Tunnel Deformation Monitoring Research Articles

Research on prediction of surrounding rock deformation and optimization of construction parameters of high ground stress tunnel based on WOA-LSTM

To accurately understand the deformation behavior of surrounding rock in the jiugongshan No.2 high ground stress tunnel and optimize construction parameters for improved efficiency, on-site monitoring was used to gather data on rock deformation and initial support contact pressure. A WOA-LSTM regression prediction model was proposed, and excavation advance depth was optimized using numerical simulation. The WOA-LSTM regression prediction model demonstrated high accuracy in tunnel deformation monitoring. The absolute errors between predicted and actual values of tunnel settlement and convergence at various measurement points average 2.53− 04 mm and 1.96− 04 mm, with relative errors averaging 2.15% and 2.34%. These results meet the requirements for guiding construction. Additionally, based on the results of numerical simulation calculations, when the step length is 12 m during the construction of high-stress tunnels using the benching method, excavation advances of 2 m and 3 m result in settlement and convergence increases of 35.1% and 63.4%, and 25.5% and 55.2%, respectively, compared to an excavation advance of 1 m. However, no sudden jumps in these values were observed, indicating that with a 3 m excavation advance, the integrity of the surrounding rock remains in good condition, effectively guiding safe and rapid construction methods.

Tunnel full-space deformation measurement based on improved downsampling and registration approach by point clouds

To fully leverage the volume measurement advantage of three-dimensional laser scanning technology in tunnel deformation monitoring, a full-space deformation analysis is proposed. A Box Improved Moving Average (BIMA) method featured by the adaptive window size downsampling is proposed which aims to achieve varying levels of downsampling in different densities regions. the combination of Maximal Cliques (MAC) and Iterative Closest Point (ICP) is utilized to achieve accurate registration. Point normal information is utilized to determine the correspondence to calculate deformation which shows an average accuracy of 7 % higher than the ICP by a convergence measurement test. The deformation convergence accuracy can go beyond 76 % when the point cloud reserved rate is approximately 10 %. Comparison between the method proposed in this study and the traditional point cloud cross sectional fitting method shows that the discrepancy between these two methods is approximately within 5 mm, with a minimum value of only 0.15 mm.

Integrated early warning and reinforcement support system for soft rock tunnels: A novel approach utilizing catastrophe theory and energy transfer laws

The initial support strength of deep-buried soft rock tunnels during excavation is often inadequate, and the timing of reinforcement support is inappropriate. This inadequacy leads to significant deformations in the tunnel, causing the energy released by the surrounding rock to exceed the energy that the support system can absorb, ultimately resulting in tunnel collapse. Tunnel deformation monitoring and anchorage support, when combined with energy transfer laws, are thus imperative for studying collapse prediction, the timing of reinforcement support, and parameters of the reinforcement support system of the surrounding rock. In this paper, a novel approach for early warning and support control in tunnel construction facing large deformations is introduced, and a predictive model for surrounding rock instability utilizing catastrophe theory and energy transfer laws is developed. By analyzing the correlations of rock deformation and energy dissipation, the timing of anchor reinforcement support was determined using function fitting techniques. Further, an energy transfer model under reinforced anchoring support was established, and the energy absorption parameters of the support body were determined. Also, the support parameter design scheme was proposed. The results indicate that the early warning and reinforcement support scheme proposed in this paper reduces the release of potential energy by 35.71 %; decreases the number of steps needed to reduce kinetic energy to zero by 66.67 %; reduces the total dissipated energy in joint shear by 33.68 %; increases the total stored strain energy in the material by 5.70 %; and reduces the total dissipated work in plastic deformation by 26.15 %. Additionally, this plan effectively controls the originally predicted large deformation area of the meter level to within 350 mm.

