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Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

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Abstract

Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

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References

  • Adhikari GR (1999) Studies on flyrock at limestone quarries. Rock Mech Rock Eng 32:291–301

    Article  Google Scholar 

  • Aghajani-Bazzazi A, Osanloo M, Azimi Y (2009) Flyrock prediction by multiple regression analysis in Esfordi phosphate mine of Iran. In: Proceedings of the 9th international symposium on rock fragmentation by blasting. Granada, Spain, pp 649–657

  • Amini H, Gholami R, Monjezi M, Torabi SR, Zadhesh J (2011) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl. doi:10.1007/s00521-011-0631-5

    Google Scholar 

  • Bajpayee TS, Rehak TR, Mowrey GL, Ingram DK (2002) A summary of fatal accidents due to flyrock and lack of blast area security in surface mining, 1989–1999. In: Proceedings of the 28th annual conference on explosives and blasting technique. Las Vegas, pp 105–118

  • Bajpayee TS, Rehak TR, Mowrey GL, Ingram DK (2004) Blasting injuries in surface mining with emphasis on flyrock and blast area security. J Safety Res 35:47–57

    Article  Google Scholar 

  • Berta G (1990) Explosives: an engineering tool. Italesplosivi, Millano

    Google Scholar 

  • Bhandari S (1997) Engineering rock blasting operations. Taylor & Francis, Boca Raton

    Google Scholar 

  • Bianchini F, Hewage K (2012) Probabilistic social cost-benefit analysis for green roofs: a lifecycle approach. Build Environ 58:152–162. doi:10.1016/j.buildenv.2012.07.005

    Article  Google Scholar 

  • Dunn WL, Shultis JK (2009) Monte Carlo methods for design and analysis of radiation detectors. Radiat Phys Chem 78:852–858. doi:10.1016/j.radphyschem.2009.04.030

    Article  Google Scholar 

  • Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-015-0720-2

    Google Scholar 

  • Esmaeili M, Osanloo M, Rashidinejad F, Aghajani Bazzazi A, Taji M (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30:549–558

    Article  Google Scholar 

  • Faradonbeh RS, Monjezi M, Jahed Armaghani D (2015) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput. doi:10.1007/s00366-015-0404-3

    Google Scholar 

  • Fletcher LR, D’Andrea DV (1987) Reducing accident through improved blasting safety. USBM IC, 9135. In: Proceedings of bureau of mines technology transfer SEM, Chicago, pp 6–18

  • Ghasemi E, Sari M, Ataei M (2012) Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int J Rock Mech Min Sci 52:163–170

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, 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

    Article  Google Scholar 

  • Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghanid D, Farazmand A (2015a) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

    Article  Google Scholar 

  • Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2015b) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput. doi:10.1007/s00366-015-0425-y

    Google Scholar 

  • Hemphill GB (1981) Blasting operations. McGraw-Hill, New York

    Google Scholar 

  • Institute of Makers of Explosives (IME) (1997) Glossary of commercial explosive industry terms. Safety Publication, Washington DC: Institute of Makers of Explosives. No 12

  • ISRM (2007) In: Ulusay and Hudson (eds) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. Suggested methods prepared by the commission on testing methods, International Society for Rock Mechanics

  • Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian J Geosci 7:5383–5396

    Article  Google Scholar 

  • Jahed Armaghani D, Hasanipanah M, Tonnizam Mohamad E (2015a) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput. doi:10.1007/s00366-015-0408-z

    Google Scholar 

  • Jahed Armaghani D, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015b) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arabian J Geosci. doi:10.1007/s12517-015-1908-2

    Google Scholar 

  • Jahed Armaghani D, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2015c) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput. doi:10.1007/s00366-015-0402-5

    Google Scholar 

  • Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam

    Google Scholar 

  • Kecojevic V, Radomsky M (2005) Flyrock phenomena and area security in blasting-related accidents. Safety Sci 43:739–750

    Article  Google Scholar 

  • Khandelwal M, Monjezi M (2013a) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  • Khandelwal M, Monjezi M (2013b) Prediction of flyrock in open pit blasting operation using machine learning method. Int J Rock Mech Min Sci Technol 23:313–316

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125

    Article  Google Scholar 

  • Ladegaard-Pedersen A, Holmberg R (1973) The dependence of charge geometry on flyrock caused by crater effects in bench blasting. Report DS1973, Swedish Detonic Res Found (SweDeFo), pp 1–38

  • Little TN, Blair DP (2010) Mechanistic Monte Carlo models for analysis of flyrock risk. Rock fragmentation by blasting. Taylor and Francis, London, pp 641–647

    Google Scholar 

  • Liu MM (2014) Probabilistic prediction of green roof energy performance under parameter uncertainty. Energy 77:667–674

    Article  Google Scholar 

  • Lundborg N (1981) The probability of flyrock. Report DS1981, Swedish Detonic Res Found (SweDeFo)

  • Lundborg N, Persson N, Ladegaard-Pedersen A, Holmberg R (1975) Keeping the lid on flyrock in open pit blasting. Eng Min J 176:95–100

    Google Scholar 

  • Mandal SK (1997) Causes of flyrock damages and its remedial measures. Course on: recent advances in blasting techniques in mining and construction projects, HRD-CMRI Dhanbad, pp 130–136

  • Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J (Article ID 643715). http://dx.doi.org/10.1155/2014/643715

  • Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neurogenetic approach. Arabian J Geosci 5:441–448

    Article  Google Scholar 

  • Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643

    Article  Google Scholar 

  • Morin AM, Ficarazzo F (2006) Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz-Ram model. Comput Geosci 32:352–359

    Article  Google Scholar 

  • Raina AK, Murthy VMSR, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ. doi:10.1007/s10064-014-0588-6

    Google Scholar 

  • Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Safety Sci 49:298–305

    Article  Google Scholar 

  • Richards AB, Moore AJ (2004) Flyrock control-by chance or design. In: Proceedings of 30th ISEE conference on explosives and blasting technique, New Orleans

  • Roth JA (1979) A model for the determination of flyrock range as a function of shot condition. US Department Commerce, NTIS Report no. PB81222358

  • Roy PP (2005) Rock blasting effects and operations. Taylor & Francis, Boca Raton

    Google Scholar 

  • Sari M, Ghasemi E, Ataei M (2013) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mech Rock Eng. doi:10.1007/s00603-013-0438-z

    Google Scholar 

  • Solver F (2010) Premium solver platform. User Guide, Frontline Systems, Inc

  • SPSS Inc (2007) SPSS for Windows (Version 16.0). Chicago: SPSS Inc

  • Trivedi R, Singh TN, Gupta N (2015) Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech Geolog Eng. doi:10.1007/s10706-015-9869-5

    Google Scholar 

  • US EPA Technical Panel (1997) Guiding principles for Monte Carlo analysis. Us Epa 1–35

  • Verakis HC, Lobb TE (2003) An analysis of blasting accidents in mining operations. In: Proceedings of 29th annual conference explosives and blasting technique. Cleveland: International Society of Explosives Engineers, vol 2, pp 119–129

Download references

Acknowledgments

The authors would like to express their sincere appreciation to the anonymous reviewers for their valuable and constructive suggestions.

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Correspondence to Manoj Khandelwal.

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Armaghani, D.J., Mahdiyar, A., Hasanipanah, M. et al. Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting. Rock Mech Rock Eng 49, 3631–3641 (2016). https://doi.org/10.1007/s00603-016-1015-z

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  • DOI: https://doi.org/10.1007/s00603-016-1015-z

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