Browse > Article
http://dx.doi.org/10.9711/KTAJ.2013.15.2.081

A study on the fast prediction of the fragmentation zone using artificial neural network when a blasting occurs around a tunnel  

You, Kwang-Ho (University of Suwon, Dept. of Civil Engineering)
Jeon, Seok-Won (Seoul National Univ. Dept. of Urban and Geosystem Engineering)
Publication Information
Journal of Korean Tunnelling and Underground Space Association / v.15, no.2, 2013 , pp. 81-95 More about this Journal
Abstract
When collapse occurs due to explosion near a tunnel, fragmentation zone should be comprehended quickly to recover the function of the tunnel itself. In this study, a method to interpret explosion behavior and predict the fragmentation zone fast. For this purpose, the various 3D-meshes were generated using SolidWorks and explosion analyses were carried out using AUTODYN. The influence of explosion variables such as source location on fragmentation volume were examined by performing sensitivity analyses. Also, a training database for an artificial neural network analysis had been established and the optimal training model was selected, and the predicted results for fragmentation volume and radius were verified. The suggested method had demonstrated that it could be effective for the fast prediction of fragmentation zone.
Keywords
Blasting; Tunnel; Artificial neural network; Fragmentation zone;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Ahn, M.S., Ryu C.H., Park, J.N., Kwun J.A. (2001), "A study on the safe blast design to increase slope stability", The Journal of Korea Society for Explosives and Blasting Engineering, Vol. 19, No. 1, pp. 85-92.   과학기술학회마을
2 ANSYS, Inc. (2010), ANSYS AUTODYN, Ver. 13, ANSYS Inc., USA.
3 Cho, J.W., Yu, S.H., Jeon, S.W., Chang, S.H. (2008), "Numerical study on rock fragmentation by TBM disc cutter", Journal of Korea Tunnelling Association, Vol. 10, No. 2, pp. 139-152.   과학기술학회마을
4 Konya, C.J., Walter, E.J. (1991), Rock blasting and overbreak control, National Highway Institute, p. 430.
5 Math Works Inc. (2010), MATLAB : Neural Network $Toolbox^{TM}$ User's Guide, Ver. R2011b, Math Works Inc., p. 404.
6 Pao, Y. (1989), Adaptive pattern recognition and neural networks, Addison - Wesley, p. 309.
7 Park, J.W. (2012), Analysis of structure subjected to blast load using parallel and domain, Master Thesis, Hanyang University, p. 50
8 Riedel, W., Thoma, K., Hiermaier, S., Schmolinske, E. (1999), "Penetration of reinforced concrete by BETAB-500 numerical analysis using a new macroscopic concrete model for hydrocodes" The 9th Int. Sym. Interaction of the Effects of Munitions with Structures, Berlin, Germany, pp. 315-322.
9 SolidWorks Corp. (2011), SolidWorks 3D, Ver. 2011, SolidWorks Corp, Massachusetts, USA.
10 Wasserman, P.D. (1989), Neural computing : Theory and practice, Van Nostrand Reinhold Co., New York, USA, p. 230.
11 You, K.H., Kim, D.H. (2012), "A study on the influence of blasting location on tunnel fragmentation zone", 2012 Korean Geotechnical Society, Geo Expo, pp. 1611-1615.
12 You, K.H., Son, M.K. (2013), "Hauling time prediction of the muck generated by a blasting around a tunnel", Journal of Korean Tunnelling and Underground Space Association, Vol. 15, No. 1, pp. 33-47.   과학기술학회마을   DOI   ScienceOn
13 Shin, H.S., Kwon, Y.C. (2009), "Development of a window-shifting ANN training method for a quantitative rock classification in unsampled rock zone", Journal of Korea Tunnelling Association, Vol. 11, No. 2, pp. 151-162.   과학기술학회마을
14 You. K.H., Song, W.Y. (2012), "A case study on a tunnel back analysis to minimize the uncertainty of ground properties based on artificial neural network", Journal of Korean Tunnelling and Underground Space Association, Vol. 14, No. 1, pp. 37-53.   과학기술학회마을   DOI   ScienceOn