An Overview of Flyrock and its Prediction in Surface Mine Blasting using Soft Computing Techniques

Yıl 2021, , 105 - 119, 31.12.2021

Vineeth Balakrishnan , Piyush Rai

https://doi.org/10.53501/rteufemud.986903

Öz

The occurrence of flyrocks due to blasting has certainly gained tremendous attention by recent researchers as well as mine operators since time immemorial. With the increase in demand for the mineral and subsequent thrust on the surface mining operations, the mining projects are expanding their scale of operations. As such, not only the mines, but also the nearby inhabitants are endangered. So flyrock is one of the most hazardous side effects of blasting operation in surface mining. There are several empirical methods for predicting flying rocks. The poor performance of these different methods is due to the complexity and difficulty of rock analysis. The existence of various influential parameters and their unknown relationships are the main reasons for the inaccuracy of empirical models. In this light, the present paper gives an overview of state-of-art researches and their outcome in the area of control and prediction of flyrocks. The paper discusses the significant contribution of soft computing techniques in controlling and minimizing the flyrocks. Furthermore, it lays emphasis on scientific and categorical identification of most significant rock explosive and blasting design parameters in the prediction models to enhance their precision and universalization.

Anahtar Kelimeler

Flyrocks, Safety, Prediction Models, Blasting

Kaynakça

• Abraham, A. (2004). Meta learning evolutionary artificial neural networks original research article. Neurocomputing, 56(1), 1-38.
• Adhikari, G.R. (1999). Studies on flyrock at limestone quarries. Rock Mechanics and Rock Engineering, 32(4), 291-301.
• Ajpayee, T.S., Rehak, T.R., Mowrey, G.L., Ingram, D.K., (2000). A summary of fatal accidents due to flyrock and lack of blast area security in surface mining. In: Proceedings of the 27th Annual Conference on Explosives and Blasting Technique, Cleveland, USA, 55-62.
• Amini, H., Gholami, R., Monjezi, M., Torabi, S.R., Zadhesh, J. (2011). Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Computing and Applications, 21(8), 2077-2085.
• Armaghani, D.J., Hajihassani, M., Mohamad, E.T., Marto, A., Noorani, S.A. (2013). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7(12), 5383-5396.
• Armaghani, D.J., Hajihassani, M., Monjezi, M., Mohamad, E.T., Marto, A., Moghaddam, M.R. (2015). Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arabian Journal of Geosciences, 8(11), 9647-9665.
• Armaghani, D.J., Mohamad, E. T., Hajihassani, M., Alavi Nezhad Khalil Abad, S. V., Marto, A., Moghaddam, M. R. (2015). Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Engineering with Computers, 32(1), 109–121. doi:10.1007/s00366-015-0402-5
• Atashpaz Gargari, E., Hashemzadeh, F., Rajabioun, R., Lucas, C. (2008). Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics, 1(3), 337-355.
• Atashpaz-Gargari, E., Lucas, C., (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 4661-4667.
• Azarafza, M., Akgün, H., Feizi-Derakhshi, M.R., Azarafza, M., Rahnamarad, J., Derakhshani, R. (2020). Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method. Heliyon, 6(5), 7-12.
• Bajpayee, T.S., Rehak, T.R., Mowrey, G.L., Ingram, D.K. (2004). Blasting injuries in surface mining with emphasis on flyrock and blast area security. Journal of Safety Research, 35(1), 47-57.
• Bhagat, N.K., Rana, A., Mishra, A.K., Singh, M.M., Singh, A., Singh, P.K. (2021). Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique. Geomatics, Natural Hazards and Risk, 12(1), 1715-1740.
• Cristianini, N., Shawe Taylor, N.J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press.
• Esmaeili, M., Osanloo, M., Rashidinejad, F., Bazzazi, A.A., Taji, M. (2014). Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers, 30(4), 549-558.
• Fletcher, L.R., Andrea, D.V., (1986). Control of flyrock in blasting. In: Proceedings of the 12th Annual Conference on Explosives and Blasting Technique. Cleveland, USA, 167-177.
• Garret, J.H. (1994). Where and why artificial neural networks are applicable in civil engineering. Journal of Computer in Civil Engineering, 8, 129-130.
