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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 628496, 10 pages
Research Article

Prediction of Ultimate Bearing Capacity of Cohesionless Soils Using Soft Computing Techniques

1Department of Civil Engineering, TKM College of Engineering, Kerala, Kollam 691005, India
2Department of Civil Engineering, National Institute of Technology, Kerala, Calicut 673601, India
3Department of Civil Engineering, National Institute of Technology, Karnataka, Surathkal, Mangalore 575025, India
4Department of Civil Engineering, College of Engineering, Thiruvananthapuram 695016, India

Received 31 July 2011; Accepted 7 September 2011

Academic Editor: M. Abbod

Copyright © 2012 S. Adarsh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study examines the potential of two soft computing techniques, namely, support vector machines (SVMs) and genetic programming (GP), to predict ultimate bearing capacity of cohesionless soils beneath shallow foundations. The width of footing ( 𝐡 ), depth of footing ( 𝐷 ), the length-to-width ratio ( 𝐿 / 𝐡 ) of footings, density of soil ( 𝛾 or 𝛾 ξ…ž ), angle of internal friction ( Ξ¦ ), and so forth were used as model input parameters to predict ultimate bearing capacity ( π‘ž 𝑒 ). The results of present models were compared with those obtained by three theoretical approaches, artificial neural networks (ANNs), and fuzzy inference system (FIS) reported in the literature. The statistical evaluation of results shows that the presently applied paradigms are better than the theoretical approaches and are competing well with the other soft computing techniques. The performance evaluation of GP model results based on multiple error criteria confirms that GP is very efficient in accurate prediction of ultimate bearing capacity cohesionless soils when compared with other models considered in this study.