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Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

DSpace at IIT Bombay

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Title Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils
 
Creator ERZIN, Y
GUMASTE, SD
GUPTA, AK
SINGH, DN
 
Subject pedo-transfer functions
uniaxial compressive strength
natural soft clays
pedotransfer functions
fuzzy model
geotechnical centrifuge
electrical-resistivity
sand-bentonite
water-content
prediction
artificial neural networks
fine-grained soils
centrifuge modeling
falling-head tests
hydraulic conductivity
 
Description This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-a-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.
 
Publisher NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS
 
Date 2011-08-19T09:37:53Z
2011-12-26T12:56:10Z
2011-12-27T05:44:27Z
2011-08-19T09:37:53Z
2011-12-26T12:56:10Z
2011-12-27T05:44:27Z
2009
 
Type Article
 
Identifier CANADIAN GEOTECHNICAL JOURNAL, 46(8), 955-968
0008-3674
http://dx.doi.org/10.1139/T09-035
http://dspace.library.iitb.ac.in/xmlui/handle/10054/10308
http://hdl.handle.net/10054/10308
 
Language en