Mountain Tunnel Research Articles

A fast and automatic establishment method of the calculation formula for the tunnel face safety factor based on analytical solution

Assessing rock tunnel face stability when constructing mountain tunnels is of utmost importance because it is influenced by various factors, including the tunnel diameter, depth, excavation dimension, geological strength index, uniaxial compressive strength, and Hoek-Brown constant. This study established a tunnel face stability database based on analytical solutions that considered the correlation between the influencing parameters. Finally, a safety factor prediction equation was constructed by combining response surface methodology and the multivariate adaptive regression spline algorithm. The results of this study find applications at construction sites and provide a reference for the rapid evaluation of rock tunnel faces during construction.

Probabilistic dislocation capacity model for mountain tunnel crossing strike–slip creeping fault

Fault dislocations significantly affect the performances of cross–fault tunnels. However, there is a lack of rapid and accurate methods for estimating the maximum fault dislocation that tunnels can withstand (i.e., the dislocation capacity of tunnels). This study systematically investigates the dislocation capacity of circular tunnels crossing strike–slip creeping faults while considering uncertainties in the lining parameters, rock strength, and crossing angles. Through pseudostatic numerical simulations, the dislocation capacities of tunnels under various working conditions were determined, and a probabilistic dislocation capacity model was developed using a support vector machine. The proposed model can estimate the probability distribution of the dislocation capacity of a tunnel, with its median closely aligned with the numerical result. Another advantage lies in its direct correlation with the lining parameters, rock strength, and crossing angles. This facilitates a rapid assessment of the tunnel dislocation capacity based on existing parameters. The dislocation capacity and vulnerability curves of a case tunnel are estimated using our probabilistic model, which serves as a demonstration. The results demonstrate that the proposed model is convenient for engineering applications and can assist in the design and optimization of cross–fault tunnels.

Effects of Reverse Fault Dislocation Application Method for Tunnelling Through Active Faults

Active faults seriously threaten the structural integrity of mountain tunnels in seismic zones, and reverse faults are the most hazardous. A tunnel project in the western region was used as a reference to analyze the damage mechanism of tunnels under different modes of reverse fault displacement. The ABAQUS finite element analysis software was employed for the numerical simulation, and a quasi-static method was adopted to analyze the displacement and stress response patterns of the tunnel structure traversing the fault under three typical modes of reverse fault displacement. This led to deriving the tunnel structure’s longitudinal damage modes and impact zones based on reverse fault displacement. The study revealed that the damage modes of the tunnel under different fault displacement modes varied, which was reflected in the different degrees of shear and compression. Regardless of the fault displacement mode, the tunnel structure located within the fault fracture zone was severely damaged, with the most severe damage occurring at the interface between the fixed plate and the fault displacement section. Therefore, in the design, special attention should be paid to the displacement resistance performance of the dangerous sections of the tunnel. The research results provide significant reference and guidance for similar projects.

The Time-Dependent Interfacial Adhesion between Artificial Rock and Fresh Mortar Modified by Nanoclay.

The time-dependent interfacial adhesion between rock and fresh mortar is key for printing concrete linings in mountain tunnels. However, a scientific deficit exists in the time-dependent evolution of the interfacial adhesion, which can cause adhesion failure when printing tunnel lining. Nanoclay has the potential to increase the interfacial adhesion and eliminate the adhesion failure. Before the actual printing of tunnel linings, the time-dependent interfacial adhesion between artificial rock and fresh mortar modified by nanoclay should be understood. This paper studied the time-dependent interfacial adhesion based on fast tack tests, fast shear tests, and isothermal calorimetry tests. With the addition of nanoclay, the maximum tensile stress and the maximum shear stress increased. Compared with a reference series, the maximum interfacial tensile stress in a 0.3% nanoclay series increased by 106% (resting time 1 min) and increased by 209% (resting time 32 min). A two-stage evolution of the interfacial adhesion was found with the addition of nanoclay. In the first stage, the time-dependent interfacial adhesion increased rapidly. A 0.3% NC series showed an increase rate six times higher than that of the reference series. As the matrices aged, the increase rate slowed down and followed a linear pattern of increase, still higher than that of the reference series. The stiffening of fresh matrices resulted in the interface failure mode transition from a ductile failure to a brittle failure. The effect of nanoclay on flocculation and on accelerating the hydration contributed to the time-dependent interfacial adhesion between artificial rock and fresh mortar.

