›› 2006, Vol. 27 ›› Issue (S1): 686-690.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Evaluation of slope stability based on improved BP neural network algorithm

SUN Yong-fu1,2, LIU Hong-jun3, XUE Xin-hua4   

  1. 1. College of Marine Geoscience，Ocean University of China，Qingdao 266003, China；2. First Institute of Oceanography, SOA, Qingdao 266061, China； 3. College of Environmental Science and Engineering，Ocean University of China，Qingdao 266003, China； 4. Institute of Geotechnical Engineering，Zhejiang University，Hangzhou 310027, China
  • Received:2006-08-09 Published:2006-12-15

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Abstract: The factors, which control and affect slope stability, are random and uncertain. As artificial neural network can consider both quantitative and qualitative factors, it is suitable to solve the uncertain problems such as the estimation of slope stability. Combining the parallel searching structure of genetic algorithms with the probabilistic jumping property of simulated annealing, a genetic algorithm and simulated annealing algorithm（GASA）hybrid strategy is proposed for the weights learning of BP networks to speed up training process, improve starting solutions robustness and generalization ability and overcome local minimum. With the consideration of the influencing factors of the stability of the slope, a BP neural network model based on GASA algorithm is established for predicting slope stability and a lot of engineering data are collected to train and examine the model. Some valuable results are derived at last.

Key words: genetic algorithm, simulated annealing, GASA hybrid strateqy, slope stability, evaluation

CLC Number: 

  • TU 457
  • 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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