Dynamic Stability Analysis of Rockmass: A Review

Dong, Longjun; Wang, Junhui; Li, Xibing; Peng, Kang

doi:https://doi.org/10.1155/2018/4270187

Advances in Civil Engineering

On this page

Abstract Introduction Conclusions Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Dynamic Failure Characteristics and Behavior of Rock Materials

View this Special Issue

Review Article | Open Access

Volume 2018 | Article ID 4270187 | https://doi.org/10.1155/2018/4270187

Dynamic Stability Analysis of Rockmass: A Review

Longjun Dong,¹Junhui Wang,¹Xibing Li,¹and Kang Peng^2,3

Guest Editor: Gaofeng Zhao

Received10 May 2018

Accepted09 Sept 2018

Published29 Nov 2018

Abstract

There are a series of human or natural activities, including earthquakes, explosions, and rockbursts, which have caused a number of safety accidents in geotechnical engineering. This review paper summarized the theories and methods for dynamic stability analysis of rockmass. First, numerical simulation methods, including finite element method (FEM), discrete element method (DEM), finite difference method (FDM), boundary element method (BEM), discontinuous deformation analysis method (DDA), and numerical manifold method (NMM), are summarized. Second, the laboratory experiments, containing shaking table test, split-Hopkinson pressure bar test (SHPB), improved true-triaxial test, dynamic centrifugal model test, acoustic emission (AE) technique, and dynamic infrared monitoring, are considered. Third, the in situ tests including microseismic (MS) technique, velocity tomography, stress-strain monitoring, and electromagnetic radiation monitoring are considered. Finally, some comprehensive analysis methods based on statistical theories are also provided. It is pointed out that the study foundation for the dynamic stability of rockmass is weak to explain the mechanism. So, a set of general comprehensive theories integrating the different methods, including theoretical analysis, numerical methods, laboratory experiments, and in situ tests, should be completely established. This is the most effective way for further investigation.

1. Introduction

The dynamic stability of rockmass is mainly in the aspects of earthquakes, explosions, and rockbursts. The threats of earthquakes and their associated geological disasters such as landslides to mankind have lasted for thousands of years. Under the influence of large magnitude earthquakes including Wenchuan earthquake [1] and Ya’an earthquake [2], people have realized the urgency and importance of the study on structural stability under seismicity [3, 4]. Because of the diversity and complexity of earthquakes, prediction of earthquake is very difficult. The researches on the failure mechanism of geotechnical engineering including slopes, underground adits, and other geotechnical engineering under the seismic condition provide a scientific reference for the selection of engineering seismic performance, which is currently the hotspot in rock mechanics.

The explosions have great influence on geotechnical engineering, such as the stability of tunnels and slopes. The impact of the explosions on the rockmass is mainly caused by the stress wave generated by the explosion stress wave and the explosive gas. Harmful gases from explosions can affect mine production and environment [5]. The stress effect of the explosive gas cannot be ignored. To solve this problem, gas pressure can be considered as a quasi-static process [6, 7]. It has a certain impact on the living environment of residents near construction sites [8, 9]. Besides, the seismicity caused by the explosions may lead to the collapse of the goafs and seriously threaten the mine production [10, 11]. Most of the common explosions are due to chemical explosions caused by detonation of explosives. In order to reduce the environmental pollution caused by the explosions of toxic and harmful gases, CO₂ blasting has been gradually developed in the fields of mining engineering and geotechnical engineering [12–14]. The effect of shock wave and explosive gas on rockmass is shown in Figure 1.

(a)

(b)

Rockbursts are common disasters in underground engineering of high stress [15, 16]. When the rockburst occurs, the energy releases sharply, which makes the surrounding rockmasses suddenly broken under unknown conditions. It threatens the construction workers, underground equipment, and building safety. Rockbursts can be divided into two types: strain rockburst and impact rockburst. Both phenomena occur on the surfaces of the rockmasses, but the sources of force and time are different [17]. Rockbursts in coal mines have a large degree of damage and a wide range of influences, which may lead to serious disasters such as gas explosions [18]. Furthermore, rockbursts can cause fire, water inrush, and the destruction of the ventilation system, which are of great concern in rock dynamics [19–22].

It is obvious that numerical simulation software, including ANSYS, ABAQUS, UDEC, 3DEC, FLAC, and PFC, have contributed greatly to the application of numerical simulation methods to the study of rock and soil mass [23–27]. In addition, in terms of experimental instruments, several advanced testing machines such as MTS and Instron series of rock mechanics large-scale testing machines have been manufactured to meet the needs of scientific researches. Besides, in situ tests containing MS technique, velocity tomography, stress-strain monitoring, and electromagnetic radiation monitoring have been improved dramatically. Furthermore, some comprehensive analysis methods based on statistical theories including the Bayesian network are constantly considered. However, it must be clearly realized that there are many theories and methods to be improved, such as the standardization of numerical simulation and the technological improvement of experimental equipment. Fortunately, more manpower and material resources are invested, which has contributed greatly to the researches on the dynamic stability of rockmass.

This review aims at providing several theories and methods to dynamic stability analysis of rockmass. First, the numerical simulation methods (including FEM, DEM, FDM, BEM, DDA, and NMM) are summarized; then, some laboratory experiments and in situ tests are considered. Moreover, some comprehensive analysis methods based on statistical theories are described. Finally, a brief summary and some prospective researches are presented.

2. Numerical Simulation Methods

2.1. Finite Element Method (FEM)

FEM was developed because of the variational principle in the early stage. In the 1950s, FEM was used to realize the mechanical analysis of the aircraft. At the same time, large-scale electronic computers were put into the work of understanding the large-scale algebraic equations. Around 1960, Clough and Feng proposed “finite element,” and the finite element technology was born [28].

FEM is a numerical analysis method that obtains approximate solutions to engineering problems. It replaces the real structure with a discrete set of finite elements. Through the analysis of elements, the approximate solution of the entire structure is obtained. In the engineering design, this approximate solution can meet the needs of the project with sufficient accuracy.

The process of finite element analysis is to divide the complex geometric and force objects into elements with relatively a simple shape and then give the displacement and force description of the element nodes. Next, the element stiffness equation is constructed. Then, by the assembly of the element through the node connection relationship among the elements, the overall rigidity equation of the structure can be obtained. Finally, according to the displacement constraints and stress conditions, the boundary conditions are processed and the solution is found [29]. The schematic diagram of the basic process is shown in Figure 2.

FEM is widely used in the analysis of rockmass dynamic stability. The commonly used finite element software includes ANSYS (LS-DYNA), ABAQUS, and ADINA. Besides, some new development software, like RFPA, was developed for rockmass instability rupture and stability under dynamic and static loads [31–34]. Secondary development based on commonly used numerical simulation programs was also carried out. Some applications of the FEM in dynamic stability analysis of rockmass are summarized in Table 1. FEM is more mature in the field of numerical simulation, and the calculation is stable and reliable, so it has a wide range of applications.

FEM is widely used because it allows every element to have different shapes and material properties, has the advantages of strong geometric adaptability and flexibility to handle different physical parameters [50]. However, the spatial derivative of FEM physical quantity is obtained by deriving the shape function, so the accuracy is lower by one degree than the physical quantity itself. The finite element analysis is greatly affected by the mesh. When analyzing the complex model, the finite element mesh may be deformed and overlapped, which affects the calculation accuracy.

2.2. Discrete Element Method (DEM)

DEM was proposed by Cundall in 1971 [51]. It is suitable for studying the mechanical problems of joint systems or block assemblies under quasi-static or dynamic conditions. Initially, DEM was used to analyze the motion of rock slopes. Later, Cundall and Strack developed the BALL program for simulating two-dimensionally circular blocks of granular media and obtained results that were very consistent with experiments using photoelastic technology [52]. There were many results that had been achieved in research and application of DEM.

DEM is a numerical simulation method specifically designed to solve the problem of discontinuous media. Rocks and structural planes with different properties constitute the basic components of the rockmasses. DEM describes the two basic components of rockmasses by the laws of continuum mechanics and contact, in which the structural planes are the boundaries of the rocks. A single rock is used as an independent object for mechanical analysis. The forces and displacements are transmitted through the structural planes and other adjacent rocks.

The general solving processes of DEM are as follows: first of all, combined with the actual problem, the research object is discretized into an element array, and the elements are connected by reasonable connection methods. Then, the relationship between the forces and the relative displacements can be used to obtain the normal and tangential forces between the elements. Additionally, the forces acting in all directions among the elements and other external forces acting on the elements are combined, and the acceleration of the elements can be obtained according to the second law of Newton's motion. The acceleration is integrated to obtain the velocity and displacement of the elements. Thereby, physical quantities such as speed and acceleration of all the elements at any time are obtained [53].

DEM has been developed for several decades, and the corresponding simulation software is constantly developing and improving. In 1980, the ITASCA Consulting Group from the United States developed DEM software UDEC and launched it on the market [54]. Lorig et al. [55] developed a discrete element-boundary element coupling calculation program. The PFC developed by the ITASCA Consulting Group from the United States is also widely used. Since its debut, DEM has been widely used in the field of geotechnical engineering. With the in-depth study of rockmass dynamic stability problems, DEM has received widespread attention because of its characteristics. Some applications of the discrete element method in dynamic stability analysis of rockmass are summarized in Table 2. DEM is more fitting to the simulation analysis of broken rockmass. With the continuous improvement of this method, it obtains much applications in rockmass dynamic stability analysis.

DEM has a good effect on the study of joint cracks and dynamic problems in engineering. However, DEM has certain deficiencies, and its theoretical method still needs improvement. Most of the methods used are explicit algorithms, and the problems of error transfer brought about by continuous accumulation. The calculation speed of DEM has a great relationship with the division of the elements, and the more the elements and contact surfaces, the greater the amount of calculations. Therefore, when simulating large-scale engineering problems, it takes a lot of time.

2.3. Finite Difference Method (FDM)

FDM originated from the research results of Southwell (1940) and Otter (1966) and has a long history of development. It is mainly used to analyze stress and deformation in rockmasses. Itasca applied FDM to develop FLAC and FLAC3D for stress and deformation analysis of rock and soil masses. It has a wide range of applications in geotechnical engineering and mining engineering.

The basic idea of FDM is to use differential meshing to solve the discrete solution of the domain. The differential equation is used to convert the scientific control equation into the difference equation through the difference formula. The initial conditions and boundary conditions are used to solve the linear equations, and then, the initial and boundary conditions are combined to solve the linear equations.

FLAC and FLAC3D are intended to analyze the problem of continuous media or media with very sparse joints. They use explicit finite difference techniques to solve the problem domain control equations. Explicit finite difference techniques means that the unknown quantity of the problem can be determined step by step according to the steps of each difference equation directly through the known quantity. With this method, a large matrix does not need to be formed during the analysis, so the need for computer storage space can be reduced. The value is relatively stable during the calculation process, and the iterative solution process is optimized. In addition, the method also considers initial conditions, boundary conditions, and constitutive equation of the medium. The FLAC3D numerical model construction and simulation analysis is shown in Figure 3.

(a)

(b)

(c)

FDM has been applied to simulate and analyze the rockmass dynamics problem. Based on the engineering practice, combined with FLAC3D, the mining kinetics behavior of coal and rockmass under different propulsion speeds was studied [67]. FLAC3D was applied to study the displacement characteristics of underground adits under earthquake loads and analyze the response characteristics of underground adits under consistent/nonuniform ground motion input conditions [68, 69]. The FLAC/PFC2D-coupled calculation model was applied to study the mechanism of seismic failure of the reversed rock slope [70]. The stability of a broken chamber in the Dongguashan copper mine under dynamic load conditions was analyzed [71]. The dynamic response of horizontally layered rock slope under SV wave action was researched by FLAC [72]. Besides, Zheng et al. [73] used FLAC3D to analyze the stability of the anchor system under blasting impact loads. Mortazavin and Alavi [74] used FLAC3D to study the effect of the full-shotcrete anchor support on surrounding rock under dynamic loading. Srikrishnan et al. [75] used FLAC3D to study the failure mechanism of the slope under earthquake loads. Probabilistic statistics and FLAC3D were used to analyze the dynamic stability and failure mechanism of the slope [76]. FDM plays a certain role in dynamic stability analysis.

Numerical simulation software FLAC and FLAC3D for FDM have the following advantages:(1)They are suitable for progressive failure and instability of rock and soil, as well as large deformation analysis.(2)Using the dynamic equations of motion makes them not to encounter numerical difficulties when dealing with unstable problems.(3)The loads in any direction can be arbitrarily applied.(4)It is more reasonable to simulate plastic fracture and plastic flow than FEM by the mixed discrete method.(5)They are more comprehensive in their response to displacement changes and failure modes.(6)Using explicit solution, there is no need to store the stiffness matrix, which significantly saves memory and computation time compared to the usual implicit solution [77].

