【摘要】 在研究过程中测量油母页岩岩石、岩体弹性纵波速度,确定选取炸药类型,采用BP神经网络和经典炸药岩石匹配理论相互论证炸药性能的数据调整方案、在相同爆破参数条件下进行油母页岩炸药和岩石匹配爆破试验,采用Split-Desktop软件分析试验后岩石块度分布以验证匹配效果。在试验后分析了油母页岩岩石块度分布,发现尾矿率升高,试验结果不理想。通过对爆破试验设计的分析,排除孔网参数、装药结构等的可能性;总结出岩体节理裂隙、节理面与自由面交角、爆破延期时间3方面原因。更多还原

【Abstract】 The author measured oil shale rock, rock elastic P-wave velocity in the process of research, determined explosive types,and adopted BP neural network and the classical theory of explosive rock matching to mutually demonstrate data adjustment scheme of explosive performance. Under the same blasting parameters conditions, the author carried out oil shale rock explosives and rock match blasting experiment, and adopted Split-Desktop software to analyze rock fragmentation distribution in order to verify the matching effect. After experiment, the author found tailings rate increases, and the experiment results are not ideal. Through analyzing the blasting experiment design, the author excluded the possibility of hole pattern parameters and charging structure, and summarized the jointed rock mass, joint surface and free surface crossing angle, blasting delay time three reasons.更多还原