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The stress-induced fracture of brittle rocks, as a result of macrocrack evolution, is closely related to the evolution of microcracks. The study of such damage processes provides information about the mechanical behavior of rock cracks. In this study, we conducted research with respect to macrocracks using hypothetical damage regions constituted by correlated microcracks. A Gaussian mixture model was applied to describe the spatial distribution of microcracks. The Kullback–Leibler divergence was used to characterize the geometric variation of damage regions. The results showed that the robustness of the damage region's geometry became increasingly higher during the damage evolution and the damage region became unchanged after some time. The robustness of the damage regions could be an indicator of the nucleation of macrocracks. Moreover, a fracture nucleation indicator methodology was developed to calculate the point at which nucleation was formed. This study is considered to enhance the understanding of macrocrack nucleation and it is useful to the application of macrocrack recognition and prediction. This study presented an investigation on the robustness of the damage regions associated with macrocracks. The evolution of calculated damage regions can be considered as a process of macrocrack nucleation. The geometric variation of damage regions was studied. Results showed that the geometry of damage regions became increasingly stable during microcracking. A methodology named fracture nucleation indicator was proposed to define the fracture nucleation point, whereby the damage regions showed great robustness. This study was considered to enhance the understanding of a fracture's nucleation process and the proposed fracture nucleation indicator was a successful approach to quantitatively define macrocrack nucleation time. [ABSTRACT FROM AUTHOR]

To further understand the rock damage zone, an approach based on microcrack distribution was proposed to characterize the crack space of rock specimens in this research. Acoustic emission (AE) technology was utilized on sandstone to obtain the spatial distribution of microcracks in which uniaxial compression forms the single-cracked fracture. The proposed theoretical distribution pattern space (TDPS), 3D convex hull, and the minimum volume enclosing ellipsoid (MVEE) algorithms were adopted to analyze the geometric features of the crack space. It was found that the 3D convex hull method returned the smallest results in both area and volume of the crack space, and the largest results were provided by the proposed TDPS method. The difference between the results of the proposed TDPS method and the MVEE method became smaller after 85%. The deviation angle of the principal axis of the cracked space gradually decreased as the spatial scale decreased, while the other two major axes exhibited a tendency to increase at the 65% scale. The results indicate that a spatial scale from 65% to 85% is a reliable range for the characterization of crack space. [ABSTRACT FROM AUTHOR]

The evolution pattern of rock damage is a progressive failure process of rock materials. It is the basis for predicting failure time of rock materials. By theoretical and experimental analysis, the acoustic emission (AE) precursor characteristics of rock fracture and the gradual evolution pattern of rock damage were analyzed detailedly. Then, the time-to-failure of red sandstone was predicted and compared by several different methods. The results demonstrated that the failure process of red sandstone can be divided into the stable deformation stage and the critical acceleration failure stage. In the critical acceleration failure stage, the AE precursor of rock failure was easy to be observed, and the AE event rate occurred as jump-like increase phenomenon. Moreover, the gradual evolution pattern of rock damage obeyed an exponential function, and the damage acceleration phenomenon existed in the critical failure stage. Furthermore, the higher values of the average of rock damage was, the more obvious linear evolution pattern will be, which was beneficial to improve the prediction accuracy of time-to-failure of rocks. Clearly, the linear prediction results of rock failure time, after taking average values of five rock damage variables, had more higher accuracy when damage variable exceeded D = 0.5. The predicting result of specimen R1 was 0.2 s ahead of its actual failure time, and the predicting result of specimen R6 was 8.1 s ahead of its actual failure time. Therefore, this method is meaningful and it can be used for the early warning of rockburst. [ABSTRACT FROM AUTHOR]

The failure process of rocks is usually accompanied by numerous acoustic emission (AE) signals. For evaluating the damage state of rocks, it is important to select key disaster-causing AE signals from massive AE monitoring data. Based on the uniaxial compression test of red sandstone, the failure characteristics of AE signals were analyzed. Then, based on the principal component analysis method, the evolution pattern of key disaster-causing factors of AE signals of red sandstone has been obtained. The results demonstrated that AE parameter signals (AE energy and AE ring counts) and AE waveform signals (AE main frequency) contributed to characterize the precursor of rock failure. The values of AE energy and AE ring counts increased significantly in the critical failure stage of rocks, and there existed a short quiet period phenomenon of AE signals. Similarly, AE main frequency increased densely in the critical failure stage. Moreover, the optimized key disaster-causing signals (AE energy, AE ring counts and AE main frequency) can clearly characterize the stress fluctuation and damage state of rocks. Based on the optimized key disaster-causing AE signals, the primary early warning point and the key early warning point of rockburst were proposed. We hope this method can bring some new ideas for predicting rockburst. [ABSTRACT FROM AUTHOR]