Study on progressive failure mode of surrounding rock of shallow buried bias tunnel considering strain-softening characteristics

Mountain tunnels portal often have to pass through slope terrain unavoidably, thus forming a shallow buried bias tunnel. During the construction of shallow buried bias tunnel, disasters such as slope sliding and tunnel collapse frequently occur. The failure mode of surrounding rock obtained by current research is based on the limit equilibrium theory, which cannot reflect the progressive failure characteristics of the surrounding rock of shallow buried bias tunnel. In order to reveal the failure mechanism of the gradual instability of surrounding rock of shallow buried bias tunnel, the problem of gradual failure of the surrounding rock is reduced to an elastic–plastic analysis problem for surrounding rock considering the strain-softening characteristics. Based on the elastic–plastic analysis of the failure process of shallow buried bias tunnel, MATLAB was used to compile a program to read the finite-difference calculation result file, extract the effective information such as shear strain and tensile strain at the center point of each unit, and establish the analysis method of the progressive failure mode of shallow buried bias tunnel. The reliability of the method proposed was verified by comparing the failure process of the model test with the development process of shear strain increment. Under the condition of no support, the formation mechanism of failure plane of surrounding rock on both sides of shallow buried bias tunnel is different. The shallow buried side is the shear failure plane formed by the collapse of surrounding rock, while the deep buried side of the tunnel is the shear failure plane formed by the collapse of surrounding rock and slope sliding. Under the conditions of excavation and support, the failure plane of the shallow buried bias tunnel can be divided into three parts according to the formation sequence and reasons. The part I is the failure plane, which is formed by active shear under the influence of tunnel excavation. The part II is the failure plane formed by tensile crack of slope top. The part III is the failure plane formed by passive shear under the push of the soil in the upper part of the slope.

Reasonable vertical position of the advanced center drift in super deep buried soft rock tunnel

In super deep-buried soft-rock tunnels, significant deformation disasters often accompany high-ground stress. Advanced center drift stress release technology to release some in-situ stress in advance can effectively solve many problems caused by large deformation. Therefore, based on the Haba Snow Mountain Tunnel in the Lijiang Section of the Yunnan-Tibet Railway, the research on the vertical reasonable position of the advanced center drift is carried out. Using literature research, numerical simulation, and on-site experiments, the deformation of surrounding rock, the stress distribution of support, and the distribution of the plastic zone of surrounding rock at different vertical positions of the advanced center drift are studied. The application effect of the advanced center drift is analyzed through on-site monitoring data. The results show that different advanced center drift vertical positions have a stress-release particular impact. When the arch top of the advanced center drift is 2 m away from the main tunnel arch top, the stress release effect and support stress are relatively ideal, and the on-site measured tunnel deformation can reduce by an average of 44.52%. The research results can provide a reference for the design of advanced center drift in similar projects.

Optimized machine learning models for predicting crown convergence of plateau mountain tunnels

The precise prediction of tunnel convergence holds significant importance in ensuring the safety and efficiency of tunnel construction. This study involves the construction of seven machine learning (ML) models that are deemed reliable for predicting crown convergence (CC) of plateau mountain tunnels. These models include K-nearest neighbour (KNN), support vector regression (SVR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and automated machine learning model (Auto-ML). The Bayesian optimization (BO) technique and the Tree-based pipeline optimization tool (TPOT) were employed to optimize the hyperparameters of non-automated machine learning models and the automated machine learning model, separately. A total of 2,734 samples of crown convergence monitoring data were collected for training and testing the models. The evaluation metrics employed to assess the performance of the models’ predictions included the determination coefficient (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE). The XGBoost model demonstrated the best prediction performance with the R2, MAPE, MAE, and RMSE of 0.9887, 0.1271, 0.7759, and 1.1845 on test set. The XGBoost, CatBoost, and Auto-ML models were utilized to predict the crown convergence monitoring curves of four sections of Huzhubeishan Tunnel in Qinghai Province, China. Notably, the predicted curves exhibited a significant level of concordance with the actual monitoring curves. The results of Shapley Additive Explanations (SHAP) suggested that time, tunnel depth ratio and groundwater table were more important than other input variables. The XGBoost model showed the best robustness of prediction performance across varying dataset sizes for seven distinct machine learning models.

