When a fire occurs in an underground shield tunnel, it can result in substantial property damage and cause permanent harm to the tunnel lining structure. This is especially true for large-diameter shield tunnels that have numerous segments and joints, and are exposed to specific fire conditions in certain areas. This paper constructs a full-scale shield tunnel fire test platform and conducts a non-uniform fire test using the lining system of a three-ring large-diameter shield tunnel with an inner diameter of 10.5 m. Based on the tests, the temperature field distribution, high-temperature bursting, cracking phenomena, and deformation under fire conditions are observed. Furthermore, the post-fire damage forms of tunnel lining structures are obtained through the post-fire ultimate loading test, and the corresponding mechanism is explained. The test results illustrate that the radial and circumferential distribution of internal temperature within the tunnel lining, as well as the radial temperature gradient distribution on the inner surface of the lining, have non-uniform distribution characteristics. As a result, the macroscopic phenomena of lining concrete bursting and crack development during the fire test mainly occur near the fire source, where the temperature rise gradient is the highest. In addition, the lining structure has a deformation characteristic of local outward expansion and cannot recover after the fire load is removed. The ultimate form of damage after the fire is dominated by crush damage from the inside out of the lining joints in the fire-exposed area. The above results serve as a foundation for future tunnel fire safety design and evaluation.
Flexible joints represent the most vulnerable aspect of the immersed tunnel, necessitating effective waterproofing and the transmission of forces between tunnel segments. However, the role of longitudinal limit devices in the seismic behavior of immersed tunnels is frequently overlooked in contemporary research on their seismic robustness. This study develops a longitudinal force model for flexible joints that incorporates the longitudinal limit device, building upon the beam-spring model of the immersed tunnel. Concurrently, a scaled partial experiment on the immersed tunnel’s flexible joint is undertaken, and validated and compared to the theoretical model. Subsequently, this model is utilized in the seismic assessment of the Ruyifang immersed tunnel. The computational findings revealed a considerable improvement in the seismic resilience of the immersed tunnel following the integration of longitudinal limit devices. With the incorporation of these devices, the opening of flexible joints diminished by 20% to 50% compared to scenarios lacking such devices. In addition, the peak acceleration of the tunnel segments’ mid-point structural response decreased by approximately 50%, accompanied by a significant reduction in the internal force response within the tunnel segments. As proposed in this research, the longitudinal force model for flexible joints under longitudinal limit devices represents the behavior of immersed tunnels under seismic stress more accurately. These numerical simulation outcomes also offer valuable insights for designing flexible joints in immersed tunnels.
Fracture/fault instability induced by fluid injection in deep geothermal reservoirs could not only vary the reservoir permeability but also trigger hazardous seismicity. To address this, we conducted triaxial shear experiments on granite fractures with different architected roughnesses reactivated under fluid injection, to investigate the controls on permeability evolution linked to reactivation. Our results indicate that the fracture roughness and injection strategies are two main factors affecting permeability evolution. For fractures with different roughnesses, a rougher fracture leads to a lower peak reactivated permeability (kmax), and varying the fluid injection strategy (including the confining pressure and injection rate) has a less impact on kmax, indicating that the evolution of permeability during fluid pressurization is likely to be determined by the fracture roughness along the shear direction. Both the fracture roughness and injection strategies affect the average rates of permeability change and this parameter also reflects the long-term reservoir recovery. Our results have important implications for understanding the permeability evolution and the injection-induced fracture/fault slips in granite reservoirs during the deep geothermal energy extraction.