• Ghasemi, E., Amini, H., Ataei, M., Khalokakaei, R. (2012b). Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arabian Journal of Geosciences, 7(1), 193-202.
• Ghasemi, E., Sari, M., Ataei, M. (2012a). Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences, 52, 163-170.
• Gorgulu, K., Arpaz, E., Uysal, O., Duruturk, Y.S., Yuksek, A.G., Kocaslan, A., Dilmac, M.K. (2015). Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arabian Journal of Geosciences, 8(6), 4269- 4278.
• Hajihassani, M., Armaghani, D.J., Sohaei, H., Mohamad, E.T., Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 80, 57-67.
• Holmeberg, R., Persson, G. (1976). The effect of stemming on the distance of throw of flyrock in connection with hole diameters. Swedish Detonic Research Foundation, Report DS, 1.
• Hornik, K. (1991). Approximation capabilities of multi-layer feed forward networks. Neural Networks, 4(2), 251-257.
• Institute of Makers of Explosives (IME), 1997. Glossary of commercial explosives industry terms. Safety publication, Institute of Makers of Explosives, Washington, 12.
• Jang, H., Topal. E. (2014). A review of soft computing technology applications in several mining problems. Applied Soft Computing, 22, 638-651.
• Kahriman, A., Ozer, U., Aksoy, M., Karadogan, A., and Tuncer, G. (2006). Environmental Impacts of Bench Blasting at Hisarcik Boron Open Pit Mine in Turkey, Environmental Geology, 50 (7),1015-1023.
• Karadogan, A., Kahriman, A., and Ozer, U. (2014). A New Damage Criteria Norm for Blast Induced Ground Vibrations in Turkey, Arabian Journal of Geosciences, 7(4), 1617-1627.
• Kecojevic, V., Radomsky, M. (2005). Flyrock phenomena and area security in blasting-related accidents. Safety Science, 43(9), 739-750.
• Khandelwal, M., Singh, T.N. (2007). Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering, 27(2), 116-125.
• Koopialipoor, M., Fallah, A., Armaghani, D.J, Azizi, A., Mohamad, E.T. (2019). Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers, 35, 243–256.
• Ladegaard-Pedersen, A., Persson, A., 1973. Flyrock in Blasting II, Experimental Investigation. Swedish Detonic Research Foundation, Report DS 13, Stockholm.
• Langefors, U., Kishlstrom, B., (1963). The Modern Technique of Rock Blasting. John Wiley & Sons, New York, USA.
• Lundborg, N., (1974). The hazards of flyrock in rock blasting. Swedish Detonic Research Foundation, Reports DS 12, Stockholm.
• Lundborg, N., (1981). Risk for flyrock when blasting. Swedish Council for Building Research, BFR Report R29, Stockholm.
• Lundborg, N., Persson, A., Ladegaard-Pedersen, A., Holmberg, R. (1975). Keeping the lid on flyrock in open-pit blasting. Engineering and Mining Journal, 176, 95-100.
• Manoj, K., Monjezi, M. (2013). Prediction of flyrock in open pit blasting operation using machine learning method. International Journal of Mining Science and Technology, 23(3), 313–316.
• Marto, A., Hajihassani, M., Jahed Armaghani, D., Tonnizam Mohamad, E., Makhtar, A.M. (2014). A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific World Journal, 1-11.
• Mishra, A.K., Rout, M. (2011). Flyrocks - detection and mitigation at construction site in blasting operation. World Environment, 1(1), 1-5.
• Mohamad, E.T., Armaghani, D.J., Hajihassani, M., Faizi, K., & Marto, A. (2013). A simulation approach to predict blasting-induced flyrock and size of thrown rocks. Electronic Journal of Geotechnical Engineering, 18, 365-374.
• Mohamad, E.T., Armaghani, D.J., Motaghedi, H. (2013). The effect of geological structure and powder factor in flyrock accident, Masai, Johor, Malaysia. Electronic Journal of Geotechnical Engineering, 18, 5561-5572.
• Mohamad, E.T., Yi, C.S., Murlidhar, B.R., Saad, R. (2018). Effect of geological structure on flyrock prediction in construction blasting. Geotechnical and Geological Engineering, 36(4), 2217-2235.
• Momeni, E., Nazir, R., Armaghani, D.J., Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122-131.
• Monjezi, M., Amini Khoshalan, H., Yazdian Varjani, A. (2010a). Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arabian Journal of Geosciences, 5(3), 441-448.
• Monjezi, M., Bahrami, A., Yazdian Varjani, A. (2010b). Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences, 47(3), 476-480.
