1.1 The Overview of Rockburst

Mine dynamic hazards mainly include the rockburst, high-speed landslide, coal and gas outburst, underground debris flow, gob area catastrophic collapse, tailings dam collapse, and water inrush, etc. Rockburst is usually referred to as dynamic catastrophe, which results in causalities and roadway destruction, caused by elastic strain energy emitting in a sudden, rapid and violent way from coal or rockmass. It is often accompanied by an airblast and violent failures which can disrupt mine ventilation, pose a danger to miners, and may also cause a large release of strata gas and explosive dust into the air (Brauner 1994; Dou et al. 2012; Jiang et al. 2014; Cook 1976; Salamon 1983; Ortlepp 1994).

With the depletion of shallow mineral resources, there is an inevitable trend for mineral extraction to go deeper. Duo to the mining depth and the mined out area increases, the frequency and intensity of induced explosion will be increased simultaneously. Mining face and deep tunnels near the rockburst area will endanger the production safety. Strong rockbursts also cause engineering loss, damage people’s living environment and pose a threat to public safety. Therefore, it is of great significance to carry out the research and management of rockburst in deep mining and tunnel engineering (He et al. 2005; Zhou et al. 2005).

Rockburst is one of the most important mine dynamic hazards in China. According to the State Administration of Work Safety of China, the total casualty in mine accidents was 54,744 from 2002 to 2016, especially the accidents caused by the rockburst, water inrush, coal gas outburst and other dynamic hazards account for more than 60%. In 2016, there were 538 deaths in coal mines in China, compared with 9 in the United States. The death totally in China is 59.78 times than that of the United States, and about 80–90% of world’s major mining hazards (more than 10 people) occur in China every year.

The study of type and induced condition of rockburst is the precondition for the prevention of rockburst. It was stated that any rockburst results from a mining tremor but not all the tremors cause rockburst. In fact, two kinds of hazards (seismic hazard and rockburst hazard) were proposed and defined, which one tremor cannot be predicted (Kornowski et al. 2012). The tremors in corresponding to the rupture events were observed at scales ranging from laboratory samples to the earth’s crust, which included rock failures in laboratory tests and field experiments, landslides, mining-induced seismicity, and crustal earthquakes (Amitrano 2012). It was verified that rocks under stress emit acoustic waves in the laboratory and microseismic events in mines. Then, AEs of rocks observed in the laboratory were considered as a small-scale model of seismicity of earth’s crust (Filimonov et al. 2005; Hardy 2003; Scholz 1968).

In the past few years, although the mining industry has carried out a large number of mine rockburst induced conditions and control technology research, the mine safety production situation is still grim. If the mine rockburst induced conditions, monitoring and predicting model can not be solved in short term, it will become a bottleneck restricting for the development of mining (especially deep mining).

1.2 Current Status on Rockburst Induced Conditions

In order to solve the key technical problems related to rockbursts, it is necessary to analyze their inducing conditions. Studies have already been done to determine these inducing conditions and to lay foundations for rockburst prediction. However, these studies are incomplete. To date, there is still no unified explanation for the rock dynamic hazards, especially the triggered conditions of rockbursts. So, it is very important to study the rockburst induced conditions and analyze the rock failure behavior by various theories, which can provide reasonable suggestions for mining design, mining method and rockburst prevention measures. It also is very important meaning to mining safety and efficient recovery.

Rockburst is a nonlinear dynamic process, in which the energy is converted into kinetic energy by the broken rockmass and ejected. Thus, from the energy point of view, rockburst is an essentially process, in which the elastic strain energy accumulated in the rockmass is released abruptly. Hence, the main factors that affect the occurrence of a rockburst are the impact which can make rock into a high-energy storage. High-energy storage of rock is more likely to cause rockburst. In other words, rock has sufficient capacity to store large elastic strain energy and there is a high concentration of stress within the rock. Based on this, rockburst was induced by both a high concentration of stress and a large quantity of recoverable strain energy (Qian 2004).

