Environmental Earth Sciences

, Volume 61, Issue 2, pp 393–403

Prediction of swelling pressure of soil using artificial intelligence techniques

Authors

    • Department of Civil EngineeringNational Institute of Technology
  • Pijush Samui
    • Department of Civil EngineeringTampere University of Technology
  • Akshaya Kumar Sabat
    • Department of Civil EngineeringKIIT University
  • T. G. Sitharam
    • Indian Institute of Science
Original Article

DOI: 10.1007/s12665-009-0352-6

Cite this article as:
Das, S.K., Samui, P., Sabat, A.K. et al. Environ Earth Sci (2010) 61: 393. doi:10.1007/s12665-009-0352-6

Abstract

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg’s limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Keywords

Expansive soilSwelling pressureArtificial neural networkSupport vector machine

Copyright information

© Springer-Verlag 2009