This study aims to develop a rational theoretical model for cutterhead-soil interaction. The cutterhead-soil interaction mechanism is divided into two components: the cutting action of the cutter on the soil and the extrusion of the cutterhead on the soil. By enhancing the Mckyes-Ali model, we analyze and deduce the force state of the cutter during shield tunneling, obtaining a calculation method for determining the force on the cutter. Additionally, we conduct an in-depth analysis of the extrusion effect of the cutterhead on the soil during shield tunneling, utilizing the fundamental solution of the Kelvin problem. Based on these theoretical calculations, we validate the tunneling thrust and cutterhead torque of the shield using our self-developed multi-functional large-scale shield tunneling test platform. The test results demonstrate that the tunneling thrust and cutterhead torque derived from the established cutterhead-soil interaction model in this paper are relatively close to the experimental monitoring values. This provides a theoretical foundation for establishing reasonable shield tunneling loads.
Obtaining a comprehensive understanding of solute transport in fractured rocks is crucial for various geoengineering applications, including waste disposal and construction of geo-energy infrastructure. It was realized that solute transport in fractured rocks is controlled by stochastic discrete fracture-matrix systems. However, the impacts and specific uncertainty caused by fracture network structures on solute transport in discrete fracture-matrix systems have yet not been fully understood. In this article, we aim to investigate the influence of fracture network structure on solute transport in stochastic discrete fracture-matrix systems. The fluid flow and solute transport are simulated using a three-dimensional discrete fracture matrix model with considering various values of fracture density and size (i.e., radius). The obtained results reveal that as the fracture density or minimum fracture radius increases, the corresponding fluid flow and solute transport channels increase, and the solute concentration distribution range expands in the matrix. This phenomenon, attributed to the enhanced connectivity of the fracture network, leads to a rise in the effluent solute concentration mean value from 0.422 to 0.704, or from 0.496 to 0.689. Furthermore, when solute transport reached a steady state, the coefficient of variation of effluent concentration decreases with the increasing fracture density or minimum fracture radius in different scenarios, indicating an improvement in the homogeneity of solute transport results. The presented analysis results of solute transport in stochastic discrete fracture-matrix systems can be helpful for uncertainty management in the geological disposal of high-level radioactive waste.
During the operation of a deep geological repository in crystalline rocks for disposal of high-level radioactive waste, understanding the seepage behaviors of fractured crystalline rocks under coupled thermo-hydro-mechanical conditions is essential for the performance assessment of deep geological repositories. In this study, radial flow tests on cylindrical Beishan granite specimens with a single artificial fracture were conducted using the MTS 815 rock mechanics testing system to investigate the influence of normal stress and temperature on radial flow behaviors of rough rock fractures. Steady state method was used to measure fracture permeability, and an axial extensometer was used to measure fracture deformation during compression. A three-dimensional blue light scanner was used to characterize fracture surface morphology. Experimental results indicate that fracture permeability decreases nonlinearly with the increase of normal stress or temperature, and normal stress has a more significant influence on fracture permeability than temperature. The evolution of three-dimensional non-uniform distribution of voids under compression was numerically obtained, and the variogram was employed to quantify the non-uniform distribution characteristics of mechanical apertures. In addition, a radial flow model considering non-uniform distribution of apertures is proposed to predict the normal stress- and temperature-dependent seepage behaviors of rock fractures, and the predictions were found to be in good agreement with experimental data.
The ability to predict tunnel deformation holds great significance for ensuring the reliability, safety, and sustainability of tunnel structures. However, existing deformation prediction models often simplify or overlook the impact of spatial characteristics on deformation by treating it as a time series prediction issue. This study utilizes monitoring data from the Grand Canyon Tunnel and introduces an effective data-driven method for predicting tunnel deformation based on the spatio-temporal characteristics of the historical deformation of adjacent sections. The proposed model, a combination of graph attention network (GAT) and bidirectional long and short-term memory network (Bi-LSTM), is equipped with robust spatio-temporal predictive capabilities. Additionally, the study explores other possible spatial connections and the scalability of the model. The results indicate that the proposed model outperforms other deep learning models, achieving favorable root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.34 mm, 0.23 mm, and 0.94, respectively. The graph structure based on intuitive spatial connections proves more suitable for meeting the challenges of predicting deformation. Integrating GAT-LSTM with transfer learning technology, remains stable performance when extended to other tunnels with limited data.
