Prediction of swelling pressure of soil using artificial intelligence techniques |
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Authors: | Sarat Kumar Das Pijush Samui Akshaya Kumar Sabat T G Sitharam |
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Institution: | (1) Department of Civil Engineering, National Institute of Technology, Rourkela, 769008, India;(2) Department of Civil Engineering, Tampere University of Technology, Tampere, Finland;(3) Department of Civil Engineering, KIIT University, Bhubaneswar, 751024, India;(4) Indian Institute of Science, Banglore, 560012, India |
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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. |
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