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The dynamic mechanical behavior of frozen rock with a flaw is essential for plateau alpine region mining. However, the freezing temperature's strengthening and damage mechanism on dynamic strength is still unclear. Drop weight impact tests (single factor) and orthogonal tests (3 factors and 5 levels) were used to examine the influence of flaw dip angle (β), impact height (H), and freezing temperature (T) on the dynamic response of frozen sandstone in plateau alpine region. Different factors' effect on peak strain and failure mode was compared, as well as a significant analysis of three factors' influence on peak strain. The results reveal that with the increase of β, the ultimate strain (εu) of sandstone shows a 'M' type variation, while the linear increase of εu occurs with the increase of H and the decrease of T. At the same time, β and T have a substantial effect on εu, and T has a significant influence on the occurrence time of εu. In addition, tensile failure is the failure mode of sandstone with tensile cracks mainly parallel to the direction of impact. Moreover, a two-dimensional particle flow code was used to supply the stress–strain curve, cracking process, and energy absorption characteristics. The obvious rebound phenomenon can be observed, and the peak stress increases with the increase of H and decreases as β and T increase. Furthermore, the energy usage rate has a linear relationship with β and T, but it decreases first and then increases as impact energy increases. At the end, the effect of the strengthening mechanism of T on single-flaw sandstone dynamic strength and the damage evolution was discussed. The effect of pore ice is primarily responsible for the strength enhancement of frozen sandstone, but the frost heave damage at a certain T (− 20 °C) will inhibit the strength growth. The damage constitutive model based on logistic function can accurately describe the stress–strain properties of single-flaw frozen sandstone before εu. Highlights: The dynamic mechanical responses of single flaw frozen sandstone were studied by drop weight impact tests and numerical simulation. The influence of flaw dip angle, impact height and freezing temperature on peak strain, stress, and energy consumption were revealed. The strengthening mechanism of freezing temperature on dynamic strength was discussed. The frost heave damage formed at a certain freezing temperature (– 20 °C) will inhibit the growth of strength. [ABSTRACT FROM AUTHOR]

Mining will unavoidably cause crack expansions and stress redistributions of coal–rock masses, which may cause rockbursts in severe cases. Microseismic monitoring is nowadays playing more and more important roles in forecasting these rockbursts. However, there are lack of studies on the evolutionary law of the mining-induced coal–rock fracture expansion and periodic weighting with the variations of multi-indices of the microseismic events, which limits the effectiveness of microseismic monitoring as a three-dimensional space monitoring technology of early warning mine disasters. Correspondingly, this study investigated the characteristic law of deep mining-induced rock pressuring using microseismic monitoring with a focus on the evolutional law of the microseismic indices including the microseismic count, microseismic energy, apparent volume (VA), apparent stress (σA), energy ratio (Es/Ep), and b value. The horizontal and vertical distribution models of the stopes are first developed based on the evolution of microseismic indices. The early warning law that the microseismic count increment and energy increment reflect the precursors of periodic weighting is then proposed. It is found that the fracture expansion of the coal–rock mass caused by various mining rates can be reflected by the changes of microseismic multi-indices. As the mining rate intensifies, both the microseismic count and energy tend to increase. Furthermore, the change of the apparent volume (VA), energy index (EI), and b value reflects the law of rock pressuring. Namely, the sharp decrease of log(EI) and b value as well as the increase of the cumulative apparent volume indicate the occurrence of high pressure, which means the deterioration of the coal–rock mass stability. It is noted 85.58% of the microseismic events monitored in a working face has a ratio of ES/EP less than 10, which is mainly related to the non-shear failure of the coal–rock mass in the stope. The apparent stress distribution along the working face is relatively consistent with that of the measured field stress, which reflects the reliability of the microseismic multi-indices. Finally, a new multidimensional information entropy indicator that reflects the mining rate has been developed, which takes into account both the microseismic count and the maximum energy. Therefore, this study establishes the evolutional law of the multi-indices of the microseismic events against the rock pressuring in deep mines, which provides an important reference for predicting rock pressuring in similar mines using the microseismic monitoring technology and formulating relevant regulations. Highlights: This study investigated the characteristic law of deep mining-induced rock pressuring using microseismic monitoring with a focus on the evolutional law of the microseismic indices. Both the horizontal and vertical distribution models of the microseismic events observed in coal mines are established. The microseismic count increment and the energy increment with adjacent time are proposed to reflect the law of rock pressuring. A new multidimensional information entropy indicator reflecting the mining rate is developed. [ABSTRACT FROM AUTHOR]

