MICROMATE RANGE TRIAL

(2021b) proposed a GEP model to predict blast-induced PPV in a quarry mine, considering 102 blast instances, 20% of which were used to test the model, encompassing 6 independent parameters as input, reaching R 2 values from 0.864 to 0.880 among the 5 models generated. Lawal (2020) used an ANN with 2-5-1 architecture and an MVRA model to predict blast-induced PPV at five quarry mines in Nigeria, considering a dataset with 100 blast instances, 20% of which were used for testing, encompassing the distance from the blast face and the maximum charge per delay as input, achieving R 2 values of 0.988 and 0.738, respectively. On the other hand, an ANN with 8-8-12-6-1 architecture, a QRNN, and an RF model reached R 2 values of 0.946, 0.920, and 0.894, respectively, while an empirical model based on the USBM predictor reached the value of 0.658.

A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg.

MICROMATE RANGE TRIAL

Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. For all the blasts, the applied blast design parameters, such as burden (B), spacing (S), charge per delay (CPD), and distance from the blast face to monitoring point (DI) were measured and recorded.īlast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Multikernel Engineering with Computers ground vibration database was randomly divided with a ratio of 80-20%. Many hybrid ANN CPD, DIS, PF, SD Yu et al. Hierarchical clustering CPD, DIS, S, B, PF Rezaeineshat et al. fuzzy C-means CPD, DIS, H, S, B, D, T, PF Nguyen et al. GEP CPD, DIS, H, S, B, D, T, PF Khandelwal et al. ICA-ANN CPD, DIS, E, PF, PWV, B, S Faradonbeh et al. PSO-ANN CPD, D, NO, RD, SD, S, B, T, PF Hajihassani et al. The blast-induced FIS CPD and DIS Armaghani et al. In this study, input variables were selected based on previous research. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, u and r closer to the optimum value, and higher R2 and a20-index. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (u), standard deviation of the mean (r), coefficient of determination (R2) and a20-index. To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. Fragmenting the rock mass is considered as the most important work in open-pit mines.

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