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Application of Meta-Models (MPMR and ELM) for Determining OMC, MDD and Soaked CBR Value of Soil
Abstract
This chapter examines the capability of Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM) for prediction of Optimum Moisture Content (OMC), Maximum Dry Density (MDD) and Soaked California Bearing Ratio (CBR) of soil. These algorithms can analyse data and recognize patterns and are proved to be very useful for problems pertaining to classification and regression analysis. These regression models are used for prediction of OMC and MDD using Liquid limit (LL) and Plastic limit (PL) as input parameters. Whereas Soaked CBR is predicted using Liquid limit, Plastic limit, OMC and MDD as input parameters. The predicted values obtained from the MPMR and ELM models have been compared with that obtained from Artificial Neural Networks (ANN). The accuracy of MPMR and ELM models, their performance and their reliability with respect to ANN models has also been evaluated.
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