Identification of preferential seepage channels for a large-scale underground water-sealed oil storage cavern is of great importance for controlling groundwater seepage and ensuring water-sealing performance. In this study, a new approach to identifying preferential seepage channels in the surrounding rock of the underground water-sealed oil storage cavern is proposed, focusing on the spatial distribution and connectivity of microfractures obtained from microseismic monitoring in the surrounding rock masses during cavern construction. The focal mechanism and the spatial occurrence of microfractures are revealed through inversion and analysis of the microseismic moment tensor. By adopting the graph traversal method, the spatial connectivity of microfractures is determined and preferential seepage channels are identified. An underground water-sealed oil storage cavern in Jinzhou, China, is selected as a case study. Five preferential seepage channels, which originate from the water curtain borehole and the surrounding rock masses between the oil storage caverns, and extend downwards along microfracture clusters are identified. The feasibility and validity of the proposed method are verified by the water supply data in the water curtain borehole and field survey. The research results can provide some reference for seepage channel detection, water-sealing performance evaluation and grouting design of large-scale underground water-sealed oil storage caverns. Highlights: The formation of preferential seepage channel is accompanied by the spatial connectivity of microfractures. Preferential seepage channels can be identified using microseismicity and pore pressure data in rocks. The microfractures caused by excavation-induced unloading are mainly tensile cracks in underground oil storage caverns. The occurrence of microfracture around the water curtain tunnel differs from that near the oil storage cavern. [ABSTRACT FROM AUTHOR]

As a 3D real-time monitoring method, microseismic (MS) monitoring technique has been widely used in various underground engineering applications. However, in such applications, it is still challenging to acquire precise and efficient MS locations. Here, we examined the applicability and accuracy of a fully convolutional neural network for source localization, where the modified loss function was utilized. The Shuangjiangkou underground powerhouse in southwestern China served as the engineering background. The dataset was made of the MS events that occurred near the main powerhouse from September 2018 to December 2019. A fully convolutional neural network, named MS-location Net, was then built. The original waveform data were directly used as the input of the neural network, while 3D Gaussian distribution functions of the monitoring area were used as the output of the neural network. The epicenter error, focal depth error and absolute error were applied as indicators to evaluate the model. The results show that all the three indicators, namely the epicenter error, focal depth error and absolute error, were less than 5 m for all the MS events in the test set. The average time for locating an MS event was 0.01435 s using a usual computer configuration, which greatly improves the positioning efficiency. The proposed location method in this paper overcomes the shortcomings of the traditional localization methods, e.g., the inaccuracy of velocity model and arrival picking. Highlights: A fully convolutional neural network, named MS-location Net, was built for microseismic source localization. The applicability and efficiency of the proposed location method were validated via a case study. The proposed location method overcomes the shortcomings of the traditional localization methods, e.g., the inaccuracy of velocity model and arrival picking. [ABSTRACT FROM AUTHOR]

Tensile strength is a vital mechanical property of rock and the deformation and failure process of rock under tension is of much significance in rock mechanics and engineering. In this study, a micro analysis on rock direct tensile failure was made based on the newly introduced quantitative analysis of acoustic emission (AE) waveforms. Direct tensile tests of common rocks were carried out, accompanied by real-time AE monitoring. The spatial and temporal distributions of AE signals were studied over the whole loading process. Distribution and evolution laws of dominant frequencies of AE waveforms with normalized applied stress were statistically analyzed. The statistical relationship between the energy ratios of AE waveforms distributed in low and high dominant frequency bands (L-type and H-type waveforms) and peak strengths of rock specimens was built. Microstructural observations with SEM were further conducted. Results show that the dispersion degree of rock tensile strength corresponds to the complexity of mineral composition. Initial release moments of L-type waveforms appeared earlier than H-type waveforms. L-type waveforms of the same proportion carry more energy compared to H-type waveforms in rock subjected to tension. There is an overall downward trend for peak strength of rock specimens with the increasing energy ratios of L-type waveforms. The tensile strength of rock obtained by fitting in this study is smaller than that obtained by averaging. There exist micro shear fractures during the macro tensile failure process of rock according to the microstructural observations with SEM. The peak strength corresponding to the case that L-type waveforms (or micro tensile failures) account for 100% can be considered as the "ideal" tensile strength from the microscopic perspective. The determined tensile strength by fitting can be used as a conservative design parameter for rock engineering. Highlights: Statistical relationship between energy ratios of AE waveforms distributed in low and high dominant frequency bands and peak strengths of rocks under tension is built. There exist micro shear fractures during the macro tensile failure process of rock. Peak strength in the case that L-type waveforms (or micro tensile failures) reach 100% can be considered as the "ideal" tensile strength. The determined tensile strength by fitting can be used as a conservative design parameter for rock engineering. [ABSTRACT FROM AUTHOR]

