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• W2073949043 abstract "Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three-phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed-forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd." @default.
• W2073949043 created "2016-06-24" @default.
• W2073949043 creator A5077298121 @default.
• W2073949043 date "2007-09-14" @default.
• W2073949043 modified "2023-10-02" @default.
• W2073949043 title "Estimation of effective stress parameter of unsaturated soils by using artificial neural networks" @default.
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• W2073949043 doi "https://doi.org/10.1002/nag.660" @default.
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