Probabilistic analysis of tunnel face seismic stability in layered rock masses using Polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.

Numerical manifold method for thermo-mechanical coupling simulation of fractured rock mass

As a calculation method based on the Galerkin variation,the numerical manifold method(NMM)adopts a double covering system,which can easily deal with discontinuous deformation problems and has a high calculation accuracy.Aiming at the thermo-mechanical(TM)coupling problem of fractured rock masses,this study uses the NMM to simulate the processes of crack initiation and propagation in a rock mass under the influence of temperature field,deduces related system equations,and proposes a penalty function method to deal with boundary conditions.Numerical examples are employed to confirm the effectiveness and high accuracy of this method.By the thermal stress analysis of a thick-walled cylinder(TWC),the simulation of cracking in the TWC under heating and cooling conditions,and the simulation of thermal cracking of the SwedishÄspöPillar Stability Experiment(APSE)rock column,the thermal stress,and TM coupling are obtained.The numerical simulation results are in good agreement with the test data and other numerical results,thus verifying the effectiveness of the NMM in dealing with thermal stress and crack propagation problems of fractured rock masses.

Rock Mass Quality Rating Based on the Multi-Criteria Grey Metric Space

Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.

A Novel ISSA–DELM Model for Predicting Rock Mass Permeability

In pumped storage projects,the permeability of rock masses is a crucial parameter in engineering design and construction.The rock mass permeability coefficient(K)is influenced by various geological parameters,and previous studies aimed to establish an accurate relationship between K and geological parameters.This study uses the improved sparrow search algorithm(ISSA)to optimize the parameter settings of the deep extreme learning machine(DELM),constructing a prediction model with flexible parameter selection and high accuracy.First,the Spearman method is applied to analyze the correlation between geological parameters.A sample database is built by comprehensively selecting four geological indexes:burial depth,rock quality designation(RQD),fracture density characteristic index(FD),and rock mass integrity designation(RID).Hence,the defects of the sparrow search algorithm(SSA)are enhanced using the improved strategy,and the initial input weights of the DELM are optimized.Finally,the proposed ISSA–DELM model is employed to predict the permeability coefficient of rock mass in the entire study area.The results showed that the predictive performance of the model is superior to that of the DELM and SSA–DELM.Therefore,this model successfully provides insights into the distribution characteristics of rock mass permeability at engineering sites.

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.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Effect of eccentric and inclined loading on the bearing capacity of strip footing placed on rock mass

This paper deals with the bearing capacity determination of strip footing on a rock mass in hilly area by considering the influence of inclined and eccentric loading. Applying the generalized HoekBrown failure criterion, the failure behavior of the rock mass is modeled with the help of the power cone programming in the lower bound finite element limit analysis framework. Using bearing capacity factor(Ns), the change in bearing capacity of the strip footing due to the occurrence of eccentrically inclined loading is presented. The variations of the magnitude of Ns are obtained by examining the effects of the Hoek-Brown rock mass strength parameters(uniaxial compressive strength(sci), disturbance factor(D), rock parameter(mi), and Geological Strength Index(GSI)) in the presence of different magnitudes of eccentricity(e) and inclination angle(λ) with respect to the vertical plane, and presented as design charts. Both the inclined loading modes, i.e., inclination towards the center of strip footing(+λ) and inclination away from the center of strip footing(-λ), are adopted to perform the investigation. In addition, the correlation between the input parameters and the corresponding output is developed by utilizing the artificial neural network(ANN). Additionally, from sensitivity analysis, it is observed that inclination angle(λ) is the most sensitive parameter. For practicing engineers, the obtained design equation and design charts can be beneficial to understand the bearing capacity variation in the existence of eccentrically inclined loading in mountain areas.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Failure behavior and strength model of blocky rock mass with and without rockbolts

To better understand the failure behaviours and strength of bolt-reinforced blocky rocks,large scale extensive laboratory experiments are carried out on blocky rock-like specimens with and without rockbolt reinforcement.The results show that both shear failure and tensile failure along joint surfaces are observed but the shear failure is a main controlling factor for the peak strength of the rock mass with and without rockbolts.The rockbolts are necked and shear deformation simultaneously happens in bolt reinforced rock specimens.As the joint dip angle increases,the joint shear failure becomes more dominant.The number of rockbolts has a significant impact on the peak strain and uniaxial compressive strength(UCS),but little influence on the deformation modulus of the rock mass.Using the Winkler beam model to represent the rockbolt behaviours,an analytical model for the prediction of the strength of boltreinforced blocky rocks is proposed.Good agreement between the UCS values predicted by proposed model and obtained from experiments suggest an encouraging performance of the proposed model.In addition,the performance of the proposed model is further assessed using published results in the literature,indicating the proposed model can be used effectively in the prediction of UCS of bolt-reinforced blocky rocks.

