Title : ( Prediction of Pile Behavior Using Artificial Neural Networks Based on Standard Penetration Test Data )
Authors: Fereydoon Pooya Nejad , Mark B. Jaksa ,Abstract
ABSTRACT: This paper presents an artificial neural network (ANN) model for the prediction of non-linear behavior of vertically loaded piles based on the results of standard penetration test (SPT) data. The geotechnical literature has included many methods, both theoretical and experimental, to predict pile behavior. Most of the available methods simplify the problem by incorporating several assumptions associated with the factors that affect pile behavior. With respect to the design of pile foundations, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. Approximately, 1,000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design.
Keywords
, Pile Behavior, Artificial Neural Networks, SPT@inproceedings{paperid:1020549,
author = {Pooya Nejad, Fereydoon and Mark B. Jaksa},
title = {Prediction of Pile Behavior Using Artificial Neural Networks Based on Standard Penetration Test Data},
booktitle = {13th International Conference of the International Association for Computer Methods and Advances in Geomechanics},
year = {2011},
location = {Melbourne, AUSTRALIA},
keywords = {Pile Behavior; Artificial Neural Networks;SPT},
}
%0 Conference Proceedings
%T Prediction of Pile Behavior Using Artificial Neural Networks Based on Standard Penetration Test Data
%A Pooya Nejad, Fereydoon
%A Mark B. Jaksa
%J 13th International Conference of the International Association for Computer Methods and Advances in Geomechanics
%D 2011