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在分析影响岩体可爆性的基础上,选取平均裂隙间距、岩体普氏系数、岩体波阻抗、动弹性模量为评价指标,建立岩体可爆性分级的粗糙元神经网络模型。依据黒岱沟矿岩体数据,对该矿的岩体可爆破性进行了分级评价。结果表明,所建立的模型能够很好的解决岩体可爆性分级问题,该模型可以避免主观因素对权重确定的影响,评价结果客观准确。
Based on the analysis of the influence of the explosive nature of the rock mass, the average crack spacing, the Platts coefficient of rock mass, the wave impedance of rock mass and the dynamic elastic modulus are selected as evaluation indexes to establish a rough neural network model of rock mass burst classification. Based on the data of the rock mass in the Blackdale ditch, the blastability of the rock mass in this mine was evaluated. The results show that the model can well solve the problem of blasting grade of rock mass. The model can avoid the influence of subjective factors on weight determination, and the evaluation results are objective and accurate.