At present, relatively few studies focus on the conditions of rockburst and each of them has different emphases. In this area of study work is as follows:

It was established an analytical model for the nonlinear instability mechanism of coal and rockmass which based on non-equilibrium thermos dynamics and dissipative structure theory, the three-dimensional model of coal and rockmass instability, coal and rock damage model by considering the dynamic instability model according to the Hoek-Brown strength theory (Jiang et al. 2005). It was found that the migration of microseismic events was mainly caused by stress redistribution, which was related to the collapse of the main caves, while the secondary microseismic clusters were related to the small-scale damage (Trifu et al. 2008). It was found that main non-uniformity stress would appear after injecting large-volume fluid into the granite body and along the fault surface shear stress would be released. The occurrence of microseismic events and fault surface shape indicating the fault sliding was quasi-static (Scotti et al. 1994). It was also found that a microseismic event with a magnitude of less than 0 can be used to assess a 2.9 level destructive rockburst event. The stress inversion and the main quantification component analysis were of good correlation. What’s more, it could be used to derive microseismic failure surfaces and structural maps (Ttifu et al. 1996). It was found that AE can be used to monitor the material destruction process and quantify the waveform analysis of microseismic event magnitude (Arastehl et al. 1997). AE event was occured in the direction of rupture tip during loading. At the same time, the macro-rupture was occurred at the unloading event. A typical loading event was an essentially component in the tensile stress zone, while the unloading usually was a shear stress.

From a research perspective, the deep mining rockburst were mainly concentrated on the induced conditions. The research work so far mainly analyzed the lab experiment under the uniaxial condition and deduced the model which mainly included the time-consuming, energy storage and energy consumption, the distribution of precursor information before rock failure. However, multiple factors of rockmass, which leaded to rockburst in the deep mining, were in complex stress environment conditions. Actually, lab experiment is only a special case of scene and it has a certain difference compared the actual rockburst, experimental investigation is a similar understanding for the real situation of rockburst hazard. At present, rock mechanics is still not enough explaining to the induced factors of rockburst, the monitoring criterion and the predicting model. Especially, the excavation leads to the stress redistribution and the fault activity induced by the microseismic event is still insufficient. It is necessary to do some further study in the lab experiments and practical production applications. Therefore, it is very important to analyze the characteristics, influencing factors, formation mechanism; emissive events induced condition, rockburst key predicting point and predicting model.

1.3 Current Status on the Precursor Characteristics of Rockbursts

The macroscopic fracture and failure of rockmass are due to the nucleation, expansion and penetration of microfracture. The secondary stress redistribution of rockmass by excavation is affected by the stress field disturbance and resulting in dislocation, disintegration and migration of mesome. With the development of rock mechanics, it has become an important method of dynamic hazards monitoring that includes the monitoring of stress wave and the maturation of AE positioning technology, microseismic monitoring technology and AE technology. By analyzing the strength, the frequency and the location of microseismic activity, we can get access to some hazards precursor information to achieve risk assessment and predicting. However, the implementation of rock test on the site is more difficult and can’t easily get the results. At the same time, lab experiment is an effective way to study the destruction and regularity of rockmass. So, it is an effective means to conduct AE test and combine with the field monitoring results. Therefore, it is the primary task to carry out the research on the prediction of dynamic hazards by summing up the characteristics and evolution patterns of AE in the process of rock rupture.

To date, less methods aim at studying the distribution of precursory information of rockburst and they have different emphases. The main work in this area as follows:

In a uniaxial loading test, it is known that the number of AE events is small during the initial loading stage. In the later stage of rock elastic and plastic deformation, the number of cumulative AE events increased rapidly before the damage and maintain a low release rate during the rock fracture process (Liu et al. 2011). From the experimental data, it was found that the precursory characteristics of rock samples were inferred and the heterogeneous fault was caused by the failure of specimens (Lei et al. 2004). It was found that the fault, stress and boundary deformation had a close interaction for the shear strain generation area evolved (Mekinnon 2006). It was also found the presence of AE relatively quiet period by rock uniaxial compression AE test (Schiavi et al. 2011). It was found that the AE energy of material was accelerated after destruction through the AE monitoring tests on concrete, rock and other materials under uniaxial loading conditions (Carpinteri et al. 2008).

