Rock burst is one of the major geological hazards in coal mines and is difficult to prevent. Faced with this problem, the low-frequency electromagnetic radiation (EMR) test experiments of coal and ...rock were conducted to explore the characteristics of low-frequency EMR (1 kHz) signals induced form fractures. The relation between EMR signals and paroxysmal fractures was analyzed theoretically. Time-series extraction method was put forward to represent coal and rock paroxysmal fractures. The research results showed that deformation and fracture of coal and rock is caused by the accumulation of paroxysmal and discrete fractures in the samples. The time series obtained by moving average method shows rather good correlation with interior coal rock fractures, which has more obvious precursory characteristics of coal rock instability and dynamic disasters. When the coal rocks are damaged under uniaxial compression, low-frequency EMR (1 kHz) signals are more sensitive to local fracture with more abundant precursor information than high-frequency EMR (500 kHz) signals. The paroxysmal and discrete low-frequency EMR (1 kHz) signal is quite sensitive to rock burst, and when the signals representing coal rock fractures show an increasing trend and the high signals last for some time, it can be regarded as the precursor of rock burst disaster in coal mine. The separation technology of EMR representing coal rock fractures provided the key scientific basis for the field monitoring and forewarning of EMR technology, and a new method to identify the precursor of dynamic disasters of coal and rock.
•An effective method is proposed to analyze the discreteness and continuity of electromagnetic radiation sequence.•Compared with high-frequency EMR, the low-frequency EMR are more sensitive to fractures with richer precursor information.•The processed EMR signals have more obvious precursory features for rock burst in coal mine.
To study the influence of hydraulic slotting inclination on the mechanical behaviors of coal seam during mining process, uniaxial compression experiments on coal specimens with a single pre-existing ...flaw inclined at 0°, 15°, 30°, 45°, 60°, 75°, 90° and intact specimens were conducted. Acoustic emission (AE) signals in the loading process were monitored, and fractal analysis method was introduced to investigate the AE characteristics. Additionally, the laboratory experiments were simulated by a finite element code. Both the experimental and numerical results show that the existence of a flaw reduces the mechanical properties of coal. The uniaxial compressive strength and modulus of elasticity increase polynomially and linearly with the increase of inclination angle, respectively. When the coal specimen ruptures finally, the fewer the surface secondary cracks or the more the sudden drops of stress, the smaller the peak value of AE count. According to the stress–strain curve, the loading process is divided into five stages: (I) compaction stage; (II) linear elastic stage; (III) stable crack propagation stage; (IV) accelerating crack propagation stage; (V) post peak and residual stage. AE fractal characteristics in various stages of each specimen were determined by Grassberger and Procaccia algorithm based on phase space reconstruction theory. AE count show fractal characteristics from stage III. The fractal dimension declines rapidly in stage IV, and continues to decline further or rise slightly in stage V, but both are lower than that in stage III. Therefore, the changing rule of AE fractal dimension in different loading stages can be used as a precursor to coal and rock dynamic disasters.
The fracture-induced electromagnetic radiation (FEMR) method is a promising geophysical method for the monitoring and early warning of coal-rock burst disasters. At present, the time-series ...characteristics of FEMR data are primarily used for rock burst monitoring and early warning. However, these rock burst precursor signal identification and hazard warning methods need further development. The progress made in the field of deep learning provides a new method for the identification of rock burst warning signals and the realization of intelligent early warning. In this paper, based on a deep learning algorithm, a method for identifying rock burst precursor FEMR signals is proposed. Based on bidirectional long short-term memory recurrent neural networks, this method trains and validates the model by analyzing a large number of normal FEMR signals and rock burst FEMR precursor signals. The input of the model is the FEMR data sequence, and the corresponding output is the hazard identification result. Upon the completion of training and validation, the model can perform automatic/intelligent shock hazard precursor signal recognition and quickly and accurately provide rock burst hazard early warning without requiring parameter adjustment and manual intervention. The results obtained showed that the rock burst precursor signal recognition method based on the recurrent neural network responds well to the rock burst hazard and can capture information regarding impact hazards in advance. Therefore, it is of great significance with regard to accurate rock burst monitoring and early warning.
•Uniaxial cyclic loading and unloading tests are carried out on granite and marble specimens.•The precursor information of AEs from these specimens are investigated.•A proposal that can predict rock ...failure during loading-unloading process more accurately is put forward.
