Tunnel Surrounding Rock Deformation Research Articles

Research on mechanical response and time-space distribution of supporting structure of deep-buried tunnel in naturally water-rich loess

When the water content is small, the strength and stiffness of loess is considerably high. But with the increasing of water content, the shear strength of loess decreases since the matric suction of unsaturated loess decrease. If loess is soaked in water for long time after reaching the saturated state and the linking intensity weakened, the loess would be prone to softening deformation, so as to the stress of the tunnel supporting structure, which will cause the decreasing of the stability and durability of the tunnel. Therefore, the in-situ monitoring of surrounding rock deformation and stress characteristics of supporting structure is very important and was carried out in the deep-buried tunnel in naturally water-rich loess in this paper. The time–space effect of tunnel surrounding rock deformation, mechanical response of primary support, and secondary lining were monitored and analyzed. The results indicated that the deformation of surrounding rock was quite large, and the reserved settlement was insufficient. The analysis and calculation of the surrounding rock pressure and the stress of the steel arch indicated that the primary supporting structure of the upper bench was unsafe, the arch-foot had a trend of bending deformation to the side of the tunnel′s inner contour, and the inverted arch had a trend of uplift. Through the analysis of the contact pressure and the stress of the secondary lining reinforced concrete, the sidewall had a trend to deform to a side of the tunnel′s inner contour due to squeezing of the surrounding rock. The inverted arch had a trend to uplift, indicating the significant change and redistribution of the groundwater and surrounding rock stress field. The stress and deformation of supporting structure were highly complex both in space distribution and time development. The research results could provide specific reference and guidance for the design and construction of deep-buried tunnels in naturally water-rich loess.

Study on mechanical properties of composite support structures in TBM tunnel under squeezing soft rock conditions

As more and more TBM tunnels are constructed under complex conditions such as high stress and fault fracture zones, large deformation disasters of squeezing soft surrounding rocks occur frequently. However, TBM method shows poor adaptability to major engineering challenges. Equipment damage, track deviation and support structures failure are widely observed. It is important to propose an effective support scheme and to fully clarifying the mechanical properties of the support structures in TBM tunnel. In this case, this paper aims to conduct laboratory and numerical tests considering steel–concrete interface bond-slip behavior so as to verify the effectiveness of the proposed support scheme. Thus, the quantitative relationship between arch type, arch spacing, longitudinal connection strength, concrete strength, steel fiber content, and tunnel surrounding rock deformation can be obtained. The research results show that the support scheme proposed in this paper shows good applicability for deformation control of TBM tunnels under squeezing soft rock conditions, which provides a reference for TBM tunnel support under squeezing soft rock conditions.

A surrogate model based on deep convolutional neural networks for solving deformation caused by moisture diffusion

Moisture diffusion is a common phenomenon in geotechnical engineering, and its induced deformation seriously affects the stability of the engineering structure, such as embankment slope instability and tunnel surrounding rock deformation. Numerical simulation is an effective method for moisture diffusion-deformation coupling computation, but it has a large computational cost and a high learning threshold for ordinary engineers. In this paper, a surrogate model based on deep convolutional neural networks is presented for moisture diffusion-deformation coupling computation. First, designing the neural network structure includes the dense blocks and transition layers, and hyperparameters. Then, the moisture diffusion-deformation coupling model in the finite discrete element method (FDEM) software package MultiFracS is used to obtain the high-fidelity simulation data for the moisture diffusion-deformation examples. The simulation data and the key parameter (elastic modulus and Poisson's ratio) are processed into the image data structure (matrix) for training the surrogate model. Finally, the root means square error and the correlation coefficient are used to evaluate the effectiveness of the surrogate model. The results reveal that, rather than taking several hours to run a numerical model, the surrogate model only takes a few seconds to obtain the deformation and stress under a given moisture field and material parameters, which significantly improves prediction efficiency. Using this surrogate model, the engineers can obtain the deformation law just only modifying key parameters. Moreover, the surrogate model can be packaged into a mobile app to provide support for rapid decision-making on the project site.

