Journal
ENGINEERING GEOLOGY
Volume 100, Issue 3-4, Pages 142-145Publisher
ELSEVIER
DOI: 10.1016/j.enggeo.2008.03.001
Keywords
Clays; Shear strength; Neural network; Statistical analysis
Ask authors/readers for more resources
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 (phi(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. (C) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available