Abstract: During tunnel blasting construction, the blasting quality is difficult to control. Therefore, a real-time blasting parameter optimization method based on three-dimensional(3D) laser scanning technology and back propagation(BP) neural network is proposed. This method percepts the over-and under-excavation of tunnel blasting and corrects subsequent drilling and blasting parameters in real time. The working procedures of field scanning method, data processing, point cloud extraction, and over- and under-excavation images are constructed. Finally, the BP neural network model, algorithm, and model parameters are established. The verification test results show that: (1) It is feasible to apply 3D laser scanning technology to the over- and under-excavation quality perception of tunnel blasting, which is in good agreement with the manual measurement results, and has the advantages of fast, real-time, and accurate. (2) The 3D laser scanning method can accurately evaluate the profile line of the tunnel after blasting. After obtaining precise data on blasting over- and under-excavation, the model training can be completed through sample learning to infer reasonable blasting parameters. The improved optimization scheme reduces the average over excavation by 54.8%.

Key words: tunnel, three-dimensional laser scanning, back propagation neural network; blasting parameters optimization, over- and under-excavation identification