3D DEM simulation of hard rock fracture in deep tunnel excavation induced by changes in principal stress magnitude and orientation

To achieve the loading of the stress path of hard rock,the spherical discrete element model(DEM)and the new flexible membrane technology were utilized to realize the transient loading of three principal stresses with arbitrary magnitudes and orientations.Furthermore,based on the deep tunnel of China Jinping Underground Laboratory II(CJPL-II),the deformation and fracture evolution characteristics of deep hard rock induced by excavation stress path were analyzed,and the mechanisms of transient loading-unloading and stress rotation-induced fractures were revealed from a mesoscopic perspective.The results indicated that the stressestrain curve exhibits different trends and degrees of sudden changes when subjected to transient changes in principal stress,accompanied by sudden changes in strain rate.Stress rotation induces spatially directional deformation,resulting in fractures of different degrees and orientations,and increasing the degree of deformation anisotropy.The correlation between the degree of induced fracture and the unloading magnitude of minimum principal stress,as well as its initial level is significant and positive.The process of mechanical response during transient unloading exhibits clear nonlinearity and directivity.After transient unloading,both the minimum principal stress and minimum principal strain rate decrease sharply and then tend to stabilize.This occurs from the edge to the interior and from the direction of the minimum principal stress to the direction of the maximum principal stress on theε1-ε3 plane.Transient unloading will induce a tensile stress wave.The ability to induce fractures due to changes in principal stress magnitude,orientation and rotation paths gradually increases.The analysis indicates a positive correlation between the abrupt change amplitude of strain rate and the maximum unloading magnitude,which is determined by the magnitude and rotation of principal stress.A high tensile strain rate is more likely to induce fractures under low minimum principal stress.

Inverting the rock mass P-wave velocity field ahead of deep buried tunnel face while borehole drilling

Imaging the wave velocity field surrounding a borehole while drilling is a promising and urgently needed approach for extending the exploration range of the borehole point.This paper develops a drilling process detection(DPD)system consisting of a multifunctional sensor and a pilot geophone installed at the top of the drilling rod,geophones at the tunnel face,a laser rangefinder,and an onsite computer.A weighted adjoint-state first arrival travel time tomography method is used to invert the P-wave velocity field of rock mass while borehole drilling.A field experiment in the ongoing construction of a deep buried tunnel in southwestern China demonstrated the DPD system and the tomography method.Time-frequency analysis of typical borehole drilling detection data shows that the impact drilling source is a pulse-like seismic exploration wavelet.A velocity field of the rock mass in a triangular area defined by the borehole trajectory and geophone receiving line can be obtained.Both the borehole core and optical image validate the inverted P-wave velocity field.A numerical simulation of a checkerboard benchmark model is used to test the tomography method.The rapid convergence of the misfits and consistent agreement between the inverted and observed travel times validate the P-wave velocity imaging.

Temperature effects on the failure of deep circular tunnel under true-triaxial compression

The failure characteristics of thermal treated surrounding rocks should be studied to evaluate the stability and safety of deep ground engineering under high-ground-temperature and high-ground-stress conditions.The failure process of the inner walls of fine-grained granite specimens at different temperatures(25–600℃)was analyzed using a true-triaxial test system.The failure process,peak intensity,overall morphology(characteristics after failure),rock fragment characteristics,and acoustic emission(AE)characteristics were analyzed.The results showed that for the aforementioned type of granite specimens,the trend of the failure stress conditions changed with respect to the critical temperature(200℃).When the temperature was less than 200℃,the initial failure stress increased,final failure stress increased,and failure severity decreased.When the temperature exceeded 200℃,the initial failure stress decreased,final failure stress decreased,and failure severity increased.When the temperature was 600℃,the initial and final failure stresses of the specimens decreased by 60.93%and 19.77%compared with those at 200℃,respectively.The numerical results obtained with the software RFPA3D-Thermal were used to analyze the effect of temperature on the specimen and reveal the mechanism of the failure process in the deep tunnel surrounding rock.

