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  Volume 1, Number 2, December 2020 , pages 187-202
DOI: https://doi.org/10.12989/mca.2020.1.2.187
 
 open access

A comparative study of different artificial intelligence techniques in predicting blast-induced air over-pressure
Hoang Nguyen, Xuan-Nam Bui, Panagiotis G. Asteris, Quang Hieu-Tran, Danial Jahed Armaghani, Masoud Monjezi, Manoj Khandelwal and Phonepaserth Sukhanouvong

 
Abstract
    Blasting is known as the most common approach for fragmenting rock in open-pit mines. Nevertheless, its side effects are not insignificant, for example, fly rock, ground vibration, dust, toxic by-products, air over-pressure, and back-break. These effects considerably alter the circumambient environment, particularly when pressure is higher than usual. This study proposed and compared four artificial intelligence models for predicting blast-induced air over-pressure, namely multi-layer perceptron (MLP), Random Forest (RF), isotonic regression (IR), and M5-Rules. The air over-pressure was selected as the output variable based on the input variables, i.e., stemming length (T), explosive charge per delay (W), burden (B), monitoring distance (R), and spacing (S). Several statistical performance indices, including coefficient of determination (R
 
Key Words
    blast-induced air over-pressure; artificial intelligence techniques; earth science; quarry mine; soft computing
 
Address
Hoang Nguyen and Xuan-Nam Bui: Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology,18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam/ Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology,18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi, Vietnam
Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece
Quang Hieu-Tran and Phonepaserth Sukhanouvong: Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology,18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam
Danial Jahed Armaghani: Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Masoud Monjezi: Department of Mining, Faculty of Engineering, Tarbiat Modares University, Tehran 14115-143, Iran
Manoj Khandelwal: School of Science, Engineering and Information Technology, Federation University Australia, Ballarat, Australia
 

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