Engineering practice shows that rock is commonly loaded cyclically during construction. Acoustic emissions (AEs) are still not effective for use as damage assessment precursors for rocks under cyclic loading-unloading. To explore more accurate damage precursors based on multiple AE indices, uniaxial loading-unloading tests were carried out on granite and marble specimens. The results show that at low stress levels, there are almost no AEs generated in the unloading stage but at higher stress levels, there are numerous AE events in the unloading part of the cycle. In each loading-unloading test, the AE count rate, RAE, of each cycle shows a steadily increasing trend as the test progresses but the rate increases sharply in the last cycle. During the entire loading-unloading series, the AEs exhibited Kaiser effect and Felicity effects and the Felicity ratio, RF, decreases as the cycle number increases. In addition, during rock deformation and failure, the RF variations can be divided into three phases: Phase I, RF>1; Phase II, 0.6<RF≤1; Phase III, RF≤0.6. The AEs do not show fractal characteristics during the first and second cycles but fractal features begin to appear in the third cycle. The correlation dimension, D, declines rapidly after the third cycle and then continues to decline slightly until the specimen ruptures. Combinations of RAE, RF and D can be used as signals for rock failure warnings and these combined indices can increase the accuracy of rock dynamic disaster predictions, especially for rocks under loading-unloading cycles. The research findings reported here should provide a more efficient method for issuing early warnings for dynamic rock disasters and provide guidance for the development of warning strategies.
•Threshold methods was used in forecasting outburst.•The methods were realized using multiple indices and logistic regression.•The algorithm is mainly concerned with LR model.•Forecasting accuracy of ...94% is obtained.
The fault-slip burst has the features of high frequency, considerable damage and complex mechanisms, because mining activities break the balance of energy in the system of the fault and rock stratum. ...This disruption of balance causes the sudden activation and slip of the fault with an enormous release of energy and abruptly destroys the excavation space. To identify the mechanical behavior and instability characteristics of the critical stage (meta-instability stage) before fault instability, 5 uniaxial static load experiments are conducted to investigate the count and energy characteristics of acoustic emission (AE), and 15 numerical models are carried out with PFC to analyze the distributional characteristics of the stress, displacement, fracture and force chain fields. The evolutional process of energies is monitored in the models. The results show that the AE signal is the most abundant in the meta-instability stage during the whole loading process, and that the AE count and energy account for 37.27% and 39.18%, respectively, of the whole process. The proportion of cracks matured in the meta-instability stage is negatively correlated with the inclination angle of the preexisting crack and positively correlated with the width of the preexisting crack and the speed of graded loading. The distribution of new cracks changes from the reverse airfoil to the airfoil with an increasing inclination angle of the precrack. During the failure process, energy is primarily released in the form of parallel bond strain energy. In the meta-instability stage, energy accounts for more than 49% of the whole process. The trends for the slip energy and dashpot energy are separated at the beginning of the meta-instability stage, which can be considered as a precursor of failure. The distribution of stress around the precrack is transmitted counterclockwise, forming a deformation pattern in which strain energy is released in the center. The research results provide theoretical support for the identification of the meta-instability stage of fault-slip bursts and are beneficial to research on the risk assessment of coal and rock dynamic disasters.
•The AE signal is released most abundantly in the meta-instability stage, which reflects the deformation synergy.•Precracks affect the stress and failure mode; the proportion of cracks in the meta-instability stage is the highest.•Energy is primarily released in the form of pbstrain energy, and the coal is mainly damaged in a shearing slip manner.•Stress transmits counterclockwise and evolves from low-level uniformity to local concentration.
