A comparative study of artificial neural networks in predicting blast

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Air-blast overpressure (AOp) is one of the undesirable effects caused by blasting operations in ... However, its adverse effects such as ground vibration, air- blast ...
A comparative study of artificial neural networks in predicting blast-induced airblast overpressure at Deo Nai open-pit coal mine, Vietnam Hoang Nguyen, Xuan-Nam Bui, HoangBac Bui & Ngoc-Luan Mai

Neural Computing and Applications ISSN 0941-0643 Neural Comput & Applic DOI 10.1007/s00521-018-3717-5

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ORIGINAL ARTICLE

A comparative study of artificial neural networks in predicting blastinduced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam Hoang Nguyen1



Xuan-Nam Bui1 • Hoang-Bac Bui2 • Ngoc-Luan Mai3

Received: 26 February 2018 / Accepted: 10 September 2018  The Natural Computing Applications Forum 2018

Abstract Air-blast overpressure (AOp) is one of the undesirable effects caused by blasting operations in open-pit mines. This side effect of blasting can seriously undermine surrounding residential structures and living quality. To control and mitigate this situation, this study using artificial neural networks to predict AOp implemented at Deo Nai open-pit coal mine, Vietnam. A total of 146 events of blasting were recorded, of which 80% (118 observations) was used for training and 20% (28 observations) was used for testing. A resampling technique, namely tenfold cross-validation, was performed with three repeats to increase the accuracy of the predictive models. In this paper, three different types of neural networks were developed to predict AOp including multilayer perceptron neural network (MLP neural nets), Bayesian regularized neural networks (BRNN) and hybrid neural fuzzy inference system (HYFIS). Each type was tested with ten model configurations to discover the best performing ones based on comparing standard metrics, including root-mean-square error (RMSE), coefficient of determination (R2), and a simple ranking method. Eight parameters were considered for these models, including charge per delay, burden, spacing, length of stemming, powder factor, air humidity, and monitoring distance. The results indicated that MLP neural nets model with RMSE = 2.319, R2 = 0.961 on testing datasets and a total ranking of 12 yielded the most accurate prediction over BRNN and HYFIS models. Keywords Artificial neural network  Multilayer perceptron neural network  Bayesian regularized neural networks  Hybrid neural fuzzy inference system  Air-blast overpressure  Deo Nai  Vietnam

1 Introduction Blasting is a dominant rock breaking method used in openpit mines [1, 2] with outstanding advantages, such as the large capacity of breaking rock, applicable to virtually any rock types, and not depending on weather conditions. However, its adverse effects such as ground vibration, airblast overpressure (AOp), and fly rock are also noticeable to the surrounding environment [3–5]. In Vietnam, most of the open-pit mines using blasting as the major rock

& Hoang Nguyen [email protected] 1

Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam

2

Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, Hanoi, Vietnam

3

Visagio Australia, 6/189 St Georges Terrace, Perth, Australia

breaking method are facing the challenge to minimize ground vibration and AOp [6–8]. AOp is one of the effects of blasting operation, which is caused by the vibration of the air adjacent to the explosive block or by vibration from the ground, in which the rock breaks down if blasting is conducted in the air. In the case of explosions on the ground, AOp is created directly by the pressure of the explosive product into the ambient air with great destructive power. This side effect of AOp is caused by sudden increase in air pressure that is greater than the atmospheric pressure at the passing wave. The explosion wave has a compressed waveform consisting of two components, in the frequencies that people experience with the ear (f = 20 7 20,000 Hz). At frequencies f \ 20 Hz, explosion wave is often referred to as air-blast overpressure (AOp) or explosion wave vibration and is also the frequency band that has the greatest impact on the building [9, 10]. At a sufficiently high level, AOp can violently

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