Real-time deformation monitoring of large diameter shield tunnel based on multi-sensor data fusion technique

The measurement of the shield tunnel deformation is an important tool to ensure structural integrity. The structural “deformation” of the tunnel is not merely a simple geometric deformation, which includes the elastic deformation of the segments and the rigid deformation caused by the opening and misalignment of the joints between the segments. Therefore, it is hard to monitor the deformation only relying on a single device. In this study, a data-fusion computing algorithm based on a fusion of rotation and strain measurements is proposed to improve the accuracy of tunnel deformation monitoring without the need of reference frame. SmartRock sensor was utilized to record rotation and the strain change of tunnel segments. Kalman filter was used as the fusion technique. The algorithm was successfully applied in the project of Jinan Yellow River tunnel, China. The result was compared with the result obtained by laser scanner and shows good consistency.

A Method for Convergent Deformation Analysis of a Shield Tunnel Incorporating B-Spline Fitting and ICP Alignment

The application of three-dimensional laser scanning technology in the field of tunnel deformation monitoring has changed the traditional measurement method. It provides an automated and intelligent solution for monitoring the geometric deformation of tunnel sections due to its high efficiency and independence from environmental influences. In this paper, based on B-spline fitting and iterative nearest point (ICP) alignment, the calculation of the difference between the radial distance and the design radius of a tunnel is transformed into a curve transformation that iterates over the nearest-neighbor points and calculates the difference in the distance between the corresponding points. The innovation of this paper is that the high-precision tunnel deformation monitoring method integrating B-spline fitting and ICP alignment can automatically compensate for the missing point clouds, is not affected by the point clouds of the tunnel inner and outer liner appendages, is more sensitive in the local deformation feedback and can be applied to a variety of tunnel shapes. The results indicate that our method maximally improves the accuracy of the horizontal convergence calculation by 28.6 mm and the accuracy of the vault settlement by 27.8 mm in comparison with the least squares circle fitting algorithm.

A novel high-precision photogrammetric technique for monitoring subway tunnel deformation

This paper presents a low cost and high precision tunnel deformation monitoring system based on network camera photogrammetry. The system uses digital image technology to analyze and process the reflective mark of the tunnel structure monitoring section in real time, enabling real-time monitoring and early warning of deformations in the tunnel structure. This paper focuses on how to effectively improve the monitoring accuracy of image analysis technology in photogrammetric system, specifically by achieving a deformation accuracy of 0.1 mm through the use of images with a resolution of 1 mm/pix. The robustness of the analysis algorithm and methods of optimizing and reducing the computational efforts of the algorithm for real-time analysis are also discussed. The effectiveness of these methods is verified through the deformation monitoring of the Tianjin subway tunnel.

Experiment and Application of NATM Tunnel Deformation Monitoring Based on 3D Laser Scanning

In recent years, 3D laser scanning technology has been applied to tunnel engineering. Although more intelligent than traditional measurement technology, it is still challenging to estimate the real-time deformation of NATM tunnel excavation from laser detection and ranging point clouds. To further improve the measurement accuracy of 3D laser scanning technology in the tunnel construction process, this paper proposes an improved Kriging filtering algorithm. Considering the spatial correlation of the described object, the optimization method of point cloud grid filtering is studied. By analyzing the full-space deformation field of the tunnel lining, the deformation information of the measuring points on the surface of the tunnel lining is extracted. Based on the actual project, through the on-site monitoring comparison test, the three-dimensional laser point cloud data are grid processed and analyzed, and the deformation data obtained from the test are compared with the data measured by traditional methods. The experimental results show that the Kriging filtering algorithm can not only efficiently identify and extract the tunnel profile visualization data but also efficiently and accurately obtain the tunnel deformation. The measurement results obtained by using the proposed technology are in good agreement with those obtained by using traditional monitoring methods. Therefore, tunnel deformation monitoring based on 3D laser scanning technology can better reflect the evolution of the tunnel full-space deformation field under certain environmental conditions and can provide an effective safety warning for tunnel construction.

