CAJ | 학술논문

在数据挖掘的基础上，采用粗糙集对矿岩可爆性数据进行了数据级融合，除去冗余属性，然后采用BP神经网络进行特征级融合，从而建立基于多源信息融合的矿岩可爆性评价模型。对原始数据进行了离散归一化处理，应用粗糙集对决定矿岩可爆性指数的6个因素进行了属性约简，剔除了平均合格率，而保留了漏斗体积、大块率、小块率、岩体声波速度和波阻抗等5个因素，并对约简的准确性进行了验证。分别建立了矿岩可爆性评价的BP神经网络模型和粗糙集?BP神经网络模型，前者对矿岩可爆性指数的预测值与实际值的平均偏差为8．33％，而后者为6．75％。利用建立的粗糙集?BP神经网络模型预测某矿山井下采场的矿岩可爆性指数为78．43，计算出采场的炸药单耗为0．65 kg／m3，而现场试验值为0．67 kg／m3，进一步验证了该模型的正确性。
재수거알굴적기출상，채용조조집대광암가폭성수거진행료수거급융합，제거용여속성，연후채용BP신경망락진행특정급융합，종이건립기우다원신식융합적광암가폭성평개모형。대원시수거진행료리산귀일화처리，응용조조집대결정광암가폭성지수적6개인소진행료속성약간，척제료평균합격솔，이보류료루두체적、대괴솔、소괴솔、암체성파속도화파조항등5개인소，병대약간적준학성진행료험증。분별건립료광암가폭성평개적BP신경망락모형화조조집?BP신경망락모형，전자대광암가폭성지수적예측치여실제치적평균편차위8．33％，이후자위6．75％。이용건립적조조집?BP신경망락모형예측모광산정하채장적광암가폭성지수위78．43，계산출채장적작약단모위0．65 kg／m3，이현장시험치위0．67 kg／m3，진일보험증료해모형적정학성。
After data mining, the redundant attributes were removed for rock mass blastability using data fusion with rough set theory. Then, BP neural network was used for information fusion in terms of feature, so as to build an evaluation model for rock mass blastability based on multi?source data fusion. After attribution reduction with rough set theory for 6 deciding factors of rock mass blastability index based on the discretion and normalization processing of the raw data, the attribute of average qualified percentage was deleted with attributes of crater volume, mass ratio of big rock blocks, ratio of small rock blocks and wave impedance retained. The accuracy of such reduction process has been verified. A BP neural network model and rough set?BP neural network model were respectively established for evaluating rock mass blastability, with the average deviation rate between predictive value and actual value at 8.33% and 6.75%, respectively. By using this established rough set?BP neural network model, the blastability index of rock mass in underground stope was predicated to be 78.43, and the unit consumption of explosive for the stope was calculated to be 0.65 kg/m3, which compared to the actual value of 0.67 kg/m3 from an on?site experiment, verified the model′s validity.