A Comparative Study of Soft Computing Paradigms for Modelling Soil Compaction Parameters

Estimation of optimum water content (OWC) and maximum dry density (MDD) are crucial compaction parameters of soils in the domains of geotechnical and geological engineering. However, determining these parameters through laboratory tests is time-consuming. Therefore, this study aims to estimate the OWC and MDD of soils using a widely employed soft computing paradigm called artificial neural network (ANN). To achieve this, a comprehensive database was collected for estimating the OWC and MDD of soils. The performance of the employed ANN was compared with four additional soft computing paradigms namely extreme learning machine, support vector regressor, k-nearest neighbour regressor and group method of data handling. Experimental results indicate that the ANN model successfully estimates the OWC (training RMSE = 0.0400 and testing RMSE = 0.0530) and MDD parameters (training RMSE = 0.0421 and testing RMSE = 0.0522). The employed k-nearest neighbour and group method of data handling frameworks were found to be less effective than other employed models with training RMSE = 0.1187 and testing RMSE = 0.0834 during OWC and training RMSE = 0.1214 and testing RMSE = 0.1366 during MDD predictions, respectively. Overall, the employed ANN was determined to be a best-suitable alternative to estimate the soil compaction parameters and can be used in civil engineering projects to assess the soil compaction status during the course of construction works.