Prediction of Safe Bearing Capacity of Noncohesive Soil in Arid Zone Using Artificial Neural Networks
Publication: International Journal of Geomechanics
Volume 16, Issue 2
Abstract
Estimation of safe bearing capacity (SBC) of noncohesive soil based on Indian Standard Code requires a lot of field work, viz, conducting direct shear tests to determine cohesion and angle of internal friction, performing the standard penetration test to determine the of soil, and finding the relative density and dry density of soil. The present study does away with these soil parameters except for the design value of density and uses the results of sieve analysis to determine the SBC of soil. This research proposes the use of artificial neural network (ANN) to predict the SBC of noncohesive soil as a function of coefficient of curvature, coefficient of uniformity, and design value of soil density along with footing dimensions such as depth, width and diameter (in case of circular footing), and the desired settlement of the footing. The results show that ANN is a useful technique in estimating SBC of noncohesive soil using parameters derived from sieve analysis results and match closely from the results derived from the traditional methods based on Terzaghi’s theories.
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Acknowledgments
The authors wish to express their thanks to Civil Engineering Department, Birla Institute of Technology and Science Pilani, Pilani Campus for providing the data required for this study.
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Information & Authors
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Published In
International Journal of Geomechanics
Volume 16 • Issue 2 • April 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Dec 13, 2013
Accepted: Mar 3, 2015
Published online: Jun 24, 2015
Discussion open until: Nov 24, 2015
Published in print: Apr 1, 2016
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