Pile Samples Classification Method Based on the Self-Organizing Map Neural Network

To reduce the noise in learning samples while using BP neural network to predict the bearing capacity of pile foundation, a self-organizing map neural network was adopted to classify the collected pile samples in the paper. Firstly, to maintain the SOM network at a stable situation, pile samples were discriminated into symbol codes and character codes, and a new coding model of pile character was established, by which a SOM neural network's weight formula of reduction was derived. Then, clustering of pile samples were shown by calibrating the maximum response cell of the self-organizing map neural network. Finally, case studies using the clustered samples as input vector to a BP network were presented, and the results showed that it was a good alternative approach for estimating the bearing capacity of pile foundation by using the improved solution with the characters of simplicity.


  • English

Media Info

  • Media Type: Print
  • Features: Figures; References;
  • Pagination: pp 171-176
  • Monograph Title: Recent Advancement in Soil Behavior, In Situ Test Methods, Pile Foundations and Tunneling: Selected Papers From the 2009 GeoHunan International Conference

Subject/Index Terms

Filing Info

  • Accession Number: 01140955
  • Record Type: Publication
  • ISBN: 9780784410448
  • Files: TRIS
  • Created Date: Sep 18 2009 1:57PM