✦ LIBER ✦

Prediction of residual friction angle of clays using artificial neural network

✍ Scribed by Sarat Kumar Das; Prabir Kumar Basudhar

Publisher: Elsevier Science
Year: 2008
Tongue: English
Weight: 192 KB
Volume: 100
Category: Article
ISSN: 0013-7952
DOI: 10.1016/j.enggeo.2008.03.001

No coin nor oath required. For personal study only.

✦ Synopsis

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 (ϕ r ) with index properties of soil. This paper presents a neural network model to predict the residual friction angle 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.

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