This book focus on the rockburst induced conditions and predicting, three-dimensional reconstruction model of rock fracture cracks under loading, dynamic evolution of rock fractures, experimental investigation of rockburst precursor information. The author’s work in investigation of key points of synergetic predicting of rockbursts and their predicting models based on the nondeterministic theory is also introduced.

1.4 Current Status on the Evolution of Rockburst

Rockburst is a sudden, unexpected and abrupt failure. It is a bottle neck for any geotechnical engineering being carried out at great depth. The study of rock failure process and evolution patterns has become the important issues in underground engineering. In the past, it was difficult to observe the true geometric distribution within the materials due to the limitations of test technique and equipment. With the rapid development of science and technology, Computed Tomography (CT) scanning technology has gradually become the most advanced non-destructive testing means. The application of CT scanning technology can achieve the continuous scan, dynamic observation test under load in the geotechnical engineering. In the same time, we also can get the parameters change in the process of computed tomography, the internal structure of sample, the microscopic structure of sample, the moving microscopic particulate matter, the developmental crack, the local change density, etc. CT image is a kind of grayscale image which can reflect the change of density inside the rockmass. Through the change of gray degree, the distribution of material in different areas of rockmass can be reflected in the image. What’s more, the process of structural failure can be well reflected by CT images.

In fact, many researchers analyzed CT images and three-dimensional cracks reconstruction (Kawakata et al. 1999, 2000), and it was come true that to simulate the outdoor rockmass distribution by X-ray rock CT scanning (Ueta et al. 2000). Chinese scholars focus on the analysis of changes of CT number in the CT image and the quantitative analysis was only in theory. The advantages of digital image processing technology did not totally display in the field of geotechnical engineering. It is necessary to use numerical simulation to describe the geotechnical material and reflect the real force of rockmass. Therefore, the CT image of rock, based on the digital image technology, provides a new way to analyze the process of meso-cracks reconstruction.

The process of rock failure is a significant accumulation of process, which is determined by the brittle characteristics of rock. In the process of cyclic load/unload, the higher concentrated stress will appear at the tip of rock crack, which will lead to the initiation and expansion of cracks inside the rock and gradually generate new macroscopic cracks leading to the overall destruction of specimen. With the continuous development of society and the process of human production, engineering construction is gradually increased in the influence of fractured rockmass.

Most scholars studied the cracks evolution by similar model test which could be observed intuitively and clearly about the process of cracks initiation, expansion and penetration failure. It was conducted the prefabricated oblique fissures of marble compression test which showed the process of expansion and it was asymmetric (Nolen-Hoeksema et al. 1987). What’s more, rock failure and the surface of crack distribution were observed in the test. As the existing technical level, internal cracks in the rock sample were achieved, but rock similar materials were used to study the crack propagation and transparent characteristics. All of these results in accurate observation and record was not achieved. In this book, CT scanning technology and digital image technology were used to obtain these cracks images of rock under different load levels. The nonlinear dynamics of cracks propagation process was revealed.

The study of propagation evolution patterns still rests in the whole process of crack initiation-extension-failure of microscopic level so far, and it does not explain the macroscopic failure criterion of rock in the process of crack propagation. This book intends to establish a three-dimensional visualization model of cracks which can provide a new way to study the transition from microscopic scale to macroscopic scale.

1.5 Current Status on Predicting of Rockburst

1.5.1 Study on the Synergetic Monitoring of Rockburst

In underground excavation process, rockburst caused a serious of threat to the construction workers and equipments. The predicting of rock damage and failure has become a problem to be solved urgently for the safe and efficient production of mine which has aroused the attention of many scholars. In fact, the predicting of rock failure has become an important topic in underground engineering field.

As the implementation of on-site rock load/unload test is difficult, laboratory test methods are often used in its replacement. Lab tests and engineering applications used in combination can fulfill deep level study in rock deformation behavior and predicting research. Therefore, it is of great significance to carry out the research on rockburst failure predicting in the laboratory. In addition, the existing rock failure predicting technology means a single application. Hence, it is very important to establish a multi-means to monitor the rockburst. In this book, we do some studies on dynamic hazard evolution, predicting model using traditional monitoring, AE monitoring, microseismic monitoring and infrared monitoring.

