Underwater blasting is a complicated

nonlinear

and dynamic process of energy release. It is affected by many factors

and its process has not been fully investigated at present. In order to accurately predict the peak particle velocity induced by underwater blasting based on a small amount of field measurements

the GRA-GA-BP model is established based on the grey relational analysis theory combining with the genetic neural network which has the nonlinear mapping and global searching capabilities. In the model

the potential information of the small sample is fully discovered

and the main factors affecting the vibration velocity are reasonably determined based on the grey relational analysis theory. Moreover

the problems of the neural network unable to automatically select and optimize input variables in complicated and multivariate systems are solved

which enhances the adaptability and stability of the genetic neural network. Finally

the GRA-GA-BP model is adopted to predict the peak particle velocity induced by underwater blasting at Dajin Island in the first phase of Taishan nuclear power station. Compared with the results obtained by traditional genetic neural network and the Sadaovsk formula

the prediction error of the GRA-GA-BP model is smaller and more stable. Therefore

the proposed procedure provides an appropriate way to predict the peak particle velocity induced by underwater blasting.