tunnel buried depth H s and groundwater table H w interpolated from the borehole data are used. Besides, as the presence of mudstone will severely influence the chamber pressure, the thickness of the, H m , is also included, as shown in equation (2). ese soil parameters are chosen due to the calculation of the slurry pressure in SPB tunneling [34].…”

Section: Plc Datamentioning

confidence: 99%

“…With the growing demand of urban tunneling, mechanized tunneling has become increasingly popular due to its construction efficiency and low ground disturbance [ 1 , 2 ]. Compared to earth pressure balance shield (EPB), slurry pressure balance shield (SPB) is preferred when tunneling in the ground with considerable cobbles and gravels, a common mixed-ground condition encountered when tunneling in southwestern China [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Slurry Pressure Balance with a Long Short‐Term Memory Recurrent Neural Network in Difficult Ground Condition

Wang

Xie

et al. 2021

Computational Intelligence and Neuroscience

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The safety of tunneling with shield tunnel boring machines largely depends on the tunnel face pressure, which is currently decided by human operators empirically. Face pressure control is vulnerable to human misjudgment and human errors can cause severe consequences, especially in difficult ground conditions. From a practical perspective, it is therefore beneficial to have a model capable of predicting the tunnel face pressure given operation and the changing geology. In this paper, we propose such a model based on deep learning. More specifically, a long short-term memory (LSTM) recurrent neural network is employed for tunnel face pressure prediction. To correlate with PLC data, linear interpolation is employed to transform the borehole geological data into sequential geological data according to the shield machine position. The slurry pressure in the excavation chamber (SPE) is taken as the output in the case study of Nanning Metro, which is confronted with the clogging problem due to the mixed ground of mudstone and round gravel. The LSTM-based SPE prediction model achieved an overall MAPE and RMSE of 3.83% and 10.3 kPa, respectively, in mudstone rich ground conditions. Factors that influence the model, including different kinds and length of input data and comparison with the traditional machine learning-based model, are also discussed.

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Section: Case Study Of Nanning Metromentioning

confidence: 99%

Section: Plc Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Slurry Pressure Balance with a Long Short‐Term Memory Recurrent Neural Network in Difficult Ground Condition

Wang

Xie

et al. 2021

Computational Intelligence and Neuroscience

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“…A new architecture of the settlement monitoring system of photogrammetry is designed, as shown in Figure 1, taking the high-speed rail settlement monitoring as an example. The system is designed to consist of a data acquisition system, data-processing and analysis system, and data transmission and storage system [20,21]. Using the pan-and-tilt camera with environmental sensor of the data acquisition system, the images of the barcode installed on the pier can be taken and sent to the data-processing and analysis system.…”

Section: Introductionmentioning

confidence: 99%

Research on the Design and Automatic Recognition Algorithm of Subsidence Marks for Close-Range Photogrammetry

Zou

Guo-jian

2020

Sensors

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In China, traditional techniques for measuring structural subsidence cannot keep pace with the rapid development of critical national infrastructure such as the growing network of high-speed railways. Traditional monitoring methods using leveling instruments are inefficient and time consuming when monitoring structures like bridges and tunnels. Thus, a fast, economical, and more accurate and precise way to survey building subsidence is urgently needed to address this problem. This paper introduces a new close-range photogrammetry technique that deploys a fixed camera with tilt compensator to measure changes in height over small areas. A barcode subsidence mark that can be identified automatically during digital image processing replaces the leveling points used in traditional methods. Four experiments at different locations verified that results from the new method were stable and consistent with total station measurements. This approach is simple, inexpensive, and produces accurate and precise results as our evaluation results show.

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“…However, the movement pattern of actual volume loss is not consistent with the assumed equivalent radial movement pattern in this method and has some deviation. Based on field monitoring data, Loganathan, Jiang, and other researchers [44][45][46][47][48][49][50][51] modified the stratum displacement pattern by exponential function and deduced the prediction formula of surface subsidence under non-equivalent radial movement pattern. However, most of these methods are only suitable for three-dimensional orthogonal plane or two-dimensional cases, which have great defects and need to be improved urgently.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Predicting the Greenfield Stratum Movements Caused by Shield Tunnel Construction

Tan

Zhang

et al. 2019

Applied Sciences

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Shield tunneling is becoming the preferred construction scheme for metro construction because of its advantages of fast construction speed and small disturbance. However, limited by process defects, the stratum movements induced by the construction of shield tunnels still affects the safety of nearby underground structures and aboveground buildings. Therefore, the reliable prediction of stratum movements is important. Described in this paper is an analysis method of the Greenfield stratum movements (Greenfield is an area of land that has not yet had buildings on it, stratum movements means the movement of various soil layers) caused by shield tunnel construction combining an elastic half-space model of mirror source-sink method with the use of modified analytical method. Based on the theoretical formula in this paper, not only can the curve of surface settlement trough be calculated, but also the three-dimensional displacement field of deep soil can be obtained. By comparing vertical and horizontal contour maps of Greenfield stratum movements, good consistency between theoretical formula results and centrifugal test results are shown. This solves the defects and limitations of existing two-dimensional formulas; furthermore, based on this, it is convenient to evaluate the effect on the other skewed underground structures through the elastic foundation beam and other similar methods; therefore, this paper can provide a wide guidance and service for the design and construction of underground engineering in the future.

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A real-time interaction platform for settlement control during shield tunnelling construction

Cited by 37 publications

References 43 publications

Predicting Slurry Pressure Balance with a Long Short‐Term Memory Recurrent Neural Network in Difficult Ground Condition

Predicting Slurry Pressure Balance with a Long Short‐Term Memory Recurrent Neural Network in Difficult Ground Condition

Research on the Design and Automatic Recognition Algorithm of Subsidence Marks for Close-Range Photogrammetry

An Improved Method for Predicting the Greenfield Stratum Movements Caused by Shield Tunnel Construction

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