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Open AccessArticle

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
3
Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
4
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Department of Mining Engineering, NIT, Rourkela 769008, India
7
Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(21), 4554; https://doi.org/10.3390/app9214554
Received: 4 September 2019 / Revised: 14 October 2019 / Accepted: 21 October 2019 / Published: 27 October 2019
Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure. View Full-Text
Keywords: mining; fly-rock; ANN; EANNs; ensemble technique; bench blasting; artificial intelligence mining; fly-rock; ANN; EANNs; ensemble technique; bench blasting; artificial intelligence
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Nguyen, H.; Bui, X.-N.; Nguyen-Thoi, T.; Ragam, P.; Moayedi, H. Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model. Appl. Sci. 2019, 9, 4554.

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