Deformation characteristics and damage ontologies of soft and hard composite rock masses under impact loading

As one of the most common occurring geological landforms in deep rock formations, the dynamic mechanical properties of layered composite rock bodies under impact loading have been widely studied by scholars. To study the dynamic properties of soft and hard composite rocks with different thickness ratios, this paper utilizes cement, quartz sand and gypsum powder to construct soft and hard composite rock specimens and utilizes a combination of indoor tests, numerical calculations, and theoretical analyses to investigate the mechanical properties of soft and hard composite rock bodies. The test results reveal that:(1) When the proportion of hard rock increases from 20% to 50%, the strength of the combined rock body increases by 69.14 MPa and 87 MPa when the hard rock face and soft rock face are loaded, respectively;however, when the proportion of hard rock is the same, the compressive strength of the hard rock face impact is 9%-17% greater than that of the soft rock face impact;(2) When a specimen of soft and hard combined rock body is subjected to impact loading, the damage mode involves mixed tension and shear damage, and the cracks generally first appear at the ends of the specimen, then develop on the laminar surface from the impact surface, and finally end in the overall damage of the soft rock part. The development rate and the total number of cracks in the same specimen when the hard rock face is impacted are significantly greater than those when the soft rock face is impacted;(3) By introducing Weibull’s statistical strength theory to establish the damage variables of soft-hard combined rock bodies, combined with the DP strength criterion, the damage model and the Kelvin body are concatenated to obtain a statistical damage constitutive model, which can better fit the full stress-strain curve of soft-hard combined rock body specimens under a single impact load.

Application and prospects of 3D printing in physical experiments of rock mass mechanics and engineering:materials,methodologies and models

The existence of joints or other kinds of discontinuities has a dramatic efect on the stability of rock excavations and engineering.As a result,a great challenge in rock mass mechanics testing is to prepare rock or rock-like samples with defects.In recent years,3D printing technology has become a promising tool in the feld of rock mass mechanics and engineering.This study frst reviews and discusses the research status of traditional test methods in rock mass mechanics tests of making rock samples with defects.Then,based on the comprehensive analysis of previous research,the application of 3D printing technology in rock mass mechanics is expounded from the following three aspects.The frst is the printing material.Although there are many materials for 3D printing,it has been found that 3D printing materials that can be used for rock mass mechanics research are very limited.After research,we summarize and evaluate printing material that can be used for rock mass mechanics studies.The second is the printing methodology,which mainly introduces the current application forms of 3D printing technology in rock mass mechanics.This includes printed precise casting molds and one-time printed samples.The last one is the printing model,which includes small-scale samples for mechanical tests and large-scale physical models.Then,the benefts and drawbacks of using 3D printing samples in mechanical tests and the validity of their simulation of real rock are discussed.Compared with traditional rock samples collected in nature or synthetic rock-like samples,the samples made by 3D printing technology have unique advantages,such as higher test repeatability,visualization of rock internal structure and stress distribution.There is thus great potential for the use of 3D printing in the feld of rock mass mechanics.However,3D printing materials also have shortcomings,such as insufcient material strength and accuracy at this stage.Finally,the application prospect of 3D printing technology in rock mass mechanics research is proposed.

Rock mass response for lined rock caverns subjected to high internal gas pressure

The storage of hydrogen gas in underground lined rock caverns(LRCs)enables the implementation of the first fossil-free steelmaking process to meet the large demand for crude steel.Predicting the response of rock mass is important to ensure that gas leakage due to rupture of the steel lining does not occur.Analytical and numerical models can be used to estimate the rock mass response to high internal pressure;however,the fitness of these models under different in situ stress conditions and cavern shapes has not been studied.In this paper,the suitability of analytical and numerical models to estimate the maximum cavern wall tangential strain under high internal pressure is studied.The analytical model is derived in detail and finite element(FE)models considering both two-dimensional(2D)and three-dimensional(3D)geometries are presented.These models are verified with field measurements from the LRC in Skallen,southwestern Sweden.The analytical model is inexpensive to implement and gives good results for isotropic in situ stress conditions and large cavern heights.For the case of anisotropic horizontal in situ stresses,as the conditions in Skallen,the 3D FE model is the best approach.

A 3D microseismic data-driven damage model for jointed rock mass under hydro-mechanical coupling conditions and its application

Rock mass is a fractured porous medium usually subjected to complex geostress and fluid pressure simultaneously.Moreover,the properties of rock mass change in time and space due to mining-induced fractures.Therefore,it is always challenging to accurately measure rock mass properties.In this study,a three-dimensional(3D)microseismic(MS)data-driven damage model for jointed rock mass under hydro-mechanical coupling conditions is proposed.It is a 3D finite element model that takes seepage,damage and stress field effects into account jointly.Multiple factors(i.e.joints,water and microseismicity)are used to optimize the rock mass mechanical parameters at different scales.The model is applied in Shirengou iron mine to study the damage evolution of rock mass and assess the crown pillar stability during the transition from open-pit to underground mining.It is found that the damage pattern is mostly controlled by the structure,water and rock mass parameters.The damage pattern is evidently different from the two-dimensional result and is more consistent with the field observations.This difference is caused by the MS-derived damage acting on the rock mass.MS data are responsible for gradually correcting the damage zone,changing the direction in which it expands,and promoting it to evolve close to reality.For the crown pillar,the proposed model yields a more trustworthy safety factor.In order to guarantee the stability of the pillar,it is suggested to take waterproof and reinforcement measures in areas with a high degree of damage.

