Tunnel Monitoring Research Articles

Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model

Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model

Fractional Derivative-based Burger Creep Model for Soft Rocks and its Verification Using Tunnel Monitoring Results and Experimental Data

AbstractConsidering the creep behavior of soft and weak rocks is critical for analyzing the long-term stability of underground constructions. This paper introduces a novel creep constitutive model to characterize the creep behavior of rocks under uniaxial and triaxial stress states. The fractional derivative Abel dashpot was used to improve the Burger model, and a viscoplastic component was added in series with the modified Burgers model to replicate the tertiary phase of rock creep. The effectiveness of the model was verified using creep test data from various soft rocks and monitoring measurements from a tunnel excavated in heavily jointed weak rock masses. Furthermore, a sensitivity analysis was carried out to assess the impact of the model parameters on creep deformation, and a comparative study was performed to evaluate the efficacy of the suggested model in modeling the accelerated stage of rock creep compared with some existing models. The strong agreement observed between the calculated results and both the creep test data and tunnel monitoring measurements underscores the accuracy and validity of the proposed model. The comparative analysis further revealed that the proposed model offers the highest fitting efficiency for describing the tertiary stage of rock creep. These findings suggest that the model effectively captures the creep behavior of rocks and precisely represents the entire creep process.

Tunnel Cross-Section Deformation Monitoring Based on Mobile Laser Scanning Point Cloud.

Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section's condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data.

A spatiotemporal recurrent neural network for missing data imputation in tunnel monitoring

A spatiotemporal recurrent neural network for missing data imputation in tunnel monitoring

A Novel FBG Placement Optimization Method for Tunnel Monitoring Based on WOA and Deep Q-Network

By employing the whale optimization algorithm’s (WOA) capability to reduce the probability of being stuck in a locally optimal solution, this study proposed an improved WOA-DQN algorithm based on the Deep Q-Network algorithm (DQN). Firstly, the mathematical model of Fiber Bragg Grating (FBG) sensor placement was established to calculate the reward of DQN. Secondly, the effectiveness and applicability of WOA-DQN were validated through experiments in nine cases. It indicated that the algorithm is far superior to other methods (Noisy DQN, Prioritized DQN, DQN, WOA), especially with the learning rate of 0.001, the initial noise 0.4, the hidden layer 3–512, and the updated frequency of 20. Finally, the FBG sensors were placed at [0°, 27°, 30°, 47°, 51°, 111°, 126°, 219°, 221°, 289°] to detect the accurate deformation of the tunnel with the maximum error 8.66 mm, which is better than the traditional placement. In conclusion, the algorithm provides a theoretical foundation for sensor placement and improves monitoring accuracy. It further shows great promise for deformation monitoring in tunnels.

Failure Mechanism and Control Technology of the Primary Support for Asymmetric Deformation Disaster of Deep Tunnel

Asymmetric large deformation disasters inevitably occur when highway tunnels cross active fault zones. Relying on the Nanlangshan No.1 Tunnel Project, the mechanism of asymmetric deformation is analyzed based on the in-situ engineering geology and tunnel monitoring data, and numerical calculations are used for verification. Based on the results of asymmetric large deformation disaster analysis, the control scheme of double-layer primary support is proposed, and the design is optimized with numerical calculation before engineering practice. The conclusions are as follows: 1. The original primary support program cannot withstand the asymmetric surrounding rock pressure, and the slip of the weak interlayer in the engineering geology is the main reason for the asymmetric deformation disaster in the tunnel. 2. According to the results of the numerical calculations, the tilted weak interlayer greatly reduces the stability of the surrounding rock, which leads to the local bending damage in the left shoulder of the tunnel, and the local shear deformation damage in the right arch girdle. 3. When using the double-layer primary support program, the appropriate delay of support timing for the second layer of primary support can effectively decrease the tunnel asymmetric deformation. However, if the second layer of primary support lag distance is too large, it will also lead to tunnel deformation convergence larger, or support failure and other issues. So, it is recommended that the second layer of primary support lags behind the first layer of primary support by 2 m to be applied.