Study on stress variation of advance fiberglass anchor bolts during tunnel excavation process

One of the main causes for excessive deformation within a tunnel is due to the instability of the soil or soft rock ahead of the excavation face. Fiberglass bolts have been shown to be a useful advance reinforcement method for the excavation face. In this paper, an improved ADECO-RS (Analysis of controlled deformation in rock and soils) method had been proposed for soft rock mountain tunnels, in terms of the partial (mainly the upper bench) excavation face reinforcement and also for the bench excavation method. Strain gauges were used to test the micro-strain in the fiberglass bolt to investigate how the axial force of the fiberglass bolt varied during the tunnel excavation. In addition, combined with the field tunnel deformation monitoring data, the relationship between the reinforcement parameters of the fiberglass bolts and the tunnel construction phase were discussed. The research results show that: (1) The stress state of the anchor rod is related to the reinforcement length of the anchor rod; (2) Excavation within the lap area of the fiberglass bolt leads to an increase in the axial force of the bolt, while excavation outside the lap area of the fiberglass bolt has no effect on the anchor; (3) Reducing the reinforcement area of rock mass will affect the stability of the excavation. To ensure the stability of the excavation face, the initial support construction loop should be completed as soon as possible; (4) In a future project with similar conditions, the recommended lap length of the fiberglass bolt could be 3 m utilizing the fiberglass bolt grouting face reinforcement method.

Automatic extraction of tunnel centerline and cross-sections from 3D point clouds

The accurate extraction of tunnel centerline and tunnel cross-section plays a vital role in many tunnel deformation monitoring projects. To address this issue, An automatic data extraction method for a tunnel centerline and cross-section is proposed based on the three-dimensional laser scanning data. A k-dimensional tree index is first constructed based on the horizontal projection of the tunnel point clouds, and then a series of seed points is selected as the center of several neighborhood point sets with different radii; the difference set region, which will be used to calculate the tunnel horizontal centerline point based on the convex hull operation, is obtained. Then, according to the horizontal centerline, the cross-section of the tunnel point clouds is extracted and fitted to obtain the tunnel cross-section parameters and three-dimensional centerline. Based on the design values of the three-dimensional centerline and tunnel radius, the overall model of the tunnel can be constructed and applied to the visual analysis of tunnel deformation. The experimental results show that the proposed method can accurately obtain the tunnel centerline and cross-section parameters, with a minimal 0.011 cm error for the extraction of the horizontal centerline. The proposed tunnel model could simulate the undulation of the tunnel surface well and provide reference values for similar cases.

Application of Mobile 3D Laser Scanning Technology in Metro Tunnel Deformation Monitoring

Application of Mobile 3D Laser Scanning Technology in Metro Tunnel Deformation Monitoring

Anomaly detection in long-term tunnel deformation monitoring

Tunnels are critical infrastructure that require a careful monitoring of their structural health. This paper explores the use of novelty detection algorithms for long-term tunnel monitoring projects. The algorithms are applied to the monitoring of the Waasland tunnel in Antwerp, where the deformations and temperatures have been monitored over a period of one year. The case demonstrates that novelty detection by means of principal component analysis enables the identification of minor changes in the tunnel response, and can therefore be embedded in an early detection warning system. An additional numerical analysis based on simulation data further demonstrates that this approach requires a sufficiently long training period, in order to enable distinguishing between regular deviations in the tunnel response and anomalies.

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

Tunnel deformation monitoring is an important process for ensuring the safety of the tunnel structure. This study presents a method for sensing tunnel cross-section deformation based on distributed fiber optic sensing and a neural network. To verify the feasibility of the proposed method, a laboratory test is conducted on a tunnel model made of polymethyl methacrylate. The test results confirm that the proposed method can accurately obtain the deformation of the tunnel structure, with a maximum deviation of 5.7%. Based on this result, a field application trial is conducted in a section of an actual subway tunnel to further verify the effectiveness of the method. The field observation result confirms the feasibility of applying the proposed method to tunnel cross-section deformation monitoring.

