Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods.
Mar 20, 2015 · In this study, ANN and adaptive neuro-fuzzy inference system (ANFIS) were applied predict flyrock distance using the datasets obtained from five granite quarry ...
An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, ...
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Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng. Comput., 32 (2016), pp. 109-121.
The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) ...
Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng. Comput., 32 (1) (2016), pp. 109-121.
In this study, an attempt has been made to use Classification and Regression Trees (CART) technique to predict the fly-rock distance in boulder blasting.
The objective of this study is to forecast/estimate the amount of flyrock produced during blasting by applying three creative composite intelligent models.
Evaluation And Prediction Of Flyrock Resulting From Blasting Operations Using Empirical And Computational Methods. Reference No. PB/2015/52123.
The aim of this paper is to test the capability of SVM for the prediction of flyrock in the Soungun copper mine, Iran.