Construction and Application of LSTM-Based Prediction Model for Tunnel Surrounding Rock Deformation

Tunnel surrounding rock deformation is a significant issue in tunnel construction and maintenance and has garnered attention from both domestic and international scholars. Traditional methods of predicting tunnel surrounding rock deformation involve fitting monitoring and measuring data, which is a laborious and resource-intensive process with low accuracy when predicting data with significant fluctuations. A deep learning approach can improve monitoring efficiency and accuracy while reducing labor costs. In this study, taking an actual tunnel project as an example, a long short-term memory (LSTM) network model was constructed based on the recurrent neural network algorithm with deep learning to model and analyze the tunnel monitoring and measurement data, and the model was used to analyze and predict the vault settlement of the tunnel. LSTM is a type of artificial neural network architecture that is commonly used in deep learning applications for sequence prediction tasks, such as natural language processing, speech recognition, and time-series forecasting. In predicting data with smaller fluctuations, the maximum error is 4.76 mm, the minimum error is 0.03 mm, the root mean square error is 2.64, and the coefficient of determination is 0.98. In predicting data with larger fluctuations, the maximum error is 8.32 mm, the minimum error is 0.13 mm, the root mean square error is 4.42, and the coefficient of determination is 0.88. The average error of the LSTM network model is 2.16 mm. With the growth of the prediction period, the prediction results become more and more stable and closer to the actual vault settlement, which provides a reliable reference for introducing the LSTM prediction method with deep learning to tunnel construction and promoting tunnel construction safety.

Influence Analysis of Rock Mass Mechanical Properties on Tunnel Rock Stability Based on Sensor Data Integration

This study takes the multisensor monitoring data of tunnel support structure and surrounding rock mechanical properties during TBM construction as the research object, integrates the monitoring data of multisensor through an intelligent optimization algorithm, and explores the self-consistent fusion characteristics between the multisensor structural mechanical properties and the field monitoring data. At the same time, the support structure parameters and construction progress in the tunnel construction process are dynamically adjusted and scientifically integrated through the information feedback of monitoring data. The research results are as follows: (1) The tunnel excavation rate is different under different rock strengths and surrounding rock levels. The overall performance is that the harder the surrounding rock, the lower the surrounding rock level, and the faster the tunnel excavation rate. The relationship between the FPI parameters of surrounding rock and the deformation of tunnel surrounding rock is closer, and the correlation coefficient reaches 98%. The mechanical properties of the surrounding rock have the greatest influence on its deformation. (2) The rotation speed, thrust, and torque of TBM machinery as well as the surrounding rock workability index and the tunnel surrounding rock deformation maintain a good fitting relationship, with a fitting rate of more than 85%. After the improved PSO-BP algorithm is used to calculate and fit the four parameters of torque, rotational speed, thrust, and surrounding rock drivable coefficient during TBM mechanical construction, the fitting accuracy is higher than 94%. (3) At the same time, according to the analysis of the fitting results, the fitting effect of TBM mechanical thrust is the best among the four-parameter algorithm analysis fitting degrees, which is 99.3%. (4) The measured points are evenly distributed near the surrounding rock deformation surface calculated by the improved PSO-BP algorithm, and the fitting variance is 0.972. With the tunnel excavation, the settlement deformation of surrounding rock first increases rapidly, and then, the settlement deformation enters a slow growth stage and finally reaches a relatively stable state. (5) After the jam occurs, the tunnel is expanded from the telescopic shield or tail shield to the outer side of the shield to reduce the contact area between the shield and the surrounding rock to reduce the extrusion of the surrounding rock pressure on the shield.

Application and Comparison of Machine Learning Algorithms for Predicting Rock Deformation in Hydraulic Tunnels

Prediction of tunnel surrounding rock deformation is important for tunnel construction safety evaluation. In this paper, machine learning algorithms are used to carry out a comparative study of the surrounding rock deformation prediction. The applications of Gaussian process regression (GPR), support vector machine (SVM), and long short-term memory network (LSTM) in the prediction of surrounding rock deformation sequences are compared and analyzed. The actual data of a diversion tunnel in a southwest region are used as an example to evaluate and compare the single-step prediction model and multistep prediction model established by the above algorithm. The results show that the machine learning algorithm has good operation effect on the prediction of surrounding rock deformation. Overall, the SVM model has the best prediction effect and outperforms the other two algorithms in terms of tracking the trend of data changes and the degree of data fit.

