Blasting-vibration-induced damage prediction by rough set-based fuzzy-neural network

According to the nonlinear links between blasting-vibration-induced damage degree and its influencing factors, a rough set-based fuzzy-neural network model is proposed to seek a method that can overcome the limitations in the single-factor case and improve the damage prediction precision. In the proposed prediction model, there are 10 factors to be taken into account, which include particle vibration velocity (PPV), dominant frequency, dominant frequency duration and dynamic characteristics of structures. The prediction model is trained and tested by a series of data from the observations of blasting vibration and damage degree of houses in Tonglshan Copper Mine. The training results are in agreement with the field observations. The rough set-based fuzzy-neural network can reduce data indexes and simplify input variables, and minify the decision table size and accelerate the approach to the minimal rules. The proposed method considering the manifold factors can improve the prediction precision of damage degree induced by blasting vibration.