Prediction of pile settlement using artificial neural networks based on standard penetration test data
Date
2009
Authors
Nejad, F.
Jaksa, M.
Kakhi, M.
McCabe, B.
Editors
Advisors
Journal Title
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Volume Title
Type:
Journal article
Citation
Computers and Geotechnics, 2009; 36(7):1125-1133
Statement of Responsibility
F. Pooya Nejad, Mark B. Jaksa, M. Kakhi and Bryan A. McCabe
Conference Name
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.
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Description
Copyright © 2009 Elsevier Ltd All rights reserved.