- About this Journal ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 628496, 10 pages
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.
- K. Terzaghi, Theoretical Soil Mechanics, John Wiley & Sons, New York, NY, USA, 1943.
- G. G. Meyerhof, “Some recent research on the bearing capacity of foundations,” Canadian Geotechnical Journal, vol. 1, no. 1, pp. 16–26, 1963.
- J. B. Hansen, “A general formula for bearing capacity,” Danish Geotechnical Institute Bulletin, vol. 11, 1961.
- A. S. Vesic, “Analysis of ultimate loads of shallow foundations,” Journal of Soil Mechanics and Foundation Division, vol. 99, no. 1, pp. 45–73, 1973.
- D. Padmini, K. Ilamparuthi, and K. P. Sudheer, “Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models,” Computers and Geotechnics, vol. 35, no. 1, pp. 33–46, 2008.
- M. A. Shahin, H. R. Maier, and M. B. Jaksa, “Artificial neural network applications in Geotechnical Eng,” Australian Geomechanics, vol. 36, no. 1, pp. 49–62, 2001.
- P. Samui, “Prediction of friction capacity of driven piles in clay using the support vector machine,” Canadian Geotechnical Journal, vol. 45, no. 2, pp. 288–295, 2008.
- P. Samui and T. G. Sitharam, “Least-square support vector machine applied to settlement of shallow foundations on cohesionless soils,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 32, no. 17, pp. 2033–2043, 2008.
- P. Samui, “Slope stability analysis: a support vector machine approach,” Environmental Geology, vol. 56, no. 2, pp. 255–267, 2008.
- M. Pal, “Support vector machines-based modelling of seismic liquefaction potential,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 30, no. 10, pp. 983–996, 2006.
- J. H. Holland, Adaptation in Natural and Artificial System, Ann Arbour Science Press, Ann Arbor, Mich, USA, 1975.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.
- J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Mass, USA, 1992.
- B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in 5th Annual ACM Workshop on COLT, D. Haussler, Ed., pp. 144–152, ACM Press, Pittsburgh, Pa, USA, 1992.
- V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
- C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
- A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004.
- S. Rajasekaran, S. Gayathri, and T. L. Lee, “Support vector regression methodology for storm surge predictions,” Ocean Engineering, vol. 35, no. 16, pp. 1578–1587, 2008.
- S. T. Khu, S. Y. Liong, V. Babovic, H. Madsen, and N. Muttil, “Genetic programming and its application in real-time runoff forecasting,” Journal of the American Water Resources Association, vol. 37, no. 2, pp. 439–451, 2001.
- A. A. Javadi, M. Rezania, and M. M. Nezhad, “Evaluation of liquefaction induced lateral displacements using genetic programming,” Computers and Geotechnics, vol. 33, no. 4-5, pp. 222–233, 2006.
- A. Johari, G. Habibagahi, and A. Ghahramani, “Prediction of soil-water characteristic curve using genetic programming,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 132, no. 5, pp. 661–665, 2006.
- B. S. Narendra, P. V. Sivapullaiah, S. Suresh, and S. N. Omkar, “Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: a comparative study,” Computers and Geotechnics, vol. 33, no. 3, pp. 196–208, 2006.
- S. B. Charhate, M. C. Deo, and S. N. Londhe, “Inverse modeling to derive wind parameters from wave measurements,” Applied Ocean Research, vol. 30, no. 2, pp. 120–129, 2008.
- S. Gaur and M. C. Deo, “Real-time wave forecasting using genetic programming,” Ocean Engineering, vol. 35, no. 11-12, pp. 1166–1172, 2008.
- K. Ustoorikar and M. C. Deo, “Filling up gaps in wave data with genetic programming,” Marine Structures, vol. 21, no. 2-3, pp. 177–195, 2008.
- S. S. Kashid, S. Ghosh, and R. Maity, “Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection,” Journal of Hydrology, vol. 395, no. 1-2, pp. 23–38, 2010.
- I. H. Witten and E. Frank, Data Mining, Morgan Kaufmann, San Francisco, Calif, USA, 2000.
- P. S. Yu, S. T. Chen, and I. F. Chang, “Support vector regression for real-time flood stage forecasting,” Journal of Hydrology, vol. 328, no. 3-4, pp. 704–716, 2006.
- F. D. Francone, Discipulus Owner’s Manual Version 3.0 DRAFT, Machine Learning Technologies, Littleton, Colo, USA, 1998.