Das, Sarat Kumar and Samui, Pijush and Sabat, Akshaya Kumar and Sitharam, TG (2010) Prediction of swelling pressure of soil using artificial intelligence techniques. In: Environmental Earth Sciences, 61 (2). pp. 393-403.
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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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Springer.|
|Keywords:||Expansive soil Swelling pressure Artificial neural network Support vector machine|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||12 Jul 2010 08:29|
|Last Modified:||18 Oct 2011 06:29|
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