On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst,the research progress and shortcomings in understanding t...On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst,the research progress and shortcomings in understanding the rockburst phenomenon have been comprehensively in-vestigated.This study focuses on the occurrence mechanism and monitoring and early warning technology for rockburst in coal mines.Results showed that the prevention and control of rockburst had made significant progress.However,with the increasing mining depth,several unre-solved concerns remain challenging.From the in-depth research and analysis,it can be inferred that rockburst disasters involve three main problems,i.e.,the induction factors are complicated,the mechanism is still unclear,and the accuracy of the monitoring equipment and multi-source stereo monitoring technology is insufficient.The monitoring and warning standards of rockburst need to be further clarified and im-proved.Combined with the Internet of Things,cloud computing,and big data,a study of the trend of rockburst needs to be conducted.Further-more,the mechanism of multiphase and multi-field coupling induced by rockburst on a large scale needs to be explored.A multisystem and multiparameter integrated monitoring and early warning system and remote monitoring cloud platform for rockburst should be explored and developed.High-reliability sensing technology and equipment and perfect monitoring and early warning standards are considered to be the de-velopment direction of rockburst in the future.This research will help experts and technicians adopt effective measures for controlling rock-burst disasters.展开更多
Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.T...Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.展开更多
Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological ...Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.展开更多
位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的...位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的邻接矩阵。再对北斗高精度位移、降雨量、地下水位与土壤含水率多因子监测数据进行去粗差、插值与归一化等时序数据处理,并进行时空相关性分析,结果表明位移主要受连续两个月的降雨量、三级边坡的地下水位与土壤含水率的影响。将最先进的基于图深度学习的GTS(Graph for Time Series)预测模型引入边坡位移预测中,提出适用于北斗高精度边坡变形监测的GTS-BDS位移预测模型。当预测时长为1 h时,其均方根误差(RMSE)、平均绝对误差(MAE)与平均绝对百分比误差(MAPE)指标评价分别达到0.301、0.154与3.5%,均优于LSTM与T-GCN等模型。本文所提出的位移预测方法充分利用了北斗高精度及其他传感器监测点位之间的空间拓扑与监测数据的时序特征,从整体监测体系的角度提升边坡位移预测的准确率与可靠性,在边坡安全预警中具有良好的应用前景。展开更多
基金This work was financially supported by the National Nat-ural Science Foundation of China(Nos.51634001,51774023,and 51904019).
文摘On the basis of the massive amount of published literature and the long-term practice of our research group in the field of prevention and control of rockburst,the research progress and shortcomings in understanding the rockburst phenomenon have been comprehensively in-vestigated.This study focuses on the occurrence mechanism and monitoring and early warning technology for rockburst in coal mines.Results showed that the prevention and control of rockburst had made significant progress.However,with the increasing mining depth,several unre-solved concerns remain challenging.From the in-depth research and analysis,it can be inferred that rockburst disasters involve three main problems,i.e.,the induction factors are complicated,the mechanism is still unclear,and the accuracy of the monitoring equipment and multi-source stereo monitoring technology is insufficient.The monitoring and warning standards of rockburst need to be further clarified and im-proved.Combined with the Internet of Things,cloud computing,and big data,a study of the trend of rockburst needs to be conducted.Further-more,the mechanism of multiphase and multi-field coupling induced by rockburst on a large scale needs to be explored.A multisystem and multiparameter integrated monitoring and early warning system and remote monitoring cloud platform for rockburst should be explored and developed.High-reliability sensing technology and equipment and perfect monitoring and early warning standards are considered to be the de-velopment direction of rockburst in the future.This research will help experts and technicians adopt effective measures for controlling rock-burst disasters.
基金financially supported by the National Natural Science Foundation of China(Nos.52011530037 and 51904019)。
文摘Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.
基金Project(52204084)supported by the National Natural Science Foundation of ChinaProject(FRF-IDRY-GD22-002)supported by the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities),China+2 种基金Project(QNXM20220009)supported by the Fundamental Research Funds for the Central Universities and the Youth Teacher International Exchange and Growth Program,ChinaProjects(2022YFC2905600,2022YFC3004601)supported by the National Key R&D Program of ChinaProject(2023XAGG0061)supported by the Science,Technology&Innovation Project of Xiongan New Area,China。
文摘Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.
文摘位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的邻接矩阵。再对北斗高精度位移、降雨量、地下水位与土壤含水率多因子监测数据进行去粗差、插值与归一化等时序数据处理,并进行时空相关性分析,结果表明位移主要受连续两个月的降雨量、三级边坡的地下水位与土壤含水率的影响。将最先进的基于图深度学习的GTS(Graph for Time Series)预测模型引入边坡位移预测中,提出适用于北斗高精度边坡变形监测的GTS-BDS位移预测模型。当预测时长为1 h时,其均方根误差(RMSE)、平均绝对误差(MAE)与平均绝对百分比误差(MAPE)指标评价分别达到0.301、0.154与3.5%,均优于LSTM与T-GCN等模型。本文所提出的位移预测方法充分利用了北斗高精度及其他传感器监测点位之间的空间拓扑与监测数据的时序特征,从整体监测体系的角度提升边坡位移预测的准确率与可靠性,在边坡安全预警中具有良好的应用前景。