Safety assessment of the construction of double track tunnels underneah exsiting railway tunnels

The ground disturbance caused by the tunnel construction will inevitably have an impact on the upper part of the constructed tunnel structure, and the railroad tunnel requires a very high level of control over the structural settlement deformation. For the problem of double-hole tunnel under the built tunnel, this paper takes Chongqing Mingyue Mountain Tunnel under the built Shanghai-Rong Railway Paihua Cave tunnel and Zheng-Yu Railway tunnel as the engineering background, and starts from the mechanism of ground loss caused by tunnel excavation, firstly, the settlement at the height of the existing tunnel strata is obtained through theoretical analysis, and the new Mingyue Mountain Tunnel under the Shanghai-Rong Railway tunnel is determined to be a more dangerous section. Further simulate and calculate the dynamic excavation process of the new double-hole tunnel underpass, and study the settlement deformation law of the Mingyue Mountain Tunnel underpassing the Hurong Railway Tunnel. According to the requirements of railroad tunnel for settlement deformation control, the new tunnel is determined to be constructed by step method to ensure the safety of railroad tunnel. The shortcomings of the theoretical calculation are analyzed to illustrate the important role of numerical simulation in the evaluation of tunnel underpass projects.

Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults

Fault water inflow is one of the most severe disasters that can occur during the construction of hard and brittle rock tunnels. These tunnels traverse brittle fault breccia zones comprising two key components: a damage zone dominated by low-strain fractures and an internally nested high-strain zone known as the fault core. Structural heterogeneity influences the mechanical and hydraulic properties within fault breccia zones, thereby affecting the evolving characteristics of water inflow in hard rock faulting. Based on the hydraulic characteristics within hard rock fault zones, this paper presents a generalized dual-porosity fluid-solid coupling water inflow model. The model is utilized to investigate the spatiotemporal evolution patterns of water pressure, inflow velocity, and water volume during tunneling through heterogeneous fault zones in hard rock. Research findings indicate that when tunnels pass through the damage zones, water inrush velocity is high, yet the water volume is low, and both decrease rapidly over time. Conversely, within the core regions of faults, water inflow velocity is low, yet the water volume is high, and both remain relatively stable over time. Simulation results closely align with the water inflow data from China’s largest cross-section tunnel, the Tiantai Mountain Tunnel, thus validating the accuracy of the evolutionary model proposed in this paper. These findings offer a new perspective for devising effective prevention strategies for water inflow from heterogeneous faults.

Local Site Effect of Fault Site and Its Impact on Seismic Response of Fault-Crossing Tunnel

ABSTRACT The areas where tunnels crossing faults are especially susceptible to seismic damage. Local site effects associated with fault site notably contribute to the unfavourable impact on the seismic behaviour of tunnels. In this study, large-scale shaking table tests were conducted to investigate the fault site effect and its impact on the seismic response of tunnels. Real earthquake records, synthetic seismic wave, and sinusoidal waves were selected as input motions, excited in both transverse and longitudinal directions of the tunnel. The local site effect of the fault site was evaluated by time-frequency analysis. The test results indicate that the acceleration response within the fault is significantly greater than those on both sides, regardless of excitation direction. Dynamic characteristics of different regions of the fault site exhibit distinct variations under different excitation directions, with the dynamic behaviour of the strata within the fault playing a crucial role. Marked waveform distortion and trapped waves can be observed within the fault, attributed to harmonic distortion, waveform conversion at fault interfaces, and varying seismic wave propagation pathways. The region of maximum seismic response of the fault-crossing tunnel predominantly occurs near the interface between the fault and the hanging wall. The seismic response of the tunnel located in the hanging wall exceeds that in the footwall. The dynamic characteristics of the fault-crossing tunnel are significantly affected by the dynamic characteristics of the strata around the tunnel, highlighting the necessity of comprehending strata dynamics in the seismic design of such tunnels. The aforementioned findings could offer valuable insights for the seismic design of mountain tunnels crossing fault zones.

Prediction of water inflow and analysis of surrounding rock stability in unfavorable geological mountain tunnel

During the construction of a mountain tunnel, water inflow and rock instability are common occurrences due to unfavorable geological conditions, posing serious threats to construction safety. This study focuses on a proposed mountain tunnel and employs multiple formulas to predict potential water inflow during excavation. Based on the amount of water inflow and deformation of surrounding rocks, comprehensive determinations are made for the thickness of grouting rings and permeability coefficients. The results demonstrate that: 1) Different formulas yield slightly varied outcomes but overall trends remain consistent; considering various calculations, the normal water inflow for this tunnel is approximately 115.5908×103 m3/d with a maximum at 210.9100×103 m3/d 2) Increasing grouting ring thickness or decreasing permeability coefficient can effectively reduce water inflow, but the reduction range is gradually narrowed. 3) Pre-grouting curtains have an evident effect in enhancing stability; however, their effectiveness decreases with increased thickness. 4) Taking into account both safety and economic factors, it is recommended that the grouting ring thickness be set at 8 m with a permeability coefficient equaling one 100th that of surrounding rocks for this tunnel project.

