| In order to accurately predict the blasting results and reduce the damage of blasting vibration to the building and ensure the safety of workers, BP neural network with the ability to deal with nonlinear problems was used to forecast the blasting results. Qualified blasting results were selected as the learning samples of the network model, after a certain number of times of training and learning, the feed forward characteristics of the neural network were applied to determine the thresholds and errors of each layer, then the establishment of BP neural network was completed. Results indicated that the error between the prediction result and the real result was within 10%, which conformed to the engineering requirements. By combining PAC algorithm, POS algorithm or MATLAB to optimize the network, the error can even be controlled within 5%. The damage caused by blasting operation can be reduced by establishing BP neural network prediction, with safety cost reduced and guiding the construction of blasting operation. |