›› 2003, Vol. 24 ›› Issue (4): 634-638.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Dam deformation forecasting by evolving artificial neural network

LI Shou-ju 1, LIU Ying-xi1, LIU Yu-jing2   

  1. 1.State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology, Dalian 116023, China; 2. Basic Science Department, Changchun University of Technology, Changchun 130012, China
  • Received:2002-04-10 Online:2003-08-11 Published:2014-08-19

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Abstract: Based on the measured data of Fengman dam deformations for many years, the artificial neural network evolved by a genetic algorithm was adopted for forecasting the dam deformation. The shortcomings of the traditional BP artificial neural network lie in the slowness in the convergence rate and the weakness in the generalization ability. Compared with the popular multi-factor regression model and BP artificial neural network, the forecasting model based on artificial neural network evolved by a genetic algorithm had the characteristics of accurately forecasting and global convergence. The practical application to Fengman concrete dam shows that, compared with a commonly used statistical method, the forecasting method proposed can obviously enhance the forecasting precision of dam deformation.

Key words: artificial neural network, deformation forecasting, concrete dam, genetic algorithm

CLC Number: 

  • TU454
  • 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.
  • 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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