Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines

In large open pit mines prediction of peak particle velocity (ppv) provides useful information for safe blasting. at sungun copper mine (scm), some unstable rock slopes facing to valuable industrial facilities are both expose to high intensity daily blasting vibrations, threatening their safty. so, controlling ppv by developing accurate predictors is essential. hence, this study proposes improved strategies for prediction of ppv by maximum charge per delay and distance using the concept of intelligent committee machine (icm). besides the empirical predictors (eps) and two artificial intelligence (ai) models of anfis and ann, four different icms models including simple and weighted averaging icm (saicm and waicm) and first and second order polynomial icm (fpicm and spicm) in conjunction with genetic algorithm, proposed for the prediction of ppv. performance of predictors was studied considering r^2, rsme and vaf indices. results indicate that icm methods have superiority over eps, ann and anfis, and among the icm models while saicm, waicm and fpicm performing near to each other spicm overrides all the models. r2 and rsme of the training and testing data for spicm are 0.8571, 0.8352 and 11.0454, 12.3074, respectively. finally, icms provides more accurate and reliable models rather than individual ais.