Study on InSAR deformation information extraction and stress state assessment in a railway tunnel in a plateau area

Introduction: The study proposes a method for evaluating stress distribution in high-altitude Tibetan Plateau railway tunnels using high-precision radar satellite time-series interferometric synthetic aperture radar technology.Methods: To effectively monitor and prevent geological hazards during the construction process, this method i employed, as it serves as a component of advanced geological prediction and surrounding rock deformation monitoring technology for high-altitude tunnels, particularly in the Dongelu Tunnel of the China–Tibet Railway. The study utilizes time-series interferometric synthetic aperture radar to obtain deformation information for Dongelu Tunnel area between 2022 and 2023 from Sentinel- 1A orbit images. This quantitatively investigates the upper mountain body and line-of-sight direction along the tunnel. The deformation characteristics are correlated with high-frequency and high-precision automated vertical displacement monitoring results, determining the spatiotemporal distribution of tunnel deformation. In this study, a model that determines the vertical stress state of the Dongelu Tunnel under loading near the entrance and evaluates its health status was established.Results: The results show that the surface deformation of the mountain above the tunnel axis develops slowly and is relatively small, with a maximum vertical deformation rate of 1–3 mm/year. The average stress on the tunnel arch is 5.54 MPa, with a fluctuation range of 0.01 MPa. Temporal Q9 changes in various parts of the tunnel are periodic, with maximum fluctuations observed in December 2022. The study reveals inconsistent surface settlement of the tunnel arch and mountain above it, causing minor vertical stress changes. As the tunnel construction progresses, vertical stress variation shows periodicity because of an initial imbalance in internal stress within the mountain. Stress fluctuations near the tunnel entrance occur during the initial excavation phase, gradually diminishing as the project progresses and internal stress stabilizes.Discussion: The proposed tunnel monitoring and stability assessment method can reduce its impact on engineering construction and provide guidance for advanced geological prediction.

Comprehensive calibration and laboratory validation of a micro electromechanical system sensor-based flexible inclinometer

Abstract Real-time monitoring of slopes, tunnels, and dams is important for ensuring the long-term stability and reliability of such structures. Despite the successes of current technologies in many applications, a gap still exists in certain areas such as precision in steep slopes and complex soil conditions. This study has designed a flexible inclinometer based on an array of Micro Electromechanical System (MEMS) sensors to enhance the accuracy and flexibility of existing monitoring techniques. The inclination angle of each flexible inclinometer measurement unit was measured to monitor the horizontal or vertical displacement of the target structural body. We used the Levenberg–Marquardt (LM) algorithm for optimizing the MEMS sensors based calibration and designed multiple experiments to test the accuracy of the proposed method. Experimental results show that the calibrated flexible inclinometer measurement unit has an inclination angle of less than 0.04° and the accuracy of the flexible inclinometer lies within ±0.4 mm in the horizontal attitude and 1.6 mm in the vertical attitude. Our research has developed a novel tool for geotechnical engineering monitoring that can aid in increasing the precision of real-time assessment and prediction of structural stability.

A Developed Tunnel Ventilation System Modeling for an Intelligent Transportation System

This paper presents a Laplace transform model for an urban tunnel ventilation system. This model allows one to witness higher performance for supervisory control and data acquisition (SCADA) in terms of monitoring and control of an urban area tunnel based on measurement systems. This proposed model illustrates the ventilation control system framework as well as the emergency response system for urban area tunnels such that smoother controllability and higher security in the operation of tunnels can be envisioned. The salient contributions of this work can be stated as a novel method for modeling tunnel ventilation systems and the implementation of an emergency response plan for a futuristic intelligent transportation system. The simulation results exhibit that the proposed model outperforms the ventilation system in the high-density traffic jams and further the efficient operation of the tunnel. Likewise, comparison results and experimental results are addressed to emphasize the validation of this method and to be helpful in proving the reliability of the results obtained in this study. These results show that the ventilation control system reaches the desired CO value either in high-traffic volume conditions or in normal traffic conditions.

Deformation of Existing Shield Tunnel Adjacent to Deep Excavations: Simulation and Monitoring Analysis

Deep excavations near subway tunnels can induce deformation, necessitating a comprehensive investigation into causal factors and mitigation strategies. Field measurements were conducted to assess both vertical and horizontal displacements of existing tunnels near a deep excavation in Shenzhen. Utilizing a validated three-dimensional finite element model that considers structure−strata interactions, this study analyzes tunnel displacements, ground movements, diaphragm wall impacts and the sensitivity of enclosure structure parameters. The results indicate that tunnel deformation correlates with enclosure structure deformation, particularly near the center of the pit. Moreover, shallow soil excavation significantly affects the vertical displacement of shallow-buried tunnels. However, the design parameters of the existing enclosure structures inadequately limit tunnel displacement. Therefore, it is crucial to intensify vertical displacement monitoring in shallow tunnels during early excavation stages and to enhance horizontal displacement monitoring during later phases. Implementing measures such as optimizing central support design or retaining soil at the pit bottom helps control maximum horizontal displacement. While support stiffness plays a greater role than retaining wall thickness, its impact on deep excavation projects is limited.

