Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5761
Title: The use of neural networks for CPT-based liquefaction screening
Authors: Erzin, Yusuf
Ecemiş, Nurhan
Ecemiş, Nurhan
Keywords: Artificial neural networks
Cone penetration resistance
Liquefaction resistance
Ottowa sand
Issue Date: 2014
Publisher: Springer Verlag
Source: Erzin, Y., and Ecemiş, N. (2014). The use of neural networks for CPT-based liquefaction screening. Bulletin of Engineering Geology and the Environment, 74(1), 103-116. doi:10.1007/s10064-014-0606-8
Abstract: This study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.
URI: https://doi.org/10.1007/s10064-014-0606-8
http://hdl.handle.net/11147/5761
ISSN: 1435-9529
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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