Application of Intelligent Models for Flyrock Prediction Considering Design Parameters and Bench Face Characteristics

This study tries to predict blast-induced flyrock using intelligent modelling techniques. Flyrock is a site-specific phenomenon. Design variables and rock properties highly influence flyrock throw distance. Site investigations were conducted in a sandstone quarry. Fundamental operational parameters, blast dimensions, bench face condition, and rock mass structure were monitored. Stepwise regression technique was applied for variable selection. Burden–hole diameter ratio, in situ block size, and powder factor were determined as the most significant parameters. Considering output of stepwise regression, artificial neural network, ANFIS, and Gaussian process regression techniques were applied to predict flyrock throw. A comprehensive validation was performed using twelve different performance indices. Pre-determination of input parameters supported development of successful soft computing applications. Rock block size was found to be an appropriate input variable for flyrock modelling. In addition to classical performance indices, standardized and symmetric accuracy metrics were quite useful in model validation process. The intelligent models predict flyrock range with an error less than 6 m. The mean percentage errors are lower than 10%. ANFIS seems to be the best model for flyrock prediction. The calculated mean absolute error is 5.36 for ANFIS model.