پیش بینی پرتاب سنگ ناشی از آتشباری با استفاده از تکنیک درختی M5P

نوع مقاله: مقاله پژوهشی

نویسنده

دانشکده مهندسی معدن، دانشگاه صنعتی اصفهان

10.29252/anm.8.16.45

چکیده

پرتاب سنگ یکی از مسائل بحرانی عملیات آتشباری در معادن روباز است که به شدت ایمنی پرسنل و تجهیزات را تحت تأثیر قرار می­دهد. یکی از راه­های کاهش ریسک حوادث ناشی از پرتاب سنگ، پیش­بینی دقیق آن است. طی سالیان گذشته با استفاده از روش­های هوش مصنوعی، مدل­های تجربی بسیاری برای پیش­بینی پرتاب سنگ توسعه داده شده است. اغلب این مدل­ها شفاف و قابل فهم نبوده و در آنها ارتباط بین پارامترهای ورودی و خروجی به وضوح نشان داده نشده است. هدف از این مقاله ارائه مدلی صریح و قابل فهم برای پیش­بینی پرتاب سنگ است. برای این منظور از تکنیک M5P استفاده و به کمک آن ساختاری درخت مانند برای تخمین فاصله پرتاب سنگ ارائه شده است. در این مدل پرتاب سنگ بر اساس یک سری معادله­های خطی پیش­بینی می­شود، از این­رو استفاده از آن بسیار ساده است. به منظور آموزش و آزمایش مدل درختی پیشنهادی، داده­های آتشباری معدن مس سونگون به کار گرفته شده است. در این مدل فاصله پرتاب سنگ با استفاده از مهم‌ترین پارامترهای قابل کنترل آتشباری یعنی بار سنگ، فاصله­داری چال­ها، طول گل­گذاری، طول چال، قطر چال، خرج ویژه و متوسط خرج در هر چال تخمین زده می­شود. دقت و کارایی مدل پیشنهادی با استفاده از شاخص­های آماری R2، VAF و RMSEمورد ارزیابی قرار گرفت. مقدار این شاخص­ها به ترتیب 1/92 درصد، 92 درصد و 9/3 به دست آمدند. بنابراین می­توان نتیجه گرفت که تکنیک درختی M5P ابزاری مفید و قدرتمند برای پیش­بینی پرتاب سنگ است. همچنین، نتایج نشان داد که بار سنگ و قطر چال به ترتیب با اهمیت­ترین و کم اهمیت­ترین پارامترها در پیش­بینی پرتاب سنگ هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of blasting-induced flyrock using M5P tree technique

نویسنده [English]

  • Ebrahim Ghasemi
Dept. of Mining, Isfahan University of Technology
چکیده [English]

Summary
Based on statistics, flyrock is the main reason of 20 to 40% of blasting induced accidents in mines. Therefore, the accurate prediction of flyrock has a remarkable role on reducing its detrimental effects. In this paper, a model tree was developed for flyrock prediction using M5P technique. This model was trained and tested by the blasting database of Sungun copper mine. The results showed that the proposed model can estimate the flyrock with an acceptable error.
 
Introduction
Flyrock is one of the most challenging safety issues of blasting operation in open pit mines, which always threatens the safety of personnel and equipment. Thus, the accurate prediction of flyrock seems necessary for determination of safe blasting zone. During past years, many empirical models have been developed using artificial intelligence techniques for flyrock prediction. Most of these models do not indicate the relationship between input and output parameters, leading to ambiguous relation of input and output. The main purpose of this paper is to construct a transparent and understandable model for flyrock prediction.
 
Methodology and Approaches
In this paper, a model is developed for flyrock prediction using M5P technique. This technique presents a tree structure which contains linear equations in its leaves and using these equations the flyrock can be predicted easily. In this model, the flyrock distance is estimated using the most important controllable blasting parameters (i.e. burden, spacing, stemming, blasthole length, blasthole diameter, powder factor and mean charge per blasthole).
 
Results and Conclusions
The accuracy and efficiency of the proposed model was evaluated using various statistical indices such as coefficient and determination (R2), variance account for (VAF) and root mean square error (RMSE). These indices were obtained 92.1%, 92%, and 3.9, respectively. Therefore, it can be concluded that M5P technique is a suitable and efficient means that provides an acceptable prediction for flyrock. Furthermore, the results indicated that the burden and blasthole diameter are the most and the least effective parameters on flyrock prediction, respectively. The output of this model can be considered as a preliminary estimation of flyrock, based on which the risk of hazards and potential accidents can be reduced to a desirable extent.

کلیدواژه‌ها [English]

  • Open pit mines
  • Blasting
  • Flyrock
  • Model tree
  • M5P technique
  • Sungun Copper Mine
[1]           Singh, T.N. and Singh, V. (2005). An intelligent approach to predict and control ground vibration in mines. Geotech. Geol. Eng., 23(3), 249–262.

[2]           Institute of Makers of Explosives (IME) (1997). Glossary of commercial explosives industry terms. Safety publication, No. 12, Institute of Makers of Explosives, Washington.

[3]           Bajpayee, T.S., Rehak, T.R., Mowrey, G.L. and Ingram, D.K. (2002). A summary of fatal accidents due to flyrock and lack of blast area security in surface mining, 1989 to 1999. In: proceedings of the 28th annual conference on explosives and blasting technique, international society of explosives engineers (ISEE), Feb. 10-13, Las Vegas, pp 105–118.

