NDLI: Neural computing models for prediction of permeability coefficient of coarse-grained soils

Content Provider	Springer Nature Link
Author	Yilmaz, Işık Marschalko, Marian Bednarik, Martin Kaynar, Oğuz Fojtova, Lucie
Copyright Year	2011
Abstract	Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.
Starting Page	957
Ending Page	968
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-02-17
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	ANN MLP RBF ANFIS Soft computing Soils Grain size Permeability Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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