›› 2009, Vol. 30 ›› Issue (11): 3460-3463.

• Geotechnical Engineering • Previous Articles     Next Articles

Slope stability evaluation based on generalized regression neural network

LAN Hai-tao1，LI Qian1，HAN Chun-yu2   

  1. 1.Engineering Faculty, China University of Geosciences, Wuhan 430074, China；2.School of Geosciences and Environmental Engineering, Central South University, Changsha 410083, China
  • Received:2008-06-30 Online:2009-11-10 Published:2010-01-07

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Abstract:

The slope failure is the common geological hazard and there are lots of methods to evaluate this geological hazard, which expose some disadvantages during the use process. In order to solve these problems, a model of generalized regression neural network is made to meet the slope stability evaluation, which uses the neural network tool kit in Matlab to analyze and calculate the input and output data. Then the reliability and feasibility of the model is trained and tested. It is concluded that the output data agrees well with the actual data by using the model that can overcome the disadvantage of BP neural network used before, if the smoothing factor is chosen appropriately. So the good engineering prospect will be expected when the slope stability evaluation is based on generalized regression neural network.

Key words: generalized regression neural network, slope stability, smoothing factor, neural network model

CLC Number: 

  • O 242
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