Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Load-settlement behavior modeling of single piles using artificial neural networks and CPT data
Author: Pooya Nejad, F.
Jaksa, M.
Citation: Computers and Geotechnics, 2017; 89:9-21
Publisher: Elsevier
Issue Date: 2017
ISSN: 0266-352X
Statement of
F. Pooya Nejad, Mark B. Jaksa
Abstract: Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design.
Keywords: Pile behavior; pile load test; neural networks; CPT
Rights: © 2017 Published by Elsevier Ltd.
RMID: 0030070149
DOI: 10.1016/j.compgeo.2017.04.003
Appears in Collections: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.