1.5.2 Study on the Predicting Model of Rockburst

Rockburst predicting is the basis of prevention and control of rockburst hazard. According to the predicting results, the feedback design of rock engineering and the control measures can be taken in time. It is of great theoretical and practical value for the safe and efficient recovery of deeply buried resources. Rockburst tendency evaluation is the prerequisite and basis of predicting hazard.

Bayesian theory, which was successfully applied in many research fields, provided a clear and a flexible method for making predictions using incomplete knowledge. Heckerman (1990) used a Bayesian framework to improve the process of medical diagnosis. Making full use of its strong information processing ability, a Bayesian network was applied to the monitoring and management of industrial production processes. A Bayesian model was utilized for choosing investment ventures, and displayed a good ability to cope with future uncertainty. In addition, Bayesian theory was used to identify faults in a computer system. Bayesian theory was demonstrated to be a reliable approach to address complex problems involving many variables with large uncertainties, and models that considered a multi-parameter space were better suited to predicting rockburst tendency than single-variable models. In these studies, the main factors affecting of risk and intensity of rockbursts were used to make a Bayesian model (Heckerman 1990; Weidl et al. 2003; Kemmerer et al. 2002; Jensen et al. 2001).

Matter-element analysis theory is primarily used to study the problem of incompatibility. It was used to solving multiple parameter evaluation problem by formalizing the problem and establishing the corresponding matter-element (Cai 1994; Chen et al. 2007; David and Wen 1997). The improved fuzzy matter-element evaluation method was used to assess water quality, which achieved more reliable results than that using the traditional method. Based on the matter-element method, He et al. (2011) designed a model to evaluate the urban power network planning, and Zhu et al. (2010 ) analyzed coefficients of evaluation in rockburst. The empirical analysis showed that this model was reliable and feasible. In this book, the main influencing factors of rockburst are considered based on the concept of matter-element analysis in combination with the fuzzy set and closeness degree patterns. The entropy method is also integrated in the weight calculation of this model. An integrated rockburst multi-index evaluation model is established and used to predict the rockburst tendency.

At present, there are many methods to judge rockburst tendency, such as stress method, rockmass integrity coefficient method, strength criterion discrimination method, rock fragility index method, elastic energy index method, dynamic failure time method, rockburst energy ratio method, impact energy index method, impact tendency criterion method, resistivity method and so on. All of these methods and judgment indexes are only considered the individual factors which will produce the one-sidedness and limitations of predicting results. In fact, all aspects will make the problem complicated. So, it is necessary to use the nonlinear mathematical method to comprehensively consider the interrelated factors of rockburst and analyze the rockburst tendency. However, the Bayesian predicting model and the fuzzy predicting model with high accuracy are established by using the Bayesian method and the fuzzy matter-element theory which considering more important influencing factors of rockburst. The Bayesian predicting model considers the rock strength index \(R_{b}\), the surrounding rock stress index \(R_{\theta }\) and the energy index \(W_{\text{et}}\), etc. The fuzzy element predicting model considers four rockburst influencing factors which contains the brittleness coefficient B, the impact tendency energy W CF , the rock integrity coefficient K V and the rock strength stress ratio \(R_{c} /\sigma_{1}\), etc. In this model, the interrelated factors of rockburst are considered comprehensively and the predicting results is coincidence with the field.

1.5.3 Study on the Field Predicting of Rockburst Hazard

Due to the complexity of rock mechanics behavior in deep mining, the actual situation of deep rock mechanics behavior is difficult to be characterized by theoretical calculation and analysis before the mining implementing. At the same time, the theoretical calculation itself can not reflect the dynamic changes of rock engineering conditions in real time. Therefore, it is a reliable and effective technical measure to master and evaluate the active status of rockmass excavation engineering in deep mining.

In order to predict the dynamic hazard in mining, microseismic monitoring technology has become the main technical means to realize the predicting dynamic hazard in deep mining. Using the microseismic monitoring system, the samples can be used to detect the elastic wave emitted by the rock ruptures. The waveform can determine the coordinates, the intensity and the frequency of microseismic activity. Based on microseismic monitoring technology, it can be used to determine the potential micro-earthquake activity criterion and to achieve predicting by the micro-rupture information. Microseismic monitoring is widely used to provide strong safety protection in deep mining.