Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data

The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a substantial opportunity for the application of machine learning (ML) to support the decision-making process on-site with timely and meaningful information. The observational method is now well established in geotechnical engineering and has a proven potential to save time and money relative to conventional design. ML advances the traditional observational method by employing data analysis and pattern recognition techniques, predicated on the assumption of the presence of enough data to describe the physics of the modelled system. This paper presents a comprehensive review of recent advances and applications of ML to inform tunnelling construction operations with a view to increasing their potential for uptake by industry practitioners. This review has identified four main applications of ML to inform tunnelling – namely, TBM performance prediction, tunnelling-induced settlement prediction, geological forecasting and cutterhead design optimisation. The paper concludes by summarising research trends and suggesting directions for future research for ML in the tunnelling space.

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“…by Yamamoto et al (2003) and Sun et al (2018b). In particular, Sun et al (2018b) achieved a prediction accuracy of R 2 = 0.8 using RFs.…”

Section: Geological Forecastingmentioning

confidence: 90%

Machine learning to inform tunnelling operations: recent advances and future trends

Sheil

Suryasentana

Mooney

et al. 2020

Proceedings of the Institution of Civil Engineers - Smart Infra

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“…A further consideration is that the different conditions of each tunnelling site complicate the direct comparison of data. Nevertheless, many studies regarding TBM operation have been conducted using machine data accumulated from tunnelling sites, and there always has been demand for large quantities of machine data for TBM operators, engineers, and researchers [4,5].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of EPB Shield Tunnelling with TBM Operational Condition Control Using Coupled DEM–FDM

Lee

Choi

et al. 2021

Applied Sciences

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This study demonstrates a three-dimensional numerical simulation of earth pressure balance (EPB) shield tunnelling using a coupled discrete element method (DEM) and a finite difference method (FDM). The analysis adopted the actual size of a spoke-type EPB shield tunnel boring machine (TBM) consisting of a cutter head with cutting tools, working chamber, screw conveyor, and shield. For the coupled model to reproduce the in situ ground condition, the ground formation was generated partially using the DEM (for the limited domain influenced by excavation), with the rest of the domain being composed of FDM grids. In the DEM domain, contact parameters of particles were calibrated via a series of large-scale triaxial test analyses. The model simulated tunnelling as the TBM operational conditions were controlled. The penetration rate and the rotational speed of the screw conveyor were automatically adjusted as the TBM advanced to prevent the generation of excessive or insufficient torque, thrust force, or chamber pressure. Accordingly, these parameters were maintained consistently around their set operational ranges during excavation. The simulation results show that the proposed numerical model based on DEM–FDM coupling could reasonably simulate EPB driving while considering the TBM operational conditions.

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“…When using the TBM method, it is difficult to observe the tunnel face during the tunnelling process. Besides, it is difficult to obtain the geological conditions during the construction period, so such a method is proposed [1]. Li et al used a digital optical borehole camera to directly observe the gaps in the rock mass through predrilled holes [2].…”

Section: Introductionmentioning

confidence: 99%

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

Qiu

Jian

Cheng

et al. 2021

Advances in Civil Engineering

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The current geological sketch in tunnel engineering is mainly based on sketches of workers. However, geological sketch drawn by workers always offers fundamental data purely due to its drawing mode. A novel drawing method for geological sketch has been introduced using multiview photos in this process. The images of tunnel faces are taken from multiple angles, and every two pictures have overlaps. By measuring the distance between the camera and the tunnel face using a laser range finder, the photographic scale of each photo can be confirmed. SpeededUp Robust Features (SURF) is a good practice for detecting feature points, and the sparse point cloud is reconstructed from multiview photos by structure from motion (SFM). However, the sparse point cloud is not suitable for analysis for structural planes due to its sparsity. Therefore, patch-based multiview stereo (PMVS) is used to reconstruct dense point cloud from the sparse point cloud. After 3D reconstruction, the details of the tunnel face are recorded. The proposed technique was applied to multiview photos acquired in the Xiaosanxia railway tunnel and Fengjie tunnel in Chongqing, China. In order to record the geological conditions of the tunnel face quickly and accurately, Chengdu Tianyou Tunnelkey has developed a set of software and hardware integration system called CameraPad. Besides, CameraPad was used to collect the multiview photos of the tunnel face in the No. 1 Xinan railway tunnel in Jilin, China. By comparing with traditional and existing methods, the proposed method offers a more reductive model for geological conditions of the tunnel face.

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Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data

Cited by 7 publications

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Machine learning to inform tunnelling operations: recent advances and future trends

Machine learning to inform tunnelling operations: recent advances and future trends

Numerical Simulation of EPB Shield Tunnelling with TBM Operational Condition Control Using Coupled DEM–FDM

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

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