At the same time, there are also several disadvantages:(1)Simplification of boundary conditions and material properties has an impact on the analysis results.(2)Most of the models are built by data files, and they cannot directly judge the rationality of the data inputted by the users. The preprocessing functions need to be improved. For complex engineering problems, it is often helpful to use some finite element software when building models and meshing [77].(3)The modeling workload is large and time-consuming, which lengthens the simulation cycle and increases the simulation difficulty [77].

2.4. Boundary Element Method (BEM)

BEM is a relatively accurate and effective numerical method after FEM [78]. After several decades of development until 1978, Brebbia held the first International Boundary Element Method Conference in Southampton, England, and published the monograph “The Boundary Element Method for Engineers” [79]. The boundary integral equations are derived using the weighted residual method, and it is pointed out that the weighted residual method is the most common numerical method. At this point, the name of the boundary element method has been internationally recognized.

The solution steps of BEM are as follows: first, the elements are divided on the boundary; second, the boundary integral equations are transformed into linear algebraic equations; third, the boundary value is solved to be determined at each boundary element; finally, the function value of any point in the calculation area can be found by using the analytical formula that relates the boundary value to the function value in the domain.

BEM can be divided into two basic types: the direct method and the indirect method. The direct method is to establish boundary integral equations using variables with explicit physical meanings, while the indirect method uses ambiguous variables. The virtual stresses and the virtual displacements are applied as basic unknowns to the boundaries according to certain rules. By establishing discretized equations, these variables are calculated, and then the displacements and stresses in the boundaries and regions are obtained [80].

BEM is used less independently in the field of rockmass dynamic stability and is often combined with other methods. Zhang et al. [81] used the singular BEM based on the manifold element to simulate the crack propagation of gravity dams during earthquake damage. Yin et al. [82] combined the idea of the viscoelastic boundary with BEM to establish the artificial boundary conditions of BEM to eliminate the reflection of seismic waves on the boundaries of the computational domain. BEM simplifies the research problems, reduces the error caused by discretization, and has a relatively small amount of calculation. For this reason, it is receiving increasing attention.

BEM has great advantages in solving problems including contact, fracture mechanics, motion boundary, infinite domain, semi-infinite domain, and ultrathin structure. In many application areas, BEM is recognized as an important complement to FEM. BEM also has many problems: singularity problems, full-coefficient matrix problems, domain integral problems in nonlinear and nonhomogeneous problems, thermal radiation and related coupling problems, and composite media problems [83].

2.5. Discontinuous Deformation Analysis (DDA)

DDA was proposed by Shi [84, 85] in the 1980s. It was developed specifically to simulate the movement of discrete rockmass systems [86]. DDA is similar to FEM for solving the stress-displacement problem but considers the problem of large distribution of cracks and joints in the rockmasses, especially the interaction of the rock blocks along the discontinuous surfaces. DDA is usually expressed as the working energy method, which can be derived using the principle of minimum potential energy or the Hamiltonian principle. DDA uses the step-by-step approach to solve large displacement problems caused by discontinuous motion between blocks. Because this method considers the inertial force of block quality, it can be used to solve the full dynamic problem of mass movement.

DDA has been used to analyze the impact of rockmass dynamic problems, especially the effect of earthquakes on the stability of rockmass. In order to study the influence of near-fault seismic forces on landslides, Zhang et al. [87] used the discrete dynamic numerical analysis method DDA to simulate the motion characteristics of the landslide body. Zhang et al. [88] used the improved DDA method to study the seismic dynamic response of the underground powerhouse of the Dagangshan hydropower station in western China. Huang et al. [89] used the improved DDA method to analyze the Donghekou landslide triggered by the 2008 Wenchuan earthquake. Fu et al. [90] used DDA to analyze the seismic dynamic responses of underground adits.

The continuous research of DDA makes this method more mature and more suitable for large deformation analysis in geotechnical engineering. DDA combines the static analysis with dynamic analysis and has a complete block dynamics theory, making it capable of handling structural engineering, rock mechanics, and material analysis. However, because of the short development time of DDA, this method exposes some deficiencies in the specific application. The investigation and analysis of structural planes are neglected when studying DDA [91]. Moreover, deformation of the structural planes cannot be ignored in the deformation of blocks. These problems should be emphasized in research [92].

2.6. Numerical Manifold Method (NMM)

NMM has been proposed by G. H. Shi in 1992 that deals with both continuous and discontinuous problems. The core and most innovative feature of NMM is the adoption of two coverage systems which include mathematical coverage (MC) and physical coverage (PC). MC can be considered as the union of a series of mathematical elements, and it must cover the entire physical area. Similarly, physical coverage is the union of all physical slices within a physical area [93].

NMM is regarded as a numerical method in the new century and has an excellent development prospect [85]. Because the NMM can deal with both continuous and noncontinuous problems, it is extremely suitable for simulating the deformation law of jointed rockmasses, and it has developed rapidly in the field of rock mechanics and engineering.

NMM has many limitations. For example, there are many difficulties for the NMM method from 2D to 3D to be solved; it lacks the ability to solve the nonlinear problems of the rockmass; and the practical application of engineering problems is insufficient [94]. Therefore, the modified NMM is used for rockmass dynamic analysis. Zhao et al. [95, 96] extended NMM to study wave propagation across rockmasses and developed a continuum-discontinuum-coupled model to simulate rock failure under dynamic loads. Qu et al. [97, 98] used the improved NMM to evaluate the dynamic stability of fractured rockmass slopes under seismic action. Wu and Wong [99] studied the collapse instability caused by joints or mining fissures in underground adits or tunnels using the numerical method. NMM integrates some advantages of the continuous FEM and the discontinuous DDA. In order to break through the limitations of the interelement correlation conditions, a great deal of effort in the study of the combination of the meshless method and NMM has been invested [100–102], making the NMM method able to achieve considerable development in rockmass stability analysis.

2.7. Comparison of Several Numerical Simulation Methods

Due to low cost and suitability for solving rockmass stability problems that are not suitable under many current experimental conditions, numerical simulation methods have been widely applied. Different numerical simulation methods have a different applicability to different engineering problems. Therefore, to study the dynamic stability problems of different rockmasses, choosing different numerical simulation methods has a certain influence on the reliability of the analysis results.

In addition, a variety of numerical simulation methods are used to accurately analyze the problems. For example, BEM is used to consider the influence of far-field stress to simulate the elastic properties; FEM is used as an intermediate transition to consider plastic deformation; and DEM is used to consider the near-field discontinuous deformation [103]. The concept of permanent displacement was proposed, and various numerical simulation software (including FLAC, UDEC, 3DEC, and DDA) were combined to study the stability of the slope under the action of earthquake loads [104]. Besides, FEM and DEM were applied to analyze the effect of force on collision of materials [105]. Furthermore, FLAC, ABAQUS, and engineering practice were combined to study the dynamic design method of deep hard rock tunnels [106]. For this reason, it is very necessary to develop a more general theory and method of numerical simulation, which needs comparing commonly used numerical simulation methods as the first step.

FEM is relatively mature in development and has a wide range of analysis software, represented by ANSYS/LS-DYNA, ABAQUS, ADINA, etc. It is suitable for above medium hardness, small deformation rockmasses, which are not be discontinuously damaged. However, the accuracy of FEM calculation needs to be improved. DEM has been in development for decades. The simulation software is constantly being improved, represented by UDEC, 3DEC, and PFC from Itasca. It is suitable for above medium hardness, low stress level, and large deformation rockmasses. However, DEM has deficiencies, the theoretical method does not form a complete system, the calculation amount is large, and the precision needs to be improved. FDM has a long history of development, with the software represented by FLAC and FLAC3D from Itasca. It is suitable for weak and deformed rockmasses. However, the preprocessing functions of FLAC and FLAC3D are weak. Besides, the application of FDM in rockmass dynamics analysis is not mature enough. BEM is a relatively accurate numerical method after FEM. It is suitable for above medium hardness—small deformation rockmasses—which are not be discontinuously damaged. However, it is rarely used in rockmasses dynamics analysis. DDA has been applied in the dynamic stability of rockmasses and is constantly maturing. It is suitable for large deformation analysis in geotechnical engineering and discontinuous failure of rockmasses. However, DDA application time is short, and there is a problem of ignoring the role of the structural plane. NMM is a numerical method with good development prospects in the new century. It is suitable for above medium hardness and continuous or discontinuous deformation rockmasses. However, the development of NMM is not mature enough, so it often needs to be corrected. A comparison of several numerical simulation methods is shown in Figure 4.

3. Laboratory Experiments on Rockmasses under Dynamic Load

Due to its instability and other reasons, rockmasses are difficult to accurately measure through the sites. Therefore, in addition to the commonly used numerical simulations for dynamic stability analysis, laboratory experiments combined with similar models have become common methods [108]. Commonly used experimental methods include the shaking table test, split-Hopkinson pressure bar test (SHPB), improved true-triaxial test, dynamic centrifugal model test, and AE technique, and dynamic infrared monitoring.

3.1. Shaking Table Test

By inputting seismic waves to the shaker table and stimulating the reaction of the structure on the shake table, the seismic process can be reproduced well and artificial seismic wave experiments can be performed [109–111]. The experiment should follow the principle of similarity ratio [112]. It is the most direct method for studying the structural earthquake response and failure mechanism in the laboratory. The technology of the shaking table test is also improving. A new real-time substructuring technique for the shaking table test was proposed [113]. It has played a very important role in the field of marine structural engineering, hydraulic structures, and mining engineering related to rockmass dynamics [114–116]. Because the shaking table test has the characteristics of simple operation, high efficiency, and so on, its application field is expanding. This method is one of the important means in the current seismic research [117, 118].

3.2. Split-Hopkinson Pressure Bar Test (SHPB)

The principle of the test is to clamp the specimen between two slender elastic rods (incident rods and transmissive rods), and the cylindrical bullet impacts the other end of the incident elastic rod at a certain speed, and then a compressive stress pulse is generated and propagated along the incident elastic rod in the direction of the sample [119–121]. When the stress waves are transmitted to the interface between the incident rod and the specimen, part of them are reflected back to the incident rod, and the others are loaded on the specimen and transmitted to the transmissive rod. The incident pulse, reflected pulse, and transmitted pulse can be recorded through the strain gauges attached to the incident and transmissive rods. One-dimensional stress wave theory can determine the stress, strain rate, strain change over time, and stress and strain curves on the specimen.

SHPB is widely used in rock dynamics. Li et al. [122–124] developed a modified split Hopkinson pressure bar for a coupling load experiment at medium-to-high strain rate. Zhou et al. [125] gave reasonable predictions for constitutive relationships of rock at different strain rates, which widened the application scope of SHPB. Besides, they investigated the stress evolution and cross-sectional stress uniformity along the thick bar, giving practicable guidance for SHPB test [126]. Gong et al. [127, 128] studied the dynamic characteristics and failure modes of rocks, providing guidance for deep mining. Li et al. [129] analyzed and experimentally studied the problem of normal propagation of longitudinal waves in filled rock joints. The split-Hopkinson pressure bar is shown in Figure 5.

3.3. True-Triaxial Rockburst Test

The true-triaxial test can perform stress and strain studies on the XYZ in three directions. If the dynamic load is changed in one direction, the rock sample can be subjected to a dynamic failure test. The systems consisted of true three-axis impact testing machines, linear variable differential transformers, high-speed cameras, digital cameras, and acoustic emission systems. Experiments applied dynamic loads in the X and Z directions, which can be applied by two independent dynamic loaders, with adjustable amplitude, duration, and frequency to input different stress waves. China University of Mining and Technology, Beijing, Central South University, and Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Northeastern University, and Guangxi University developed true-triaxial rockburst test systems. He et al. [130] applied the true triaxial experiment combined with acoustic emission to obtain the dynamic damage process and characteristics of limestone. Du et al. [131] explored the failure behavior of different rock types using a novel test system coupled to true triaxial static loads and local dynamic disturbance. Feng [132] summed up the research status of rockburst and mentioned that the true-triaxial test has been developed to study the mechanism of rockburst development process. Su et al. [133] used the improved true triaxial test system to study the development law of granite rockburst. True-triaxial rockburst test is shown in Figure 6 [17].

3.4. Dynamic Centrifuge Model Test

The centrifugal model is a method commonly used in engineering by using similar simulations to study physical phenomena to help solve theoretical and design problems. Earthquake dynamic response of rock and soil is a research hotspot of centrifugal model tests. When conducting studies on earthquakes, explosions, etc., the geotechnical model needs to be placed in the centrifugal field and coupled with a certain frequency of vibration. The vibration can be provided by a centrifugal shaker placed on a working basket. Tsinghua University used the geotechnical centrifugal test to generate horizontal seismic waves through a hydraulic servosystem to simulate the dynamic stability of the slope and provided a new method for analyzing the dynamic stability of the slope [134–136].

