Blasting Vibration Prediction Based on Novel HGS-ANN Model
Based on the combination of the hunger games search(HGS)algorithm and the artificial neural network(ANN),a new hybrid model of HGS-ANN was developed to predict blasting vibration.Four different prediction models were established based on group method of data handling(GMDH),support vector machines(SVM),ANN and Sadov's empirical formula,and compared with HGS-ANN model in evaluating the performance of models.For this purpose,32 sets of blasting data of an open-pit mine were collected.7 independent variables,including detonation distance,maximum single-stage charge,total charge,burden,hole spacing,number of holes and hole depth were selected as inputs,while the particle vibration velocity was selected as the output.With the root-mean-square error(RMSE)and the decisive factor(R2)as the evaluating indicators,the established models was compared in terms of their performances.The results show that the HGS-ANN model,with the RMSE and R2 of 0.833 and 0.963,respectively,has performance better than the other four models.It is proposed that the HGS-ANN model can be used as an auxiliary tool to optimize the blasting design for reducing the blasting-induced seismic effect.
blasting vibrationhunger games search algorithmartificial neural networkvibration prediction
万方数据
维普
