Study of BP and RBF Neural Networks Applied to the Prediction of Vibration Characteristics in Static Blasting of Dry Ice Powder

Abstract

The novel dry ice powder static blasting rock breaking lacked working standards, especially for the vibration safety assessment of the construction site for the protection of the building structure, in comparison with the traditional drill and blast method which had a proven operational process and safety specifications. The Sadovsky vibration velocity prediction formula could only predict the vibration velocity and was project specific. Oscillation parameters that needed to be considered in the vibration safety assessment, such as the dominant frequency of vibration, could not be obtained through empirical formulas. Using the five parameters of hole depth, blast center distance, dry ice powder mass and rock classification as the main influencing factors, BP and RBF neural network models were constructed by Matlab software to predict the peak vibration velocity, main frequency and maximum displacement of dry ice powder blasting. Projection results revealed that it is structurally simpler than the BP neural network and that the RBF was more accurate in predicting the target than the BP network. The results of the study had significant implications for the safe application of the new technology, and more samples of field data need to be obtained in the future, along with the use of more advanced predictive modelling.