Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 32, No. 4 (April 2019) 617-627    Article in Press

PDF URL: http://www.ije.ir/Vol32/No4/A/21-3061.pdf  
downloaded Downloaded: 22   viewed Viewed: 115

  PREDICTION OF SEISMIC WAVE INTENSITY GENERATED BY BENCH BLASTING USING INTELLIGENCE COMMITTEE MACHINES
 
Y. Azimi
 
( Received: January 04, 2019 – Accepted: March 07, 2019 )
 
 

Abstract    In large open pit mines prediction of Peak Particle Velocity (PPV) provides useful information for safe blasting. At Sungun Copper Mine (SCM), some unstable rock slopes facing to valuable industrial facilities are both expose to high intensity daily blasting vibrations, threatening their safty. So, controlling PPV by developing accurate predictors is essential. Hence, this study proposes improved strategies for prediction of PPV by maximum charge per delay and distance using the concept of Intelligent Committee Machine (ICM). Besides the Empirical Predictors (EPs) and two Artificial Intelligence (AI) models of ANFIS and ANN, four different ICMs models including Simple and Weighted Averaging ICM (SAICM and WAICM) and First and Second order Polynomial ICM (FPICM and SPICM) in conjunction with genetic algorithm, proposed for the prediction of PPV. Performance of predictors was studied considering R2, RSME and VAF indices. Results indicate that ICM methods have superiority over EPs, ANN and ANFIS, and among the ICM models while SAICM, WAICM and FPICM performing near to each other SPICM overrides all the models. R2 and RSME of the training and testing data for SPICM are 0.8571, 0.8352 and 11.0454, 12.3074, respectively. Finally, ICMs provides more accurate and reliable models rather than individual AIs.

 

Keywords    Adaptive Neuro-fuzzy Inference System; Artificial Neural Network; Genetic Algorithm; Fuzzy Logic; Intelligence Committee Machine; Peak Particle Velocity Prediction; Rock Blasting

 

چکیده   

پیش‌بینی حداکثر سرعت ذره¬ای (PPV) ارتعاش زمین ناشی از انفجار در معادن، روباز بزرگ می-تواند اطلاعات مفیدی را برای انجام عملیات انفجاری ایمن فراهم کند. در معدن مس سونگون یکسری شیب¬های سنگی با پتانسیل ناپایداری مشرف به برخی تاسیسات صنعتی باارزش هر دو در مواجهه با لرزش روزانه انفجارها قرار دارند، که ایمنی آن¬ها را تهدید می¬کند. از این¬رو کنترل شدت لرزش¬ها با توسعه مدل¬های صحیح پیش-بینی کننده ضروری می¬باشد. در این مطالعه استراتژی‌های بهبود یافته¬ای برای پیش¬بینیPPV بر اساس حداکثرخرج ماده منفجره شده در تاخیرات و فاصله بین مرکز انفجار و ایستگاه اندازه¬گیری‌ با استفاده از مفهوم ماشین کمیته هوشمند (ICM) ارائه شده است. از اینرو، علاوه بر روابط تجربی (EPs) و دو روش هوش مصنوعی ANN و ANFIS، چهار نوع مختلف ICM شامل ICM با متوسط¬گیری ساده و وزن (SAICM و WAICM) و ICM چند جمله‌ای مرتبه اول و دوم (FPICM و SPICM) بر اساس خروجی دو مدل ANFIS و ANN برای پبش¬بینی PPV بر اساس داده-های اندازه¬گیری شده در معدن مس سونگون پیشنهاد شده است. از این¬رو الگوریتم ژنتیک (GA) برای پیدا کردن ضرایب مدل¬های WAICM،FPICM و SPICM استفاده شد. در نهایت عملکرد هفت مدل پیش‌بینی کننده PPV با استفاده از شاخص‌های R2،RSMEوVAF مورد بررسی قرار گرفت. نتایج نشان می‌دهد که روش‌های ICM در پیش‌بینی PPV نسبت به EP ها و مدل‌های هوش مصنوعی برتری دارد. علاوه بر این، عملکرد سه مدل SAICM،WAICM و FPICM نزدیک به یکدیگر هستند، در حالیکه SPICM بهینه شده با GA عملکرد بهتری نسبت به تمام مدل¬های توسعه داده شده در این تخقیق دارد. مقدار R2 و RSME داده های آموزش و آزمون برای مدل SPICM به ترتیب برابر با 8571/0، 8352/0 و 0454/0 ، 3074/12 است. درنهایت، روش ICM مدل‌های دقیق¬تر و قابل اطمینان¬تری نسبت به مدل¬های هوش مصنوعی ارائه می¬کند.

