The prediction of air and ground vibrations is an important problem in
rock blasting activities. The aim of this study is to evaluate the prediction
of ground and air vibrations by using intelligent networks and traditional
regression model. So, fuzzy logic and artificial neural network (ANN)
models have been constructed to predict peak particle velocity and air
overpressure induced by blasting in Assiut Cement Company. For this
purpose, the peak particle velocity, air vibrations, and charge weight per
delay were recorded for 136 blast events at various distances and used
for the training of the predictor models. About new 26 data sets have
been used to test and validate the models. The performance, validity and
capability of these models to predict were proved to be successful by
statistical performance indices. These indices are variance-accounted for
(VAF) and root mean square error (RMSE). The results from these
models asserted that, intelligent networks technologies can be precisely
and effectively used for predicting the air and ground vibrations in
comparison with traditional regression analysis. Also, the comparison
indicated that the fuzzy logic model exhibited slightly better prediction
performance and generalization than the artificial neural network in
ground and air vibration prediction.