Document Type : Original Research Paper


Faculty of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran


The drilling and blasting method is the first choice for rock breakage in surface or underground mines due to its high flexibility against variations and low investment costs. However, any method has its own advantages and disadvantages. The flyrock phenomenon is one of the drilling and blasting disadvantages that the mining engineers have always been faced with in the surface mine blasting operations. Flyrock may lead to fatality and destroy mine equipment and structures, and so its risk assessment is very essential. For a flyrock risk assessment, the causing events that lead to flyrock along with their probabilities and severities should be identified. For this aim, a combination of the fuzzy fault tree analysis and multi-criteria decision-making methods are used. Based on the results obtained, the relevant causing events of flyrock in surface mines can be categorized into three major groups: design error, human error, and natural error. Finally, using the obtained probabilities and severities for these three groups, the risk matrix is constructed. Based on the risk matrix, the risk numbers of flyrock occurrence due to the design errors, human errors, and natural influence are 12, 6, and 2, respectively. Hence, in order to minimize the flyrock risk, it is very vital for the engineers to select appropriate values for the design events of blasting pattern such as burden, spacing, delays, and hole diameter.


Main Subjects

[1]. Monjezi, M., Bahrami, A., Varjani, A.Y. and Sayadi, A.K. (2011). Prediction and controlling of fly rock in blasting operation using artificial neural network. Arab J Geosci 4: 421–425.

[2]. Fan L., Shen, W. and Li, Y. (2002). The causes of flyrock and safety precautions in demolition blasting. Engineering Blasting of China 8 (1): 35-38.

[3]. Amini, H., Gholami, R., Monjezi, M., Torabi, S.R. and Zadhesh, J. (2012). Evaluation of fly rock phenomenon due to blasting operation by support vector machine. Neural Comput Appl 21: 2077–2085.

[4]. Hasanipanah, M., Armaghani D.J., Amnieh, H.B., Majid, M.Z.A. and Tahir, M.M. (2017). Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28 (1): 1043–1050.

[5]. Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research 253, 1–13.

[6]. Huang, X., WLi, L., Fang, G., Chen, Y., Zhu, L., Liu, J. and Liu, Z. (2018). Risk decision-making model for reservoir floodwater resources utilization. Environ Earth Sci 77, 555.

[7]. Osorio-Gómez, J.C., Manotas-Duque, D.F., Rivera-Cadavid, L. and Canales-Valdiviezo, I. (2018). Operational Risk Prioritization in Supply Chain with 3PL Using Fuzzy-QFD. In: García-Alcaraz J., Alor-Hernández G., Maldonado-Macías A., Sánchez-Ramírez C. (eds) New Perspectives on Applied Industrial Tools and Techniques. Management and Industrial Engineering. Springer, Cham.

[8]. Rezaei, M., Monjezi, M. and Yazdanian, V.A. (2012). Development of fuzzy model to predict fly rock in surface mining. Saf Sci 49 (298): 305.

[9]. Armaghani, D.J., Hajihassani, M., Mohamad, E.T., Marto, A. and Noorani, S.A. (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian J. Geosci 7 (12): 5383–5396.

[10]. Faradonbeh, R.S., Armaghani, D.J., Amnieh, H.B. and Mohamad, E.T. (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Computing and Applications. 29 (6): 269-281.

[11]. Dehghani, H. and Shafaghi, M. (2017). Prediction of blast-induced flyrock using differential evolution algorithm. Eng Comput 33 (1): 149–158.

[12]. Hudaverdi, T. and Akyildiz, O. (2017). A new classification approach for prediction of flyrock throw in surface mines. Bull Eng Geol Environ.

[13]. Koopialipoor, M., Fallah, A., Armaghani, D.J., Azizi, A. and Mohamad, E.T. (2018). Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput.

[14]. Paithankar, A. (2011). Hazard identification and risk analysis in mining industry. Department of Mining Engineering National Institute of Technology. Undergraduate thesis. Sahu, HB (Supervisor) Rourkela, India 74.

[15]. Zhou, Z., Li, X., Liu, X. and Wan, G. (2012). Safety Evaluation of Blasting Flyrock Risk with FTA Method.

[16]. Wang, Y., Wang, X., Tao, T., Yang, D., Wang, Y. and Zhao, M. (2017). Analysis of Flying Rock Accidents in the Method of FTA in Blasting Demolition. Electronic Journal of Geotechnical Engineering 2701-2710.

[17]. Mottahedi, A. and Ataei, M. (2019). Fuzzy fault tree analysis for coal burst occurrence probability in underground coal mining. Tunn Undergr Space Technol 83,165-174.

[18]. Zhang, M., Kecojevic, V. and Komljenovic, D. (2014). Investigation of haul truck-related fatal accidents in surface mining using fault tree analysis. Safety Sci 65: 106-117. 

[19]. Lavasani, S.M., Ramzali, N., Sabzalipour, F. and Akyuz, E. (2015). Utilization of Fuzzy Fault Tree Analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural gas wells. Ocean Engineering 108, 729-737.

[20]. Lavasani, S.M., Wang, J. and Finlay, J. (2011). Application of fuzzy fault tree analysis on oil and gas offshore pipelines. Int J Mar Sci Eng.1 (1): 29–42.

[21]. Wang, D., Zhang, P. and Chen, L. (2013). Fuzzy fault tree analysis for fire and explosion of crude oil tanks. Journal of Loss Prevention in the Process Industries 26, 1390-1398.

[22]. Renjit V.R., Madhu, G., Nayagam, V.L.G. and Bhasi A.B. (2010). Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation. Journal of Hazardous Materials 183, 103-110.

[23]. Yazdi, M., Nikfar, F. and Nasrabadi, M. (2017) Failure probability analysis by employingfuzzy fault tree analysis. Int. J. Syst. Assur. Eng. Manage.

[24]. Onisawa, T. (1988). A representation of human reliability using fuzzy concepts. Inf Sci 45, 2, 153–173.

[25]. Wang, D., Zhangk P. and Chen, L. (2013). Fuzzy fault tree analysis for fire and explosion of crude oil tanks.

[26]. Fontela, E. and Gabus, A. (1972). World Problems an Invitation to Further Thought within the Framework of DEMATEL. Battelle Geneva Research Centre. Switzerland. Geneva.

[27]. Fontela, E. and Gabus A. (1974). DEMATEL, innovative methods. Report No. 2. Structural analysis of the world problematique. Battelle Geneva Research Institute.

[28]. Fontela, E. and Gabus A. (1976). The DEMATEL observer. Battelle Institute. Geneva Research Center.

[29]. Gabus, A. and Fontela, E. (1973). Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility (DEMATEL Report no.1). Battelle Geneva Research Centre. Geneva. Switzerland.

[30]. Mohammadi, S., Ataei, M., Khaloo Kakaie, R. and Mirzaghorbanali, A. (2018) Prediction of the main caving span in longwall mining using fuzzy MCDM technique and statistical method. Journal of Mining and Environment. 9 (3): 717-726.

[31]. Norouzi Masir, R., Khalokakaie, R., Ataei, M. and Mohammadi, S. (2018). Structural analysis of impacting factors of sustainable development in underground coal mining using DEMATEL method. Journal of Mining and Environment. 9 (3): 567-579.

[32]. Saaty, T.L. (1996). The analytical network process-decision making with dependence and feedback. Pittsburgh, Pa: RWS Publication.

[33]. Saaty, T.L. and Vargas, L. (2013). Decision making with the analytic network process – economic, political, social and technological applications with benefits, opportunities, costs and risks. 2nd. New York: Springer.