【摘要】 爆堆形态是爆破效果评价的重要指标，也是露天矿精准采矿的重要影响因素.为确定爆堆数值模型材料参数，首先，通过信标试验得到了典型岩块的位移与速度，并与先前建立的离散元数值模型进行对比.其次，在岩体节理敏感性分析的基础上，建立岩体形态参数、质点轨迹与节理参数的神经网络训练集.最后，基于训练好的神经网络，通过爆堆数据来反演岩体材料参数以能正确建立台阶爆破的离散元模型.研究表明：内摩擦角对爆堆整体形态影响最大也最为敏感，其次是体积模量和剪切模量，黏聚力和抗拉强度影响最小.台阶岩石运动速度大小与最小抵抗线有关，最小抵抗线越小其运动速度越快.基于联合神经网络建立了台阶岩体节理力学参数的反演流程，通过与实测值对比可知，大部分测点的计算误差均在10%之内，能够满足工程需要.更多还原

【Abstract】 The shape of the blast pile is an important indicator for evaluating the blasting effect and a significant influencing factor for precision mining in open-pit mines. To determine material parameters of the numerical model for explosive piles, the displacement and velocity of typical rock blocks were obtained through beacon experiments, and were compared with the previously established discrete element numerical model. Using orthogonal experiments, different values of material parameters were taken to obtain the characteristic data of the explosion pile. Based on trained neural networks, the discrete element model of step blasting was accurately established by inverting rock material parameters through blasting pile data. The results show that the internal friction angle exerted a predominant and highly responsive effect on the configuration of the blasted material. It was followed in importance by the bulk and shear modulus, whereas both cohesion and tensile strength had a comparatively nominal influence. Moreover, the velocity of bench rocks was proportional to the line of least resistance; a lesser line of least resistance correlated with an augmented velocity of movement. An inversion methodology utilizing a joint neural network was developed to ascertain the mechanical parameters of stepped rock joints.Comparative analysis with empirical data indicates that the computational inaccuracies at the majority of measurement sites are below 10%, thereby satisfying the precision requirements for engineering applications.更多还原