Artificial Neural Network as a Tool for Backbreak Prediction

Geotechnical and Geological Engineering

Volumn 32, Issue 1, 2014, Pages 21-30

  Monjezi, M ^a   Hashemi Rizi, S M ^a   Majd, V Johari ^a   Khandelwal, Manoj ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b MAHARANA PRATAP UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (India)

Author keywords

Artificial neural network; Backbreak; Blasting; Sangan iron mine

Indexed keywords

BLASTING; IRON MINES; NEURAL NETWORKS; REGRESSION ANALYSIS;

BACKBREAK; BLASTING OPERATIONS; EFFECTIVE PARAMETERS; MINING PROJECTS; NETWORK TRAINING; REGRESSION MODEL; ROCK CONDITIONS; SENSITIVE PARAMETER;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; BLASTING; DATA SET; DATABASE; IRON; MINE; SENSITIVITY ANALYSIS;

IRAN;

EID: 84902436118     PISSN: 09603182     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10706-013-9686-7     Document Type: Article

References (59)

1. Ambromi TW, Turk G (2003) Prediction of subsidence due to underground mining by artificial neural networks. Comput Geosci 29: 627-637.
2. Babǔska R (2004) Fuzzy and neural control. Delft University of Technology, DISC Course Lecture Notes, Netherlands.
3. Bhandari S, Rathore SS (1997) Engineering rock blasting operations, A. A Balkema, Rotterdam.
4. Brent GF, Smith GE (2000) The detection of blast damage by borehole pressure measurement. J S Afr Inst Min Metall 100(1): 17-22.
5. Chain R, Talhi K (2005) Design of model blasting for evaluating bench slope stability. Asian J Inf Technol 4: 1170-1176.
6. Demuth, H., Beale, M. (2008). Neural network toolbox: for use with MATLAB. User's Guide, Version 4, The MathWorks Inc.
7. Ding D, Zhang Z (2004) Artificial neural network based inverse design method for circular sliding slopes. J Central South University Technol 11: 89-92.
8. Dreyfus G (2004) Neural networks: methodology and applications, 2nd edn. Published by Eyrolles, Paris.
9. Dyno Nobel group (1998) Dyno Nobel user manual and technical data sheets. (1st ed.). Sweden, Gyttorp.
10. Eskandari H, Rezaee MR, Mohammadnia M (2004) Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South-West Iran. CSEG Recorder 42: 48.
11. Etkin MB, Azarkovich AE, Sapronov AA, Vartanov VG (2001) Protecting external rock bodies from blasting damage. Power Technol Eng 35: 499-506.
12. Forsyth WW, Moss AE (1991) Investigation of development blasting practices. CANMET-MRL, 91-143.
13. Gates WCB, Ortiz LT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems. The 40th U. S. Symposium on Rock Mechanics (USRMS), Alaska, 25-29.
14. Giraudi A, Cardu M, Kecojevic V (2009) An assessment of blasting vibrations: a case study on quarry operatio. Am J Environ Sci 5: 467-473.
15. Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17: 61-72.
16. Grima MA (2000) Neuro-fuzzy modeling in engineering geology. A. A. Balkema, Rotterdam.
17. Hayakin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs.
18. Hoek E, Bray J (2004) Rock Slope Engineering, Civil and Mining. (4th ed). In: C. Duncan.
19. Hustrulid W (1999) Blasting principles for open pit mining, vol 1. Balkema, A. A.
20. Jennrich RI (1995) An introduction to computational statistics-regression analysis. N. J. Prentice-Hall, Englewood Cliffs.
21. Jhanwar JC, Jethwa JL (2000) The use of air-decks in production blasting in an open pit coal mine. Geotech Geol Eng 18: 269-287.
22. Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41: 533-538.
23. Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis fault breccia using artificial neural network. Expert Syst Appl 36: 6874-6878.
24. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289: 711-725.
25. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46: 1214-1222.
26. Kojovic T (2005) Influence of aggregate stemming in blasting on sag mill performance. Mineral Eng 18: 1398-1404.
27. Konya CJ (2003) Rock blasting and overbreak control. (2nd ed). US Department of Transportation, National Highway Institute.
28. Konya CJ, Walter EJ (1991) Rock blasting and overbreak control. (1st ed). US Department of Transportation, National Highway Institute.
