Search

Abstract Coal burst has become one of the most serious hazards in longwall coal mines of many countries, and extensive studies have been conducted on the mechanism, prediction, and prevention of coal bursts. However, most of these studies focus on qualitative descriptions of burst environments and specific case analyses. Few studies have been carried out to explore knowledge through a number of past coal bursts in longwall mines. The main objective is to quantitatively analyze the current burst‐prone conditions in longwall coal mines of China and to summarize corresponding lessons. A database consisting of 54 major coal bursts was established. The characteristics and contributing factors of involved coal bursts were analyzed quantitatively, and high‐risk areas that are common in coal mines were then identified. Finally, some suggestions and lessons were proposed. This study may help for identifying controlling factors and high‐risk areas at a specific mine, and it might provide insights into the control of coal bursts in longwall coal mines.

Abstract Real‐time monitoring of three‐dimensional stress in the field is an effective approach to detect evolving stress in roof rock and to evaluate rock bursts risk. However, the sensors or data transmission cables may be damaged due to the volatile environment found in coal mines, which can lead to the loss of relevant monitoring data, and some critical information for rock burst prediction may be missed entirely. A number of methods that use historical data to predict missing data or future structural states have been proposed. However, the performance of these methods is poor when the training data are insufficient owing to lack of data. To address this issue, a methodology framework is proposed to predict the mining‐induced stress when some monitoring data are missing. The framework uses a long short‐term memory neural network integrated with the transfer learning method. The proposed method can transfer the knowledge learned from complete monitored data of adjacent sensor to target sensor to boost forecasting. A case study has been conducted to evaluate the method. The results show that the developed model can significantly improve the prediction performance for the target domain, which can be improved further by increasing the size of the target domain training data available.

The study of the mechanical properties and failure behaviors for coal with different bedding structures at various medium strain rates is of great importance for revealing the mechanism of rock burst. In our study, we systematically analyze the uniaxial compressive strength (UCS), acoustic emission (AE) characteristics, failure pattern, and risk of rock burst on coal specimens with two bedding orientations under ranged in strain rates from 10−4 s−1 to 10−2 s−1. The results reflect that and the bedding direction and the strain rates significantly affect the UCS and failure modes of coal specimens. The UCS of coal specimens with loading directions perpendicular to bedding planes (horizontal bedding) increases logarithmically with increasing strain rate while the UCS increases first and then decreases of coal specimens with loading directions parallel to bedding planes (vertical bedding). The AE cumulative energy of the specimens with horizontal bedding is an order of magnitude higher than that of specimens with vertical bedding. However, it is independent of the strain rates. The energy release rates of these two types of bedded coal specimens increase in a power function as the strain rate increases. The coal specimens with horizontal bedding show violent failure followed by the ejection of fragments, indicating a high risk of rock burst. On the other hand, the coal specimens with vertical bedding exhibit a tensile splitting failure with a low risk of rock burst. Strain localization is a precursor of coal failure, and the concentration area of local principal strain is highly consistent with the initial damage area, and the area where the principal strain gradient is significantly increased corresponds to the fracture initiation area.

Roof rocks in coal mines are subjected to the combination of in situ stresses and dynamic stresses induced by mining activities. Understanding the mechanical properties of roof rocks under static and dynamic loads at medium strain rates is of great significance to revealing the mechanism of rock bursts. In this study, we employ the digital image correlation (DIC) technique to investigate the energy concentration and dissipation behaviors, failure mode, and deformation characteristics of roof rocks under combined static and dynamic loads. Our results show that both the static pre-stress and dynamic loading rate have significant effects on the uniaxial compressive strength of rock specimens. From the energy principle, when the static pre-stress is the same, both elastic strain energy density and dissipated energy density increase with increasing dynamic loading rate. The hazard of rock bursts increases with decreasing static pre-stress and increasing dynamic loading rate. At higher dynamic loading rates, more cracks are generated, and the failure becomes more violent. The crack initiation, propagation and coalescence processes are identified, and the failure mode is closely related to the evolution of the global principal strain field of the rock specimens.

Fiber-reinforced concrete (FRC) has a great advantage in earthquake-resistant structures, as compared with regular concrete. However, there are many difficulties in the construction and maintenance of concrete structures due to the high density and easy corrosion of the steel fiber in commonly used steel FRC. With the development of polymer material science, polyvinyl alcohol (PVA) fiber has been rapidly promoted for use in FRC because of its low density, high strength, and large elongation at break value. Dynamic uniaxial compression and splitting tensile experiments of FRC with PVA fiber were carried out with two matrix strengths (i.e., C30 and C40), which were blended with PVA fibers with a length of 12 mm in different volume contents (0, 0.2, 0.4, and 0.6%), at the age of 28 days, under different strain rates (i.e., 10−5, 10−4, 10−3, and 10−2 s−1). The results show that PVA has an obvious enhancing and toughening effect on concrete, which can improve its brittle properties and residual strength. With increasing strain rate, the compressive strength, split tensile strength, and elastic modulus increase to a certain extent, while the toughness index and the peak strain decrease to a certain degree. The post-peak deformation characteristic changes from a brittle failure of sudden caving to a ductile failure with dense cracking. The effect of PVA is different when enhancing the concrete with two different matrix strengths. The lower the matrix strength, the more obvious the enhancement effect of the fiber, showing characteristics of a higher compressive strength and low split tensile strength in FRC with low strength and a smoother post-peak stress–strain curve.