8.1 Introduction
Predicting is the basis of prevention and controlling of rockburst hazards. According to the predicting results, the feedback design of rock engineering and controlling measures are taken in time. It is of great theoretical and practical value for the safety and efficiency of deep mine. So far, there are many methods to judge rockburst tendency, such as rock integrity coefficient method, strength criterion discrimination method, rock brittleness index method, elastic energy index method, dynamic failure time method, rockburst energy ratio method, impact energy index method, impact tendency criterion method, and resistivity method. Most of these methods and judging indexes, which is only considered the individual factors, were one-sidedness, limitations and complications. Therefore, the influencing factors of rockburst are comprehensively based on the non-deterministic theory for established a more accurate rockburst predicting model. Rockburst is a complex dynamic instability phenomenon (Hedley
1992), which can occur during underground excavation in areas with large in situ stress. As a result of the sudden release of accumulated strain energy, rocks can be come loose, crack, and even eject violently (Canadian Rockburst Research Program 1996). As a result, rockbursts were considered a major technical challenge in deep mining. Duo to the characteristic of sudden, disruptive, and complex, the accurate prediction of rockbursts was difficult and an urgent problem need to be solved (Blake et al.
2003).
The phenomenon was discussed extensively by many scholars. Rockburst tendency is an important metric to quantify the risk and potential intensity of occurrences and grade the hazard of an affected mine. However, there are still no accurate prediction methods or effective control measures. In recent decades, many meaningful advances have been made by many scholars (Singh
1989; Dou et al.
2009; Marek
2009; Patynska et al.
2009; Marian
2011). Rockburst mechanism was better understand using some proposed some criteria, such as strength theory, stiffness theory and energy theory. These models could be explained the origin and mechanism of rockbursts, but were hard to apply in practice. In addition, several indexes were proposed to measure rockburst tendency, such as strength and brittleness, burst energy release, impact energy and rock integrity (Hoek et al.
1980; Cook
1965; Wiebols et al.
1968; Tan et al.
1991; Kidybiński
1981; Singh
1988; Hou et al.
1989). These criteria derived from the mechanical parameters obtained by testing rock samples. Some important values were compressive strength, tensile strength, capacity to store and release elastic strain energy, and surrounding rockmass stress and integrity.
In light of rockburst phenomenon complexity, the use of a single parameter was insufficient for predicting rockburst. Though AE, chip drilling, removal, vibration, and resistance methods were proposed and applied, each parameter was lacking in predictive power under isolation. As a multifactor, coupling induced dynamic hazard, it was essential to establish a calculation method to evaluate rockburst tendency involving the proper parameters. However, few studies were tried to combine the various factors relating to rockburst hazard. Recently, some interesting models were derived using artificial intelligence, such as a neural network (Chen et al.
2002), fuzzy theory (Adoko et al.
2013; Wang et al.
2015), and distance discriminant analysis method (Gong et al.
2007), along with other integrated analysis methods. These research results indicated that the occurrence of rockbursts was closely related to the mechanical properties of rockmass, the geological structure, and the surrounding stress. However, these attempts had not yet formed a complete theoretical system. Based on the Bayesian theory and Fuzzy element-matter theory, several critical factors were integrated into a single model for predicting rockburst tendency in this chapter.
Bayesian theory, which is successfully applied in many fields, provides a clear and a flexible method for making predictions using incomplete knowledge. Heckerman (Heckerman
1990) used a Bayesian framework to improve the process of medical diagnosis. Making full use of its strong information processing ability (Weidl et al.
2003), 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 (Kemmerer et al.
2002). In addition, Bayesian theory was used to identify faults in a computer system (Jensen et al.
2001). It was attempt that the tendency was predicted more precisely using these incomplete indexes of rockburst occurrence.
Professor Cai (
1994) analyzed a large number of examples found that people in dealing with incompatibility issues must take things, features and the corresponding value to consider together. The main idea of this method was to make things to ‘things, features, values’ to describe and analyze. Matter-element analysis is an effective way to study matter-element and to solve incompatible problems in the real world. If the magnitude of matter is ambiguous, it constitutes a fuzzy incompatibility problem. Fuzzy matter-element analysis is the combination of fuzzy mathematics, and matter-element analysis can solve this kind of fuzzy things. In recent years, this highly practical theory and method were achieved many gratifying results in the field of engineering technology. In addition, artificial intelligence methods were used, such as fuzzy inference system (FIS) and adaptive neuro-fuzzy inference systems (Adoko
2013) and Rough set theory and genetic algorithms (Yu
2009). These were seismological theory and methods were used to predict the rockburst such as the peak velocity and dynamic energy, the seismic risk assessment method and mining and seismological parameters (Srinivasan et al.
1997; Li et al.
2011; Stewart
1995). The above research works indicated that the occurrence of rockburst was closely related to the strength of surrounding rockmass, geomechanics, geological structure, hydrogeology, and the construction sequence. Matter-element analysis theory was primarily used to study the problem of in compatibility (Wang et al.
2015). It could be also used for solving multiple-parameter evaluation problems by formalizing the problem and establishing the corresponding matter-element (Cai
1994; David et al.
1997; Chen et al.
2007; Liu et al.
2007). The improved fuzzy matter-element evaluation method was used to assess water quality, which achieved more reliable results than that using the traditional method (Liu et al.
2012). Based on the matter-element method (He et al.
2011) designed a model to evaluate the urban power net work planning, and Zhu (
2010) analyzed coefficients of evaluation in rockburst. The empirical analysis showed that this model was reliable and feasible.
Therefore, in this chapter, 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. The main factors affecting of risk and intensity of rockbursts were used to make a Bayesian model. On the other hand, the main influencing factors of rockburst were considered based on the concept of matter-element analysis in combination with the fuzzy set and closeness degree rules. The entropy method was also integrated in the weight calculation in this model. An integrated rockburst multi-index evaluation model was established and used to predict the rockburst tendency in a case study.
8.4 Conclusions
By considering multiple factors, the new model can overcome the limitations of single-factor methods. In this chapter, most of parameters and methods were considered in order to eliminate subjective judgments. These variables included rock brittleness index \( R_{b} \), Russense’s \( R_{\theta } \), Kidybinski’s \( W_{\text{et}} \), the surrounding rockmass stress \( \sigma_{1} \) and \( \sigma_{\theta } \), and rock strength \( \sigma_{c} \) and \( \sigma_{t} \).
The occurrence of rockburst was correlated not only with the physi-mechanical parameters of rockmass, but also with the surrounding mining environment. This research was intended to advance the development of predicting rockburst model based on fuzzy matter element method and measurement data.
Proposed model was based on fuzzy matter-element analysis, combining with the concept to Euclid closeness degree, an integrated rockburst multi-index predicted model was proposed. In this model, the rock brittleness (\( B \)), the major principal stress (\( \sigma_{1} \)), rock integrity (\( k_{V} \)) and impact energy parameters (\( W_{CF} \)) were all considered. At the same time, the entropy theory was introduced to determine the weight of each evaluation index, preventing the subjectivity of weight distribution. Finally, we recommend the fuzzy matter-element model on predicting rockburst method in field.