Abstract Coal burst is a dynamic failure phenomenon that is caused by a sudden release of strain energy during mining. It is extremely difficult to predict coal burst accurately. In this study, experimental tests were performed under uniaxial cyclic load-unload conditions using a microcomputer-controlled electrohydraulic servo stiffness compressor with acoustic emission (AE) monitoring. The experiments data, such as the AE energy, AE energy ratio and load-unload energy response ratio, were obtained. The Y value of the load-unload response ratio (LURR) was calculated based on these data. The results show that variation characteristics of the AE energy release can be related to the failure of coal specimens. The concentrated release of AE energy occurs at the beginning of the plastic deformation stage. Then, AE energy was accumulated before coal specimens' failure. The variation characteristics of the LURR (Y 1) and Benioff strain (Y 2) were obtained. The fluctuation of Y 1 and Y 2 values was exhibited to coincide with the AE energy accumulation, which allowed predicting coal failures. The predicting time point where the Y 1 and Y 2 values were both close to 1 could be defined as the predicting key point of coal specimens' failure. These results of experimental investigation show a meaningful attempt for predicting coal failure. Highlights • Y value of the load-unload response ratio (LURR) was calculated for rock bursts prediction. • Variation characteristics of AE energy release and Y value can be related to coal specimens failure. • Y 1 and Y 2 values were close to 1 could be defined as the predicting key point of coal failure. [ABSTRACT FROM AUTHOR]

The spatio-temporal-energy evolution characteristics of strong acoustic emission (AE) events on the crack surface were investigated during the process of a complete limestone rupture, in order to demonstrate the generation, expansion, and interpenetration of microcracks in rock samples. The results show that the process of internal deformation and rock fracture could be well represented using the spatial distribution of strong AE events. The spatial distribution of strong AE events was consistent with the fracture surface, with a good linear relationship between spatial fractal dimension and stress. The formation of the fracture surface occurred mainly in stable and rapid development stages. It shows that the evolution model of strong AE events cumulative energy on the fracture surface matched with the distribution of the Gaussian Amp function. The proposed cumulative energy formula for AE events can quantitatively describe the process of rock crack development, which could be used to describe the spatio-temporal-energy evolution pattern of AE for rock fracture. • The spatio-temporal-energy evolution characteristics of AE on crack was obtained. • The spatial distribution of strong AE is consistent with the fracture surface. • Evolution model of cumulative AE energy is consistent with Gaussian distribution. [ABSTRACT FROM AUTHOR]

The implementation of multi-crack recognition requires a theoretical basis from single-crack failure results. This study investigates the spatial distribution pattern of microcracks in single-crack-damaged coal specimens subjected to uniaxial compression. The distribution mechanism of the induced microcracks is analysed in terms of macrocracks and stress states. Gaussian and Weibull distributions are selected to explore the spatial distribution of microcracks, and quantile–quantile plots are used to study the probability density plots. The results show that the updated data better demonstrate the distribution characteristics of the ellipsoid shape visually, and the microcracks are observed to converge near the envelope. Linear distribution features are found in the scattered points of the quantile–quantile plots. The goodness-of-fit tests indicate that both Gaussian and Weibull distributions can be assigned to describe the microcrack distribution on the new axis. The probability density distribution results show that the shape is preferred to obey Gaussian distribution, whereas Weibull distribution exhibits a greater possibility of shape variation. The study results can enhance the existing knowledge on crack spatial distribution of acoustic emission positioning results associated with rock materials. In addition, the statistical results can interpret the cracks by recognising them with a specified distribution pattern. [ABSTRACT FROM AUTHOR]

It is difficult to predict rock cracking and failure accurately given the complex mechanical behavior of the rock failure process under uniaxial compression. Based on the Weibull statistics distribution theory, the equivalent hypothesis theory and damage theory, the damage-time-failure predicting model was established. The damage, time and stress were synthetically considered to predict the rock failure, and the process of rock failure was analyzed. In this paper, we first briefly introduced the Tangent damage factor (TDF) based on the acoustic emission parameters of two groups' experimental data. The relationship between the damage factor and load-time was indicated, and the power law relationship was obtained. The TDF model, applied to limestone, was established. On this basis, the initial predicting points (precursor) and key predicting points (instability point) were identified according to the TDF curve in the process of rock cracking and failure. The initial predicting point was approximately located at 75% of the peak strength, and the key predicting point was the peak strength point. The interval for the precursor point and the failure point, which is the TDF (k B = 0), was divided into three predicting stages: the blue predicting stage (B-E), yellow predicting stage (E -F), and red predicting stage (F-D). The experimental results demonstrated that there is a power law relationship between the damage factor and load-time. The damage factor curves verified the phased characteristics for rapid growth-stable growth-sharp growth-stability. Identification of the predicting points based on the abnormal change behavior of the TDF in the process of rock failure is very important forewarning information. The experimental results indicated that this is a meaningful study for predicting rockburst. The established forewarning model and proposed grading prediction method contributed to predicting hierarchically rockburst hazards in the field. • The damage-time-failure predicting model was established. • The Tangent Damage Factor (TDF) model, applied to limestone, was proposed. • The initial and key predicting points were identified based on the TDF model. • The proposed model and the grading method is a meaningful predicting rockburst. [ABSTRACT FROM AUTHOR]