The abandoned roadways (ARs) in front of the longwall face catastrophic instability will seriously hamper mining progress, which is a complicated process related to the stress environment, the roadway section, and the mechanical properties of the surrounding rock. The cusp catastrophe theory is employed to establish a state identification model for the irregular coal pillar-roof system (CPRS) formed by the ARs and re-mining entries. To begin, the state discrimination equation (Δp) for the gradual CPRS is derived, and the critical value at which the system transitions into an unstable state under quasi-static conditions is determined. The results indicated that when 16.49 m ≤ L ≤ 22.63 m (L denotes the equivalent span of the intersection roof) and 0 < Re ≤ 2.61 m (Re denotes the width of the elastic zone within the triangular coal pillar), the triangular CPRS is inherently unstable. Similarly, for trapezoidal CPRS configurations where the length Lm (the span of the right-angled trapezoid roof in the propulsion direction) varies from 4.0 to 12.60 m, the system is unstable as well. Subsequently, the model was further enhanced by accounting for the impact of the Pc (advance stress increment load), where a critical criterion for the catastrophic instability of the CPRS was proposed, which represented the external energy required to transition the CPRS from an unstable state to catastrophic instability in different mining stages. After that, the stability degree of the irregular coal pillar was categorized, and a coupling zoning control technology was applied to CPR operations. Field monitoring results demonstrated the effectiveness of the zoning control technology, providing valuable guidance for similar mining practices.
Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.
The mechanical properties of the steel-plate-reinforced segmental lining are generally determined by the load-bearing capacity of reinforced joints. However, there is a lack of valid calculation methods for compression-bending bearing capacity, and researchers mainly rely on experience and analogy for the design of reinforced joints. This paper proposes an analytical model based on the deformation and stress characteristics of the joint surface to calculate the compression-bending capacity of the steel-plate-reinforced joint. After verifying the applicability of this analytical model through finite element simulations, the evalution rules of the load-bearing capacity of the reinforced joint were attained, followed by a quantitative investigation into the influence of joint parameters on it. The results show that: (1) the bearing capacity curve of the reinforced joint under different axial forces can be separated into two parts, with the maximum ultimate bending moment found at the demarcation point, where the steel plate yielding and joint failure occur simultaneously; (2) the steel plate strength and cross-sectional area have a strong influence on the bearing capacity of the reinforced joint when the axial force is under 0.15RFF, where RFF is the axial force at pure-compression failure); (3) the concrete strength and segment width have a prominent influence on the curve when the axial force is over 0.30RFF; (4) the impact of the fictitious strain, bolt strength, bolt diameter, and bolt location on the bearing capacity is minimal in range and amplitude.
Microscopic damage and macroscopic mechanical properties of granite under the coupling effect of thermal load and initial stress are crucial considerations for the safe construction of underground geo-energy engineering. However, visualizing real-time micro-crack processes in rocks under high-temperature and high-pressure conditions using the current experimental techniques remains challenging. In this study, a numerical method is developed to analyze the thermally induced damage in heterogeneous granite under the coupled influence of initial stress and thermal loading. A biaxial thermo-mechanical grain-based model considering real mineral distribution is established based on digital image processing technology, the grain-based modeling method, and heat conduction theory. The microscopic parameters are calibrated and the effectiveness of the model is verified based on thermal shock and uniaxial compression experiments. The thermal destruction mechanism of granite under initial stress from a microscopic perspective was unveiled for the first time. During the thermal shock process, the stress within the rock does not remain constant at the initial stress value. Instead, it changes continuously with the progression of heat conduction. The impact of the initial stress on the thermally induced cracks is relatively minor. Cooling causes more damage to the rock than heating during thermal shock. The intragranular cracks of quartz consistently outnumber other intragranular or intergranular cracks during thermal shock. The initial stress and thermal shock damage enhance and weaken the biaxial peak strength of granite, respectively. The weakening effect of thermal shock on the peak strength becomes more pronounced at a higher initial stress. These research findings and proposed research techniques contribute to the management and optimization of underground geo-energy engineering.
