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dc.contributor.authorNejad, F. Pooya
dc.contributor.authorJaksa, Mark B.
dc.contributor.authorKakhi, M.
dc.contributor.authorMcCabe, Bryan A.
dc.identifier.citationPooya 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:
dc.description.abstractIn 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.en_IE
dc.relation.ispartofComputers and Geotechnicsen
dc.subjectPile load testen_IE
dc.subjectPile foundationen_IE
dc.subjectNeural networksen_IE
dc.subjectCivil engineeringen_IE
dc.titlePrediction of pile settlement using artificial neural networks based on standard penetration test dataen_IE
dc.local.contactBryan Mccabe, Dept. Of Civil Engineering, Coll Engineering & Informatics, Room Eng-1040, Nui Galway. 2021 Email:

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