Support design method for deep soft-rock tunnels in non-hydrostatic high in-situ stress field

Due to the long-term plate tectonic movements in southwestern China,the in-situ stress field in deep formations is complex.When passing through deep soft-rock mass under non-hydrostatic high in-situ stress field,tunnels will suffer serious asymmetric deformation.There is no available support design method for tunnels under such a situation in existing studies to clarify the support time and support stiffness.This study first analyzed the mechanical behavior of tunnels in non-hydrostatic in-situ stress field and derived the theoretical equations of the ground squeezing curve(GSC)and ground loosening curve(GLC).Then,based on the convergence confinement theory,the support design method of deep soft-rock tunnels under non-hydrostatic high in-situ stress field was established considering both squeezing and loosening pressures.In addition,this method can provide the clear support time and support stiffness of the second layer of initial support.The proposed design method was applied to the Wanhe tunnel of the China-Laos railway in China.Monitoring data indicated that the optimal support scheme had a good effect on controlling the tunnel deformation in non-hydrostatic high in-situ stress field.Field applications showed that the secondary lining could be constructed properly.

The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.

In-situ observations of damage-fracture evolution in surrounding rock upon unloading in 2400-m-deep tunnels

The damage-fracture evolution of deep rock mass has obvious particularity,which is revealed in 2400-mdeep tunnels by field tests.The evolution of the excavation damaged zone depth is consistent with that of the fractured zone depth.The ratio of the excavation damaged zone depth to the excavation fractured zone depth is greater than 2.0 in a rock mass with both high strength and good integrity,but less than1.5 in a rock mass with lower strength or poor integrity.Zonal disintegration in a rock mass with high strength and fair integrity is more likely to occur when it contains more than two groups of primary fractures in damaged zones.Fractures develop outward in zonal disintegration but are totally different from the single-zone fracture,in which the fractures develop inward,and it is the starting position of the fractured zone when the excavation surface of the middle pilot is 7–9 m close to the pre-set borehole and it stops after the excavation surface of the baseplate is 11–14 m away.The most intense evolution occurs around 2–4 m from the pre-set borehole in the sidewall expansion stage.The research results provide a reference for the monitoring scheme and support design of CJPL-Ⅲin its future construction.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

Research on in situ stress inversion of deep-buried tunnel based on pressure/tension axis mechanism and geological structure

The investigation of the in situ stress distribution has always been a key condition for engineering design of deep tunnels and analysis of surrounding rock stability.In this paper,a comprehensive judgment method coupled with pressure/tension(P/T)axis mechanism and geological structure is proposed to invert the in situ stress in the Duoxiongla tunnel in Tibet.In the process of TBM tunnel excavation,3887 groups of microseismic events were collected by means of microseismic monitoring technology.By studying the temporal and spatial distribution of 3887 groups of microseismic events,42 groups of microseismic data were selected for in situ stress inversion.Then the focal mechanisms of 42 groups of microseisms were inverted.Combined with the analysis of the previous geological survey,the inversion results of the in situ stress were analyzed.According to the focal mechanism of the tunnel area,the linear in situ stress inversion method was used to invert the in situ stress in the source area.Finally,according to the PTGS(pressure/tension axis mechanism and geological structure)comprehensive judgment method proposed in this paper,the in situ stress of the tunnel microseismic region was determined.The results show that there are mainly three groups of fissures and joint surfaces in the tunnel area,and the in situ stress is dominated by the horizontrun tectonic stress;the main driving force of the rupture surface in the excavation process of Duoxiongla tunnel is the horizontal tectonic stress;the distribution of the maximum and minimum principal stress obtained by the inversion is consistent with the distribution of the P/T axis;combined with the linear in situ stress inversion method and the comprehensive judgment of PTGS,the azimuth and dip angles of the three principal stresses are finally determined as N90.71°E,4.06°,N5.35°W,3.06°,and N8.10W,85.32°,respectively.The study verifies the feasibility of microseismic inversion of in situ stress.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Assessment of strain bursting in deep tunnelling by using the finite-discrete element method

Rockbursting in deep tunnelling is a complex phenomenon posing significant challenges both at the design and construction stages of an underground excavation within hard rock masses and under high in situ stresses. While local experience, field monitoring, and informed data-rich analysis are some of the tools commonly used to manage the hazards and the associated risks, advanced numerical techniques based on discontinuum modelling have also shown potential in assisting in the assessment of rockbursting. In this study, the hybrid finite-discrete element method(FDEM) is employed to investigate the failure and fracturing processes, and the mechanisms of energy storage and rapid release resulting in bursting, as well as to assess its utility as part of the design process of underground excavations.Following the calibration of the numerical model to simulate a deep excavation in a hard, massive rock mass, discrete fracture network(DFN) geometries are integrated into the model in order to examine the impact of rock structure on rockbursting under high in situ stresses. The obtained analysis results not only highlight the importance of explicitly simulating pre-existing joints within the model, as they affect the mobilised failure mechanisms and the intensity of strain bursting phenomena, but also show how the employed joint network geometry, the field stress conditions, and their interaction influence the extent and depth of the excavation induced damage. Furthermore, a rigorous analysis of the mass and velocity of the ejected rock blocks and comparison of the obtained data with well-established semi-empirical approaches demonstrate the potential of the method to provide realistic estimates of the kinetic energy released during bursting for determining the energy support demand.