References   

1. Azimi, Y., Khoshrou, S., Osanloo, M. and Sadeghee, A., "Seismic wave monitoring and ground vibration analysis for bench blasting in sungun open pit copper mine", in Rock Fragmentation by Blasting, (2010 of Conference), 561-570.
2. Duvall, W.I. and Fogelson, D.E., "Review of criteria for estimating damage to residences from blasting vibrations, US Department of the Interior, Bureau of Mines,  (1962).
3. United States. Bureau of Mines and Siskind, D., "Structure response and damage produced by ground vibration from surface mine blasting, US Department of the Interior, Bureau of Mines New York,  (1980).
4. Dowding, C.H. and Dowding, C., "Construction vibrations, Prentice Hall Upper Saddle River, NJ,  Vol. 81,  (1996).
5. Singh, T. and Singh, V., "An intelligent approach to prediction and control ground vibration in mines", Geotechnical & Geological Engineering,  Vol. 23, No. 3, (2005), 249-262.
6. Khandelwal, M., "Blast-induced ground vibration prediction using support vector machine", Engineering with Computers,  Vol. 27, No. 3, (2011), 193-200.
7. Monjezi, M., Hasanipanah, M. and Khandelwal, M., "Evaluation and prediction of blast-induced ground vibration at shur river dam, iran, by artificial neural network", Neural Computing and Applications,  Vol. 22, No. 7-8, (2013), 1637-1643.
8. Nguyen, H., Bui, X.-N., Tran, Q.-H., Le, T.-Q. and Do, N.-H., "Evaluating and predicting blast-induced ground vibration in open-cast mine using ann: A case study in vietnam", SN Applied Sciences,  Vol. 1, No. 1, (2019), 125.
9. Ghasemi, E., Ataei, M. and Hashemolhosseini, H., "Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining", Journal of Vibration and Control,  Vol. 19, No. 5, (2013), 755-770.
10. Mohamed, M.T., "Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations", International Journal of Rock Mechanics and Mining Sciences,  Vol. 48, No. 5, (2011), 845.
11. Fişne, A., Kuzu, C. and Hüdaverdi, T., "Prediction of environmental impacts of quarry blasting operation using fuzzy logic", Environmental Monitoring and Assessment,  Vol. 174, No. 1-4, (2011), 461-470.
12. Faradonbeh, R.S., Armaghani, D.J., Majid, M.A., Tahir, M.M., Murlidhar, B.R., Monjezi, M. and Wong, H., "Prediction of ground vibration due to quarry blasting based on gene expression programming: A new model for peak particle velocity prediction", International Journal of Environmental Science and Technology,  Vol. 13, No. 6, (2016), 1453-1464.
13. Armaghani, D.J., Hajihassani, M., Mohamad, E.T., Marto, A. and Noorani, S., "Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization", Arabian Journal of Geosciences,  Vol. 7, No. 12, (2014), 5383-5396.
14. Hajihassani, M., Armaghani, D.J., Monjezi, M., Mohamad, E.T. and Marto, A., "Blast-induced air and ground vibration prediction: A particle swarm optimization-based artificial neural network approach", Environmental Earth Sciences,  Vol. 74, No. 4, (2015), 2799-2817.
15. Hajihassani, M., Armaghani, D.J., Marto, A. and Mohamad, E.T., "Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm", Bulletin of Engineering Geology and the Environment,  Vol. 74, No. 3, (2015), 873-886.
16. Agrawal, H. and Mishra, A., "Probabilistic analysis on scattering effect of initiation systems and concept of modified charge per delay for prediction of blast induced ground vibrations", Measurement,  Vol. 130, No., (2018), 306-317.
17. Murmu, S., Maheshwari, P. and Verma, H.K., "Empirical and probabilistic analysis of blast-induced ground vibrations", International Journal of Rock Mechanics and Mining Sciences,  Vol. 103, No., (2018), 267-274.
18. Arthur, C.K., Temeng, V.A. and Ziggah, Y.Y., "Novel approach to predicting blast-induced ground vibration using gaussian process regression", Engineering with Computers,  Vol., No., (2019), 1-14.
19. Nadiri, A., Hassan, M.M. and Asadi, S., "Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification", Transportation Research Record: Journal of the Transportation Research Board,  Vol., No. 2528, (2015), 96-105.
20. Kadkhodaie-Ilkhchi, A., Rezaee, M.R. and Rahimpour-Bonab, H., "A committee neural network for prediction of normalized oil content from well log data: An example from south pars gas field, persian gulf", Journal of Petroleum Science and Engineering,  Vol. 65, No. 1-2, (2009), 23-32.