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.

A high resolution detection approach combining probe drilling and horizontal cross-hole resistivity tomography to interpret water conducting channels ahead of the tunnel: A case study in Yunnan, China

The geological conditions in Yunnan Province, China, are intricate, and numerous challenges are anticipated during tunnel excavation. The goal of this study was to develop a high resolution detection approach combining probe drilling and horizontal cross-hole resistivity tomography to interpret water conducting channels ahead of the tunnel. A complex section of the Xianglu Mountain tunnel was taken as an example. Drilling data from 10 boreholes are used to establish the initial resistivity model ahead of the tunnel face. The utilization of drilling data as a priori information refines the horizontal cross-borehole resistivity tomography imaging results, providing a more accurate depiction of the subsurface conditions. In practical applications, the use of multiple boreholes for high resolution detection via probe drilling analysis and horizontal cross-hole resistivity tomography is relatively uncommon in complex underground environments and represents an engineering innovation. At the Xianglu Mountain tunnel site, 13 electrical resistivity tomography (ERT) profiles were executed to thoroughly assess the geological conditions. A 3-D resistivity model was constructed via interpolation of this dataset. The 3-D resistivity model not only facilitated precise estimation of the 3-D hydrogeological environment at the Xianglu Mountain tunnel site but also enhanced the accuracy of identifying the water conducting channels by revealing low-resistivity interconnected zones. This approach offers a precise guide for determining the location of the critical water conducting channels and for optimizing grouting treatment strategies based on the characterization, geometry, resistivity spatial distribution, and hydrogeological conditions of the studied zones. The acquired results, which are in good accordance with the reality of the studied area in terms of locating the water conducting channels, can be utilized to develop preventative techniques for water and mud inrush disasters during tunnel construction.

A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance

In the context of mountain tunnel mining through the drilling and blasting method, the recognition of lithology from palm face images is crucial for the comprehensive analysis of geological conditions and the prevention of geological risks. However, the complexity of the background in the acquired palm face images, coupled with an insufficient data sample size, poses challenges. While the incorporation of deep learning technology has enhanced lithology recognition accuracy, issues persist, including inadequate feature extraction and suboptimal recognition accuracy. To address these challenges, this paper proposes a lithology recognition network integrating attention mechanisms and a feature Brownian distance covariance approach. Drawing inspiration from the Brownian distance covariance concept, a feature Brownian distance covariance module is devised to enhance the network’s attention to rock sample features and improve classification accuracy. Furthermore, an enhanced lightweight Convolutional Block Attention Module is introduced, with upgrades to the multilayer perceptron in the channel attention module. These improvements emphasize attention to lithological features while mitigating interference from background information. The proposed method is evaluated on a palm face image dataset collected in the field. The proposed method was evaluated on a dataset comprising field-collected images of a tunnel rock face. The results illustrate a significant enhancement in the improved model’s ability to recognize rock images, as evidenced by improvements across all objective evaluation metrics. The achieved accuracy rate of 97.60% surpasses that of the current mainstream lithology recognition neural network.

A novel numerical framework to simulate mechanical response and damage characteristics of mountain tunnels under faulting

This paper presents a novel numerical framework that can simulate the mechanical response and damage characteristics of mountain tunnels under faulting more precisely and efficiently. Both the submodeling and two-stage models are adopted in the framework. The submodeling technique is used to obtain a precise simulation of tunnel-fault interaction while ensuring computational efficiency. The two-stage models can acquire accurate initial stress of the tunnel lining and simulate the tunnel-rock interaction during fault movement, which can also achieve the transition from static to dynamic analysis. The computational efficiency can be improved by about 73 % via the proposed numerical framework compared to the conventional numerical analysis when keeping the same mesh sizes. Moreover, overall lining damage indices and cross-sectional ovalization are introduced to quantitatively evaluate the respective damage and deformation of the tunnel lining cross-section, and damage level classifications for tunnel lining under faulting are established. Parametric sensitivity analyses are performed to investigate key factors affecting the mechanical damage behavior of mountain tunnels under strike-slip faulting, including fault dislocation, fault zone width, and the tunnel-fault intersection angle. The increasing fault dislocation causes larger tunnel-rock interaction around the fault plane, making the damage and deformation of tunnel lining around the fault plane more severe. The fault zone width has a significant impact on the damage and deformation range of tunnel lining. The tunnel-fault intersection angle contributes great influence on the magnitude and range of tunnel lining damage and deformation. The tunnel lining would suffer relatively severe damage and deformation when the two fault blocks move away from each other along the tunnel axial.