To explore the load-bearing performance and the failure patterns of the lining structures, a full-scale loading test on the three-ring staggered assembled shield tunnel segments is carried out through a hydraulic loading system. In the experimental study, the segments’ internal force, convergence deformation, and displacement, and the bolts’ internal force, are analyzed. According to the experimental results, the relationship between internal force and deformation is obtained to determine the residual bearing capacity of the shield tunnel at each stage. Three stages are specified for the evolution of the segment’s maximum bending moment during the loading process, in which, the elastic stage is the main and longest stage, in which the bending moment of the segment increases the most. There are two stages for convergence deformation development and segment misalignment development. At the end of loading, the segment’s maximum positive and negative convergence values reach 61.22 and −57.69 mm, respectively. Besides, the maximum segment misalignment is 3.67 mm, which occurs in the direction of 90°. The segment cracks when its maximum convergence value reaches 25.03 mm. Moreover, there are signs of fracturing on the waist joint of the segment when its maximum convergence value reaches 32.73 mm. The concrete at the waist joint starts fracturing in pieces when the segment’s maximum convergence value reaches 38.93 mm, which is defined as the type of shear failure. Finally, the bearing capacity of shield tunnels during segment failure period can be evaluated by using the corresponding relationship between deformation and internal force.
High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection. However, due to the curvature of the tunnel, there are variations in object distance that exceed the depth of field of the lens, resulting in uneven defocus blur in the captured images. This can significantly affect the accuracy of defect recognition. While existing deblurring algorithms can improve image quality, they often prioritize results over inference time, which is not ideal for high-speed tunnel image acquisition. To address this issue, we developed a lightweight tunnel structure defect deblurring network (TSDDNet) for curved-tunnel line scanning with wide-angle lenses. Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time. The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). A raw dataset containing tunnel blur images was created using a high-resolution line scan camera and used to train and test our model. TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888, while using one-third of the parameters of comparable alternatives. Moreover, our method exhibited a higher computational speed than that of conventional methods, with inference times of 8.82 ms for a single 512 × 512 pixel image patch and 227.22 ms for completely processing a 2048 × 2560 pixel image. The test results indicated that the structural scalability of the network allows it to accommodate large inputs, making it effective for high-resolution images.
As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substantial threat. This study introduces a novel framework known as “robust mobility deformation detection” (RM2D), designed for real-time tunnel deformation detection. RM2D employs mobile LiDAR scanner to capture real-time point cloud data within the tunnel. This data is then voxelized and analyzed using covariance matrices to create a voxel-based multi-distribution representation of the rugged tunnel surface. Leveraging this representation, we assess deformations and scrutinize results through machine learning models to swiftly pinpoint tunnel deformation locations. Extensive experimental validation confirms the framework’s capacity to successfully detect deformations, including floor heave, side rib spalling, and roof fall, with remarkable accuracy. For deformation levels at 0.15 m, RM2D was able to successfully detect deformations with an area greater than 2 m2. For deformation areas of (3 ± 0.5) m2, RM2D successfully detected deformations of levels at (0.05 ± 0.01) m, and its detection capability meets the standard criteria for mining tunnel deformation detection. When compared to two conventional methods, RM2D demonstrates its real-time deformation detection capability in complex environments and on rough surfaces with precision, all at speeds below 10 km/h. Furthermore, we evaluated the predictive performance using multiple evaluation metrics and provided insights into the decision mechanism of the machine learning employed in our research, thereby offering valuable information for practical engineering applications in tunnel deformation detection.
