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

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 ( R 2 ), 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, R 2  = 0.961 on testing datasets and a total ranking of 12 yielded the most accurate prediction over BRNN and HYFIS models.

[1]  Stefano Tarantola,et al.  Estimating the approximation error when fixing unessential factors in global sensitivity analysis , 2007, Reliab. Eng. Syst. Saf..

[2]  Lianru Gao,et al.  A new kernel method for hyperspectral image feature extraction , 2017, Geo spatial Inf. Sci..

[3]  J. W. Reed,et al.  Atmospheric attenuation of explosion waves , 1977 .

[4]  Nick Yugo,et al.  Analysis of blasting damage in adjacent mining excavations , 2015 .

[5]  Larry T. Looper,et al.  Neural Networks: A Primer , 1991 .

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[8]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[9]  Shuzhi Sam Ge,et al.  Role Playing Learning for Socially Concomitant Mobile Robot Navigation , 2017, CAAI Trans. Intell. Technol..

[10]  Luc Thorel,et al.  Ground vibration isolation with geofoam barriers : Centrifuge modeling , 2009 .

[11]  Debasis Deb,et al.  Estimation of blast induced peak particle velocity at underground mine structures originating from neighbouring surface mine , 2010 .

[12]  Mohammad Ataei,et al.  Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations , 2012 .

[13]  Per-Anders Persson,et al.  Rock Blasting and Explosives Engineering , 1993 .

[14]  M. T. Mohamed,et al.  Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry , 2009 .

[15]  Tuan Ngo,et al.  Blast Loading and Blast Effects on Structures – An Overview , 2007, Electronic Journal of Structural Engineering.

[16]  Fei Wang,et al.  Urban land-use classification by combining high-resolution optical and long-wave infrared images , 2017, Geo spatial Inf. Sci..

[17]  Ercan Arpaz,et al.  Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems , 2017, Environmental Earth Sciences.

[18]  Michael J. Watts,et al.  FuNN/2 - A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition , 1997, Inf. Sci..

[19]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[20]  Mahdi Hasanipanah,et al.  Several non-linear models in estimating air-overpressure resulting from mine blasting , 2015, Engineering with Computers.

[21]  Mahdi Hasanipanah,et al.  A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure , 2016, Engineering with Computers.

[22]  Lucy Bastin,et al.  Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization , 2017, Geo spatial Inf. Sci..

[23]  Ebru Akcapinar Sezer,et al.  Application of two non-linear prediction tools to the estimation of tunnel boring machine performance , 2009, Eng. Appl. Artif. Intell..

[24]  D. R. Hush,et al.  Classification with neural networks: a performance analysis , 1989, IEEE 1989 International Conference on Systems Engineering.

[25]  Hassan Bakhshandeh Amnieh,et al.  Predicting ground vibration caused by blasting operations in Sarcheshmeh copper mine considering the charge type by adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[26]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[27]  Alireza Karami,et al.  Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system (ANFIS) , 2013 .

[28]  Jean-Claude Léon,et al.  Functional restructuring of CAD models for FEA purposes , 2015 .

[29]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[30]  J. J. Shann,et al.  A fuzzy neural network for rule acquiring on fuzzy control systems , 1995 .

[31]  Nikola K. Kasabov,et al.  HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.

[32]  Maureen Caudill,et al.  Neural networks primer, part III , 1988 .

[33]  Candan Gokceoglu,et al.  Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation , 2006 .

[34]  Maoguo Gong,et al.  Two-phase clustering algorithm with density exploring distance measure , 2018, CAAI Trans. Intell. Technol..

[35]  D. E. Siskind,et al.  Structure response and damage produced by airblast from surface mining , 1980 .

[36]  Alexander Remennikov,et al.  Predicting the effectiveness of blast wall barriers using neural networks , 2007 .

[37]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[38]  Mohd Hazreek Zainal Abidin,et al.  Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence , 2018 .

[39]  Aminaton Marto,et al.  Neuro-fuzzy technique to predict air-overpressure induced by blasting , 2015, Arabian Journal of Geosciences.

[40]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[41]  Masoud Monjezi,et al.  Evaluation of effect of blasting pattern parameters on back break using neural networks , 2008 .

[42]  Mahdi Hasanipanah,et al.  Airblast prediction through a hybrid genetic algorithm-ANN model , 2018, Neural Computing and Applications.

[43]  I. Kanellopoulos,et al.  Strategies and best practice for neural network image classification , 1997 .

[44]  M. Monjezi,et al.  Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method , 2013, Rock Mechanics and Rock Engineering.

[45]  Masoud Monjezi,et al.  Prediction and controlling of flyrock in blasting operation using artificial neural network , 2011 .

[46]  Mahdi Hasanipanah,et al.  A combination of the ICA-ANN model to predict air-overpressure resulting from blasting , 2015, Engineering with Computers.

[47]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[48]  Masoud Monjezi,et al.  Prediction of flyrock in open pit blasting operation using machine learning method , 2013 .

[49]  Alireza Karami,et al.  Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function , 2012 .

[50]  Bhatawdekar Ramesh Murlidhar,et al.  Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique , 2016, Environmental Earth Sciences.

[51]  Saeid R. Dindarloo,et al.  Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks , 2016 .

[52]  Aminaton Marto,et al.  Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization , 2014 .

[53]  Zoran Obradovic,et al.  Training an artificial neural network to discriminate between magnetizing inrush and internal faults , 1994 .

[54]  Hima Nikafshan Rad,et al.  The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting , 2018, Engineering with Computers.

[55]  C. Kuzu,et al.  Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries , 2009 .

[56]  R. Nateghi.  Evaluation of blast induced ground vibration for minimizing negative effects on surrounding structures , 2012 .

[57]  Masoud Monjezi,et al.  Prediction of blast-induced ground vibration using artificial neural networks , 2011 .

[58]  Azzedine Zerguine,et al.  Multilayer perceptron-based DFE with lattice structure , 2001, IEEE Trans. Neural Networks.