›› 2003, Vol. 24 ›› Issue (2): 225-228.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Predicting terrane crack depth of complex fire hole using artificial neural network

CHANG Bin, LI Ning, ZHANG Ping   

  1. Institute of Rock and Soil Engineering, Xi’an University of Technology, Xi’an 710048, China
  • Received:2002-04-08 Online:2003-02-10 Published:2014-08-19

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Abstract: By using ANN and its powerful nonlinear mapping ability and combining production of FEM dynamic analysis, a new way to predicting terrane crack depth of complex fire hole is offered. The result has indicated that the ANN can incarnate the connotative nonlinear relation between explosive parameters and terrane crack depth fully. This method has higher fitting precision, and its result coinsides with the result of FEM.

Key words: complex fire hole, BP network, speed of study, network training, local minimum

CLC Number: 

  • TU 43
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
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