Systematic Approach for Tunnel Deformation Monitoring with Terrestrial Laser Scanning

The use of terrestrial laser scanning (TLS) point clouds for tunnel deformation measurement has elicited much interest. However, general methods of point-cloud processing in tunnels are still under investigation, given the high accuracy and efficiency requirements in this area. This study discusses a systematic method of analyzing tunnel deformation. Point clouds from different stations need to be registered rapidly and with high accuracy before point-cloud processing. An orientation method of TLS in tunnels that uses a positioning base made in the laboratory is proposed for fast point-cloud registration. The calibration methods of the positioning base are demonstrated herein. In addition, an improved moving least-squares method is proposed as a way to reconstruct the centerline of a tunnel from unorganized point clouds. Then, the normal planes of the centerline are calculated and are used to serve as the reference plane for point-cloud projection. The convergence of the tunnel cross-section is analyzed, based on each point cloud slice, to determine the safety status of the tunnel. Furthermore, the results of the deformation analysis of a particular shield tunnel site are briefly discussed.

Health Monitoring System with Hybrid Laser Sensor Networks and Cloud Computing for Subway Tunnels

The settlement and deformation monitoring for existing tunnels would encounter the difficulties in sensor installation and health evaluation in real time. Thus, it is necessary to develop effective methods for settlement and deformation measurements in the existing tunnels. This study proposed a more practical tunnel deformation monitoring theory and developed remote wireless tunnel deformation real-time monitoring and early warning system based on infrared laser ranging technology and cable sensing technology. The system could monitor the settlement and deformation of the tunnel in real time from a long distance and transmit the deformation data wirelessly in real time; then, three-dimensional space model of tunnel deformation was established, which intuitively and comprehensively reflected the actual deformation of the tunnel and intelligently identified the deformation. Finally, based on the practice of deformation control during tunnel excavation at Xiaohongshu Station of Wuhan Metro Line 8 of China, the measurement results of this new method were compared with the measurement results of particle image velocimetry technology (PIV), the reliability of the new tunnel deformation monitoring method was proved, the settlement error of each monitoring point was found to be less than 1 mm, and the deformation trend was consistent with the actual deformation. The results of this research could provide useful guidance for tunnel deformation monitoring.

Development of Optic‐Electric Hybrid Sensors for the Real‐Time Intelligent Monitoring of Subway Tunnels

The settlement and deformation monitoring of subway tunnels had difficult in long‐distance and real time measurement. This study proposed an optic‐electric hybrid sensor based on infrared laser ranging technology and cable‐sensing technology. The working principle, hardware layer, design details, laboratory calibration and field validation were presented and discussed. The optic‐electric hybrid sensor implemented the real‐time intelligent analysis modulus for the whole system which could analysis the measurement errors and improve the accuracy. The laboratory calibration tests were carried out and the results shown that the hybrid sensors had measurement resolution of 1 mm with the maximum measurement range of 100 m. A remote real‐time intelligent monitoring system is established based on the hybrid sensors. The system contains an edge computing module, real‐time communication module and warning light signal with three colors. The stability of data acquisition and transmission of the intelligent control monitoring system under long‐term conditions was examined. Test results shown that the system was quite stable for the long‐term measurement. The whole system was verified in a constructing subway tunnel of Wuhan Metro Line 8, China. According to the field monitoring results, the deformations and the state of health safety of the tunnel was evaluated. The results of this study could provide useful guidance for tunnel deformation monitoring and has great practical value in civil engineering.

