Estimation of peak particle velocity using soft computing technique approaches: a review

Abstract

Ground vibrations caused by blasting are undesirable consequences in the mining industry. It can cause serious damage to the nearby buildings and facilities. Hence, such vibrations have to be controlled to reduce the damage to the environment, and this may be achieved by predicting the blast peak particle velocity. The induced peak particle velocity has influenced by a number of parameters. They are spacing, burden, distance from the blast site, maximum explosive charge per delay, number of holes, stemming, and hole diameter. Existed empirical predictor approaches were used to evaluate the ground vibrations based on two parameters: distance and maximum explosive charge per hole only. To overcome the limitations of empirical methods, soft computing techniques are employed to estimate the peak particle velocity accurately. Since the late 1990s, several soft computing techniques such as artificial neural networks, fuzzy logic, and genetic algorithms have been proposed for achieving accurate prediction. This article presents a summary review of the developed soft computing in the 10 years following 2006 with particular emphasis on the number of influenced parameters and coefficient of determination ( R2).