Prediction of pile settlement using artificial neural networks based on standard penetration test data
Nejad, F. Pooya
Jaksa, Mark B.
McCabe, Bryan A.
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Pooya Nejad, F., Jaksa, Mark B., Kakhi, M., & McCabe, Bryan A. (2009). Prediction of pile settlement using artificial neural networks based on standard penetration test data. Computers and Geotechnics, 36(7), 1125-1133. doi: http://dx.doi.org/10.1016/j.compgeo.2009.04.003
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. (C) 2009 Elsevier Ltd. All rights reserved.