[4]           Rehak, T.R., Bajpayee, T.S., Mowrey, G.L. and Ingram, D.K. (2001). Flyrock issues in blasting. In: proceedings of the 27th annual conference on explosives and blasting technique, international society of explosives engineers (ISEE), Jan. 28-31, Orlando, pp 165–175.

[5]           Raina, A.K., Murthy, V.M.S.R. and Soni, A.K. (2015). Flyrock in surface mine blasting: understanding the basics to develop a predictive regime. Current Science, 108(4), 660–665.

[6]           Mishra, A.K. and Mallick, D.K. (2013). Analysis of blasting related accidents with emphasis on flyrock and its mitigation in surface mines. In: proceedings of rock fragmentation by blasting, Fragblast 10, Taylor and Francis, London, pp 555–561.

[7]           Kecojevic, V. and Radomsky, M. (2005) Flyrock phenomena and area security in blasting-related accidents. Saf. Sci., 43(9), 739–750.

[8]           Little, T.N. (2007). Flyrock risk. In: proceedings of EXPLO Conference, Sep. 3-4, Wollongong, pp 35–43.

[9]           Verakis, H.C. and Lobb, T.E. (2003). An analysis of blasting accidents in mining operations. In: proceedings of 29th annual conference explosives and blasting technique, international society of explosives engineers (ISEE), Feb. 2-5, Nashville, pp 119–129.

[10]         Ghasemi, E., Sari, M. and 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.

[11]         Raina, A.K., Murthy, V.M.S.R. and Soni, A.K. (2014). Flyrock in bench blasting: a comprehensive review. Bull. Eng. Geol. Environ., 73, 1199–1209.

[12]         Monjezi, M., Bahrami, A. and Yazdian Varjani, A. (2010). Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. Sci., 47(3), 476–480.

[13]         Rezaei, M., Monjezi, M. and Yazdian Varjani, A. (2011). Development of a fuzzy model to predict flyrock in surface mining. Saf. Sci., 49(2), 298–305.

[14]         Monjezi, M., Amini Khoshalan, H. and Yazdian Varjani, A. (2012). Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab. J. Geosci., 5(3), 441–448.

[15]         Amini, H., Gholami, R., Monjezi, M., Torabi, S.R. and Zadhesh, J. (2012). Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput. Appl., 21(8), 2077–2085.

[16]         Khandelwal, K. and Monjezi, M. (2013). Prediction of flyrock in open pit blasting operation using machine learning method. Int. J. Min. Sci. Technol., 23(3), 313–316.

[17]         Ghasemi, E., Amini, H., Ataei, M. and Khalokakaei, R. (2014). Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab. J. Geosci., 7(1), 193–202.

[18]         Jahed Armaghani, D., Hajihassani, M., Tonnizam Mohamad, E., 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. Arab. J. Geosci., 7(12), 5383–5396.

[19]         Marto, A., Hajihassani, M., Jahed Armaghani, D., Tonnizam Mohamad, E. and Makhtar, A.M. (2014). A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci. World J., Article ID 643715.

[20]         Trivedi, R., Singh, T.N. and Raina, A.K. (2014). Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J. Rock Mech. Geotech. Eng., 6(5), 447–454.

[21]         Trivedi, R., Singh, T.N. and Gupta, N. (2015). Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech. Geol. Eng., 33(3), 875–891.

[22]         Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., Alavi Nezhad Khalil Abad, S.V., Marto, A. and Moghaddam, M.R. (2016). Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng. Comput., 32(1), 109–121.

[23]         Shirani Faradonbeh, R., Jahed Armaghani, D., Monjezi, M. and Tonnizam Mohamad, E. (2016). Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int. J. Rock Mech. Min. Sci., 88, 254–264.

[24]         Yari, M., Bagherpour, R., Jamali, S. and Shamsi, R. (2016). Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety. Neural Comput. Appl., 27(3), 699–706.

[25]         Raina, A.K. and Murthy, V.M.S.R. (2016). Importance and sensitivity of variables defining throw and flyrock in surface blasting by artificial neural network method. Current Science, 111(9), 1524–1531.

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

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

[28]         Quinlan, J.R. (1992). Learning with continuous classes. In: proceedings of the fifth Australian joint conference on artificial intelligence, world scientific, Singapore, pp 343–348.

[29]         Wang, Y. and Witten, I.H. (1997). Induction of model trees for predicting continuous lasses. In: proceedings of the poster papers of the European conference on machine learning, Prague, Czech Republic.

[30]         Ghasemi, E., Kalhori, H., Bagherpour, R. and Yagiz, S. (2018). Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks. Bull. Eng. Geol. Environ., 77(1), 331–343.

[31]         Jung, N.-C., Popescu, I., Kelderman, P., Solomatine, D.P. and Price, R.K. (2010). Application of model trees and other machine learning techniques for algal growth prediction in Yongdam Reservoir, Republic of Korea. J. Hydroinform., 123, 262–274.

[32]         WEKA (Waikato Environment for Knowledge Analysis), Version 3.6.12 (2014). The University of Waikato, Hamilton, New Zealand, available at: http://www.cs.waikato.ac.nz/ml/weka.

[33]         Montgomery, D.C., Peck, E.A. and Vining, G.G. (2012). Introduction to linear regression analysis, 5th edn. Wiley, New Jersey.