Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6761
Title: The use of neural networks for the prediction of cone penetration resistance of silty sands
Authors: Erzin, Yusuf
Ecemiş, Nurhan
Ecemiş, Nurhan
Keywords: Cone penetration resistance
Artificial neural networks
Silty sand
Horizontal coefficient of consolidation
Issue Date: Dec-2017
Publisher: Springer Verlag
Source: Erzin, Y., and Ecemiş, N. (2017). The use of neural networks for the prediction of cone penetration resistance of silty sands. Neural Computing and Applications, 28, 727-736. doi:10.1007/s00521-016-2371-z
Abstract: In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.
URI: http://doi.org/10.1007/s00521-016-2371-z
http://hdl.handle.net/11147/6761
ISSN: 0941-0643
1433-3058
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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