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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1134

Title: Prediction of Residual Friction Angle of Clays using Artificial Neural Network
Authors: Das, S K
Basudhar, P K
Keywords: Clays;
Shear strength;
neural network;
statistical analysis.
Issue Date: 2008
Publisher: Elsevier
Citation: Engineering Geology, Volume 100, Issues 3-4, 1 September 2008, Pages 142-145
Abstract: The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (r) with index properties of soil. This paper presents a neural network model to predict the residual friction angles based on clay fraction and Atterberg‟s limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters.
URI: http://dx.doi.org/10.1016/j.enggeo.2008.03.001
http://hdl.handle.net/2080/1134
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