A rockfall is a typical dynamic problem of a discontinuous block system originating from a dangerous rock mass and always presents serious geo-hazards along highway slopes in mountainous areas. This study aims to investigate the failure mechanisms and movement characteristics of rockfalls through a three-dimensional discontinuous deformation analysis (3D DDA) method and attempts to comprehensively examine the complicated kinematic process of rockfall disasters. In terms of the initial failure and post-movement characteristics (i.e., motion trajectory and kinetic energy) of a rockfall, the effectiveness of 3D DDA is verified by comparing its results with those of laboratory experiments. Taking the K4580 typical high-steep slope undergoing rockfalls along the G318 national highway in Tibet as an example, the initiation and failure of a single boulder and a large-scale rock mass at the source area were simulated by 3D DDA. Then, the movement characteristics of the boulder and massive collapsing rocks along the slopes of different geometrical characteristics, i.e., the slopes before the landslide and after the shallow and deep landslides, were studied. The results show that the 3D DDA has significant advantages in analysing the failure mechanisms of slope rockfalls and can satisfactorily simulate the spatial movement (e.g., lateral deviation and deflection) of blocks by considering the 3D geometry of the slopes and blocks. The 3D DDA numerical simulation can predict the movement range, deposition position, and affected area of rockfall disasters, which can provide a basis for formulating disaster prevention countermeasures in actual projects. [ABSTRACT FROM AUTHOR]

Due to the susceptibility of microseismic activity to variations in construction parameters and geological conditions, deep-buried tunnel rockbursts often exhibit different precursor characteristics. Consequently, when rockburst intelligent early-warning methods are applied long-term during construction, manual intervention is required to adjust the warning model to maintain accuracy. In light of this issue, this paper focuses on the development of an intelligent dynamic early-warning method for rockbursts, using the Qinling Water Diversion Tunnel of the Hanjiang-to-Weihe Water Diversion Project as a case study. Daily microseismic parameters, construction progress, and records of previously occurred rockbursts serve as input parameters, while the occurrence and intensity level of rockbursts within the next day serve as output parameters. Sequential samples are constructed, and a dynamic early-warning model for rockbursts based on Recurrent Neural Network is established. The dynamic warning capabilities of the model were validated through two test sets, and its mechanism is explained. The results show that the records of occurred rockbursts as input parameters are indispensable. Increasing the sequence length appropriately can improve prediction accuracy. In comparison with other common classification algorithms, the Recurrent Neural Network exhibits superior performance. Ultimately, the model is applied to the long-term rockburst monitoring and early-warning work of the Hanjiang-to-Weihe Water Diversion Project, achieving a rockburst prediction accuracy of 91.2% and a rockburst level prediction accuracy of 86.0%. The findings of this study can provide valuable insights for the research and application of intelligent rockburst early-warning methods. Highlights: An intelligent dynamic early warning model for rockbursts was developed, offering a solution to the dependence on manual intervention. The model incorporates daily microseismic parameters, construction progress, and previous rockburst records as sequential input parameters. By learning from newly acquired rockburst situations and monitoring data, the model dynamically adapts to the ever-changing rockburst conditions. The model demonstrated high prediction accuracy in rockburst occurrence (91.2%) and rockburst level (86.0%) in long-term application. [ABSTRACT FROM AUTHOR]