• Monjezi, M., Dehghani, H. (2008). Evaluation of effect of blasting pattern parameters on back break using neural networks. International Journal of Rock Mechanics and Mining Sciences, 45(8), 1446-1453.
• Monjezi, M., Mehrdanesh, A., Malek, A., Khandelwal, M. (2012). Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Computing and Applications, 23(2), 349-356.
• Murlidhar, B.R. Kumar, D., Armaghani, D.J., Mohamad, E.T., Roy, B., Pham B.T. (2020). A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock. Natural Resources Research, 29(6), 4103-4120.
• Mutlu, B., Sezer, E.A., Nefeslioglu, H.A. (2017). A defuzzification-free hierarchical fuzzy system (DF-HFS): Rock mass rating prediction. Fuzzy Sets and Systems, 307, 50-66.
• Persson, P.A., Holmberg, R., Lee, J., Coursen, D.L., Davis, W.C. (1994). Book review: rock blasting and explosives engineering. Journal of Energetic Materials, 12(1), 85-88.
• Rad, H.N., Bakhshayeshi, I., Jusoh, W.A.W., Tahir, M.M., Foong, L.K. (2020). Prediction of flyrock in mine blasting: a new computational intelligence approach. Natural Resources Research, 29(2), 609-623.
• Raina, A.K., Chakraborty, A.K., Choudhury, P.B., Sinha, A. (2011). Flyrock danger zone demarcation in opencast mines: a risk based approach. Bulletin of Engineering Geology and the Environment, 70(1), 163-172.
• Rajabioun, E., Atashpaz-Gargari, E., Lucas, C. (2008). Colonial competitive algorithm as a tool for nash equilibrium point achievement. Computational Science and Its Applications, Berlin, Germany, 680-695.
• Rehak, T.R., Bajpayee, T.S., Mowrey, G.L., Ingram, D.K., (2001). Flyrock issues in blasting. In: Proceedings of the 27th Annual Conference on Explosives and Blasting Technique, I. Cleveland, USA, 165-175.
• Rezaei, M., Monjezi, M., Varjani, A.Y. (2011). Development of a fuzzy model to predict flyrock in surface mining. Safety Science, 49(2), 298-305.
• Roth, J., (1979). A model for the determination of flyrock range as a function of shot conditions. Management Science Associates, Los Altos, USA.
• Sadeghi, F., Monjezi, M., Armaghani, D.J. (2020). Evaluation and optimization of prediction of toe that arises from mine blasting operation using various soft computing techniques. Natural Resources Research, 29(2), 887-903.
• Saghatforoush, A., Monjezi, M., Faradonbeh, R.S., Armaghani, D.J. (2015). Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Engineering with Computers, 32(2), 255-266.
• Sepehri Rad, H., Lucas, C. (2008). Application of imperialistic competition algorithm in recommender systems. In Proceedings of the 13th International CSI Computer Conference, Kish Island, Iran.
• Shea, C.W., Clark, D., (1998). Avoiding tragedy: lessons to be learned from a flyrock fatality. Coal Age, 103(2), 51-54.
• Singh, T.N., Singh, V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical and Geological Engineering, 23(3), 249-262.
• Siskind, D.E., Kopp, J.W., (1995). Blasting accidents in mines: a 16-year summary. In: Proceedings of the 21st Annual Conference on Explosives and Blasting Technique, Cleveland, USA, 224-239.
• Trivedi, R., Singh, T.N., Raina, A.K. (2014). Prediction of blast-induced flyrock in Indian limestone mines using neural networks. Journal of Rock Mechanics and Geotechnical Engineering, 6(5), 447-454.
• Uysal, O., Cavus, M. (2013), Effect of a pre-split plane on the frequencies of blast induced ground vibrations. Acta Montanistica Slovaca, 18(2), 101-109.
• Vapnik, V.N. (1998). Statistical learning theory. John Wiley and Sons Inc.
• Verakis H.C., Lobb T.E. (2003), An analysis of blasting accidents in mining operations. In: Proceedings of the 29th annual conference on explosives and blasting technique, Cleveland, USA, 119-129.
• Yan, Yu., Hou, X., Fei, H. (2020). Review of predicting the blast-induced ground vibrations to reduce impacts on ambient urban communities. Journal of Cleaner Production, 260, 121135.
• Zhou, J., Aghili, N., Ghaleini, E.N., Bui, D.T., Tahir, M.M., Koopialipoor, M. (2019). A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Engineering with Computers, 36(2), 713-723.
• Zimmermann, H.J. (2001). Zimmermann Fuzzy Set Theory-and its Applications Springer Science, New York, USA, 1-525.