Deformation Prediction of Tunnel-Surrounding Rock Considering the Time Effect of the Viscosity Coefficient: A Case of an NATM-Excavated Tunnel

Considering engineering problems such as complicated stress and the difficulty in controlling large deformation while a tunnel passes through a soft rock stratum, a theoretical prediction model of convergence deformation of tunnel-surrounding rock is proposed. Based on the longitudinal displacement profile curve reflecting the “space effect” of the excavation surface, the Hoek formula with better applicability was introduced to analyze and theoretically deduct the “time–space effect comprehensively.” By taking the influence of the “time effect” coefficient into account, an improved Nishihara model was established to derive the analytical equation of the viscoelastic–viscoplastic convergence of surrounding rock. Taking the Dingxi Tunnel of Wujing Expressway in Hunan Province, the physical and mechanical parameters of surrounding rock in the tunnel were firstly determined then they were used to calculate and predict the vault down of three typical sections with the scoping equation of surrounding rock deformation. Based on the calculation results, the causes of the differences between the measured and theoretical values were analyzed; moreover, it is indicated that the minimum root-mean-square error is 1.68, the minimum average error is 1.3%, and the correlation coefficient is 0.99. The comparison shows that the theoretical prediction results agree well with the corresponding field test results. The improved Nishihara model can accurately predict the final deformation of the surrounding rock. Simultaneously, it is also proved that the relevant parameters and the hypothesis and correlation of the nonlinear viscosity coefficient equation are reasonable, with particular effectiveness and applicability in practical engineering. This study is significant for further studying the tunnel-surrounding rock’s stability and accumulating theoretical and practical experience to develop rheological theory.

Evaluation and Deformation Control Study on the Bias Pressure of Layered Rock Tunnels

In the process of tunnel construction, the bias of layered rock mass tunnels has always been a prominent problem that troubles the construction and safe operation of tunnels. In this paper, a comprehensive method that combines monitoring technology and discrete element (3DEC) numerical simulation is proposed to analyze the deformation characteristics of the surrounding rock in the layered rock tunnel and the deformation law of the bias tunnel. The results indicate that the tunnel surrounding rock deformation in the study area showed the characteristics of bias. Based on the bias mechanism, the surrounding rock deformation law, the construction deformation control, and the optimization measures of layered rock mass in the bias tunnel were studied by means of combining monitoring technology with discrete element (3DEC) numerical simulation. Based on the research results, appropriate methods for controlling the deformation of the surrounding rock of the tunnel with comprehensive consideration of the anchor rod length, anchor rod angle, and anchor rod layout spacing were proposed. The method proposed in this paper could visually reveal the deformation characteristics of the surrounding rock of layered rock tunnels and the deformation law of bias tunnels. It could also better solve the problem of deformation control in the tunnel construction process. This approach provides a novel idea for special layered rock mass tunnel bias evaluation and deformation control parameter optimization and serves as a valuable reference for analogous engineering cases through engineering case analysis.

Reconstitution method for tunnel spatiotemporal deformation based on 3D laser scanning technology and corresponding instability warning

The three dimensional deformation and corresponding temporal evolution of surrounding rock is a key information for tunnel stability analysis and safety warning. To reconstruct the 3D deformation process of tunnel based on point cloud data by laser scanning, a semi-automatic method is presented to align the point cloud data of the tunnel surfaces at different stages. This method includes three steps: (i) initial alignment, splitting, denosing and merging of the tunnel surface’s point clouds; (ii) coordinate transformation between the tunnel surface’s point clouds according to the arch’s geometric characteristics; and (iii) calculation and visualization of the tunnel surface’s sequential deformation based on the multiscale model cloud comparison algorithm. The technical application of the time-dependent deformation analysis for an experimental tunnel in a mine indicated that this method can not only fully expose the evolution process of tunnel surrounding rock deformation, i.e., the spatial inhomogeneity, partitioning and temporal characteristics, but also can warn instability of tunnel by discerning hot deformation islands, i.e., zones with relatively large deformation.

Construction Sequence Optimization and Settlement Control Countermeasures of Metro Tunnels Underpassing Expressway

The Chongqing metro line 6 underpass expressway around the city is taken as an engineering background, and the optimal excavation sequence and corresponding control countermeasures for the triangular-distributed three-line metro tunnel underpass expressway are studied. The influences of excavation sequence on the tunnel surrounding rock deformation, surrounding rock stress, supporting structure stress, plastic zone, and surface settlement are analyzed by using MIDAS/GTS NX finite element software. The numerical simulation results showed that Case 1 is the optimal excavation sequence of the metro tunnel. However, the surface settlement under the optimal excavation sequence exceeds the limit value of 30 mm, which cannot guarantee the safety of expressway traffic. On this basis, the control measure for strengthening the three-line tunnels with advanced small pipe grouting and reinforcing the middle tunnel with concrete-filled steel tube piles are proposed. Moreover, the excavation process of the metro tunnel with and without reinforcement schemes is numerically simulated. The results show that the reinforcement scheme can effectively control the surface settlement value within the limited value (16.47 mm), which is close to the maximum surface settlement of 18.31 mm after the metro tunnel excavation is completed, indicating that the proposed reinforcement scheme is beneficial to ensure the safety of metro tunnel construction and the driving safety of the expressway.