Tunnel deformation prediction during construction: An explainable hybrid model considering temporal and static factors

This paper presents a novel hybrid model designed for predicting mountain tunnel deformation during construction, incorporating both temporal and static factors. Utilizing a boosting ensemble technique, the model effectively integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network—acclaimed for its proficiency with time-series data—with the Light Gradient Boosting Machine (Light GBM) model—recognized for its adept handling of tabular data. In the prediction procedure, the Bi-LSTM and Light GBM modules are engaged to process time-dependent and static factors, respectively. The model’s performance was evaluated against seven other established machine learning models using data from the Liangwangshan Tunnel, whose outcomes demonstrated the superiority of our model in terms of its local and overall reduction in prediction errors. By introducing static factors associated with each monitoring section via the Light GBM, our model offers a robust solution to the issue of time delayed prediction—a challenge inadequately addressed in time series prediction. Finally, we used the SHAP (SHapley Additive exPlanations) method to interpret the hybrid model’s decision-making mechanisms. The findings reveal a significant correlation between the prediction rules employed by our model and the fundamental principles of tunnel engineering and physical mechanics, thereby underlining its reliability and potential applicability in practical scenarios.

Landslide control in the excavation of a shallowly buried tunnel with a large front slope

The tunnel with a large front slope is a common type of entrance in mountain tunnels. Excavation of this type of tunnel disrupts the original balance of the slope, which can easily lead to landslides or collapses. It is therefore important to choose suitable support for early reinforcement. This paper presents a case study of landslide control during the tunnelling of a shallowly buried tunnel with a large front slope. Three types of pre-support, including anti-slide pile, pipe roof and surface grouting, were used to control the landslide. In addition, a three-dimensional analysis was carried out to investigate the reinforcing effect of single and multiple pre-supports on the landslide and tunnel structure. The results showed that the deformation of the landslide and tunnel structure at the end of the entrance was the greatest, that is, the most dangerous, and the maximum slope displacement without pre-support was 15.1 cm. In tunnel structures, the arch area was the most prone to instability, while the bottom area did not experience significant deformation. The horizontal displacement control effect of the landslide body was ranked from strong to weak as surface grouting, pipe roof, and anti-slide pile, which is a decrease of 4.8%, 20.5% and 40.8% compared to the maximum displacement. While the vertical displacement control effect of the landslide was ranked from strong to weak as pipe roof, surface grouting, and anti-slide pile, which is a decrease of 4.3%, 45.1% and 63.7% compared to the maximum displacement. The reinforcement effect when three types of pre-supports were applied simultaneously was basically equal to the sum of the reinforcement effects when each pre-support was applied separately. This is mainly due to the different reinforcement mechanisms and areas of the three pre-supports, resulting in better overall effects.

Study on Risk Management of Rescue Construction of Mountain Tunnels

Study on Risk Management of Rescue Construction of Mountain Tunnels

Study on the Driving Risk Reduction in the Mountain Highway Tunnel Group under the Perspective of Visual Load

More highway built in the mountains in recent years, the driving risk in the tunnel group is becoming a new issue. This paper analyzed the driving risk in the mountain highway tunnel group from the perspective of visual load. Based on vehicle test in the Pengshui‐Xiantang tunnel group in China, the evolution characteristics of MTPA were quantitatively analyzed, and the random forest model was constructed to discuss the effect factors of the maximum transient velocity value of the pupil area (MTPA) in different sections. The results are as follows: (1) The MTPA frequently presents a tendency of steep rise and fall in the tunnel group. MTPA in the second tunnel is significantly higher than the first tunnel. (2) The mountain tunnel group can be divided into nine sections; the velocity, design luminance, measured luminance, and location have different effects on MTPA in each section. Due to the complex terrain conditions, the location has a more significant impact on MTPA in the second tunnel. (3) The first tunnel entrance, the first tunnel exit to the second tunnel entrance, and the second tunnel exit are the areas with more significant pressure on drivers in the tunnel group. The visual load of drivers in the exit section of the last tunnel is the greatest. The driving risk reduction recommendations include improving the transition lighting design of the second tunnel, clarifying the tunnel group identification, and adding safety features at the tunnel connection section, in order to clarify the driver’s expectations and reduce the fear of the unknown mountain environment.

Dynamic response of mountain tunnel, bridge, and embankment along the Sichuan-Tibet transportation corridor to active fault based on model tests

Dynamic response of mountain tunnel, bridge, and embankment along the Sichuan-Tibet transportation corridor to active fault based on model tests