摘要
借助支持向量机模型并考虑影响爆堆前冲距离和后冲距离的影响因素,提出了爆堆前冲和后冲的支持向量机(SVM)预测模型。结合工程实例和爆破作业方式,本次实验选择第1排炮孔的孔深、孔距、抵抗线距离、坡角、超钻长度、药量、炮孔堵塞长度,以及第2~8排炮孔的孔深、孔距、排距、超钻长度、药量、炮孔堵塞长度作为影响因素,以爆堆的前冲距离和后冲距离作为因变量,并分别采用网格搜索的方式(GSM)和遗传算法(GA)对基于支持向量机预测模型的超参数进行优化调节,利用从现场收集来的40组爆破实例构建和评价预测模型。最终的预测结果表明,从现场收集来的相关参数与爆堆位移之间构成一定的映射关系,结合启发式算法的爆堆位移支持向量机预测模型能够得到较好的预测精度。
With the help of support vector machine model and considering the factors that affect the distance of forward replacement and backward replacement,the prediction model of support vector machine(SVM)for the forward and back⁃ward displacement of blasted pile was proposed.Combined with the engineering examples and blasting operation patterns,this experiment selects the hole depth,hole distance,resistance line distance,slope angle,over drilled length,charge quantity,stemming length of the first row of blasthole,as well as the hole depth,hole distance,row distance,over drilled length,charge quantity and stemming length of the second to eighth rows of blastholes as the influencing factors,and takes the forward and backward replacement distance of the blasting pile as the output variables.In addition,GSM(grid search method)and GA(genetic algorithm)are used to optimize and adjust the hyper-parameters of SVM prediction models.40 groups of blasting examples collected from the field blasting operation are used to develop and evaluate the prediction model.The final prediction results show that there is a certain mapping relationship between the relevant parameters collected from the field and the blasting pile replacement,and the support vector machine prediction model of blasting displacement com⁃bined with heuristic algorithm can get higher prediction accuracy.
作者
何晓华
He Xiaohua(Sinosteel Maanshan General Institute of Mining Research Co.,Ltd.,Maanshan 243000,China)
出处
《金属矿山》
CAS
北大核心
2020年第6期177-183,共7页
Metal Mine
关键词
爆堆前冲
爆堆后冲
支持向量机
预测
Forward blasting pile
Backward blasting file
Support vector machine
Prediction