3.5. Acoustic Emission (AE) Technique

As a result of either dislocations at the microlevel or at the macrolevel by twinning and grain boundary movements, or initiation and propagation of fractures between mineral grains, AE occurs by a crack formed in rocks under high stress. AE is also described as the breaking and destruction of large-area materials on a giant scale or the relative movement between structural elements. When new cracks are created or cracks propagate, the elastic energy stored in the rock suddenly releases. With the occurrence of this process, elastic stress waves are generated and transmitted from the interior of the rock to the boundary, where it is observed as an AE signal [130]. AE technique can effectively monitor the development of microcracks inside the rock until the macroscopic damage process [21, 137]. Indoor AE experiments were widely used in the study of rockmass dynamic damage and achieved certain results [138, 139]. AE technology was also used to monitor coal rock and gas dynamic disasters, as well as slope failure [140, 141]. Moreover, the AE source localization algorithm is constantly improving [142].

3.6. Dynamic Infrared Monitoring

It is used to determine the state of rock, mainly by the infrared thermal imager to observe the distribution and change of infrared radiation temperature during the process of rock loading until its damage [143–145]. Combined with the stress variation curve of the rockmasses, the variation characteristics of the infrared radiation temperature of the rockmasses during the stress process and the impending damage are summarized. The ultimate goal is to use the space-time evolution law of infrared radiation in the process of rockmass to judge, monitor, and predict the stress concentration and damage of rockmass and provide theoretical basis and technology for the early warning of disasters related to rockmass damage. It is important for prediction of rockbursts and earthquakes [146].

Laboratory experiments can obtain the physical and mechanical properties of rock samples and realize the strength of rock under dynamic loading conditions. Compared with numerical simulation methods, it is more intuitive and credible. However, laboratory experiments are costly and cannot be studied for many specific complex conditions. In addition, due to the limitations of the specifications and techniques of experimental instruments, when studying large-scale geotechnical engineering, it is necessary to reduce the proportion of research objects to create similar models, resulting in deviations between experimental results and actual projects. Therefore, in order to overcome these problems, it is necessary to improve the experimental techniques while comparing with the numerical simulation methods to enhance the reliability of the research results.

4. In Situ Tests and Monitoring

4.1. Microseismic (MS) Technique

With the exploitation of deep resources and the utilization of underground space, underground projects continue to develop deeper, and the frequency of rockbursts and other events have greatly increased. Therefore, it is particularly important for the stability monitoring of rockmass. It is an indispensable method to study the dynamic stability of rockmass by analyzing the field data through engineering practice. The MS technique developed based on geophysics can effectively monitor the location of rock microcracks and has been widely used in mines and hydropower projects [147–149]. The location of microfractures obtained through MS technique is used to determine the potential activities of the mine’s dynamic disasters and to achieve safety early warning functions [150].

The essence of the MS event is the manifestation of a series of dynamic processes including rock stress-strain and instability damage. Therefore, the MS signal contains a lot of useful information on rock failure and geological defect activation process. The MS technique is widely used in geotechnical engineering, including rockbursts, rockmass collapses, and many other aspects [151]. Enough sensors are placed in the underground space to form a spatial network structure. The sensor network can monitor the stress wave signal emitted during the internal destruction of the rockmass. After filtering, P-wave (S-wave) pick-up, source location, and focal mechanism analysis, the spatial-temporal characteristics of MS events are determined, and quantitative mechanical properties of rockmass are described by quantitative seismological methods. Thereby, the stability of rockmass is analyzed.

The research directions of MS technique are mainly in four aspects: localization, identification, focal mechanism, and early warning.

First, for localization, many various advanced source localization algorithms have been proposed, such as the simplex method, genetic algorithm, and double-difference localization method. Ge [152, 153] summarized the current main iterative methods and analytical methods. Feng et al. [154, 155] introduced an efficient global-optimization algorithm and particle swarm optimization to improve localization accuracy. Li et al. [156] developed a nonlinear MS source localization method using the simplex method, which can search the MS source directly in the error space through four deformations of the simplex figures. However, the accuracy of localization methods based on the arrival time difference is usually affected by the premeasured wave velocity, the iterative algorithm, and the initial value.

To reduce the effects of abnormal arrivals, Dong et al. [157–161] proposed the analytical solution without the need to determine the wave velocity. Figure 7 shows the application of MS localization method without prior velocity measurement in mine. Explicit formulas were developed to resolve the accurate analytical solutions for cuboid and cube sensor networks to avoid the iterative error [147, 162, 163]. However, the geometric shapes of monitoring networks can hardly be special in the practical layout. To solve the above problems, the three-dimensional comprehensive analytical solutions (TDCAS) without premeasured velocity under random sensor networks was proposed [164]. Then, a collaborative localization method using analytical and iterative solutions (CLMAI) was proposed, which combined with the arrivals of multisensor and inversion of the real-time average wave velocity to seek the optimal locating results [165]. The analytical localization method is used to remove abnormal arrivals since it has a stable solution with the high precision when the input data are accurate. The iterative solution without the need of premeasured P-wave velocity is used to improve the locating accuracy since it can optimize results using the advantage of multiple sensors. This method not only eliminates the influence of premeasured wave velocity on localization but also eliminates the influence of abnormal arrivals. Figure 8 shows the application scatter diagram of P-wave velocity solved by the CLMAI method of the mining area for Yongshaba mine.

Figure 7

Propagation paths of P-wave with blasting sources and microseismic sources located at different coordinates. In the traditional methods such as STT and STD methods, the average velocity value of blasting source A is equal to the average value of v1 to v8, which is calculated through their own distances and travel times. However, the average velocity values of other blasting sources B, C, D, and E will not equal the source A due to the anisotropy of rock. Similarly, the authentic velocity value of a microseismic source is not equal to the velocity value used in the localization process. Thus, they are inaccurate locating sources using the premeasured average velocity value of the blasting sources, where the velocity paths are different from the sources to be located [164].

(a)

(b)

(c)

(d)

Secondly, the identification of MS signals is the analysis of P-wave and S-wave arrival times, wavefield characteristics, and signal denoising. The monitored MS signal was identified as the rock fracture signal, drilling signal, electrical signal, heavy vehicle passing signal, and blast signal [166]. Besides, based on large amounts of data and statistical methods, blasts and seismic events were identified. Dong et al. [148, 149] and Zhao et al. [167] used the Fisher Classifier, Naive Bayesian Classifier, and logistic regression to establish discriminators. Databases from three Australian and Canadian mines were established for training, calibrating, and testing the discriminant models. The classification performances and discriminant precision of the three statistical techniques were discussed and compared. The proposed discriminators have explicit and simple functions which can be easily used by workers in mines or researchers.

Thirdly, the focal mechanism refers to the physical and mechanical process of MS [168]. MS focal mechanism models are mainly divided into fault models [169, 170], expansion models [171–173], and force-couple models [174]. Moment tensor analysis was performed on the monitored MS information to understand the differences in the gestation mechanism of the strain type and strain-structure-slip type 2 rockbursts in the instantaneous rockburst [175]. Ma et al. [176] fully used waveform inversion and statistical methods to investigate the different MS source mechanisms in a more quantitative way. The caving and moving laws were analyzed to the overlying rock in the repetitive mining, the structure of the key blocks in heavy rock formations, the stress conditions in different regions, and the characteristics of fracture instability. Besides, the mechanism of the mine shock caused by repeated mining was obtained [177].

Finally, the MS warning is to analyze the time and probability of occurrence of MS events and provide guidance for safe construction. Using the cloud computing and Internet of Things, Dong et al. [178] established a prealarm model for tailing the dam based on real-time monitoring and numerical simulation. Combined with the trend of real-time monitoring deformation, as well as calculated dynamic safety factor, random reliability, and interval nonprobabilistic reliability, the stable or dangerous warning signals of the tailings dam can be obtained by the remote real-time prealarm system. By using MS technique, the occurrence of rockbursts was predicted in deep tunnel TBM excavation [179]. According to the measured evolution rules of MS information, Feng et al. [180] used the early warning of rockburst grades to conduct feedback analysis and dynamic control to avoid rockbursts, reduce the level, or delay the time of rockbursts. Taking into account the temporal and spatial characteristics of the seismic sequence before the large magnitude seismic events, Dong et al. [181] used the Gutenberg–Richter magnitude-frequency relationship, Hurst exponent, and Benioff strain to reveal the characteristics of the precursor seismicity. The MS warning technique can be more widely used in the field of rockmass dynamics.

The MS technique method is increasingly becoming one of the mainstream methods for in situ stability monitoring of geotechnical engineering. Numerical simulation and MS are combined to make the research more fully validated [182, 183]. The study of rockmass reliability considering MS loads also provides a new idea for rockmass stability analysis [184]. However, due to the anisotropy of rockmasses, the attenuation of wave velocity propagation in rockmasses and other theoretical studies are still not mature enough, making the monitoring results often have some errors. In addition, the MS technique costs a lot, requires a lot of manpower and material resources for installation and maintenance, and has certain requirements for the performance of the computer. However, with the development of advanced technologies such as artificial intelligence and cloud computing, these problems can be greatly improved.

4.2. Velocity Tomography

As a new geophysical method, velocity tomography, based on the MS technique, has broad application prospects in the field of rockmass dynamics [185]. This technique relies on seismic waves, specifically p-waves, through rockmasses. Velocity tomography can be divided into two types: “active” and “passive.” Active sources are generally artificially activated, including explosives, hammers strikes, and vibrations generated by shearer. Passive sources are generally naturally occurring seismic events [186]. The technique involves the forward algorithm and the inversion algorithm. Forward algorithm is used to accurately determine the propagation path of seismic waves, which is a key link in seismic tomography. According to the ray travel path obtained by the ray travel time and the forward algorithm, the wave velocity distribution in the medium is reversed, which is called the inversion algorithm [187].

Velocity tomography has the characteristics of large amount of information and high resolution, so it is widely used in engineering fields. The potential of rockburst in a longwall coal mine was studied by using passive seismic velocity tomography [188]. The three-dimensional velocity tomography technique was used to image the stress redistribution around the longwall of the coal seam to better understand the mechanism of rockburst. It was indicated that velocity tomography was an appropriate technique for monitoring the redistribution of stress in the underground mines [189]. Using the MS events and the arrival time data of the blasting, the P-wave tomogram of the Yongshaba deposit in the underground mining area of Guizhou Province, China, was inferred [190].

4.3. Stress-Strain Monitoring

The stress-strain change of rockmass during the construction process is a very complicated process. It is affected by factors such as the nature of rockmass and the order of excavation. By applying the stress-strain monitoring technique, engineers and technicians can fully grasp the ground pressure state caused by blasting, excavation, etc., so as to ensure the safety of construction and production.

The instrument system used to monitor the stress or strain of the rockmasses is generally composed of three parts: (1) sensors, which can transform the sensed measurement information into electrical signals or other identifiable signals according to certain rules; (2) transmission systems, which can transmit the signals emitted by sensors to the reading systems; (3) reading systems, which convert data into applicable forms for calculation and analysis [191].

The basic principle of stress-strain monitoring is that the surrounding rockmass stresses act on the sensors, stimulating sensors to generate identifiable signals. By the transmission systems, the signals are transmitted or amplified directly to the reading systems. By intermittently or continuously recording the signals, the stress or strain of the rockmasses around the measuring points can be obtained [191].

Stress-strain monitoring is widely used in engineering sites. The Institute of Geology of the Chinese Academy of Sciences (IGN) collaborated with Kumamoto University in Japan to design an improved method known as the compact conical ended borehole overcoring method (CCBO) for determining the stress state of the rockmasses [192]. In addition, the in situ dynamic monitoring technology was used to monitor the stress and strain of the Nantun Coal Mine, which helped to determine the maximum depth of the fracture zone under the coal seam and the width of the significant stress-increasing phase. It was an important factor in controlling mine water inrush [193]. Furthermore, the resistance strain gauge was used to monitor the surrounding rock strain and analyze the failure mode of the specimen, so as to study the secondary stress distribution characteristics and failure mechanism of the tunnel [194]. The stress-strain monitoring technique is constantly improving, which is also an important technology for rock laboratory experiments [195].

4.4. Electromagnetic Radiation Monitoring

The study of electromagnetic radiation monitoring in rockmass damage provides new methods and means for understanding the process, mechanism, monitoring, and forecasting of rockmass dynamic disaster occurrence and development.

