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G. Turk, J. Logar and B. Majes

Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia

doi:10.4203/ccp.62.5.5

G. Turk, J. Logar, B. Majes, "Modelling the Soil Behaviour in Uniaxial Strain Conditions by Neural Networks", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 139-146, 1999. doi:10.4203/ccp.62.5.5

Abstract

The feed-forward neural network was used to simulate the behaviour of soil samples in uniaxial strain conditions, i.e. to predict the oedometer test results only on the basis of the basic soil properties. Artificial neural network was trained using the database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and stress-strain soil behaviour and demonstrates that artificial neural network can be successfully used as an effective alternative empirical material model.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 62 ARTIFICIAL INTELLIGENCE APPLICATIONS IN CIVIL AND STRUCTURAL ENGINEERING Edited by: B. Kumar and B.H.V. Topping Paper V.5 Modelling the Soil Behaviour in Uniaxial Strain Conditions by Neural Networks G. Turk, J. Logar and B. Majes Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia doi:10.4203/ccp.62.5.5 purchase the full-text of this paper Full Bibliographic Reference for this paper G. Turk, J. Logar, B. Majes, "Modelling the Soil Behaviour in Uniaxial Strain Conditions by Neural Networks", in B. Kumar, B.H.V. Topping, (Editors), "Artificial Intelligence Applications in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 139-146, 1999. doi:10.4203/ccp.62.5.5 Abstract The feed-forward neural network was used to simulate the behaviour of soil samples in uniaxial strain conditions, i.e. to predict the oedometer test results only on the basis of the basic soil properties. Artificial neural network was trained using the database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and stress-strain soil behaviour and demonstrates that artificial neural network can be successfully used as an effective alternative empirical material model. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £50 +P&P)
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