Precursor information of rock fracture is a prerequisite for rock dynamic disaster (RDD) prediction. To investigate the precursor information of rock fracture based on pressure stimulated current ...(PSC), progressive loading (PL) and step-like loading (SLL) experiments on sandstone samples were conducted. The response characteristics of PSCs from sandstone samples being actively loaded in the laboratory were analyzed and potential precursors to rock fracture based on PSCs are discussed. The results show that weak currents are generated instantaneously when a load is applied on sandstone samples and the PSCs correspond well to stress variations. In the PL experiments, the PSC responses are different in the different stages of deformation. In the compaction stage, the PSC first increases linearly and rapidly as the stress becomes greater but then decreases slowly a few seconds later; In the elastic stage, the current increases slowly and linearly with stress but it begins to rise with an increasing rate when the sample enters the plastic deformation stage, reaching the peak value of PSC the instant the final fracture occurs. In the SLL experiments, when stress is increased from a lower to a higher stress step, the PSCs increase rapidly and reach peak values the moment the stress stops increasing. In addition, the peak current increases exponentially with stress rate with a negative exponent. During stress maintenance, the PSCs decay slowly before finally reaching a stable value, and the PSC decaying process obeys the non-extensive statistical mechanics. The accumulated charge increases linearly with the relative stress, but the charge increasing rate are different in the three deformation stages. The variation rate of accumulated charge in plastic deformation stage is approximately eleven times as that in elastic deformation stage. The variation of current has a precursor response to rock fracture: the increase of PSC with an accelerative rate can be used as a precursor of rock failure during a progressive loading, the increasingly intensive abrupt drops during the PSCs decay are precursors for rock fracture during creep, and the rapid increase of accumulated charge variation rate can be used as a precursor of rock fracture both under progressive and step-like loadings. These research results are expected to provide new ideas and methods for RDD prediction.
Microseismic, acoustic emission, and electromagnetic radiation (MS, AE, and EMR) are promising methods for the monitoring and early warning of rock bursts. In the underground mining process, ...personnel activities and electromechanical equipment produce MS, AE, and EMR interference signals that affect the accuracy of MS, AE, and EMR monitoring. This study is aimed at overcoming the difficulties in identifying MS, AE, and EMR interference signals by means of on-site monitoring and deep learning methods. First, MS waveform signals and AE and EMR time series signals are collected by the SOS monitoring system and GDD12 monitor. According to the field records, the MS effective waveform, MS interference waveform, AE and EMR interference signals were labeled, and training sets and verification sets were made. An identification model for MS waveforms based on the ResNet-50 convolutional neural network and an identification model for AE and EMR interference signals based on the recurrent neural network were constructed using the original MS, AE, and EMR data. In addition, the identification accuracy and generalization ability of the models were analyzed. The results show that the proposed method can respond positively to MS, AE, and EMR interferences and accurately eliminate MS, AE, and EMR interference signals. It can significantly improve the reliability of MS, AE, and EMR monitoring data and effectively monitor rock burst disasters.
•A MS identification model based on CNN and an AE-EMR identification model based on RNN were established.•The models improve the identification accuracy of interference signals.•The models can provide strong support for monitoring and early warning of rock burst disasters.
Dynamic disasters in Chinese coal mines pose a significant threat to coal productivity. Thus, a thorough understanding of the deformation and failure processes of coal is necessary. In this study, ...the energy dissipation rate is proposed as a novel indicator of coal deformation and failure under static and dynamic compressive loads. The relationship between stress-strain, uniaxial compressive strength, displacement rate, loading rate, fractal dimension, and energy dissipation rate was investigated through experiments conducted using the MTS C60 tests (static loads) and split Hopkinson pressure bar system (dynamic loads). The results show that the energy dissipation rate peaks are associated with stress drop during coal deformation, and also positively related to the uniaxial compressive strength. A higher displacement rate of quasi-static loads leads to an initial increase and then a decrease in energy dissipation rate, whereas a higher loading rate of dynamic loads results in larger energy dissipation rate. Theoretical analysis indicates that a sudden increase in energy dissipation rate suggests partial fracture occurring within coal under both quasi-static and dynamic loads. Hence, the energy dissipation rate is an essential indicator of partial fracture and final failure within coal, as well as a prospective precursor for catastrophic failure in coal mine.
The novel dry ice powder static blasting rock breaking lacked working standards, especially for the vibration safety assessment of the construction site for the protection of the building structure, ...in comparison with the traditional drill and blast method which had a proven operational process and safety specifications. The Sadovsky vibration velocity prediction formula could only predict the vibration velocity and was project specific. Oscillation parameters that needed to be considered in the vibration safety assessment, such as the dominant frequency of vibration, could not be obtained through empirical formulas. Using the five parameters of hole depth, blast center distance, dry ice powder mass and rock classification as the main influencing factors, BP and RBF neural network models were constructed by Matlab software to predict the peak vibration velocity, main frequency and maximum displacement of dry ice powder blasting. Projection results revealed that it is structurally simpler than the BP neural network and that the RBF was more accurate in predicting the target than the BP network. The results of the study had significant implications for the safe application of the new technology, and more samples of field data need to be obtained in the future, along with the use of more advanced predictive modelling.
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