Title : ( Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data )
Authors: Fereydoon Pooya Nejad , Mark B. Jaksa ,
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Abstract
. In this paper, an ANN model is developed for predicting pile settlement based on the results of cone penetration test (CPT) data. Approximately, 300 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 compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.
Keywords
, Pile load test, Pile foundation, Settlement, Neural networks
@inproceedings{paperid:1015192,
author = {Pooya Nejad, Fereydoon and Mark B. Jaksa},
title = {Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data},
booktitle = {GeoFlorda Conference 2010},
year = {2010},
location = {West Palm Beach, USA},
keywords = {Pile load test; Pile foundation; Settlement; Neural networks},
}
%0 Conference Proceedings
%T Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
%A Pooya Nejad, Fereydoon
%A Mark B. Jaksa
%J GeoFlorda Conference 2010
%D 2010
