Paper Title:
Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network
  Abstract

Based on BP neural network, this paper had a prediction on ultimate bearing capacity of prestressed pipe pile. Taking pile diameter, effective pile length, ultimate average value of friction standard value, ultimate average value of end resistance standard value as influences factors, the prediction model of pile bearing capacity based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of pile bearing capacity compared with the observed value for 70 groups of independent variables training BP neural network model was 3.1498%; And the average value of absolute value for the relative error of prediction value of pile bearing capacity compared with the observed value for 10 groups of independent variables validating BP neural network model was 3.50126% whose precision was better than ANFIS’5.32293%. The following conclusion can be drawn that, the prediction model of ultimate bearing capacity of prestressed pipe pile based on BP neural network is feasible.

  Info
Periodical
Chapter
Chapter 2: Simulation and Engineering Optimization
Edited by
Di Zheng, Yiqiang Wang, Yi-Min Deng, Aibing Yu and Weihua Li
Pages
228-231
DOI
10.4028/www.scientific.net/AMM.101-102.228
Citation
J. P. Jiang, "Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network", Applied Mechanics and Materials, Vols. 101-102, pp. 228-231, 2012
Online since
September 2011
Authors
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Price
$32.00
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