AXIAL CAPACITY PREDICTION FOR DRIVEN PILES USING ANN: MODEL COMPARISON

A comparison of three different models using back-propagation neural network for estimation of pile bearing capacity from dynamic stress wave data was made. The pile bearing capacity predicted by TNOWAVE was employed as the desired output in training. The study shows that the neural network models generally predict total bearing capacity more favorably if both the stress wave data and the properties of the driven pile are considered as the input parameters. In addition, better selection of input parameters rather than the increase number of input parameters will improve the accuracy of the prediction.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0784407444
• Corporate Authors:

  American Society of Civil Engineers

  Geo Institute, 1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Lok, TMH
  • Che, W F
• Conference:
  • Geotechnical Engineering for Transportation Projects, Geo-Trans 2004
  • Location: Los Angeles, California
  • Date: 2004-7-27 to 2004-7-31
• Publication Date: 2004

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: 8 p.
• Serial:
  • ASCE Geotechnical Special Publication
  • Issue Number: 126
  • Publisher: American Society of Civil Engineers

Subject/Index Terms

• TRT Terms: Axial loads; Backpropagation; Bearing capacity; Data collection; Neural networks; Pile driving; Piles (Supports); Stresses; Training; Waveform analysis
• Subject Areas: Bridges and other structures; Design; Education and Training; Highways; I24: Design of Bridges and Retaining Walls;

Filing Info

• Accession Number: 00987961
• Record Type: Publication
• ISBN: 0784407444
• Report/Paper Numbers: Volume 1
• Files: TRIS
• Created Date: Mar 23 2005 12:00AM