Research on Deformation Control Measures of Surrounding Rock in Soft Rock Tunnel

In order to study the control measures of soft rock tunnel surrounding rock deformation, based on the restraint effect of shotcrete anchor support on it, a support model was constructed, and numerical simulation was used to compare and analyze the restraint effects of different bolts controlling the surrounding rock deformation. The results show that: With the reduction of the bolt spacing (d), the resin bolt has a significant supporting effect compared with the mortar bolt support, the maximum displacement of the surrounding rock is reduced by 70.69%, and the maximum stress is reduced by 58.81%. With the increase of bolt length (L), compared with mortar bolt support, resin bolt has a more obvious restraint on surrounding rock deformation. The maximum displacement of surrounding rock is reduced by 57.89% and the maximum stress is reduced by 40.23%. When 4.5m <L< 6.m, 0.6m< d< 0.7m, the resin bolt has a significant effect on controlling the deformation of surrounding rock. In other cases, the use of mortar bolts or resin bolts to support the surrounding rock deformation has little significant difference.

Application of Improved Grey Model Based on Cumulative Method to Deformation Prediction of Tunnel Surrounding Rock

The traditional GM(1,1) model was improved by using the cumulative to increase its prediction accuracy. Based on the measured data of the Laomu tunnel in section 10 of shiqian to yuping expressway, the traditional grey prediction model and the improved grey prediction model were used to predict the surrounding rock deformation of the tunnel. The research shows that the improved model prediction can reduce the probability of outliers and the data series fluctuation of traditional gray prediction model, and makes the predicted value closer to the measured value. Although the improved gray prediction model has a slightly single modeling step, it can effectively increase the prediction accuracy of the model in a short period of time, and has certain reference value for similar tunnel surrounding rock deformation monitoring projects.

Large deformation analysis of tunnel-surrounding rock along the expressway from Wenchuan to Maerkang, China

The rock surrounding the Wenchuan–Malkang Tunnel, which consists of metasandstone, slate, and phyllite (including carbonaceous phyllite), is strongly affected by geological and tectonic factors. The large deformation of the tunnel-surrounding rock is a concerning problem. Based on the analysis of the geological characteristics of the tunnel-surrounding rock, Hoek predictive equation was used to study the relationship between the relative deformation of the tunnel-surrounding rock and the rock strength, geostress, geological strength index, and strength-to-stress ratio. Using the strength-to-stress ratio, we predicted the large deformation of the tunnel-surrounding rock and compared it with the deformation phenomena revealed during tunnel construction. Our study shows that the large deformation of tunnel-surrounding rock along the expressway from Wenchuan to Maerkang occurs primarily in soft rock, such as slate and phyllites, which are strongly affected by the geology and tectonics factors. Deformation of the tunnel-surrounding rock grows rapidly in soft rock with decreasing geological strength index and strength-to-stress ratio and with increasing ground stress. Moreover, the relationship between the schistosity strike of the steeply layered rock mass and the tunnel axis has an important influence on the tunnel-surrounding rock deformation. Tunnels where the schistosity of the surrounding rock has a strike at a small angle with the tunnel axis are more prone to large deformation.

The risk assessment of tunnel surrounding rock deformation based on entropy weight method and extension matter element method

The deformation of surrounding rock is an expected geological hazard when the tunnel passes through the area with unfavorable geological conditions. If the surrounding rock deformation is too large, it will lead to serious safety hazards. In this paper, the entropy weight method is adopted to calculate the weight of each index. Based on the extension theory, the risk evaluation model of tunnel surrounding rock deformation is established. Five evaluation indexes, such as surrounding rock grade, tunnel depth, groundwater status, the uniaxial compressive strength of rock, and rock integrity, were selected to evaluate the deformation risk of surrounding rock in 10 sections of Sheta Village tunnel of Zhangjihuai high-speed railway. The results show that the surrounding rock deformation is high in 2 of the 10 cavities to be evaluated, and the workers must take reinforcement measures to strengthen the surrounding rock, which is of considerable significance to guide the scientific and reasonable construction of the tunnel.

Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network

Tunnel surrounding rock deformation is dynamic, sensitive to time and space, nonlinear, and highly complicated. By combining the firefly algorithm (FA) and nonlinear auto-regressive (NAR) dynamic neural network method, an algorithm model was proposed for predicting dynamic nonlinear surrounding rock deformation. The FA improved the prediction accuracy of the NAR dynamic neural network by determining the optimum values of two network parameters—delay order and number of units in the hidden layer; combined with the monitoring results of Beishan exploration tunnel (BET), this is demonstrated by a comparative analysis of predictions yielded by the FA–NAR dynamic neural network and by the least squares support vector machine (LS-SVM). In general, the comparation shows that the FA–NAR dynamic neural network model yielded predictions that are fundamentally consistent with measurements and exhibits higher prediction accuracy than the LS-SVM. Results also show that the surrounding rock deformation prediction of BET for March 4, 2020 was marginally smaller than 2.43 mm.

Effect of joint characteristics on surrounding rock stability and stress mechanism of bolt under blasting excavation

Joint characteristics of rock mass are the key factors of tunnel surrounding rock failure. This paper relies on the two-lane mountain tunnel project and takes the IV class surrounding rock as the research object. By means of on-site investigation, numerical simulation and on-site monitoring, the effects of different joint development characteristics under blasting vibration on tunnel surrounding rock deformation, instability mode and force mechanism of rock bolts are studied. It is found that the instability mode of tunnel is obviously affected by the larger joint dip angles. When the dip angle of one group of consecutive joints is kept unchanged and the dip angle of another group of joints is changed, the vault settlement decreases first and then increases with the increase of joint dip angles. The influence of horizontal joint spacing on surrounding rock deformation and vault settlement is much greater than that of vertical joint. Under the most disadvantageous working conditions of the two sets of consecutive joints, the effective range of the axial force of rock bolts depends on joint dip angles and has little correlation with joint spacing. The axial force of rock bolts decreases with the increase of horizontal joint spacing, while the influence of vertical joint spacing on bolt is less.

A Prediction Model of Tunnel Surrounding Rock Deformation Based on Induced Ordered Weighted Averaging Theory and its Application

For the different prediction accuracy of tunnel surrounding rock deformation prediction model are different. Based on induced ordered weighted averaging theory(IOWA), IOWA modified combined forecasting model of tunnel surrounding rock deformation has been put forward with the relationship between the accuracy and the weight of a single prediction model. With the monitoring data of Mila Mountain tunnel of highways from Lasa to Linzhi, coupling the time series prediction model of deformation and exponential smoothing model of two kinds of single tunnel, IOWA modified combination prediction model for tunnel surrounding rock deformation has been built up. The result shows that predicted displacement of IOWA modified combination prediction model for tunnel surrounding rock deformation is closer to the measured value and has higher accuracy. The combined model for the prediction of tunnel surrounding rock deformation is feasible.

Study on deformation law of surrounding rock of super long and deep buried sandstone tunnel

The finite difference software Flac3D is used to study the influence of tunnel burial depth, tunnel diameter and lateral pressure coefficient of original rock stress on the stress and deformation of tunnel surrounding rock under sandstone condition. The results show that the maximum shear stress, the radius of the plastic zone and the maximum displacement in the surrounding rock increase with the increase of the diameter of the tunnel. When the lateral pressure coefficient is 1, it is most favorable for surrounding rock and lining structure, with the increase or decrease of lateral pressure coefficient, the maximum principal stress, surrounding displacement and plastic zone range of surrounding rock and lining show a sharp increase trend, the plastic zone on the lining increases with the increase of buried depth.

Optimization design of pre-reinforcement scheme for large cross section shallow buried tunnel in Xi’an Metro

In order to control the deformation of the surrounding rock of the large section of the tunnel, the author analyzes the stratum displacement law caused by the tunnel excavation by means of on-site monitoring. And the optimized design of the existing tunnel pre-reinforcement scheme is carried out. The FLAC3D numerical simulation method is used to verify the feasibility of the pre-reinforcement optimization scheme. Field test showed that the tunnel surrounding rock deformation in the allowable range, and the deformation control measures of the surrounding rock are reasonable and effective. The research results have important guiding significance for future similar project.

Supporting Force Characteristics and Extrusion Deformation of Tunnel Surrounding Rock in Layered Stratum Under High Ground Stress Condition

Supporting Force Characteristics and Extrusion Deformation of Tunnel Surrounding Rock in Layered Stratum Under High Ground Stress Condition