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Strength and deformation characteristics of irregular columnar jointed rock mass: A combined experimental and theoretical study

The irregularity of jointed network poses a challenge to the determination of field mechanical param-eters of columnar jointed rock mass(CJRM),and a reasonable prediction of deformation and strength characteristics of CJRM is important for engineering construction.The Voronoi diagram and three-dimensional printing technology were used to make an irregular columnar jointed mold,and the irregular CJRM(ICJRM)specimens with different dip directions and dip angles were prepared.Uniaxial compression tests were performed,and the anisotropic strength and deformation characteristics of ICJRM were described.The failure modes and mechanisms were revealed in accordance with the final appearances of the ICJRM specimens.Based on the model test results,the empirical correlations for determining the field deformation and strength parameters of CJRM were derived using the dip angle and modified joint factor.The proposed empirical equations were used in the Baihetan Project,and the calculated mechanical parameters were compared with the field test results and those obtained from the tunneling quality index method.Results showed that the deformation parameters determined by the two proposed methods are all consistent with the field test results,and these two methods can also estimate the strength parameters effectively.

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

Determination of rock mass integrity coefficient using a non-invasive geophysical approach

Determination of rock mechanical parameters is the most important step in rock mass quality evaluation and has significant impacts on geotechnical engineering practice.Rock mass integrity coefficient(KV)is one of the most efficient parameters,which is conventionally determined from boreholes.Such approaches,however,are time-consuming and expensive,offer low data coverage of point measurements,require heavy equipment,and are hardly conducted in steep topographic sites.Hence,borehole approaches cannot assess the subsurface thoroughly for rock mass quality evaluation.Alternatively,use of geophysical methods is non-invasive,rapid and economical.The proposed geophysical approach makes useful empirical correlation between geophysical and geotechnical parameters.We evaluated the rock mass quality via integration between KV measured from the limited boreholes and inverted resistivity obtained from electrical resistivity tomography(ERT).The borehole-ERT correlation provided KV along various geophysical profiles for more detailed 2D/3D(two-/three-dimensional)mapping of rock mass quality.The subsurface was thoroughly evaluated for rock masses with different engineering qualities,including highly weathered rock,semi-weathered rock,and fresh rock.Furthermore,ERT was integrated with induced polarization(IP)to resolve the uncertainty caused by water/clay content.Our results show that the proposed method,compared with the conventional approaches,can reduce the ambiguities caused by inadequate data,and give more accurate insights into the subsurface for rock mass quality evaluation.

Anisotropic creep behavior of soft-hard interbedded rock masses based on 3D printing and digital imaging correlation technology

Three-dimensional(3D)printing technology is increasingly used in experimental research of geotechnical engineering.Compared to other materials,3D layer-by-layer printing specimens are extremely similar to the inherent properties of natural layered rock masses.In this paper,soft-hard interbedded rock masses with different dip angles were prepared based on 3D printing(3DP)sand core technology.Uniaxial compression creep tests were conducted to investigate its anisotropic creep behavior based on digital imaging correlation(DIC)technology.The results show that the anisotropic creep behavior of the 3DP soft-hard interbedded rock mass is mainly affected by the dip angles of the weak interlayer when the stress is at low levels.As the stress level increases,the effect of creep stress on its creep anisotropy increases significantly,and the dip angle is no longer the main factor.The minimum value of the long-term strength and creep failure strength always appears in the weak interlayer within 30°–60°,which explains why the failure of the layered rock mass is controlled by the weak interlayer and generally emerges at 45°.The tests results are verified by comparing with theoretical and other published studies.The feasibility of the 3DP soft-hard interbedded rock mass provides broad prospects and application values for 3DP technology in future experimental research.

Multi-domain equivalent method for prediction of elastic modulus of complex fractured rock mass

Reliable estimation of deformation and failure behaviors of fractured rock mass is important for practical engineering design.This study proposes a multi-domain equivalent method for fracture network to estimate the deformation properties of complex fractured rock mass.It comprehends both the advantages of the discrete fracture network model and the equivalent continuum model to capture the features of discontinuities explicitly while reducing computational intensity.The complex fracture network is stochastically split into a number of subfracture networks according to the domain,length or angle.An analytical solution is derived to infer theoretically the relationship between the elastic moduli of the original complex fractured rock mass and the split subfractured rock masses by introducing a correction term based on the deformation superposition principle.Numerical simulations are conducted to determine the elastic moduli of split subfractured rock masses using universal distinct element code(UDEC),while the elastic modulus of the original model is estimated based on the currently proposed analytical relationship.The results show that the estimation accuracy with the current domainbased splitting model is far superior compared to those with the other two splitting models.Thus,the estimation method of elastic modulus of complex fractured rock mass based on domain splitting mode of fracture network is identified as the multi-domain equivalent method proposed in this paper.The reliability of this method is evaluated,and its high computational efficiency is demonstrated through exemplification with regard to different geometric configurations for stochastically artificial discrete fracture network.The proposed multi-domain equivalent method constructs the theoretical framework except for the regression analysis hypothesis compared to the density-reduced model equivalent method.

Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.