A phenomenon in which the heterogeneous bodies, such as coal or rock, radiate energy outward during loading is called electromagnetic radiation. It is formed by the displacement motion of charged particles. It occurs in the process of charge migration and crack propagation caused by displacement deformation of various parts of the heterogeneous bodies. The electromagnetic radiation intensity mainly reflects the loading degree and deformation failure strength of the rockmasses [196]. When an abnormality occurs in the level of electromagnetic radiation monitoring, such as sudden increase, gradual increase, or strong fluctuation, it indicates the occurrence of rockburst. Also, the electromagnetic radiation monitoring value typically exceeds the warning threshold when the rockburst occurs.

This technique is mainly used to monitor and forecast accidents such as mine coal and rock dynamic disasters. Wang et al. [197] developed the KBD5 electromagnetic radiation monitor for coal and gas outburst. Electromagnetic radiation technology was widely used in the prediction of coal and gas outburst and impact ground pressure disasters, roof stability monitoring in goafs, surrounding rock stress distribution and mine pressure observation, and determination of the width of the pressure relief belt [198–200]. Song et al. [201] studied the electromagnetic radiation characteristics of coal-rock samples under uniaxial loading. It has a great theoretical and practical value for using electromagnetic radiation technology to evaluate the mechanical state of coal and rock, as well as accurately monitoring and predicting coal-rock dynamic disasters.

5. Some Comprehensive Analysis Methods Based on Statistical Theories

After a large number of literature statistics and analysis, in addition to the commonly used numerical simulation, experimental methods, and in situ tests, some effective methods have been developed for rockmass dynamic stability problems. Most of these methods are based on a large number of statistical data. Through the collection of geology, rockmasses properties and other related data, models can be established, with applying statistical methods to find the laws, which guide to solve the practical problems.

A three-dimensional Earth model of heterogeneous geomechanics was established in combination with physical and mechanical properties of rockmass and geological survey data. The variations of longitudinal wave velocity and shear velocity in each layer of reservoir rock were taken into account during modeling [202]. Rockbursts are often caused by the superposition of static and dynamic loads. During underground activities, the accumulated elastic energy is suddenly released in the rockmasses, causing failure with a sudden. By using random forest, support vector machine, and naive Bayes classification, Dong et al. obtained nonlinear methodologies for identifying the seismic event and nuclear explosion [203]. Figure 9 shows the application of ROC curves to compare the discrimination performance of different methods. Bayesian networks were used to be able to deal with the characteristics of incomplete data to predict rockbursts [204]. Quantitative statistical analysis was conducted through a large number of geological survey data and seismic data [205]. Adoko et al. [206] used fuzzy inference system (FIS), adaptive neurofuzzy inference systems (ANFIS), and field measurements data to predict rockburst intensity. He et al. [207] applied data mining (DM) techniques to the database to develop predictive models for the rockburst maximum stress and rockburst risk index that required the determination of the test results. Feng et al. [208] comprehensively studied the evolution process of different types of rockbursts in deep tunnels using a large number of indoor and field tests data. Liu et al. [209] proposed a microseismic method for early warning the occurrence probability of rockburst risk. Zhao et al. [210] applied microseismic monitoring data to comparatively study the microseismic characteristics and rockburst risk of deep tunnel excavated by TBM-drilling and blasting method. Feng et al. [211] proposed a fractal calculation method to study the self-similarity of the energy distribution of microseismic events during the development of immediate rockbursts. These methods are effective and have a great reference value for the study of rockmass dynamic stability.

6. Conclusions

It is obviously that numerical simulation, laboratory experiments, and in situ tests have wide applications in the dynamic stability analysis of rockmass. In addition, some comprehensive methods based on statistical theories have also been applied. However, there are corresponding deficiencies in different research methods, which require to be continuously improved.(1)According to the current development of the methods and the stability of rockmass dynamics, FEM, DEM, and FDM are commonly used. Some advantages, including low costs, wide range of applications, and simple using conditions, make the numerical simulation methods widely used. However, because the models are simplified, numerical simulation methods have problems such as lack of intuitiveness, which affect the results.(2)Laboratory experiments are various. At present, the split-Hopkinson pressure bar test, true-triaxial rockburst test, etc. are used to analyze the dynamic stability of the rockmasses. Laboratory experiments are more practical, reliable, and intuitive. However, the methods require the use of large-scale testing machines. The costs of the experiments are high, and the analyses are limited to certain rock samples.(3)In situ tests are most suitable for engineering practice, and the obtained data best meet engineering issues. However, these methods require the installation of sensors and other equipment, which have high costs and take long time. In addition, the relevant personnels need to be supervised, and the implementation is difficult.(4)Some comprehensive analysis methods based on statistical theories utilize a large amount of statistical data, so that they have a certain representation. They can find common rules in a large number of data. It is of great significance to study the natural laws of dynamic stability of rockmass. However, this work requires a lot of time to find data, and it can hardly cover all different situations. The obtained conclusions are not reliable.(5)There are many uncertainties under the influence of seismic loads including earthquakes, explosions, and rockbursts. In addition, the influence of geological structures, groundwater, etc. cannot be ignored in considering the stability of rockmass. These problems must be paid more attention in the process of studying the stability of rockmass dynamics.(6)It has a certain gap to the demand for solutions to many rockmass dynamic problems represented by earthquakes, explosions, and rockbursts, that the dynamic stability analysis of rockmass lacks perfect theories and methods. Therefore, in order to effectively solve the problems of the dynamic stability of rockmass that are becoming more noticeable, we should conduct more in-depth research on the theories and methods of analyzing problems in this field. It is important to note that when analyzing dynamic factors, it is recommended to consider uncertain information.(7)Through a large number of literatures, it is found that the traditional rockmass dynamic stability analysis methods are more suitable for studying earthquakes and explosions. Due to the difficulty of predicting rockbursts, numerical simulation methods have rarely studied rockbursts. The research methods for rockbursts are mainly MS technique and statistical data analysis. It is urgent to strengthen theoretical research and develop software suitable for rockburst analysis.

Numerical simulation software, laboratory experiments equipment, and in situ tests technique should be constantly improved to more accurately analyze the stability of rockmass under dynamic disturbances. Besides, a set of general comprehensive theories should be completely established to solve the errors caused by different methods. Furthermore, the most effective way to explore comprehensive analysis methods is to integrate the methods, including theoretical analysis, numerical methods, laboratory experiments, and in situ tests, so that they can adapt to different engineering problems and cross-validate each other.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors wish to acknowledge financial support from the National Natural Science Foundation of China (51774327, 51822407, 51504288, and 41630642), Young Elite Scientists Sponsorship Program by CAST (YESS20160175), Natural Science Foundation of Hunan Province, China (2018JJ1037), and National Basic Research Program of China (2015CB060200).