21. Labani, M.M., Kadkhodaie-Ilkhchi, A. and Salahshoor, K., "Estimation of nmr log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the iranian part of the south pars gas field, persian gulf basin", Journal of Petroleum Science and Engineering,  Vol. 72, No. 1-2, (2010), 175-185.
22. Chen, C.-H. and Lin, Z.-S., "A committee machine with empirical formulas for permeability prediction", Computers & Geosciences,  Vol. 32, No. 4, (2006), 485-496.
23. Iphar, M., Yavuz, M. and Ak, H., "Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system", Environmental Geology,  Vol. 56, No. 1, (2008), 97-107.
24. Samareh, H., Khoshrou, S.H., Shahriar, K., Ebadzadeh, M.M. and Eslami, M., "Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm", Journal of African Earth Sciences,  Vol. 133, No., (2017), 36-45.
25. Faradonbeh, R.S. and Monjezi, M., "Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms", Engineering with Computers,  Vol. 33, No. 4, (2017), 835-851.
26. Torres, V.N., Silveira, L.G., Lopes, P.F. and de Lima, H.M., "Assessing and controlling of bench blasting-induced vibrations to minimize impacts to a neighboring community", Journal of Cleaner Production,  Vol. 187, No., (2018), 514-524.
27. Ambraseys, N., Rock mechanics in engineering practice. 1968.
28. Langefors, U. and Kihlström, B., "The modern technique of rock blasting, Wiley New York,  (1963).
29. Standard, I., "Criteria for safety and design of structures subjected to under ground blast", ISI., IS-6922,  Vol., No., (1973).
30. Roy, P., "Putting ground vibration predictions into practice", Colliery Guardian,  Vol. 241, No. 2, (1993), 63-67.
31. Azimi, Y., "Investigation of seismic wave due to blasting in sungun copper mine", Amirkabir Unversity Department of Mining and Metallurgical Engineering, Tehran Iran, MSc,  (2006), 
32. Fenjan, S.A., Bonakdari, H., Gholami, A. and Akhtari, A., "Flow variables prediction using experimental, computational fluid dynamic and artificial neural network models in a sharp bend", International Journal of Engineering-Transactions A: Basics,  Vol. 29, No. 1, (2016), 14-21.
33. Jafari, M.M. and Khayati, G., "Artificial neural network based prediction hardness of al2024-multiwall carbon nanotube composite prepared by mechanical alloying", International Journal of Engineering-Transactions C: Aspects,  Vol. 29, No. 12, (2016), 1726-1733.
34. Jang, J.-S.R., Sun, C.-T. and Mizutani, E., "Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence",  Vol., No., (1997).
35. Takagi, T. and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control", IEEE transactions on Systems, man, and Cybernetics,  Vol. 15, No. 1, (1985), 116-132.
36. Azadnia, A., Siahi, A. and Motameni, M., "An adaptive fuzzy neural network model for bankruptcy prediction of listed companies on the tehran stock exchange", International Journal of Engineering-Transactions C: Aspects,  Vol. 30, No. 12, (2017), 1879-1884.
37. Dunn, J.C., "A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters",  Vol., No., (1973).
38. Jafari, S., Mashohor, S. and Varnamkhasti, M.J., "Committee neural networks with fuzzy genetic algorithm", Journal of Petroleum Science and Engineering,  Vol. 76, No. 3-4, (2011), 217-223.
39. Holland, J. and Goldberg, D., "Genetic algorithms in search, optimization and machine learning", Massachusetts: Addison-Wesley,  Vol., No., (1989).
40. Azimi, Y. and Osanloo, M., "Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm", Archives of Mining Sciences,  Vol. 56, No. 2, (2011), 189–212.
41. Jafari, M.M., Khayati, G., Hosseini, M. and Danesh-Manesh, H., "Modeling and optimization of roll-bonding parameters for bond strength of ti/cu/ti clad composites by artificial neural networks and genetic algorithm", International Journal of Engineering-Transactions C: Aspects,  Vol. 30, No. 12, (2017), 1885-1893.
42. Azimi, Y., Osanloo, M., Aakbarpour-Shirazi, M. and Bazzazi, A.A., "Prediction of the blastability designation of rock masses using fuzzy sets", International Journal of Rock Mechanics and Mining Sciences,  Vol. 47, No. 7, (2010), 1126-1140.


Download PDF 



International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir