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Prediction of the blast induced ground vibration in tunnel blasting using ANN, moth fame optimized ANN, and gene expression programming

Lawal Abiodun Ismail, Kwon Sangki, Kim Geon Young

Acta Geophysica

2021

Vol. 69, no. 1

161--174

The blast-induced ground vibration (BIGV) is a severe environmental impact of blasting as it can afect the integrity of the structures and cause civil unrest. In this study, the BIGV of Daejeon tunnel was predicted taking into consideration parameters such as hole length, the charge per delay, number of holes, total charge, distance from the measuring station to the blasting point and the rock mass rating as the input parameters, while the peak particle velocity (PPV) was the targeted output parameter. An artifcial neural network (ANN) model was frst simulated. The optimum ANN structure obtained was optimized using a novel moth-fame optimization algorithm (MFO). The gene expression program (GEP) was also used to develop another new model. The proposed models were compared with the multilinear regression (MLR) model and the selected empirical models for the PPV predictions. The performance of the proposed model was evaluated using statistical indices such as adjusted coefcient of determination (adj R2 ), mean square error (MSE), mean absolute error (MAE), and the variance accounted for (VAF). The proposed MFO-ANN outperformed other models with the adj R2 of 0.9702 and 0.9577, VAF of 97.0472 and 95.9832, MSE of 0.0009 and 0.0008, and MAE of 0.0233 and 0.0216 for the respective training and testing phases. The sensitivity analysis was conducted using the weight partitioning method (WPM), and the charge per delay has the highest infuence on the predicted PPV. This study indicates the suitability of the proposed models for the prediction of PPV.