References

P. Z. Zhang, X. W. Xu, X. Z. Wen, and Y. K. Ran, “Slip rates and recurrence intervals of the Longmen Shan active fault zone, and tectonic implications for the mechanism of the May 12 Wenchuan earthquake, 2008, Sichuan, China,” Chinese Journal of Geophysics, vol. 51, no. 4, pp. 1066–1073, 2008.
View at: Google Scholar
B. H. Tang and L. L. Zhang, “Ya’an earthquake,” The Lancet, vol. 381, no. 9882, pp. 1984-1985, 2013.
View at: Publisher Site | Google Scholar
Y. Ouyang, “Earthquake tests China’s emergency system,” The Lancet, vol. 381, no. 9880, pp. 1801-1802, 2013.
View at: Publisher Site | Google Scholar
A. P. Tang and A. H. Wen, “An intelligent simulation system for earthquake disaster assessment,” Computers & Geosciences, vol. 35, no. 5, pp. 871–879, 2009.
View at: Publisher Site | Google Scholar
L. J. Dong, W. W. Shu, X. B. Li, and J. M. Zhang, “Quantitative evaluation and case studies of cleaner mining with multiple indexes considering uncertainty factors for phosphorus mines,” Journal Of Cleaner Production, vol. 183, pp. 319–334, 2018.
View at: Publisher Site | Google Scholar
C. Wei, W. Zhu, B. Yu, and L. Niu, “Numerical simulation on cutting seam cartridge blasting under different in-situ stress conditions,” Explosion & Shock Waves, vol. 36, no. 2, pp. 161–169, 2016.
View at: Google Scholar
Z. M. Zhu, H. P. Xie, and B. Mohanty, “Numerical investigation of blasting-induced damage in cylindrical rocks,” International Journal of Rock Mechanics and Mining Sciences, vol. 45, no. 2, pp. 111–121, 2008.
View at: Publisher Site | Google Scholar
M. P. Roy, P. K. Singh, M. Sarim, and L. S. Shekhawat, “Blast design and vibration control at an underground metal mine for the safety of surface structures,” International Journal of Rock Mechanics and Mining Sciences, vol. 83, pp. 107–115, 2016.
View at: Publisher Site | Google Scholar
Y. Yang, X. Xie, and R. Wang, “Numerical simulation of dynamic response of operating metro tunnel induced by ground explosion,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 2, no. 4, pp. 373–384, 2010.
View at: Google Scholar
Y. Potvin, “Strategies and tactics to control seismic risks in mines,” Journal of the South African Institute of Mining and Metallurgy, vol. 109, no. 3, pp. 177–186, 2009.
View at: Google Scholar
L. J. Dong, W. W. Shu, X. B. Li, and J. M. Zhang, “Quantitative evaluation and case study of risk degree for underground goafs with multiple indexes considering uncertain factors in mines,” Geofluids, vol. 183, pp. 319–334, 2017.
View at: Google Scholar
H. D. Chen, Z. F. Wang, X. E. Chen, X. J. Chen, and L. G. Wang, “Increasing permeability of coal seams using the phase energy of liquid carbon dioxide,” Journal of CO₂ Utilization, vol. 19, pp. 112–119, 2017.
View at: Publisher Site | Google Scholar
S. P. Singh, “Non-explosive applications of the PCF concept for underground excavation,” Tunnelling and Underground Space Technology, vol. 13, no. 3, pp. 305–311, 1998.
View at: Publisher Site | Google Scholar
N. Vidanovic, S. Ognjanovic, N. Ilincic, N. Ilic, and R. Tokalic, “Application of unconventional methods of underground premises construction in coal mines,” TTEM—Technics Technologies Education Management, vol. 6, no. 4, pp. 861–865, 2011.
View at: Google Scholar
M. Dietz, G. M. Oremek, D. A. Groneberg, and M. H. K. Bendels, “What is a rock burst?” Zentralblatt Für Arbeitsmedizin Arbeitsschutz Und Ergonomie, vol. 68, no. 1, pp. 45–49, 2018.
View at: Publisher Site | Google Scholar
Y. Potvin and J. Wesseloo, “Towards an understanding of dynamic demand on ground support,” Journal of the Southern African Institute of Mining And Metallurgy, vol. 113, no. 12, pp. 913–922, 2013.
View at: Google Scholar
M. C. He, D. Q. Liu, W. L. Gong et al., “Development of a testing system for impact rockbursts,” Chinese Journal of Rock Mechanics and Engineering, vol. 33, no. 9, pp. 1729–1739, 2014.
View at: Google Scholar
Y. D. Jiang, Y. S. Pan, F. X. Jiang, L. M. Dou, and Y. Ju, “State of the art review on mechanism and prevention of coal bumps in China,” Journal of China Coal Society, vol. 39, no. 2, pp. 205–213, 2014.
View at: Google Scholar
M. Ji, H. J. Guo, Y. D. Zhang, T. Li, and L. S. Gao, “Hierarchic analysis method to evaluate rock burst risk,” Mathematical Problems in Engineering, vol. 2015, Article ID 184398, 8 pages, 2015.
View at: Publisher Site | Google Scholar
L. J. Dong, X. B. Li, and K. Peng, “Prediction of rockburst classification using Random Forest,” Transactions of Nonferrous Metals Society of China, vol. 23, no. 2, pp. 472–477, 2013.
View at: Publisher Site | Google Scholar
M. C. He, J. L. Miao, D. J. Li, and C. G. Wang, “Experimental study on rockburst processes of granite specimen at great depth,” Chinese Journal of Rock Mechanics and Engineering, vol. 26, no. 5, pp. 865–876, 2007.
View at: Google Scholar
Q. Qian, “Definition, mechanism, classification and quantitative forecast model for rockburst and pressure bump,” Rock and Soil Mechanics, vol. 35, no. 1, pp. 1–6, 2014.
View at: Google Scholar
M. M. D. Banadaki and B. Mohanty, “Numerical simulation of stress wave induced fractures in rock,” International Journal of Impact Engineering, vol. 40-41, pp. 16–25, 2012.
View at: Google Scholar
X. M. Wang, Z. M. Zhu, M. Wang, P. Ying, L. Zhou, and Y. Q. Dong, “Study of rock dynamic fracture toughness by using VB-SCSC specimens under medium-low speed impacts,” Engineering Fracture Mechanics, vol. 181, pp. 52–64, 2017.
View at: Publisher Site | Google Scholar
Y. L. Gui, H. H. Bui, J. Kodikara, Q. B. Zhang, J. Zhao, and T. Rabczuk, “Modelling the dynamic failure of brittle rocks using a hybrid continuum-discrete element method with a mixed-mode cohesive fracture model,” International Journal of Impact Engineering, vol. 87, pp. 146–155, 2016.
View at: Publisher Site | Google Scholar
P. Sun, Y. P. Yin, S. R. Wu, and L. W. Chen, “Does vertical seismic force play an important role for the failure mechanism of rock avalanches? A case study of rock avalanches triggered by the Wenchuan earthquake of May 12, 2008, Sichuan, China,” Environmental Earth Sciences, vol. 66, no. 5, pp. 1285–1293, 2012.
View at: Publisher Site | Google Scholar
M. Cai, P. K. Kaiser, H. Morioka et al., “FLAC/PFC coupled numerical simulation of AE in large-scale underground excavations,” International Journal of Rock Mechanics and Mining Sciences, vol. 44, no. 4, pp. 550–564, 2007.
View at: Publisher Site | Google Scholar
Ansys, ANSYS 14.5, ANSYS Inc., Canonsburg, PA, USA, 2012.
P. Zeng, Finite Element Analysis and Application, Tsinghua University Press, Beijing, China, 2004.
K. J. Bathe, Finite Element Procedures, Prentice Hall, Pearson Education, Inc., Watertown, MA, USA, 2006.
W. C. Zhu, Y. J. Zuo, S. M. Shang, Z. H. Li, and C. A. Tang, “Numerical simulation of instable failure of deep rock tunnel triggered by dynamic disturbance,” Chinese Journal of Rock Mechanics and Engineering, vol. 26, no. 5, pp. 915–921, 2007.
View at: Google Scholar
W. C. Zhu, C. A. Tang, Z. P. Huang, and M. Z. Pang, “Numerical simulation on splitting failure mode of rock under static and dynamic loadings,” Chinese Journal of Rock Mechanics and Engineering, vol. 24, no. 1, pp. 1–7, 2005.
View at: Google Scholar
C. A. Tang and W. Zhao, “RFPA2D system for rock failure process analysis,” Chinese Journal of Rock Mechanics and Engineering, vol. 16, no. 5, pp. 507-508, 1997.
View at: Google Scholar
C. A. Tang, “Numerical simulation of ae in rock failure,” Chinese Journal of Rock Mechanics and Engineering, vol. 16, no. 4, pp. 368–374, 1997.
View at: Google Scholar
X. Cheng, Y. Ren, X. Du, and Y. Zhang, “Seismic stability of subsea tunnels subjected to seepage,” Scientific World Journal, vol. 2014, Article ID 631925, 8 pages, 2014.
View at: Google Scholar
X. S. Cheng, C. H. Dowding, and R. R. Tian, “New methods of safety evaluation for rock/soil mass surrounding tunnel under earthquake,” Journal of Central South University, vol. 21, no. 7, pp. 2935–2943, 2014.
View at: Publisher Site | Google Scholar
D. Shirole, C. Moormann, and K. G. Sharma, “A new continuum based model for the simulation of a seismically induced large-scale rockslide,” Plasticity and Impact Mechanics, vol. 173, pp. 1755–1762, 2017.
View at: Publisher Site | Google Scholar
Y. Y. Jiao, H. N. Tian, H. Z. Wu, H. B. Li, and H. M. Tang, “Numerical and experimental investigation on the stability of slopes threatened by earthquakes,” Arabian Journal of Geosciences, vol. 8, no. 7, pp. 4353–4364, 2015.
View at: Publisher Site | Google Scholar
Q. C. Lv, Y. R. Liu, and Q. Yang, “Stability analysis of earthquake-induced rock slope based on back analysis of shear strength parameters of rock mass,” Engineering Geology, vol. 228, pp. 39–49, 2017.
View at: Publisher Site | Google Scholar
A. L. Che, H. K. Yang, B. Wang, and X. R. Ge, “Wave propagations through jointed rock masses and their effects on the stability of slopes,” Engineering Geology, vol. 201, pp. 45–56, 2016.
View at: Publisher Site | Google Scholar
Y. R. Liu, Z. He, K. D. Leng, Y. Q. Huang, and Q. Yang, “Dynamic limit equilibrium analysis of sliding block for rock slope based on nonlinear FEM,” Journal of Central South University, vol. 20, no. 8, pp. 2263–2274, 2013.
View at: Publisher Site | Google Scholar
N. W. Xu, F. Dai, Z. Z. Liang, Z. Zhou, C. Sha, and C. A. Tang, “The dynamic evaluation of rock slope stability considering the effects of microseismic damage,” Rock Mechanics And Rock Engineering, vol. 47, no. 2, pp. 621–642, 2014.
View at: Publisher Site | Google Scholar
Y. Yang, J. T. Chen, and M. Xiao, “Analysis of seismic damage of underground powerhouse structure of hydropower plants based on dynamic contact force method,” Shock and Vibration, vol. 2014, Article ID 859648, 13 pages, 2014.
View at: Publisher Site | Google Scholar
B. Y. Zhao, Z. Y. Ma, and X. L. Ding, “Seismic response of a large underground rock cavern groups considering different incident angles of earthquake waves,” Chinese Journal of Rock Mechanics and Engineering, vol. 29, no. s1, pp. 3395–3402, 2010.
View at: Google Scholar
A. Kaya, K. Karaman, and F. Bulut, “Geotechnical investigations and remediation design for failure of tunnel portal section: a case study in northern Turkey,” Journal of Mountain Science, vol. 14, no. 6, pp. 1140–1160, 2017.
View at: Publisher Site | Google Scholar
A. Morales-Esteban, J. L. De Justo, J. Reyes, J. M. Azanon, P. Durand, and F. Martinez-Alvarez, “Stability analysis of a slope subject to real accelerograms by finite elements. Application to San Pedro cliff at the Alhambra in Granada,” Soil Dynamics and Earthquake Engineering, vol. 69, pp. 28–45, 2015.
View at: Publisher Site | Google Scholar
J. H. Yang, W. B. Lu, Y. G. Hu, M. Chen, and P. Yan, “Numerical simulation of rock mass damage evolution during deep-buried tunnel excavation by drill and blast,” Rock Mechanics and Rock Engineering, vol. 48, no. 5, pp. 2045–2059, 2015.
View at: Publisher Site | Google Scholar
N. Jiang, C. B. Zhou, S. W. Lu, and Z. Zhang, “Propagation and prediction of blasting vibration on slope in an open pit during underground mining,” Tunnelling and Underground Space Technology, vol. 70, pp. 409–421, 2017.
View at: Publisher Site | Google Scholar
X. P. Li, J. H. Huang, Y. Luo et al., “Numerical simulation of blast vibration and crack forming effect of rock-anchored beam excavation in deep underground caverns,” Shock and Vibration, vol. 2017, Article ID 2490431, 10 pages, 2017.
View at: Publisher Site | Google Scholar
I. M. Smith and D. V. Griffiths, Programming the Finite Element Method, John Wiley and Sons, New York, NY, USA, 4th edition, 2005.
P. A. Cundall, “A computer model for simulating progressive, large-scale movements in blocky rock systems,” in Proceedings of International Symposium on Rock Fracture, vol. 2, pp. 11–18, Nancy, France, October 1971.
View at: Google Scholar
P. A. Cundall and O. D. L. Strack, “A discrete numerical model for granular assembles,” Geotechnique, vol. 29, no. 1, pp. 47–65, 1979.
View at: Publisher Site | Google Scholar
Y. Wang, Numerical Simulation of Jointed and Fractured Rock Masses Blasting, Wuhan University of Technology, Wuhan, China, 2011.
P. A. Cundall and Associates Virginia Water, UDEC—A Generalized Distinct Element Program for Modelling Jointed Rock, Defense Technical Information Center, Fort Belvoir, VA, USA, 1980.
L. J. Lorig, B. H. G. Brady, and P. A. Cundall, “Hybrid distinct element-boundary element analysis of jointed rock,” International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, vol. 23, no. 4, pp. 303–312, 1986.
