Hassan Bakhshandeh Amnieh

TL;DR: The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach and revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock.

TL;DR: The development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp, found the GA-ANN model to be better than ANN model in estimating AOp induced by blasting.

TL;DR: The PSO–SVR–RBF model receives better results in comparison with other developed hybrid models in the field of AOp prediction, and can be used as a reliable algorithm to train the SVR model.

TL;DR: In this research work, classification and regression tree (CART), multiple regression (MR), and different empirical models are used to develop predictions for ground vibrations induced by blasting operations conducted in the Miduk copper mine, Iran to reveal a better performance when compared with empirical and MR models and has the capacity to generalize.

TL;DR: A new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp, and the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.