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.
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16 February 2021
A Correction to this paper has been published: https://doi.org/10.1007/s00521-021-05773-6
References
Persson P-A, Holmberg R, Lee J (1993) Rock blasting and explosives engineering. CRC Press, Boca Raton
Wilkinson GM, Pronko SG (1996) Method and apparatus for blasting hard rock. Google Patents
Hajihassani M, Armaghani DJ, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67
Bach NV, Thanh NV (1996) Impact of large explosions and some measures to protect the works. Min Ind J 4:13–14 (in Vietnamese)
Bach NV, Quyen LV, Nam BX, An ND, Phuc NV (2006) Measures to minimize the effects of ground vibration in Nui Beo open pit coal mine. Min Ind J 14:58–62 (in Vietnamese)
Bach NV, Nam BX (2007) Minimizing bad impacts on environment when using blasting method in mining. In: International workshop on geoecology and environmental technology, pp 164–173
Bach NV (2008) Fundamental issues for blasting operation in Vietnamese surface mines. In: International conference on advances in mining and technology, pp 150–155
Ninh LN, Bach NV, Vinh LQ (2010) Research on blasting to reduce shocks, dust and harmful gases from large boreholes for quarries near residential areas. In: Science technology conference, Hanoi University of Mining and Geology, Hanoi, Vietnam
Thang DT, Nam BX, Hieu TQ (2015) Blasting in mining and construction industries. Science and Technics Publishing House, Hanoi
Hieu TQ, An ND, Viet PV, Duc TM, V.A B (2014) Effects of climatic conditions on air blast overpressure when blasting near residents area at surface coal mines in Quang Ninh. Paper presented at the Proceedings of the 3rd International Conference on Advances in Mining and Tunneling, Vung Tau, Vietnam
Mayor R, Flanders R (1990) Technical manual simplified computer model of air blast effects on building walls. US Department of State, Office of Diplomatic Security, Washington DC
Army U (1998) Technical manual design and analysis of hardened structures to conventional weapons effects. Army TM5-855-1, Washington DC
Nateghi R (2012) Evaluation of blast induced ground vibration for minimizing negative effects on surrounding structures. Soil Dyn Earthq Eng 43:133–138
Ngo T, Mendis P, Gupta A, Ramsay J (2007) Blast loading and blast effects on structures–an overview. Electron J Struct Eng 7(S1):76–91
Remennikov AM, Rose TA (2007) Predicting the effectiveness of blast wall barriers using neural networks. Int J Impact Eng 34(12):1907–1923
Murillo C, Thorel L, Caicedo B (2009) Ground vibration isolation with geofoam barriers: centrifuge modeling. Geotext Geomembr 27(6):423–434
Kuzu C, Fisne A, Ercelebi S (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70(3):404–411
Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455
Siskind DE, Stachura VJ, Stagg MS, Kopp JW (1980) Structure response and damage produced by airblast from surface mining. Twin Cities Research Center, Bureau of Mines, Twin Cities, MN
Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45(8):1446–1453
Li M, Jiang R, Ge SS, Lee TH (2017) Role playing learning for socially concomitant mobile robot navigation. arXiv preprint arXiv:170510092
Ma J, Jiang X, Gong M (2018) Two-phase clustering algorithm with density exploring distance measure. CAAI Trans Intell Technol 3(1):59–64
Guan X, Liao S, Bai J, Wang F, Li Z, Wen Q, He J, Chen T (2017) Urban land-use classification by combining high-resolution optical and long-wave infrared images. Geo-Spat Inf Sci 20(4):299–308
Zhao B, Gao L, Liao W, Zhang B (2017) A new kernel method for hyperspectral image feature extraction. Geo-Spat Inf Sci 20(4):309–318
Tracewski L, Bastin L, Fonte CC (2017) Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization. Geo-Spat Inf Sci 20(3):252–268
Mohamed MT (2009) Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46(2):426–431
Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4(3–4):421–425
Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26(1):46–50
AminShokravi A, Eskandar H, Derakhsh AM, Rad HN, Ghanadi A (2018) The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Eng Comput 34(2):277–285
Alel MNA, Upom MRA, Abdullah RA, Abidin MHZ (2018) Optimizing blasting’s air overpressure prediction model using swarm intelligence. J Phys Conf Ser 995:012046
Armaghani DJ, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171
Armaghani DJ, Hasanipanah M, Mahdiyar A, Majid MZA, Amnieh HB, Tahir MM (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29(9):619–629
Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396