Multi-layer linings have been widely used in deep rheological soft rock tunnels for the excellent performance in preventing large-deformation hazards. Previous studies have focused on the bearing capability of multi-layer lining, however, its failure characteristics and synergistic load-bearing mechanisms under high geo-stress are still unclear. To fill the gap, three-dimensional geomechanical model tests were conducted and synergistic mechanisms were analysed in this study. The model test was divided into normal loading, excavating, and overloading stages. The surrounding rock deformation was monitored by using an improved high-precise extensometer measurement system. Results show that the largest radial deformation appears on the sidewall, followed by the floor and vault during the excavating stage. The relative convergence deformation of sidewalls springing reaches 1.32 mm. The failure characteristics of the multi-layer linings during the overloading stage undergo an evolution of stability, crack initiation, local failure, and collapse, with a safety factor of 1.0-1.6, 1.6-2.0, and 2.0-2.2, respectively. The synergistic load-bearing mechanism analysis results suggest that the early stiffness and late yielding deformation capacity of large deformation support measures play important roles in stability maintenance both in the construction and operation of deep soft rock tunnels. Therefore, the combination of yielding support or a compressible layer with reinforced support is recommended to mitigate the effect of the high geo-stress.
Shield tail grouting is an important measure to control tunnelling-induced ground deformation by injecting prepared grouting materials to fill the tail gap. The working performance of grout is usually invisible and hard to obtain in construction. This paper carries out an experimental study to investigate the tail grout behavior in ground. In the current research, a testing device is developed to explore the grout behavior in varying soils. The grout working performance is evaluated not only by the liquid grout properties such as fluidity, consistency, bleeding rate, stone rate and compressed deformation but also solid grout properties such as unconfined compressive strength and permeability. Three typical grouts are chosen and their behaviors in the various soils are observed. To take an insight on the behaviors, scanning electron microscopy and mercury intrusion porosimetry analysis are employed. The microstructure of solid grout is a sign of its working performance. The observation shows that the solid grout micro-structure is influenced by grout proportions, pressure, and ground permeabilities. The experimental results are applied in the case of Beijing Metro Line 12 for validation and as a result, the ground movement is inhibited due to high performance of tail grout.
The prediction of rock cutting force is critical for tunnel boring machine performance and cutterhead design. This paper presents a novel model for rock cutting force prediction based on the Colorado School of Mines (CSM) model, which incorporates the installation position of disc cutters by introducing installation radius and synergistic effect factors. Linear cutting tests in the laboratory and large-scale rotary cutting simulations in MatDEM software were conducted to examine the impact of these factors. Results indicate that the normal and rolling forces increase and stabilize as the installation radius increases. The synergistic effect produces three force modes in a cutting circle, with mode α having the largest cutting force, mode β having a smaller force, and mode γ having the smallest force. The impact of installation radius and synergistic effect varies with rock-cutter parameters. Multiple regression analysis was used to determine the introduced factors. The proposed model was validated with rock strength and operation data from the Irtysh River conveyance project. The results demonstrate that the proposed model outperforms the CSM model in predicting cutting force in field conditions.
Earth pressure balance machine (EPBM) operation is sensitive to the properties of the excavated soil due to the requirements of proper soil conditioning and maintenance of necessary chamber pressure. However, soil properties are invariably only available at a limited number of borehole explorations and soil samplings conducted during the subsoil investigation. Thus, it is crucial to identify properties of the tunnel excavation face, such as clay-sand mixed conditions, grain size distributions, and clogging potential along the whole alignment beside the few borehole locations to attain optimally efficient EPBM operation. Therefore, this paper presents the development of machine learning prediction models (i.e., classifiers and regressors) to estimate the properties of the tunnel excavation face using EPBM operational data collected during the tunneling operation as input features. Geotechnical data collected from boreholes and soil samples can be used to validate prediction models and calibrate them. To develop such models, the Northgate Link Extension (N125) tunneling project, constructed in Seattle, Washington, the USA, is used to capture and identify the true ground conditions. The results indicate successful prediction performances by the models, providing indication that EPBM parameters are crucial pointers of the tunnel excavation face properties to help attain optimally efficient EPBM operation.