基于萤火虫算法优化BP神经网络的爆破振速预测

Prediction of peak particle velocity of blast vibration based on BP neural network model optimized by firefly algorithm

爆破振动是岩体爆破开挖工程中典型的有害效应,实现爆破振动精准预测对爆破过程中的风险控制意义重大。为实现这一目标,借助萤火虫算法(FA)对反向传播神经网络(BPNN)的权值和阈值进行优化,构建FA-BP神经网络预测模型,以露天矿山台阶爆破中多个设计参数和爆心距为模型输入参数,对峰值质点振速进行预测,并比较FA-BP神经网络模型、统计预测方程、BPNN模型和随机森林方法的预测结果。最后,借助归一化互信息分析各输入参数对FA-BPNN预测结果的敏感性。研究结果表明:FA-BPNN模型能有效预测爆破峰值质点速度,预测结果对应的均方根误差、平均绝对误差和决定系数分别为1.445,1.182和0.973,预测效果较其余3种方法更好;单段最大装药量、爆心距、炸药单耗、台阶高度与抵抗线长度之比对爆破峰值振速预测结果影响较大。

Blast vibration is a typical harmful effect in rock blast excavation projects.Accurate prediction of blast vibration is of great significance for risk control during blasting.To achieve this goal,the weights and thresholds of the back propagation neural network(BPNN)were optimized with the firefly algorithm(FA),and an FA-BPNN prediction model was established.Taking multiple design parameters and blast center distance of bench blasting in an open-pit mine as the model input parameters,the peak particle velocity(PPV)of blast vibration was predicted,and the prediction results of FA-BP neural network model,statistical prediction equation,BPNN model and random forest method were compared.The sensitivity of each parameter to the prediction results of the FA-BPNN model was analyzed by normalized mutual information.The results indicated that the FA-BPNN model was effective in predicting the PPV,the corresponding root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination were 1.445,1.182,and 0.973 respectively,the FA-BPNN model was more effective than the other three methods.The maximum charge of a single section,the distance between the blasting center,the unit consumption of explosives,and the ratio of step height to the length of resistance line have great influences on the prediction results of the PPV.