›› 2006, Vol. 27 ›› Issue (2): 277-281.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Predicting durability of geosynthetics by neural networks

MIN Xing, ZHANG Meng-xi, TAO Chen   

  1. Department of Civil Engineering, Shanghai University, Shanghai 200072, China
  • Received:2004-07-01 Online:2006-02-10 Published:2013-10-25

Abstract: The ANN model has great potential in predicting durability of geasynthetics, because of its unique learning. training and prediction characteristics. By contrast to the regression method , the ANN method doesn’t demand the regulation of the experiment data. Based on 31 groups of experimental data obtained from aging tests of non-woven polyester, a BP neural network model for predicting durability of geosynthetics is developed. The model proposed has four parameters (the temperature, relative humidity, aging time and ultraviolet radiation) at input layer and two parameters (strength and percentage elongation) at output layer. The comparison between the prediction values and experimental results shows that the model proposed has high accuracy for predicting durability of geocynthetics.

Key words: neural network, geosynthetics, durability, strength, percentage elongation

CLC Number: 

  • TU 443
  • 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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