Application of BP Neural Networks in Damage Prediction of Multilayer Medium Penetration and Blasting

Xiao Ling Liu; Ting Lei; Yong Yao

doi:10.4028/www.scientific.net/AMR.446-449.1417

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Reserch for Anchor Mechanical Character of Fully Grouted Bolt
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Application of BP Neural Networks in Damage Prediction of Multilayer Medium Penetration and Blasting
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 446-449Application of BP Neural Networks in Damage...

Application of BP Neural Networks in Damage Prediction of Multilayer Medium Penetration and Blasting

Abstract:

Back-propagation method (BP method) is the supervised learning algorithm that is the most widely and successfully used in feed forward network nowadays. This paper dealt with the penetration and blasting experimental data by BP Neural networks, including of the influence of the velocity and attack angles to damage of multilayer medium penetration and blasting. Through handling of the experimental data by the BP Network system, coupled effects of quantity of explosive and buried depth can be uncoupled. The curves of infundibular crater radius vs. quantity of explosive and infundibular crater depth vs. buried depth of explosive was given. Base on computing results, it is shown that the neural networks method can be used to predict the damage of multilayer medium penetration and blasting.

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Periodical:

Advanced Materials Research (Volumes 446-449)

Pages:

1417-1420

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.446-449.1417

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Online since:

January 2012

Authors:

Xiao Ling Liu, Ting Lei, Yong Yao

Keywords:

BP Neural Network (BPNN), Damage Prediction, Multilayer Medium

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References

[1] D.M. Yang ,X.M. Wang.Journal of Ballistics, Vol.16(2004), pp.83-87.

Google Scholar

[2] Q.K. Jin. Acta Armamentarii，Vol.27(2006),pp.10-14．

Google Scholar

[3] A.E. Ma. Explosion and Shock Waves,Vol.27(2008), pp.33-37.

Google Scholar

[4] AmaVolri S I. IEEE Trans. On Electronic Computers. EC-16(1967), pp.299-307.

Google Scholar

[5] Bryson A and Ho Y C. Applied Optimal Control. New York. 1969.

Google Scholar

[6] Werbos P.．New tools for prediction and analysis in the behavioral science．Harvard University. (1974)

Google Scholar

[7] Braun H ENZO-M．A hybrid approach for optimizing neural networks by evolution and learning. in Davider Y et al. Parallel problem solving from nature．Berlin. Springer-Verlag. 1994, pp.440-451.

DOI: 10.1007/3-540-58484-6_287

Google Scholar

[8] Z.W. Shen, Y.X. Sun, etc. Journal of Experimental Mechanics. Vol.16(2001), pp.264-269.

Google Scholar

[9] Q.J. Wang, W.B.Gu,etc. Engineering Blasting,Vol.9(2003), pp.7-11.

Google Scholar