Study on monitoring error of distributed optical fiber using fixed-point layout for tunnel deformation monitoring

Study on monitoring error of distributed optical fiber using fixed-point layout for tunnel deformation monitoring

Research on the Layout and Data Processing Method of Distributed Optical Fiber in Shield Tunnel Monitoring

With the increase of subway service time, shield tunnels will produce different degrees of damage and destruction. This is related to external factors such as differences in stratum soil, groundwater seepage, construction disturbance to soil, and operating vehicle load. With the increase of subway service time, shield tunnels will produce different degrees of damage and destruction. This is related to external factors such as differences in stratum soil, groundwater seepage, construction disturbance to soil, and operating vehicle load. In order to ensure the operation safety of the subway, long-term and large-scale safety condition monitoring of the shield tunnel is very important. This study summarizes the technical characteristics, layout methods, data collection and processing of distributed optical fiber in shield tunnel deformation monitoring. The feasibility of distributed optical fiber monitoring technology is verified through the segmental deformation monitoring project of Beijing Metro Airport Line and Line 10 shield tunnel. In addition, some experiences in fiber layout and data processing are summarized, which has reference value for similar projects.

Research on tunnel engineering monitoring technology based on BPNN neural network and MARS machine learning regression algorithm

Tunnel engineering is affected by a variety of factors, which results in large detection errors in tunnel engineering. In order to improve the monitoring effect of tunnel engineering, based on BPNN and MARS machine learning regression algorithm, this research constructs a tunnel engineering monitoring and prediction model. Moreover, the gray residual BP neural network designed in this study uses a series combination, and the residuals obtained from the gray model are used as the input data of the BP neural network, and the output of the combined model is used as the prediction result. By applying the monitoring data of the convergence of the upper surrounding of the tunnel surface section and deformation of the arch subsidence, it is verified that the proposed method based on the combined model of BPNN and MASR can predict and analyze the tunnel deformation monitoring data very well.

Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models

Correctly distinguishing microseismic and blasting events in underground excavations is fundamental to subsequent geophysical analysis activities such as rock burst early warning and tunnel deformation monitoring. Conventional distinguishing approaches such as waveform analysis require operating personnel strong physical and signal processing knowledge for predetermining man-crafted identification criteria, which may not achieve decent recognition accuracy since the collected signals are always varied. In addition, increasingly growing monitoring data cause conventional methods to be time-consuming and inefficient. Based on these considerations, machine learning approach was introduced to efficiently and accurately distinguishing microseismic/blasting events without developing explicit identification instructions for signals. This study investigated the performances of ten frequently-used machine learning models for microseismic/blasting events recognition. A complete identification process encompassing feature selection, model training, hyperparameter tuning was demonstrated to achieve ten models' precedence measured by five selected performance indicators. Then, models' performances were comprehensively considered by a fuzzy evaluation model to generate final conclusions: The Logistic regression model yielded the best performance for microseismic/blasting events identification while the Gaussian process model did the worst. The results gained from this study should serve to encourage machine learning in microseismic/blasting events identification, as well as give geological engineers reliable prior knowledge in model selection.

Construction Monitoring and Numerical Simulation of Water-Rich Tuff Soft Rock Tunnel

With the rapid development of China’s high-speed railway construction, many difficulties have been encountered, and many difficulties and challenges have been encountered. Usually, there are safety accidents such as excessive deformation of the surrounding rock that causes serious intrusion, and even serious flooding, which not only restricts the construction period, Caused significant economic losses. The main reasons for these problems are insufficient understanding of the deformation mechanism of tunnel surrounding rocks, especially water-rich soft rock tunnels, and the lack of targeted control measures. This article takes the widely distributed tuffs in Zhejiang as an example. The tunnel deformation monitoring and measurement work can be used to grasp the dynamic evolution law of the surrounding rock. After the tunnel is excavated, the surrounding clearance of the surrounding rock and the settlement of the vault are determined. The key basis of process accuracy, the monitoring results are used to guide the next stage of construction and predict the deformation law of the surrounding rock in front of the palm face. At the same time, a finite element numerical model is established based on the surrounding rock structure of the tunnel to analyze the displacement and stress changes of the surrounding rock during the tunnel excavation process.