Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse cons...Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse consequences of blasting. Since there are several effective variables on ground vibrations that have highly nonlinear interactions, no comprehensive model of the blast-induced vibrations are available. In this study, the genetic expression programming technique was employed for prediction of the frequency of the adjacent ground vibrations. Nine input variables were used for prediction of the vibration frequencies at different distances from the blasting face. A high coefficient of determination with low mean absolute percentage error(MAPE) was achieved that demonstrated the suitability of the algorithm in this case. The proposed model outperformed an artificial neural network model that was proposed by other authors for the same dataset.展开更多
文摘Excessive ground vibrations, due to blasting, can cause severe damages to the nearby area. Hence, the blast-induced ground vibration prediction is an essential tool for both evaluating and controlling the adverse consequences of blasting. Since there are several effective variables on ground vibrations that have highly nonlinear interactions, no comprehensive model of the blast-induced vibrations are available. In this study, the genetic expression programming technique was employed for prediction of the frequency of the adjacent ground vibrations. Nine input variables were used for prediction of the vibration frequencies at different distances from the blasting face. A high coefficient of determination with low mean absolute percentage error(MAPE) was achieved that demonstrated the suitability of the algorithm in this case. The proposed model outperformed an artificial neural network model that was proposed by other authors for the same dataset.