29. Kuzu C, Ergin H (2005) An assessment of environmental impacts of quarry-blasting operation; A case study in Istanbul, Turkey. Environ Geol 48: 211-217.
30. Langefors U, Kihlström B (1978) The modern technique of rock blasting, 3rd edn. John Wiley, New York.
31. Latham JP, Ping Lu (1999) Development of an assessment system for the blastability of rock masses. Int J Rock Mech Min Sci 36: 41-55.
32. Lopez JC, Lopez JE (1995) Drilling and blasting of rocks. A. A Balkema, Rotterdam.
33. Lyall W, Eloranta J (2010) The effects of blasting on crushing and grinding efficiency and energy consumption. Online e-book at: http://www. elorantaassoc. com/Blasteffects_Lyall. pdf.
34. MacKenzie AS (1967) Optimum blasting. Proceedings of the 28th Annual Minnesota Mining Symposium, p 181-188.
35. Maity D, Saha A (2004) Damage assessment in structure from changes in static parameters using neural networks. Sadhana 29: 315-327.
36. Mandal SK, Singh MM, Dasgupta S (2008) Theoretical concept to understand plan and design smooth blasting pattern. Geotech Geol Eng 26: 399-416.
37. Meulenkamp F, Grima AM (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36: 29-39.
38. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45(8): 1446-1453.
39. Monjezi M, Yazdian A, Hesami SM (2006) Use of back propagation neural network to estimate burden in tunnel blasting. India. Mines Metals and Fuels 54: 424-429.
40. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37: 2637-2643.
41. Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5(3): 441-448.
42. Murthy VMSR, Dey K, Raitani R (2003) Prediction of overbreak in underground tunnel blasting a case study. J Can Tunn 109-115.
43. Musil M, Plesinger A (1996) Discrimination between local micro earthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps. Bull Seismol Soc Am 86(4): 1077-1090.
44. Nilson K, Kristiansen J (1996) Blasting, crushing, grinding: Optimization of an integrated comminution system. Rock Fragmentation by Blasting, FragBlast5, Rotterdam, Balkema, 269-283.
45. Oliver PH (2003) Changes to drill pattern and adequate inter-row delay time improve blasting performance. CIM Bulletin, Proquest Science Journals.
46. ÖzgenKaracan C (2007) Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams. Int J Coal Geol 72: 221-239.
47. Pompanna YV, Chikkareddy FV (1993) Improving blasting performance by air-decking; An Expriment. India, Proceeding of Drill and Blast, 63.
48. Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79: 1541-1552.
49. Rowlands DM (1989) Separating explosive charges with air gaps to improve fragmentation whilst reducing explosive usage. Proceeding of Second Large Open Pit Mining Conference, 105.
50. Sebastia M, Olmo IF, Irabien A (2003) Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures. Cem Concr Res 33: 1137-1146.
51. Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23: 249-262.
52. Stegemanna JA, Buenfeld NR (2003) Prediction of unconfined compressive strength of cement paste containing industrial wastes. Waste Manage 23: 321-332.
53. Stephen H, Chung, Graham, Mustoe GW (2007) Effects of particles shape and size distribution on stemming performance in blasting, Discrete Element Methods. Numerical Modeling of Discontinua Proceedings of the Third International Conference on Discrete Element Methods, 288-293.
54. TantaviMohammad M (2009) Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46: 426-431.
55. Tawadrous AS (2006) Evaluation of artificial neural networks as a reliable tool in blast design. Int Soc Explos Eng 1: 1-12.
56. Thote NR, Singh DP (2000) Effect of air decking on fragmentation: a few case studies of Indian mining. Explosive and Blasting Techniques, Rotterdam.
57. Tiryaki B (2008) Application of artificial neural networks for predicting the cutability of rocks by drag tools. Tunn Undergr Sp Technol 23: 273-280.
58. Tsekouras GJ, Koukoulis J, Mastorakis NE (2010) An optimized neural network for predicting settlements during tunneling excavation. WSEAS Trans Sys 9(12): 1153-1167.
59. Yong He, Sixin Xu, Kejun Z, Ting L, Yue Li (2008) A genetic-neural method of optimizing cut-off grade and grade of crude ore. Lect Notes Comput Sci 5264: 588-597.