Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Prediction of pile settlement using artificial neural networks based on standard penetration test data
Author: Nejad, F.
Jaksa, M.
Kakhi, M.
McCabe, B.
Citation: Computers and Geotechnics, 2009; 36(7):1125-1133
Publisher: Elsevier Sci Ltd
Issue Date: 2009
ISSN: 0266-352X
Statement of
F. Pooya Nejad, Mark B. Jaksa, M. Kakhi and Bryan A. McCabe
Abstract: In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 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. © 2009 Elsevier Ltd. All rights reserved.
Keywords: Pile load test
Pile foundation
Neural networks
Description: Copyright © 2009 Elsevier Ltd All rights reserved.
DOI: 10.1016/j.compgeo.2009.04.003
Published version:
Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.