Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Random Forest Based Approach

Document Type : Research Papers

Authors

1 Department of Faculty Engineering, Imam Khomeini International University, Qazvin, Iran

2 Imam Khomeini international university

3 Department of Architecture and Energy, Faculty of Architecture and Urbanism, University of Art

Abstract

Determining the ultimate bearing capacity (UBC) is vital for design of shallow foundations. Recently, soft computing methods (i.e. artificial neural networks and support vector machines) have been used for this purpose. In this paper, Random Forest (RF) is utilized as a tree-based ensemble classifier for predicting the UBC of shallow foundations on cohesionless soils. The inputs of model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and internal friction angle (ϕ). A set of 112 load tests data were used to calibrate and test the developed RF-based model. The used data set consists of 47 full-scale observations and 65 small-scale laboratory footing load tests. To demonstrate the efficiency of proposed RF-based model, the results are compared with some popular classical formulas that are most commonly used for determining the UBC. The results show the efficiency and capabilities of the proposed RF-based model as a practical tool in evaluating the UBC of shallow foundations in a fast and accurate way.

Keywords

Main Subjects

References

 
Meyerhof, G.G. (1963). "Some recent research on the bearing capacity of foundations", Canadian Geotechnical Journal, 1(1), 16-26.
Padmini, D., Ilamparuthi, K., and Sudheer, K. (2008). "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models", Computers and Geotechnics, 35(1), 33-46.
Samui, P. (2012). "Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil", International Journal for Numerical and Analytical Methods in Geomechanics, 36(1), 100-110.
Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2004). "Data division for developing neural networks applied to geotechnical engineering", Journal of Computing in Civil Engineering, 18(2), 105-114.
Shahnazari, H. and Tutunchian, M.A. (2012). "Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach", KSCE Journal of Civil Engineering, 16(6), 950-957.
Tatsuoka, F., Okahara, M., Tanaka, T., Tani, K., Morimoto, T. and Siddiquee, M. (1991). "Progressive failure and particle size effect in bearing capacity of a footing on sand", Proceedings of Geotechnic Engrgineering Congress, ASCE Geotechnical Special Publication, 788-802.
Terzaghi, K. (1943). Theoretical soil mechanics, John Wiley & Sons, New York.
Tsai, H.-C., Tyan, Y.-Y., Wu, Y.-W. and Lin, Y.-H. (2013). "Determining ultimate bearing capacity of shallow foundations using a genetic programming system", Neural Computing and Applications, 23(7-8), 2073-2084.
Vesic, A.S. (1974). "Analysis of ultimate loads of shallow foundations", International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 11(11), A230, Pergamon.
Witten, I.H. and Frank, E. (2005). Data mining: Practical machine learning tools and techniques, Morgan Kaufmann.
Yamaguchi, H., Kimura, T. and Fujii, N. (1977).  "On the scale effect of footings in dense sand", Proceedings of the 9th  International Conference on Soil Mechanism and Foundation Engineering, 795-798.

Files

History

  • Receive Date: 03 June 2015
  • Revise Date: 05 February 2017
  • Accept Date: 01 March 2017

Share

How to cite

Statistics