Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

Abstract

Purpose The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran. Design/methodology/approach For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output. Findings After modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R2 of 0.939 was the most precise model for predicting the PPV in the present study. Originality/value In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

“…Behzadafshar et al [44] ICA-linear R 2 = 0.939; RMSE = 0.320; VAF = 92.18%; MBE = 0.22; MAPE = 0.038 Tian et al [45] GA-power R 2 = 0.977; RMSE = 0.285 Hasanipanah et al [46] FS-ICA R 2 = 0.942; RMSE = 0.22; VAF = 94.2% Nguyen et al [12] HKM-ANN R 2 = 0.983; RMSE = 0.554; VAF = 97.488% Nguyen et al [11] HKM-CA R 2 = 0.995; RMSE = 0.475; MAE = 0.373 Zhang et al [8] PSO-XGBoost R 2 = 0.968; RMSE = 0.583; MAE = 0.346, VAF = 96.083…”

Experimental and Numerical Study of the Key Non-Dimensional Geometrical Parameters on the Noise Level of Dry-Type Cast Resin Transformers

“…According to these papers magnetostrictive strains which has the alternative frequency twice the power system supply and magnetic flux frequency is the main cause of noise generation. Also, as a new research work [18], the purpose of the paper [18], is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran. In addition, according to reference [19], the magnetostriction damps the vibrations at some frequencies and increases them at some other ones.…”

Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam

“…In recent years, various advanced techniques and approaches have been developed to predict and reduce the undesirable effects of blast-induced PPV in open-cast [38] ANN 182 R 2 = 0.949 Armaghani et al [39] PSO-ANN 44 R 2 = 0.930; MSE = 10.71 Saadat et al [21] ANN 69 R 2 = 0.957; MSE = 0.000722 Hasanipanah et al [6] SVM 80 R 2 = 0.957; RMSE = 0.340 Amiri et al [40] ANN-KNN 75 R 2 = 0.880; RMSE = 0.540 Hasanipanah et al [41] PSO 80 R 2 = 0.938; RMSE = 0.240 Faradonbeh and Monjezi [42] GEP 115 R 2 = 0.874; RMSE = 6.732 Taheri et al [23] ABC-ANN 89 R 2 = 0.920; RMSE = 0.220 Armaghani et al [43] ICA 73 R 2 = 0.940; RMSE = 0.370 Behzadafshar et al [44] ICA 76 R 2 = 0.939; RMSE = 0.320 Abbaszadeh Shahri and Asheghi [45] ANN 37 R 2 = 0.954; RMSE = 0.157 Mokfi et al [46] GMDH 102 R 2 = 0.911; RMSE = 0.889 Torres et al [47] MLR-Empirical 178 R 2 = 0.898 [26] used random forest (RF) and support vector machine (SVM) algorithms to predict blast-induced PPV; 93 blasting events were used for development of the RF and SVM models in their study. Their results showed that the RF and SVM models were acceptable and the SVM model was better than the RF model throughout the PPV predicted values on the testing dataset.…”