Temperature insensitive distributed wide-dynamic-range strain sensing based on polarization-maintaining photonic crystal fiber

In this paper, a temperature insensitive wide-dynamic-range distributed strain sensor with centimeter-level spatial resolution based on optical frequency domain reflection technology is proposed. It adopts a polarization-maintaining photonic crystal fiber as the sensing fiber, and experimental results demonstrate that its temperature sensitivity is much smaller than that of the standard polarization-maintaining fiber. Benefited from the specially designed cross-correlation algorithm, the sensor achieves the maximum measurable strain reaches 7000 μϵ with spatial resolution of 1.66 cm. This provides a new approach to address current issues with special fiber sensors, such as single-point sensing and low spatial resolution, which shows great significance for structural health monitoring of buildings, bridges and tunnels with urgent requirement of large dynamic range.

Full-Section Deformation Monitoring of High-Altitude Fault Tunnels Based on Three-Dimensional Laser Scanning Technology.

In traditional tunnel monitoring, the characteristic points of an object within a tunnel are measured to obtain information about the object. Considering the limitations of the traditional method in measuring the complex surface structure of tunnels, such as limited monitoring points, a long measurement period, and low precision, this study introduces an approach that uses three-dimensional (3D) laser scanning for monitoring tunnel cross-section deformation. Using this approach, the soft surrounding rock of a high-altitude ultralong tunnel was taken as the monitoring object. The test tunnel was first scanned using a 3D laser scanner, and the collected data were processed. The internal structural data of the tunnel were subsequently compared with its actual contour lines and the data of its primary branch and secondary lining on different dates. The results indicate that the arch roof of the tunnel tended to be stable within a certain time range when the positions of the primary branch and secondary lining were at different measuring points with different pile numbers. The deformation of the pile number on the left and right sides did not generally exceed 0.02 m, except at a few measuring points. A comparison between the actual cross section of the initial branch and that of the designed section showed that the actual elevation of the arch of the initial branch of the tunnel was greater than its designed elevation by no more than 0.3 m. Hence, through this study, a convenient and practical method is presented for monitoring deformation in complex curved tunnel structures.

Construction monitoring and analysis of low mountain ridge tunnel

In order to obtain the change rule of surrounding rock structure displacement and supporting structure internal force with time during the construction of the low mountain ridge tunnel, this paper relies on the Xishan Tunnel Project as the background. During tunneling, the displacement around the tunnel, the subsidence of the surface, the internal force of the steel arch and the pressure between the two layers of support are monitored dynamically. According to the above monitoring and measurement data, and the monitoring data analysis and nonlinear regression fitting, the predicted trend curve is obtained, the displacement change rules and characteristics of various surrounding rocks of the tunnel are obtained, to ensure the construction safety and stability requirements of supporting structure, and to provide a reasonable opportunity for the construction of secondary lining.

Numerical Simulation Study on Deformation Mechanism of Tunnel–Landslide Orthogonal Systems and Early Warnings of Imminent Sliding

This paper takes a tunnel through a landslide in Northwest China as an example, constructs a mechanical model of a tunnel–landslide orthogonal system, and explores the deformation mechanism of a tunnel–landslide system and the technology of early warnings of near-slips. Given the problem that it is difficult to accurately monitor the deformation and damage characteristics of the tunnel–landslide system using conventional methods, FLAC3D was used to analyze the deformation mechanism of the tunnel–landslide orthogonal system and numerical simulation of the NPR constant resistance large deformation anchors for the early warning of near-slips. Based on the strength reduction method, by reducing the mechanical parameters of the shear strength of the slip zone and simulating different degrees of landslides, we obtained the change rules of the displacement and the axial force of the NPR constant-resistance large deformation anchor cable in the tunnel–landslide orthogonal system, established the warning mode of the Newtonian force tunnel–landslide orthogonal system, and successfully issued a near-slip warning in actual engineering applications. The above research is of great significance to the stability monitoring and risk assessment of tunnel–landslide systems.

