Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*
CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.048398
Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable
effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined
with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony
optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran.
Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine
(KELM), and general regression neural network (GRNN), are employed, and their performances are compared to
those of four hybrid SVR models. After modeling,… More >