View at: Publisher Site | Google Scholar
R. J. Tan, M. S. Li, P. X. Xu, R. L. Hu, and Y. Su, “Numerical simulation of dynamic stability of slope rock mass under seismic loading,” Chinese Journal of Rock Mechanics and Engineering, vol. 28, pp. 3986–3992, 2009.
View at: Google Scholar
Z. Cui and Q. Sheng, “Seismic response of underground rock cavern dominated by a large geological discontinuity subjected to near-fault and far-field ground motions,” Chinese Journal of Rock Mechanics and Engineering, vol. 36, no. 1, pp. 53–67, 2017.
View at: Google Scholar
T. J. Cui, Y. D. Ma, and L. G. Wang, “Simulation and analysis of earthquake stability for open-pit coal mine slope,” Earthquake Engineering and Engineering Dynamics, vol. 35, no. 6, pp. 219–225, 2015.
View at: Google Scholar
X. J. Hu, K. Bian, P. C. Li, L. Z. Chen, and Z. P. Liu, “Simulation of dynamic failure process of horizontal thick-layered rock slopes using particle flow code,” Chinese Journal of Rock Mechanics and Engineering, vol. 36, no. 9, pp. 2156–2168, 2017.
View at: Google Scholar
S. Wang, Q. Sheng, Z. Q. Zhu, and X. D. Fu, “Dynamic stability evaluation for rock structure around underground cavern based on frictional energy on joints,” Chinese Journal of Rock Mechanics and Engineering, vol. 33, no. s2, pp. 4049–4055, 2014.
View at: Google Scholar
H. B. Li, T. T. Liu, Y. Q. Liu, J. C. Li, X. Xia, and B. Liu, “Numerical modeling of wave transmission across rock masses with nonlinear joints,” Rock Mechanics and Rock Engineering, vol. 49, no. 3, pp. 1115–1121, 2016.
View at: Publisher Site | Google Scholar
J. Zhao, J. G. Cai, X. B. Zhao, and H. B. Li, “Dynamic model of fracture normal behaviour and application to prediction of stress wave attenuation across fractures,” Rock Mechanics and Rock Engineering, vol. 41, no. 5, pp. 671–693, 2008.
View at: Publisher Site | Google Scholar
L. Yang, Y. J. Jiang, B. Li, S. C. Li, and G. Wang, “Estimation of dynamic behaviors of bedrock foundation subjected to seismic loads based on FEM and DEM simulations,” KSCE Journal of Civil Engineering, vol. 17, no. 2, pp. 342–350, 2013.
View at: Publisher Site | Google Scholar
T. J. Cui, Y. D. Ma, and L. G. Wang, “Simulated study on the influence of the slope blasting height on the slope stability,” Journal of Safety and Environment, vol. 17, no. 3, pp. 896–900, 2017.
View at: Google Scholar
H. B. Li, X. A. Xia, J. C. Li, J. A. Zhao, B. Liu, and Y. Q. Liu, “Rock damage control in bedrock blasting excavation for a nuclear power plant,” International Journal of Rock Mechanics and Mining Sciences, vol. 48, no. 2, pp. 210–218, 2011.
View at: Google Scholar
X. B. Li, C. J. Li, W. Z. Cao, and M. Tao, “Dynamic stress concentration and energy evolution of deep-buried tunnels under blasting loads,” International Journal of Rock Mechanics and Mining Sciences, vol. 104, pp. 131–146, 2018.
View at: Publisher Site | Google Scholar
H. F. Ma, C. M. Li, J. Z. Li, A. Y. Yuan, W. R. Liu, and C. Q. Zhu, “Response characteristics of mining-induced mechanical behavior for coal and rock mass under different advancing speed and its control,” Journal of Safety Science and Technology, vol. 12, no. 12, pp. 74–79, 2016.
View at: Google Scholar
H. B. Li, X. D. Ma, J. R. Li, H. C. Dai, and K. Q. Xiao, “Study on influence factors of rock cavern displacement under earthquake,” Chinese Journal of Geotechnical Engineering, vol. 28, no. 3, pp. 358–362, 2006.
View at: Google Scholar
H. B. Li, L. Zhu, T. Lu, J. H. Yang, X. Xia, and Y. Q. Liu, “Seismic response analysis of an underground cavern groups in rock subjected to spatially non-uniform seismic ground motion,” Chinese Journal of Rock Mechanics and Engineering, vol. 27, no. 9, pp. 1757–1766, 2008.
View at: Google Scholar
L. Liu, L. Chen, Z. H. Cui, and H. Li, “FLAC/PFC∼(2D) hybrid simulation for seismically induced failure process of toppling rock slope,” Journal of Engineering Geology, vol. 22, no. 6, pp. 1257–1262, 2014.
View at: Google Scholar
Y. J. Xu, W. Y. Qi, and L. F. Zeng, “Dynamic response of surrounding rock with high stress in deep chamber,” Hydrogeology and Engineering Geology, vol. 36, no. 6, pp. 94–98, 2009.
View at: Google Scholar
Z. F. Zhan and S. W. Qi, “Numerical study on dynamic response of a horizontal layered-structure rock slope under a normally incident Sv wave,” Applied Sciences-Basel, vol. 7, no. 7, p. 716, 2017.
View at: Publisher Site | Google Scholar
X. G. Zheng, J. B. Hua, N. Zhang, X. W. Feng, and L. Zhang, “Simulation of the load evolution of an anchoring system under a blasting impulse load using FLAC3D,” Shock and Vibration, vol. 2015, Article ID 972720, 8 pages, 2015.
View at: Publisher Site | Google Scholar
A. Mortazavi and F. T. Alavi, “A numerical study of the behavior of fully grouted rockbolts under dynamic loading,” Soil Dynamics and Earthquake Engineering, vol. 54, pp. 66–72, 2013.
View at: Publisher Site | Google Scholar
S. Srikrishnan, J. L. Porathur, and H. Agarwal, “Impact of earthquake on mining slopes-a numerical approach,” Arabian Journal of Geosciences, vol. 7, no. 12, pp. 5193–5208, 2014.
View at: Publisher Site | Google Scholar
D. L. Li, X. R. Liu, X. W. Li, and Y. Q. Liu, “The impact of microearthquakes induced by reservoir water level rise on stability of rock slope,” Shock and Vibration, vol. 2016, Article ID 7583108, 13 pages, 2016.
View at: Publisher Site | Google Scholar
L. Zou and X. Z. Peng, “Talking about the application principle, advantages, disadvantages and improvement measures of FLAC‐3D,” Sichuan Architecture, vol. 27, no. 1, pp. 152–156, 2007.
View at: Google Scholar
L. Q. Wang, Engineering Numerical Analysis, Shandong University Press, Jinan, Shangdong, China, 2002.
C. A. Brebbia, The Boundary Element Method for Engineers, Pentech Press, London, UK, 1978.
I. J. Stewart, “18 a static relaxation method for the analysis of excavations in discontinuous rocks,” in Proceedings of Design and Performance of Underground Excavations: ISRM Symposium, Thomas Telford, Cambridge, UK, September 1984.
View at: Google Scholar
G. X. Zhang, F. Jin, and G. L. Wang, “Seismic failure simulation of gravity dam by manifold-based singular boundary element method,” Engineering Mechanics, vol. 18, no. 4, pp. 18–27, 2001.
View at: Google Scholar
D. S. Yin, W. X. Qing, and C. S. Li, “Block element analysis for earthquake response of joint rock slope,” in Applied Mechanics and Materials, Trans Tech, Zürich, Switzerland, 2012.
View at: Google Scholar
X. W. Gao, H. F. Peng, K. Yang, and J. Wang, Higher Boundary Element Method: Theory and Procedures, China Science Press, Beijing, China, 2015.
G. H. Shi, Block System Modeling by Discontinuous Deformation Analysis, Computational Mechanics Publications, Southampton, UK, 1984.
G. H. Shi, Numerical Manifold Method (NMM) and Discontinuous Deformation Analysis (DDA), China Tsinghua University Press, Beijing, China, 1997.
D. D. Xu and A. Q. Wu, “Discontinuous deformation analysis method based on the explicit time integration scheme,” Chinese Journal of Rock Mechanics and Engineering, vol. 35, no. s2, pp. 3526–3534, 2016.
View at: Google Scholar
Y. B. Zhang, G. Q. Chen, L. Zheng, Y. G. Li, and J. Wu, “Effects of near-fault seismic loadings on run-out of large-scale landslide: a case study,” Engineering Geology, vol. 166, pp. 216–236, 2013.
View at: Publisher Site | Google Scholar
Y. H. Zhang, X. D. Fu, and Q. Sheng, “Modification of the discontinuous deformation analysis method and its application to seismic response analysis of large underground caverns,” Tunnelling and Underground Space Technology, vol. 40, pp. 241–250, 2014.
View at: Publisher Site | Google Scholar
D. Huang, Y. X. Song, D. F. Cen, and G. Y. Fu, “Numerical modeling of earthquake-induced landslide using an improved discontinuous deformation analysis considering dynamic friction degradation of joints,” Rock Mechanics and Rock Engineering, vol. 49, no. 12, pp. 4767–4786, 2016.
View at: Publisher Site | Google Scholar
X. D. Fu, Q. Sheng, Y. H. Zhang, Y. Q. Zhou, and F. Dai, “Boundary setting method for the seismic dynamic response analysis of engineering rock mass structures using the discontinuous deformation analysis method,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 39, no. 15, pp. 1693–1712, 2015.
View at: Publisher Site | Google Scholar
N. Li, F. X. Chen, Z. Q. Cui, and P. B. Huang, “A 3D block deformation analysis for J.Y. underground openings in jointed rock mass,” Chinese Journal of Rock Mechanics and Engineering, vol. 16, no. 1, pp. 22–28, 1997.
View at: Google Scholar
J. Liu, Z. K. Li, and Y. Xiao, Discontinuous Deformation Analysis Method and Its Application in Underground Engineering, Geological Press, Beijing, China, 2006.
D. D. Xu, H. Zheng, Y. T. Yang, and A. Q. Wu, “Multiple crack propagation based on the numerical manifold method,” Chinese Journal of Theoretical and Applied Mechanics, vol. 47, no. 3, pp. 471–481, 2015.
View at: Google Scholar
S. C. Li and S. C. Li, Numerical Method and Engineering Application of Rock Failure Process of Intermittent Joints, Science Press, Benjing, China, 2007.
G. F. Zhao, X. B. Zhao, and J. B. Zhu, “Application of the numerical manifold method for stress wave propagation across rock masses,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 38, no. 1, pp. 92–110, 2014.
View at: Publisher Site | Google Scholar
G. F. Zhao, N. Khalili, J. N. Fang, and J. Zhao, “A coupled distinct lattice spring model for rock failure under dynamic loads,” Computers and Geotechnics, vol. 42, pp. 1–20, 2012.
View at: Publisher Site | Google Scholar
X. L. Qu, Y. Wang, G. Y. Fu, and G. W. Ma, “Efficiency and accuracy verification of the explicit numerical manifold method for dynamic problems,” Rock Mechanics and Rock Engineering, vol. 48, no. 3, pp. 1131–1142, 2015.
View at: Publisher Site | Google Scholar
X. L. Qu, C. Z. Qi, G. Y. Fu, and X. Z. Li, “Effects of time integration schemes on discontinuous deformation simulations using the numerical manifold method,” International Journal for Numerical Methods in Engineering, vol. 112, no. 11, pp. 1614–1635, 2017.
View at: Publisher Site | Google Scholar
Z. J. Wu and L. N. Y. Wong, “Underground rockfall stability analysis using the numerical manifold method,” Advances in Engineering Software, vol. 76, pp. 69–85, 2014.
View at: Publisher Site | Google Scholar
R. Tian, Finite-Cover-Based Element-Free Method for Continuous and Discontinuous Deformation Analysis with Applications in Geotechnical Engineering, Dalian University of Technology, Dalian, China, 2000.
T. Belytschko, Y. Krongauz, D. Organ, M. Fleming, and P. Krysl, “Meshless methods: an overview and recent developments,” Computer Methods in Applied Mechanics and Engineering, vol. 139, no. 1–4, pp. 3–47, 1996.
View at: Publisher Site | Google Scholar
X. Liu, D. M. Zhu, M. W. Lu, and X. Zhang, “Study on meshless method based on manifold cover ideas,” Chinese Journal of Computational Mechanics, vol. 18, no. 1, pp. 21–27, 2001.
View at: Google Scholar
Y. J. Wang and J. B. Xing, Discrete Element Method and Its Application in Geomechanics, Northeast Institute of Technology Press, Jorhat, India, 1991.
H. B. Li, H. J. Jiang, J. Zhao, L. X. Huang, and J. R. Li, “Some problems about safty analysis of rock engeering under dynamic load,” Chinese Journal of Rock Mechanics and Engineering, vol. 22, no. 11, pp. 1887–1891, 2003.
View at: Google Scholar
S. H. Li, D. H. Tang, and J. Wang, “A two-scale contact model for collisions between blocks in CDEM,” Science China Technological Sciences, vol. 58, no. 9, pp. 1596–1603, 2015.
View at: Publisher Site | Google Scholar
X. T. Feng, C. Q. Zhang, S. L. Qiu, H. Zhou, Q. Jiang, and S. J. Li, “Dynamic design method for deep hard rock tunnels and its application,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 8, no. 4, pp. 443–461, 2016.
View at: Publisher Site | Google Scholar
X. H. Diao, “Discussion on the numerical simulation method of rock mechanics in underground mining engineering,” in Proceedings of National Metal Mine Mining Special Topics, Mineral Exploration Special Academic Discussion and Technical Exchange Conference, Xinzhou, China, 2008.
View at: Google Scholar
W. B. Jian, R. B. Hong, X. F. Fan, and B. R. Zhou, “Experimental study of dynamic response of jointed rock slopes under cyclic loads,” Chinese Journal of Rock Mechanics and Engineering, vol. 35, no. 12, pp. 2409–2416, 2016.
View at: Google Scholar
C. W. Li, X. Y. Sun, T. B. Gao, Y. F. Sun, B. J. Xie, and X. M. Xu, “Research on the variation characteristics of natural frequency in the process of coal and rock vibration damage,” Journal of China Coal Society, vol. 40, no. 10, pp. 2422–2429, 2015.
