Prediction of pile settlement using artificial neural networks based on cone penetration test data
Date
2010
Authors
Nejad, F.
Jaksa, M.
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Conference paper
Citation
GeoFlorida 2010 : advances in analysis, modeling, & design : proceedings of the GeoFlorida 2010 Conference ; February 20-24, 2010, West Palm Beach, Florida / D. Fratta, A. J. Puppala, B. Muhunthan (eds.): pp.1432-1441
Statement of Responsibility
F. Pooya Nejad and Mark B. Jaksa
Conference Name
GeoFlorida Conference (2010 : West Palm Beach, Florida)
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 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. © 2010 ASCE.
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© 2010 ASCE