摘要
爆破地震危害是矿山开采过程中最为显著的负面效应之一,准确预测质点峰值振动速度(PPV)对于有效预防爆破振动引发的建(构)筑物失稳破坏具有极大的工程实际意义。设计并开展了露天矿山开挖爆破现场监测试验,采用灰色关联分析法对PPV影响因素进行敏感性分析,确定各影响因素之间的主次关系。在此基础上,建立最小二乘支持向量机(LS-SVM)模型对PPV进行预测,并通过粒子群算法(PSO)局部寻优确定LS-SVM模型中正则化参数和核函数宽度系数的最佳参数组合,最后将PSO-LSSVM模型预测结果与BP神经网络模型、LS-SVM模型及传统萨道夫斯基公式的预测结果进行了对比分析。结果表明:PSO-LSSVM模型对PPV预测的拟合相关系数(R^(2))、均方根误差(RMSE)、平均相对误差(MRE)及纳什系数(NSE)分别为97.38%、2.68%、1.36%和99.98%,PSO-LSSVM模型预测精度更高,且具有更好的泛化能力,用于多因素影响下的矿山爆破PPV预测切实可行。
Blasting seismic hazard is one of the most significant negative effects in the process of mining.Accurate prediction of peak particle velocity(PPV)is of great engineering practical significance to effectively prevent the instability and damage of buildings(structures)caused by blasting vibration.The blasting vibration test of open-pit mine excavation was designed and carried out,the sensitivity analysis of the influencing factors of PPV was analyzed by the grey correlation analysis method,and the primary and secondary relationships between the influencing factors were determined.On this basis,the least squares support vector machine(LS-SVM)model is established to predict PPV,and the optimal parameter combination of regularization parameters and kernel function width coefficient in the LS-SVM model is determined by local optimization of particle swarm optimization(PSO).Finally,the prediction results of PSO-LSSVM model are compared with those of BP neural network model,LS-SVM model and traditional Sadowski formula.The results show that the fitted correlation coefficient(R^(2)),root mean square error(RMSE),mean relative error(MRE)and Nash-Sutcliffe efficiency coefficient(NSE)of the PSO-LSSVM model for PPV prediction are 97.38%,2.68%,1.36%and 99.98%,respectively,and the PSO-LSSVM model has higher prediction accuracy and better generalization ability,which is feasible for mine blasting PPV prediction under multi-factor influence.
作者
何理
刘易和
李琳娜
陈江伟
姚颖康
刘昌邦
HE Li;LIU Yihe;LI Linna;CHEN Jiangwei;YAO Yingkang;LIU Changbang(Hubei Key Laboratory of Systems Science in Metallurgical Process,Wuhan 430065,China;Hubei Key Laboratory of Blasting Engineering,Jianghan University,Wuhan 430056,China;China Construction Seventh Engineering Bureau Co.,Ltd.,Zhengzhou 450004,China;State Key Laboratory of Precision Blasting,Jianghan University,Wuhan 430056,China;Wuhan Explosion&Blasting Co.,Ltd.,Wuhan 430056,China)
出处
《金属矿山》
CAS
北大核心
2022年第7期145-150,共6页
Metal Mine
基金
国家自然科学基金项目(编号:51904210)
冶金工业过程系统科学湖北省重点实验室基金项目(编号:Z202001)
爆破工程湖北省重点实验室基金项目(编号:BL2021-11)
湖北省重点研发计划项目(编号:2020BCA084)。
关键词
振动速度预测
敏感性分析
最小二乘支持向量机模型
粒子群算法
泛化能力
vibration velocity prediction
sensitivity analysis
least squares support vector machine model
particle swarm algorithm
generalization capability
作者简介
何理(1986-),男,副教授,博士,硕士研究生导师。