Manoj K, Monjezi M (2013) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Min Sci Technol 23(3):313–316
Mohamadnejad M, Gholami R, Ataei M (2012) Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunn Undergr Space Technol 28:238–244
Ghiasi M, Askarnejad N, Dindarloo SR, Shamsoddini H (2016) Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks. Int J Min Sci Technol 26(2):183–186
Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32(4):631–644
Karami A, Afiuni-Zadeh S (2013) Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system (ANFIS). Int J Min Sci Technol 23(6):809–813
Armaghani DJ, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8(12):10937–10950
Mohammadi SS, Amnieh HB, Bahadori M (2011) Predicting ground vibration caused by blasting operations in Sarcheshmeh copper mine considering the charge type by adaptive neuro-fuzzy inference system (ANFIS). Arch Min Sci 56(4):701–710
Karami A, Afiuni-Zadeh S (2012) Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function. Int J Min Sci Technol 22(4):459–463
Koçaslan A, Yüksek AG, Görgülü K, Arpaz E (2017) Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems. Environ Earth Sci 76(1):57
Limited VNCaMIHC (2010) Report of coal reserve in Quang Ninh province, Vietnam (in Vietnamese)
Vinacomin (2001) Report on geological exploration of Deo Nai open pit coal mine, Quang Ninh, Vietnam
Reed JW (1977) Atmospheric attenuation of explosion waves. J Acoust Soc Am 61(1):39–47
Alcudia AD, Stewart RR, Hall KW, Gallant EV (2008) Field comparison of 3-C geophones and microphones to high-precision blasting sensors. CREWES Res Rep 20:1–20
Yugo N, Shin W (2015) Analysis of blasting damage in adjacent mining excavations. J Rock Mech Geotech Eng 7(3):282–290
Deb D, Jha A (2010) Estimation of blast induced peak particle velocity at underground mine structures originating from neighbouring surface mine. Min Technol 119(1):14–21
Schalkoff RJ (1997) Artificial neural networks, vol 1. McGraw-Hill, New York
Zerguine A, Shafi A, Bettayeb M (2001) Multilayer perceptron-based DFE with lattice structure. IEEE Trans Neural Netw 12(3):532–545
Perez LG, Flechsig AJ, Meador JL, Obradovic Z (1994) Training an artificial neural network to discriminate between magnetizing inrush and internal faults. IEEE Trans Power Deliv 9(1):434–441
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171:12–29
Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Kim J, Kasabov N (1999) HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Netw 12(9):1301–1319
Horikawa S-I, Furuhashi T, Uchikawa Y (1992) On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans Neural Netw 3(5):801–806
Hung CC (1993) Building a neuro-fuzzy learning control system. AI Expert 8(11):40–49
Kasabov NK, Kim J, Watts MJ, Gray AR (1997) FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition. Inf Sci 101(3–4):155–175
Lin C-T, Lee CSG (1991) Neural-network-based fuzzy logic control and decision system. IEEE Trans Comput 40(12):1320–1336
Shann J, Fu H (1995) A fuzzy neural network for rule acquiring on fuzzy control systems. Fuzzy Sets Syst 71(3):345–357
Rumelhart D, Hinton G, Williams J (1986) Learning Internal Representations by Error Propagation. In: Rumelhart DE, McClelland JL (eds) Parallel Distributed Processing. MIT Press, Cambridge, MA
Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250
Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, pp 277–280
Kanellopoulos I, Wilkinson G (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18(4):711–725
Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22(4–5):808–814
Sonmez H, Gokceoglu C, Nefeslioglu H, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235
Caudill M (1988) Neural networks primer. Part III. AI Expert 3(6):53–59
Mohamad ET, Armaghani DJ, Hasanipanah M, Murlidhar BR, Alel MNA (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75(2):174
Tarantola S, Gatelli D, Kucherenko S, Mauntz W (2007) Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliab Eng Syst Saf 92(7):957–960
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270
Acknowledgements
This research was supported by Hanoi University of Mining and Geology (HUMG) and Ministry of Education and Training of Vietnam (MOET). We also thank the Center for Mining, Electro-Mechanical research of Hanoi University of Mining and Geology for supporting the instruments for data collecting.
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Nguyen, H., Bui, XN., Bui, HB. et al. A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput & Applic 32, 3939–3955 (2020). https://doi.org/10.1007/s00521-018-3717-5
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DOI: https://doi.org/10.1007/s00521-018-3717-5