Burden prediction in blasting operation using rock geomechanical properties

Arabian Journal of Geosciences

Volumn 5, Issue 5, 2012, Pages 1031-1037

  Rezaei, Mohammad ^a   Monjezi, Masoud ^a   Moghaddam, Saeed Ghorbani ^a   Farzaneh, Farhad ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Artificial neural network; Blasting; Burden; Mouteh gold mine; Statistical model

Indexed keywords

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; BLASTING; DATABASE; ERROR ANALYSIS; GEOCHEMICAL SURVEY; GEOMECHANICS; GOLD MINE; NUMERICAL MODEL; PARAMETERIZATION; PERFORMANCE ASSESSMENT; ROCK; ROCK PROPERTY;

IRAN;

EID: 84885021301     PISSN: 18667511     EISSN: 18667538     Source Type: Journal    
DOI: 10.1007/s12517-010-0269-0     Document Type: Article

References (38)

1. Adhikari G. R (1999) Burden calculation for partially changed blast design conditions. Int J Rock Mech Min Sci 36:235-256
2. Alvarez Grima M, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339-349
3. Ash R. L (1973) The influence of geological discontinuities on rock blasting. Ph.D. thesis, University of Minnesota
4. Basheer I. A, HajmeerM(2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 43:3-31
5. Cai J. G, Zhao J, (1997) Use of neural networks in rock tunneling. In: Proceedings of computing methods and advances in geomechanics, IACMAG, China, pp 613-18
6. Chungsik Y., Kim JM (2007) Tunneling performance prediction using an integrated GIS and neural network. Comput Geotech 34:19-30
7. Demuth H., Beal M, Hagan M (1996) Neural network tool box 5 user's guide. The Math Work, Natick
8. Ermini L., Catani F, Casagli N(2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327-343
9. 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
10. Finol J., Guo YK, Dong Jing X (2001) A rule-based fuzzy model for the prediction of petrophysical rock parameters. J Petrol Sci Eng 29:97-113
11. 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
12. Gomez H., Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11-27
13. Haykin S. (1999) Neural networks, a comprehensive foundation, USA, 2nd edn. Prentice Hall, USA
14. Hustrulid W. (1999) Blasting principles for open pit mining, vol 1. Balkema, Rotterdam
15. ISRM (International Society for Rock Mechanics) (1981) Suggested methods for determining hardness and abrasiveness of rocks. In: Brown E. T (ed) Rock characterization, testing and monitoring-ISRM suggested methods. Pergamon, Oxford, pp 95-96
16. Jennrich R. I (1995) An introduction to computational statistics-regression analysis. Prentice-Hall, Englewood Cliffs
17. Jimeno C. L, Jimeno EL, Carced FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam
18. Jong Y. H, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41:533-538
19. Khandelwal M., Singh TN (2002) Prediction of waste dump stability by an intelligent approach. In: Proceedings of the national symposium on new equipment-new technology, management and safety, ENTMS, Bhubaneshwar, pp 38-45
20. Khandelwal M., Roy MP, Singh PK (2004) Application of artificial neural network in mining industry. Ind Min Eng J 43:19-23
21. Lee S., Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820-833
22. Maity D., Saha A (2004) Damage assessment in structure from changes in static parameters using neural networks. Sadhana 29:315-327
23. Maulenkamp F., Grima MA (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
24. Monjezi M., Dehghani H (2008) Evaluation of effect of blasting pattern parameters on backbreak using neural networks. Int J Rock Mech Min Sci 45:1446-1453
25. Monjezi M., Singh TN, Khandelwal M, Sinha S, Singh V, Hosseini I (2006) Prediction and analysis of blast parameters using artificial neural network. Noise Vib Worldw 37:8-16
26. Monjezi M., Dehghan H, Samimi Namin F (2007) Application of TOPSIS method in controlling fly rock in blasting operations. In: Proceedings of the 7th international science conference SGEM, Sofia, Bulgaria pp 41-9
27. Monjezi M., Rezaei M, Yazdian Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46:1273-1280
28. Monjezi M., Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637-2643
29. Neaupane K. M, Achet SH (2004) Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74:213-236
30. Neaupane K. M, Adhikari NR (2006) Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunn Undergr Space Technol 21:151-159
31. Qiang W., Siyuan Y, Jia Y (2008) The prediction of size-limited structures in a coal mine using artificial neural networks. Int J Rock Mech Min Sci 45:999-1006
32. Singh V. K, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rock. Int J Rock Mech Min Sci 38:269-284
33. Singh T. N, Kanchan R, Saigal K, Verma AK (2004) Prediction of Pwave velocity and anisotropic properties of rock using artificial neural networks technique. J Sci Ind Res 63:32-38
34. Sonmez H., Gokceoglua C, Nefeslioglub HA, Kayabasic A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224-235
35. Tawadrous A. S (2006) Evaluation of artificial neural networks as a reliable tool in blast design. In: Proceedings of the 32nd annual conference on explosives and blasting techniques. International Society of Explosives Engineers, Dallas
36. Tzamos S., Sofianos AI (2006) Extending the Q system's prediction of support in tunnels employing fuzzy logic and extra parameters. Int J Rock Mech Min Sci 43:938-949
37. Yesilnacar E., Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251-266
38. Yong L. (2005) Underground blast induced ground shock and its modeling using artificial neural network. Comput Geotech 32:164-178