View at: Google Scholar
Y. L. Lin, Y. X. Li, G. L. Yang, and Y. Li, “Experimental and numerical study on the seismic behavior of anchoring frame beam supporting soil slope on rock mass,” Soil Dynamics and Earthquake Engineering, vol. 98, pp. 12–23, 2017.
View at: Publisher Site | Google Scholar
Y. L. Chung, T. Nagae, T. Hitaka, and M. Nakashima, “Seismic resistance capacity of high-rise buildings subjected to long-period ground motions: E-defense shaking table test,” Journal of Structural Engineering, vol. 136, no. 6, pp. 637–644, 2010.
View at: Publisher Site | Google Scholar
G. X. Chen, Z. H. Wang, X. Zuo, X. L. Du, and H. M. Gao, “Shaking table test on the seismic failure characteristics of a subway station structure on liquefiable ground,” Earthquake Engineering and Structural Dynamics, vol. 42, no. 10, pp. 1489–1507, 2013.
View at: Publisher Site | Google Scholar
S. K. Lee, E. C. Park, K. W. Min, and J. H. Park, “Real-time substructuring technique for the shaking table test of upper substructures,” Engineering Structures, vol. 29, no. 9, pp. 2219–2232, 2007.
View at: Publisher Site | Google Scholar
A. R. Ghaemmaghami and M. Ghaemian, “Shaking table test on small-scale retrofitted model of Sefid-rud concrete buttress dam,” Earthquake Engineering and Structural Dynamics, vol. 39, no. 1, pp. 109–118, 2010.
View at: Publisher Site | Google Scholar
A. R. Ghaemmaghami and A. Ghaemian, “Experimental seismic investigation of Sefid-rud concrete buttress dam model on shaking table,” Earthquake Engineering and Structural Dynamics, vol. 37, no. 5, pp. 809–823, 2008.
View at: Publisher Site | Google Scholar
P. B. Morin, P. Leger, and R. Tinawi, “Seismic behavior of post-tensioned gravity dams: shake table experiments and numerical simulations,” Journal of Structural Engineering, vol. 128, no. 2, pp. 140–152, 2002.
View at: Publisher Site | Google Scholar
Z. M. Shi, Y. Q. Wang, M. Peng, J. F. Chen, and J. Yuan, “Characteristics of the landslide dams induced by the 2008 Wenchuan earthquake and dynamic behavior analysis using large-scale shaking table tests,” Engineering Geology, vol. 194, pp. 25–37, 2015.
View at: Publisher Site | Google Scholar
Z. M. Shi, Y. Q. Wang, M. Peng, S. G. Guan, and J. F. Chen, “Landslide dam deformation analysis under aftershocks using large-scale shaking table tests measured by videogrammetric technique,” Engineering Geology, vol. 186, pp. 68–78, 2015.
View at: Publisher Site | Google Scholar
M. Huang, K. Tang, J. W. Zhan, and T. Deng, “Dynamic properties of grouting-reinforced rock mass and its damage mechanics model,” Journal of Vibration and Shock, vol. 36, no. 10, pp. 63–68, 2017.
View at: Google Scholar
L. Z. Tang, L. P. Cheng, C. Wang, J. B. Shu, J. L. Wu, and Y. Chen, “Dynamic characteristics of serpentinite under condition of high static load and frequent dynamic disturbance,” Rock and Soil Mechanics, vol. 37, no. 10, pp. 2737–2745, 2016.
View at: Google Scholar
X. B. Li, M. Tao, F. Q. Gong, Z. Q. Yin, and K. Du, “Theoretical and experimental study of hard rock spalling fracture under impact dynamic loading,” Chinese Journal of Rock Mechanics and Engineering, vol. 30, no. 6, pp. 1081–1088, 2011.
View at: Google Scholar
X. B. Li, F. Q. Gong, M. Tao et al., “Failure mechanism and coupled static-dynamic loading theory in deep hard rock mining: a review,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 9, no. 4, pp. 767–782, 2017.
View at: Publisher Site | Google Scholar
X. B. Li, T. Zhou, D. Y. Li, and Z. W. Wang, “Experimental and numerical investigations on feasibility and validity of prismatic rock specimen in SHPB,” Shock and Vibration, vol. 2016, Article ID 7198980, 13 pages, 2016.
View at: Publisher Site | Google Scholar
X. B. Li, F. Q. Gong, J. Zhao, K. Gao, and T. B. Yin, “Test study of impact failure of rock subjected to onedimensional coupled static and dynamic loads,” Chinese Journal of Rock Mechanics and Engineering, vol. 29, no. 2, pp. 251–260, 2010.
View at: Google Scholar
Z. L. Zhou, X. B. Li, Z. Y. Ye, and K. W. Liu, “Obtaining constitutive relationship for rate-dependent rock in SHPB tests,” Rock Mechanics and Rock Engineering, vol. 43, no. 6, pp. 697–706, 2010.
View at: Publisher Site | Google Scholar
Z. L. Zhou, X. B. Li, A. H. Liu, and Y. Zou, “Stress uniformity of split Hopkinson pressure bar under half-sine wave loads,” International Journal of Rock Mechanics and Mining Sciences, vol. 48, no. 4, pp. 697–701, 2011.
View at: Publisher Site | Google Scholar
F. Q. Gong, X. B. Li, and X. L. Liu, “Preliminary experimental study of characteristics of rock subjected to 3d coupled static and dynamic loads,” Chinese Journal of Rock Mechanics and Engineering, vol. 30, no. 6, pp. 1179–1190, 2011.
View at: Google Scholar
F. Q. Gong, X. B. Li, X. L. Liu, and J. Zhao, “Experimental study of dynamic characteristics of sandstone under one-dimensional coupled static and dynamic loads,” Chinese Journal of Rock Mechanics and Engineering, vol. 29, no. 10, pp. 2076–2085, 2010.
View at: Google Scholar
J. C. Li, G. W. Ma, and X. Huang, “Analysis of wave propagation through a filled rock joint,” Rock Mechanics and Rock Engineering, vol. 43, no. 6, pp. 789–798, 2010.
View at: Publisher Site | Google Scholar
M. C. He, J. L. Miao, and J. L. Feng, “Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions,” International Journal of Rock Mechanics and Mining Sciences, vol. 47, no. 2, pp. 286–298, 2010.
View at: Publisher Site | Google Scholar
K. Du, M. Tao, X. B. Li, and J. Zhou, “Experimental study of slabbing and rockburst induced by true-triaxial unloading and local dynamic disturbance,” Rock Mechanics and Rock Engineering, vol. 49, no. 9, pp. 3437–3453, 2016.
View at: Publisher Site | Google Scholar
X. T. Feng, Rockburst: Chapter 18-Conclusions and Future Developments, Elsevier, Kidlington, UK, pp. 549-550, 2018.
View at: Publisher Site
G. S. Su, X. T. Feng, J. H. Wang, J. Q. Jiang, and L. H. Hu, “Experimental study of remotely triggered rockburst induced by a tunnel axial dynamic disturbance under true-triaxial conditions,” Rock Mechanics and Rock Engineering, vol. 50, no. 8, pp. 2207–2226, 2017.
View at: Publisher Site | Google Scholar
L. P. Wang and G. Zhang, “Centrifuge model test study on pile reinforcement behavior of cohesive soil slopes under earthquake conditions,” Landslides, vol. 11, no. 2, pp. 213–223, 2014.
View at: Publisher Site | Google Scholar
K. G. Zornberg and F. Arriaga, “Strain distribution within geosynthetic-reinforced slopes,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 129, no. 1, pp. 32–45, 2003.
View at: Publisher Site | Google Scholar
N. I. Thusyanthan, S. P. G. Madabhushi, and S. Singh, “Tension in geomembranes on landfill slopes under static and earthquake loading-centrifuge study,” Geotextiles and Geomembranes, vol. 25, no. 2, pp. 78–95, 2007.
View at: Publisher Site | Google Scholar
S. L. Li and H. Y. Tang, “Acoustic emission characteristics in failure process of rock under different uniaxial compressive loads,” Chinese Journal of Geotechnical Engineering, vol. 32, no. 1, pp. 147–152, 2010.
View at: Google Scholar
Y. X. Gong, M. C. He, Z. H. Wang, and Y. T. Yin, “Research on time-frequency analysis algorithm and instantaneous frequency precursors for acoustic emission data from rock failure experiment,” Chinese Journal of Rock Mechanics and Engineering, vol. 32, no. 4, pp. 787–799, 2013.
View at: Google Scholar
M. C. He, F. Zhao, S. Du, and M. J. Zheng, “Rockburst characteristics based on experimental tests under different unloading rates,” Rock and Soil Mechanics, vol. 35, no. 10, pp. 2737–2747, 2014.
View at: Google Scholar
J. G. Li and H. Liu, “Application conditions on acoustic emission (AE) technique monitoring coal and rock dynamic disasters in mines,” Materials Science and Engineering Application Ii, vol. 413, pp. 235–240, 2012.
View at: Google Scholar
G. Michlmayr, A. Chalari, A. Clarke, and D. Or, “Fiber-optic high-resolution acoustic emission (AE) monitoring of slope failure,” Landslides, vol. 14, no. 3, pp. 1139–1146, 2017.
View at: Publisher Site | Google Scholar
L. J. Dong, X. B. Li, and G. N. Xie, “An analytical solution for acoustic emission source location for known P wave velocity system,” Mathematical Problems in Engineering, vol. 2014, Article ID 290686, 6 pages, 2014.
View at: Publisher Site | Google Scholar
S. J. Liu and L. X. Wu, “Comparison of infrared radiations of brittle rock and polymethyl mechacrylate induced by stress,” Chinese Journal of Rock Mechanics and Engineering, vol. 26, no. s2, pp. 4183–4188, 2008.
View at: Google Scholar
Y. F. Dong, L. G. Wang, X. F. Liu, and L. Q. Dai, “The experimental research of the infrared radiation in the process of rock deformation,” Rock and Soil Mechanics, vol. 22, no. 2, pp. 134–137, 2001.
View at: Google Scholar
L. X. Wu, S. J. Liu, Y. H. Wu, and C. Y. Wang, “Precursors for rock fracturing and failure—Part I: IRR image abnormalities,” International Journal of Rock Mechanics and Mining Sciences, vol. 43, no. 3, pp. 473–482, 2006.
View at: Publisher Site | Google Scholar
F. Wang, Infrared Radiation Characteristics of Mode I Fracture of Sandstone, China University of Mining and Technology, Xuzhou, China, 2016.
X. B. Li and L. J. Dong, “An efficient closed-form solution for acoustic emission source location in three-dimensional structures,” AIP Advances, vol. 4, no. 2, pp. 152–160, 2014.
View at: Publisher Site | Google Scholar
L. J. Dong, J. Wesseloo, Y. Potvin, and X. B. Li, “Discriminant models of blasts and seismic events in mine seismology,” International Journal of Rock Mechanics and Mining Sciences, vol. 86, pp. 282–291, 2016.
View at: Publisher Site | Google Scholar
L. J. Dong, J. Wesseloo, Y. Potvin, and X. B. Li, “Discrimination of mine seismic events and blasts using the Fisher classifier, naive bayesian classifier and logistic regression,” Rock Mechanics and Rock Engineering, vol. 49, no. 1, pp. 183–211, 2016.
View at: Publisher Site | Google Scholar
L. J. Dong and X. B. Li, “Main influencing factors for the accuracy of microseismic source location,” Science and Technology Review, vol. 31, no. 24, pp. 26–32, 2013.
View at: Google Scholar
Q. H. Qian, “Challenges faced by underground projects construction safety and countermeasures,” Chinese Journal of Rock Mechanics and Engineering, vol. 31, no. 10, pp. 1945–1956, 2012.
View at: Google Scholar
M. C. Ge, “Analysis of source location algorithms-Part I: Overview and non-iterative methods,” Journal of Acoustic Emission, vol. 21, pp. 14–28, 2003.
View at: Google Scholar
M. C. Ge, “Analysis of source location algorithms-Part II: Iterative methods,” Journal of Acoustic Emission, vol. 21, pp. 29–51, 2003.
View at: Google Scholar
G. L. Feng, X. T. Feng, B. R. Chen, and Y. X. Xiao, “Performance and feasibility analysis of two microseismic location methods used in tunnel engineering,” Tunnelling and Underground Space Technology, vol. 63, pp. 183–193, 2017.
View at: Publisher Site | Google Scholar
G. L. Feng, X. T. Feng, B. R. Chen, Y. X. Xiao, and Q. Jiang, “Sectional velocity model for microseismic source location in tunnels,” Tunnelling and Underground Space Technology, vol. 45, pp. 73–83, 2015.
View at: Publisher Site | Google Scholar
N. Li, E. Y. Wang, M. C. Ge, and Z. Y. Sun, “A nonlinear microseismic source location method based on Simplex method and its residual analysis,” Arabian Journal of Geosciences, vol. 7, no. 11, pp. 4477–4486, 2014.
View at: Publisher Site | Google Scholar
L. J. Dong, X. B. Li, L. Z. Tang, and F. Q. Gong, “Mathematical functions and parameters for microseismic source location without premeasuring speed,” Chinese Journal of Rock Mechanics and Engineering, vol. 30, no. 10, pp. 2057–2067, 2011.
View at: Google Scholar
L. J. Dong, X. B. Li, J. Ma, and L. Z. Tang, “Three-dimensional analytical comprehensive solutions for acoustic emission/microseismic sources of unknown velocity system,” Chinese Journal of Rock Mechanics and Engineering, vol. 36, no. 1, pp. 186–197, 2017.
View at: Google Scholar
L. J. Dong, D. Y. Sun, X. B. Li, and K. Du, “Theoretical and experimental studies of localization methodology for AE and microseismic sources without pre-measured wave velocity in mines,” IEEE Access, vol. 5, pp. 16818–16828, 2017.
View at: Publisher Site | Google Scholar
L. J. Dong, W. W. Shu, G. J. Han, X. B. Li, and J. Wang, “A multi-step source localization method with narrowing velocity interval of cyber-physical systems in buildings,” IEEE Access, vol. 5, pp. 20207–20219, 2017.
View at: Publisher Site | Google Scholar
L. J. Dong and X. B. Li, “A microseismic/acoustic emission source location method using arrival times of PS waves for unknown velocity system,” International Journal of Distributed Sensor Networks, vol. 9, no. 10, pp. 485–503, 2013.
View at: Google Scholar
L. J. Dong, X. B. Li, Z. L. Zhou, G. H. Chen, and J. Ma, “Three-dimensional analytical solution of acoustic emission source location for cuboid monitoring network without pre-measured wave velocity,” Transactions of Nonferrous Metals Society of China, vol. 25, no. 1, pp. 293–302, 2015.
View at: Google Scholar
L. J. Dong and X. B. Li, “Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network,” Transactions of Nonferrous Metals Society of China, vol. 22, no. 12, pp. 3087–3094, 2012.
View at: Publisher Site | Google Scholar
L. J. Dong, W. W. Shu, X. B. Li, G. J. Han, and W. Zou, “Three dimensional comprehensive analytical solutions for locating sources of sensor networks in unknown velocity mining system,” IEEE Access, vol. 5, pp. 11337–11351, 2017.
View at: Google Scholar
L. J. Dong, W. Zou, X. B. Li, W. W. Shu, and Z. W. Wang, “Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining,” Engineering Fracture Mechanics, pp. 1–18, 2018, In press.
View at: Publisher Site | Google Scholar
B. R. Chen, Q. P. Li, X. T. Feng, Y. X. Xiao, G. L. Feng, and L. X. Hu, “Microseismic monitoring of columnar jointed basalt fracture activity: a trial at the Baihetan Hydropower Station, China,” Journal of Seismology, vol. 18, no. 4, pp. 773–793, 2014.
View at: Publisher Site | Google Scholar
G. Y. Zhao, J. Ma, L. J. Dong, X. B. Li, G. H. Chen, and C. X. Zhang, “Classification of mine blasts and microseismic events using starting-up features in seismograms,” Transactions of Nonferrous Metals Society of China, vol. 25, no. 10, pp. 3410–3420, 2015.
View at: Publisher Site | Google Scholar
B. H. Zhang and J. H. Deng, Microseismic Mechanism of Engineering Rockmass and its Application, Science Press, Benjing, China, 2016.
H. F. Reid, “The elastic-rebound theory of earthquakes,” University of California Publications in Geological Sciences, vol. 6, pp. 413–444, 1911.
View at: Google Scholar
K. Aid and P. G. Richards, Quantitative Seismology: Theory and Methods, Freeman, San Francisco, CA, USA, 1980.
A. Nur, “Dilatancy, pore fluids, and premonitory variations of ts/tp travel times,” Bulletin of the Seismological society of America, vol. 62, no. 5, pp. 1217–1222, 1972.
View at: Google Scholar
J. H. Whitcomb, J. D. Garmany, and D. L. Anderson, “Earthquake prediction: variation of seismic velocities before the San Francisco earthquake,” Science, vol. 180, no. 4086, pp. 632–635, 1973.
View at: Publisher Site | Google Scholar
C. H. Scholz, The Mechanics of Earthquakes and Faulting, Cambridge University Press, Cambridge, UK, 2002.
G. R. Foulger, R. E. Long, P. Einarsson, and A. Bjornsson, “Implosive earthquakes at the active accretionary plate boundary in northern Iceland,” Nature, vol. 337, no. 6208, pp. 640–642, 1989.
View at: Publisher Site | Google Scholar
X. T. Feng, B. R. Chen, H. J. Ming et al., “Evolution law and mechanism of rockbursts in deep tunnels: immediate rockburst,” Chinese Journal of Rock Mechanics and Engineering, vol. 31, no. 3, pp. 433–444, 2012.
View at: Google Scholar
J. Ma, L. J. Dong, G. Y. Zhao, and X. B. Li, “Discrimination of seismic sources in an underground mine using full waveform inversion,” International Journal of Rock Mechanics and Mining Sciences, vol. 106, pp. 213–222, 2018.
View at: Publisher Site | Google Scholar
F. X. Jiang, S. L. Yao, Q. D. Wei et al., “Tremor mechanism and disaster control during repeated mining,” Journal of Mining and Safety Engineering, vol. 32, no. 3, pp. 349–355, 2015.
View at: Google Scholar
L. J. Dong, W. W. Shu, D. Y. Sun, X. B. Li, and L. Y. Zhang, “Pre-alarm system based on real-time monitoring and numerical simulation using Internet of Things and cloud computing for tailings dam in mines,” IEEE Access, vol. 5, pp. 21080–21089, 2017.
View at: Publisher Site | Google Scholar
B. R. Chen, X. T. Feng, X. H. Zeng et al., “Real-time microseismic monitoring and its characteristic analysis during tbm tunneling in deep-buried tunnel,” Chinese Journal of Rock Mechanics and Engineering, vol. 30, no. 2, pp. 275–283, 2011.
View at: Google Scholar
X. T. Feng, C. Q. Zhang, B. R. Chen et al., “Dynamical control of rockburst evolution process,” Chinese Journal of Rock Mechanics and Engineering, vol. 31, no. 10, pp. 1983–1997, 2012.
View at: Google Scholar
L. J. Dong, D. Y. Sun, W. W. Shu, X. B. Li, and L. Y. Zhang, “Statistical precursor of induced seismicity using temporal and spatial characteristics of seismic sequence in mines,” in Proceedings of World Conference on Acoustic Emission-2017, Xi’an, China, 2018.
View at: Google Scholar
B. Li, T. Li, N. W. Xu, F. Dai, W. F. Chen, and Y. S. Tan, “Stability assessment of the left bank slope of the Baihetan hydropower station, southwest China,” International Journal of Rock Mechanics and Mining Sciences, vol. 104, pp. 34–44, 2018.
View at: Publisher Site | Google Scholar
N. W. Xu, J. Y. Wu, F. Dai, Y. L. Fan, T. Li, and B. Li, “Comprehensive evaluation of the stability of the left-bank slope at the Baihetan hydropower station in southwest China,” Bulletin of Engineering Geology and the Environment, vol. 77, no. 4, pp. 1567–1588, 2017.
View at: Google Scholar
L. J. Dong, D. Y. Sun, X. B. Li, J. Ma, L. Y. Zhang, and X. J. Tong, “Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels,” Tunnelling and Underground Space Technology, vol. 81, pp. 326–335, 2018.
View at: Publisher Site | Google Scholar
W. Cai, L. M. Dou, Z. L. Li, S. Y. Gong, R. J. Han, and J. Liu, “Verification of passive seismic velocity tomography in rock burst hazard assessment,” Chinese Journal of Geophysics, vol. 59, no. 1, pp. 252–262, 2016.
View at: Google Scholar
A. Y. Cao, L. M. Dou, W. Cai, S. Y. Gong, S. Liu, and G. C. Jing, “Case study of seismic hazard assessment in underground coal mining using passive tomography,” International Journal of Rock Mechanics and Mining Sciences, vol. 78, pp. 1–9, 2015.
View at: Publisher Site | Google Scholar
X. Z. Qi, Methodology Research on Concrete Ultrasonic Computerized Tomography, Chang’an University, Xi’an, China, 2016.
N. Hosseini, “Evaluation of the rockburst potential in longwall coal mining using passive seismic velocity tomography and image subtraction technique,” Journal of Seismology, vol. 21, no. 5, pp. 1101–1110, 2017.
View at: Publisher Site | Google Scholar
K. Luxbacher, E. Westman, P. Swanson, and M. Karfakis, “Three-dimensional time-lapse velocity tomography of an underground longwall panel,” International Journal of Rock Mechanics and Mining Sciences, vol. 45, no. 4, pp. 478–485, 2008.
View at: Publisher Site | Google Scholar
Z. W. Wang, X. B. Li, D. P. Zhao, X. Y. Shang, and L. J. Dong, “Time-lapse seismic tomography of an underground mining zone,” International Journal of Rock Mechanics and Mining Sciences, vol. 107, pp. 136–149, 2018.
View at: Publisher Site | Google Scholar
C. X. Yang, Investigation on the Technologies of High Efficiency Mining and Ground Pressure Monitoring and Control for Deep Metal Deposit Mining, Central South University, Changsha, China, 2007.
P. Waclawik, L. Stas, J. Nemcik, P. Konicek, and T. Kalab, “Determination of stress state in rock mass using strain gauge probes CCBO,” in Proceedings of International Conference on Manufacturing Engineering and Materials, ICMEM 2016, vol. 149, pp. 544–552, Nový Smokovec, Slovakia, June 2016.
View at: Google Scholar
H. Y. Yin, L. Lefticariu, J. C. Wei, J. B. Guo, Z. J. Li, and Y. Z. Guan, “In situ dynamic monitoring of stress revolution with time and space under coal seam floor during longwall mining,” Environmental Earth Sciences, vol. 75, no. 18, p. 1249, 2016.
View at: Publisher Site | Google Scholar
D. Li, B. W. Xia, H. Chen, and S. W. Bai, “Research on stability of tunnel in anisotropic layered rockmass with low inclination angle bedding by model test,” Rock and Soil Mechanics, vol. 30, no. 7, pp. 1933–1938, 2009.
View at: Google Scholar
B. Tarasov and Y. Potvin, “Universal criteria for rock brittleness estimation under triaxial compression,” International Journal of Rock Mechanics and Mining Sciences, vol. 59, pp. 57–69, 2013.
View at: Publisher Site | Google Scholar
Z. X. Cao, B. Peng, C. W. Wang, M. Cao, and X. Q. Wang, “Integrated acoustic emission and electromagnetic radiation monitoring warning technology,” Safety in Coal Mines, vol. 41, no. 11, pp. 58–60, 2010.
View at: Google Scholar
E. Y. Wang, X. Q. He, Z. T. Liu, L. M. Dou, S. N. Zhou, and B. S. Nie, “Electromagnetic radiation detector of coal or rock dynamic disasters and its application,” Journal of China Coal Society, vol. 28, no. 4, 2003.
View at: Google Scholar
E. Y. Wang, H. L. Jia, Z. H. Li, X. Q. He, and Z. T. Liu, “Monitoring and forecasting roof stability of goaf by electromagnetic emission,” Journal of China Coal Society, vol. 31, no. 1, p. 3, 2006.
View at: Google Scholar
E. Y. Wang, X. F. Liu, Z. H. Li, and X. Q. He, “Application of electromagnetic radiation technology in monitoring and warning on coal and rock dynamic disasters,” Journal of Liaoning Technical University (Natural Science Edition), vol. 31, no. 5, pp. 642–645, 2012.
View at: Google Scholar
V. Frid, “Rockburst hazard forecast by electromagnetic radiation excited by rock fracture,” Rock Mechanics and Rock Engineering, vol. 30, no. 4, pp. 229–236, 1997.
View at: Publisher Site | Google Scholar
X. Y. Song, X. L. Li, Z. H. Li et al., “Study on the characteristics of coal rock electromagnetic radiation (EMR) and the main influencing factors,” Journal of Applied Geophysics, vol. 148, pp. 216–225, 2018.
View at: Publisher Site | Google Scholar
M. Vishkai, J. Y. Wang, R. C. K. Wong, C. R. Clarkson, and I. D. Gates, “Modeling geomechanical properties in the montney formation, alberta, Canada,” International Journal of Rock Mechanics and Mining Sciences, vol. 96, pp. 94–105, 2017.
View at: Publisher Site | Google Scholar
L. J. Dong, X. B. Li, and G. N. Xie, “Nonlinear methodologies for identifying seismic event and nuclear explosion using random forest, support vector machine, and naive Bayes classification,” Abstract and Applied Analysis, vol. 2014, Article ID 459137, 8 pages, 2014.
View at: Publisher Site | Google Scholar
N. Li, R. Jimenez, and X. D. Feng, “The influence of Bayesian networks structure on rock burst hazard prediction with incomplete data,” ISRM European Rock Mechanics Symposium Eurock, vol. 191, pp. 206–214, 2017.
View at: Publisher Site | Google Scholar
T. Gorum, C. J. Van Westen, O. Korup, M. Van Der Meijde, X. M. Fan, and F. D. Van Der Meer, “Complex rupture mechanism and topography control symmetry of mass-wasting pattern, 2010 Haiti earthquake,” Geomorphology, vol. 184, pp. 127–138, 2013.
View at: Publisher Site | Google Scholar
A. C. Adoko, C. Gokceoglu, L. Wu, and Q. J. Zuo, “Knowledge-based and data-driven fuzzy modeling for rockburst prediction,” International Journal of Rock Mechanics and Mining Sciences, vol. 61, pp. 86–95, 2013.
View at: Publisher Site | Google Scholar
M. C. He, L. R. E. Sousa, T. Miranda, and G. L. Zhu, “Rockburst laboratory tests database—Application of data mining techniques,” Engineering Geology, vol. 185, pp. 116–130, 2015.
View at: Publisher Site | Google Scholar
X. T. Feng, B. R. Chen, S. J. Li et al., “Studies onthe evolution process of rockbursts in deep tunnels,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 4, no. 4, pp. 289–295, 2012.
View at: Publisher Site | Google Scholar
G. F. Liu, X. T. Feng, G. L. Feng, B. R. Chen, D. F. Chen, and S. Q. Duan, “A Method for Dynamic Risk Assessment and Management of Rockbursts in Drill and Blast Tunnels,” Rock Mechanics and Rock Engineering, vol. 49, no. 8, pp. 3257–3279, 2016.
View at: Publisher Site | Google Scholar
Z. N. Zhao, X. T. Feng, Y. X. Xiao, and G. L. Feng, “Microseismic characteristics and rockburst risk of deep tunnel constructed by different excavation methods,” Chinese Journal of Geotechnical Engineering, vol. 38, no. 5, pp. 867–876, 2016.
View at: Google Scholar
X. T. Feng, Y. Yu, G. L. Feng, Y. X. Xiao, B. R. Chen, and Q. Jiang, “Fractal behaviour of the microseismic energy associated with immediate rockbursts in deep, hard rock tunnels,” Tunnelling and Underground Space Technology, vol. 51, pp. 98–107, 2016.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2018 Longjun Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

2444

Downloads

1870

Citations