A method for monitoring the uneven settlement of shield tunnels considering the flattening effect using distributed strain data measured from BOTDA sensors

When investigating the uneven settlement monitoring of shield tunnels, the influence of the flattening effect under longitudinal bending is rarely considered, which leads to inaccurate and incomplete settlement monitoring. To address this issue, a method for monitoring the uneven settlement of shield tunnels that considers the flattening effect is proposed, which is achieved using high-density strain data from distributed Brillouin optical time domain analysis (BOTDA) sensors. The high-density measured strain data is used to combine global settlement with the additional influence of the flattening effect, which includes extrusion and shear effects. This approach allows the trend of uneven settlement to be effectively monitored. Furthermore, a finite element model updating process is implemented in advance to improve the accuracy of soil parameters and minimize calculation errors in practical engineering. To demonstrate the effectiveness of the proposed method, an application example from the Jinan Rail Transit Line 3 was utilized for numerical simulations and practical tests. The results of the application example reveal that the proposed method has a high accuracy, with measurements comparable to the total station-based method. This indicates that the proposed method is an effective and robust settlement monitoring approach and provides a referential basis for further tunnel operations and maintenance.

Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction

In the Republic of Korea, large-scale deep excavation construction is being conducted adjacent to structures owing to overpopulation in urban areas. Securing the safety of earth-retaining and underground structures is crucial for adjacent excavation work in urban areas. Accordingly, an automated measurement system is used to monitor the behaviors of subway tunnels and track structures; however, the utilization of its results is extremely low. Existing techniques evaluate the safety of track and tunnel structures based on the measured maximum values. This study introduces an evaluation technique for improving behavior monitoring in subway tunnels and track structures. A substantial amount of long-term measurement data on subway tunnels and track deformation was quantitatively evaluated using the Gaussian probability density function. In addition, the results from the same location where the tunnel convergence meter (TL) and track bed settlement (RM) sensors were located were compared. The comparison results revealed a difference in the vertical displacement of the tunnel and track structure owing to adjacent excavation work. A technique to analyze the continuous behavior of the tunnel was presented by numerically analyzing the tunnel measurement results as input data. In addition, they emphasized the need for simultaneous monitoring of tracks and tunnels.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Mechanical Behavior Monitoring and Load Inversion Analysis of Large-Diameter Underwater Shield Tunnel during Construction.

The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.

Adaptive Control of Underwater Tunnel Monitoring Robot Based on IoT and Fuzzy Neural Network Algorithm

Abstract To improve the navigation ability of underwater tunnel monitoring robots at fixed distances, directions, depths, and heights and to improve the accuracy of tunnel monitoring, an adaptive control method for underwater tunnel monitoring robots based on the Internet of Things (IoT) and fuzzy neural network algorithms is proposed. The structure of underwater tunnel monitoring robots is analyzed based on the IoT, the convolutional neural network algorithm is used to extract the tracking target characteristics of the underwater tunnel monitoring robot, and the obstacle avoidance process of the underwater tunnel monitoring robot is analyzed. The membership degree of the input variable is calculated by the fuzzy control algorithm. The control rule optimizes the neural network algorithm, obtains the target characteristics displayed by the visual tracking of the underwater tunnel monitoring robot based on the fuzzy neural network, uses the adaptive control to estimate the optimal parameters, and finally obtains the adaptive sliding mode control of the underwater tunnel monitoring robot. The experimental results show that the proposed method can accurately realize the target tracking task of the underwater tunnel monitoring robot and has better obstacle avoidance ability.

Southbank Place development: tunnel movement predictions and monitoring performance

The Southbank Place project comprised the redevelopment of the UK headquarters of the Shell Company (formerly known as the Shell Centre) in the Waterloo area of London. The project included the demolition of all structures within the site except for the iconic tower building, local extension of the existing basement and the construction of eight new mixed-use buildings. London Underground's (LU) Bakerloo Line and Northern Line running tunnels pass beneath the site. The excavation of the basement and construction of the Shell Centre in the late 1950s caused movements of the underlying Bakerloo Line tunnels that were much larger than expected. The monitoring of these tunnels between 1958 and 1995 has provided a classic case history for the behaviour of London Clay. The proposed redevelopment offered an opportunity to analyse and monitor the behaviour of LU tunnels over a period of 60 years and two phases of significant construction. This paper presents a back-analysis of the historical monitoring of the Bakerloo Line tunnels and the calibration of a sophisticated finite-element model. The model was subsequently used to predict the impact of the redevelopment on the tunnels as part of the assurance procedure with LU.