Numerical simulation of the effect of coupling support of bolt-mesh-anchor in deep tunnel

The mechanical effects of bolt-mesh-anchor coupling support in deep tunnels were studied by using a numerical method, based on deep tunnel coupling supporting techniques and non-linear deformation mechanical theory of rock mass at great depths.It is shown that the potential of a rigid bolt support can be efficiently activated through the coupling effect between a bolt-net support and the surrounding rock.It is found that the accumulated plastic energy in the surrounding rock can be sufficiently transformed by the coupling effect of a bolt-mesh-tray support.The strength of the surrounding rock mass can be mobilized to control the deforma-tion of the surrounding rock by a pre-stress and time-space effect of the anchor support.The high stress transformation effect can be realized by the mechanical coupling effect of the bolt-mesh-anchor support, whereby the force of the support and deformation of the surrounding rock tends to become uniform, leading to a sustained stability of the tunnel.

Experimental investigation on influence of loading rate on rockburst in deep circular tunnel under true-triaxial stress condition

To investigate the influence of loading rate on rockburst in a circular tunnel under three-dimensional stress conditions,the true-triaxial tests were conducted on 100 mm×100 mm×100 mm cubic sandstone specimens with d50 mm circular perforated holes,and the failure process of hole sidewall was monitored and recorded in real-time by the microcamera.The loading rates were 0.02,0.10,and 0.50 MPa/s.The test results show that the rockburst process of hole sidewall experienced calm period,pellet ejection period,rock fragment exfoliation period and finally formed the V-shaped notch.The rockburst has a time lag and vertical stress is high when the rockburst occurs.The vertical stress at the initial failure of the hole sidewall increases with loading rate.During the same period after initial failure,the rockburst severity of hole sidewalls increased significantly with increasing loading rate.When the vertical stress is constant and maintains a high stress level,the rockburst of hole sidewall under low loading rate is more serious than that under high loading rate.With increasing loading rate,the quality of rock fragments produced by the rockburst decreases,and the fractal dimension of rock fragments increases.

Excavation-induced microseismicity and rockburst occurrence:Similarities and differences between deep parallel tunnels with alternating soft-hard strata

Excavation-induced microseismicity and rockburst occurrence in deep underground projects provide invaluable information that can be used to warn rockburst occurrence,facilitate rockburst mitigation procedures,and analyze the mechanisms responsible for their occurrence.Based on the deep parallel tunnels with the maximum depth of 1890 m created as part of the Neelum–Jhelum hydropower project in Pakistan,similarities and differences on excavation-induced microseismicity and rockburst occurrence between parallel tunnels with soft and hard alternant strata are studied.Results show that a large number of microseismic(MS)events occurred in each of the parallel tunnels during excavation.Rockbursts occurred most frequently in certain local sections of the two tunnels.Significant differences are found in the excavation-induced microseismicity(spatial distribution and number of MS events,distribution of MS energy,and pattern of microseismicity variation)and rockbursts characteristics(the number and the spatial distribution)between the parallel tunnels.Attempting to predict the microseismicity and rockburst intensities likely to be encountered in subsequent tunnel based on the activity encountered when the parallel tunnel was previously excavated will not be an easy or accurate procedure in deep tunnel projects involving complex lithological conditions.

Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil

The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

Numerical modeling of large deformation and nonlinear frictional contact of excavation boundary of deep soft rock tunnel

Roadways excavated in soft rocks at great depth are difficult to be maintained due to large deformation of surrounding rocks, which greatly influences the safety and efficiency of deep resources exploitation. During the excavation process of a deep soft rock tunnel, the rock wall may be compacted due to large deformation. In this paper, the technique to address this problem by a two-dimensional (2D) finite element software, large deformation engineering analyses software (LDEAS 1.0), is provided. By using the Lagrange multiplier method, the kinematic constraint of non-penetrating condition and static constraint of Coulomb friction are introduced to the governing equations in the form of incremental displacement. The numerical example demonstrates the efficiency of this technology. Deformations of a transportation tunnel in inclined soft rock strata at the depth of 1 000 m in Qishan coal mine and a tunnel excavated to three different depths are analyzed by two models, i.e. the additive decomposition model and polar decomposition model. It can be found that the deformation of the transportation tunnel is asymmetrical due to the inclination of rock strata. For extremely soft rock, large deformation can converge only for the additive decomposition model. The deformation of surrounding rocks increases with the increase in the tunnel depth for both models. At the same depth, the deformation calculated by the additive decomposition model is smaller than that by the polar decomposition model.

Support pressure assessment for deep buried railway tunnels using BQ-index

Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different rock mass classification systems,such as Q system,GSI system and RMR system.However,various rock mass classification systems are based on different tunnel geologic conditions in various regions.Therefore,each rock mass classification system has a certain regionality.In China,the BQ-Inex(BQ system)has been widely used in the field of rock engineering ever since its development.Unfortunately,there is still no estimation method of support pressure with BQ-index as parameters.Based on the field test data from 54 tunnels in China,a new empirical method considering BQ-Inex,tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods.And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system,Q system and GSI system.Finally,the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.

Effect of layered joints on rockburst in deep tunnels

The existence of joints in the surrounding rock mass has a considerable efect on tunnel rockbursts.Herein,we studied the efect of layered joints with diferent inclination angles and spacings on rockburst in deep tunnels and investigated the failure area,deformation process of the surrounding rock mass,stress change inside the surrounding rock mass,velocity of the failed rock,and the kinetic energy of the failure.The failure type of the surrounding rock mass can thus be determined.The results showed that the intensity of rockburst increases as rock quality designation(RQD)decreases,while the deformation rate of the surrounding rock mass frst increases and then decreases.The deformation rate exhibits a turning point between RQD=50 and 70,below which the deformation rate of the surrounding rock mass gradually decreases,ultimately ceasing to be a rockburst.Rockburst always occurs perpendicular to the direction of the joint.Whenσ_(x)=σ_(y),as the joint inclination angle changes from 45°to 90°,the intensity of a rockburst frst decreases(from 45°to 60°),and then increases(from 60°to 90°).When combined with the evolution law of stress and strain energy,the rockburst process can be divided into four stages.

Key technologies for construction of Jinping traffic tunnel with an extremely deep overburden and a high water pressure

Jinping traffic tunnel is one of the deepest traffic tunnels in the world with a maximum overburden of 2 375 m and the overburden over 73% of its total length is larger than 1 500 m. The tunnel is 17.5 km long and designed to provide a shortcut road between two hydropower stations： Jinping I and Jinping II of the Jinping Hydropower Project, located on Yalong River, Liangshan State, Sichuan Province, China. The tunnel is so deep that building any shafts is impossible. The construction starts from both ends （east and west ends）, and the construction length from the west end is 10 km with a blind heading. This paper deals with an overview of this project and analysis of the engineering features, as well as key technologies developed and applied during the construction, including geological prediction, rock burst prevention under a super high in-situ stress, sealing of groundwater with a high pressure and big flow rate, ventilation for a blind heading of 10 km, wet spraying of shotcrete at zones of rock burst and rich water, etc. The application of the new technologies to the construction achieved a high quality tunnel within the contract period.

Zonal disintegration phenomenon in enclosing rock mass surrounding deep tunnels——Elasto-plastic analysis of stress field of enclosing rock mass

The zonal disintegration phenomenon （ZDP） is a typical phenomenon in deep block rock masses. In order to investigate the mechanism of ZDP, an improved non-linear Hock-Brown strength criterion and a bi-linear constitutive model of rock mass were used to analyze the elasto-plastic stress field of the enclosing rock mass around a deep round tunnel. The radius of the plastic region and stress of the enclosing rock mass were obtained by introducing dimensionless parameters of radial distance. The results show that tunneling in deep rock mass causes a maximum stress zone to appear in the vicinity of the boundary of the elastic and the plastic zone in the surrounding rock mass. Under the compression of a large tangential force and a small radial force, the rock mass in the maximum stress zone was in an approximate uniaxial loading state, which could lead to a split failure in the rock mass.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.