Artificial Neural Network Modeling for Predicting Compaction Parameters based on Index Properties of Soil

Compaction is simple ground improvement technique where the soil is densified through external compactive effort. The important parameters of compaction are Optimum Moisture Content (OMC) and Maximum Dry Density (MDD) which depends on the index properties of soil. Compaction increases the density of soil thereby, increasing shear strength and bearing capacity. These above parameters which are determined from laboratory tests are laborious and time consuming. However index soil properties test is relatively inexpensive, simple and can be performed within less time with utmost accuracy. In this research work, an attempt has been made to predict the compaction parameters from index properties of the soil in terms of Liquid Limit, Plasticity Index, soil particles finer than 75microns and greater than 75microns size. A feed forward neural network model is developed to predict the compaction parameters of the soil using index properties of the soil and the analysis is done by using Artificial Neural Network (ANN) methodology. Using this model, compaction parameters can be easily predicted by performing simple index properties tests in the laboratory. The R2 values of OMC of ANN model for training and testing dataset were found to be 0.8526 and 0.7568 respectively. The R2 values of MDD of ANN model for training and testing dataset were found to be 0.8801 and 0.8071 respectively. The R2 values of OMC and MDD for simulation dataset for this parameter are 0.9463 and 0.9478 respectively. Hence, it was proved that the developed neural network model can predict OMC and MDD with reasonable degree of accuracy.