Predicting the maximum dry density and optimum moisture content from soil index properties using efficient soft computing techniques

This study aims to achieve the densest possible state of soil for constructing dams and roads. This requires assessing the compaction characteristics, Optimum Moisture Content (OMC), and Maximum Dry Density (MDD) to determine the soil's suitability for earthworks. However, this process is resource-intensive and time-consuming. To streamline the assessment, the study incorporates six parameters: gravel (G), sand (S), fine (F) contents, plastic limit (PL), liquid limit (LL), and plasticity index (PI). Four different models are used to predict compaction characteristics: artificial neural network (ANN), nonlinear regression (NLR), linear regression (LR), and multilinear regression (MLR). The study utilized a substantial dataset of 2162 entries, considering various soil gradation and plasticity properties as input variables. To evaluate the models' effectiveness, several statistical measures, including coefficient of determination (R²), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), a20-index, and Objective (OBJ) value, were employed. The ANN model outperformed other models in predicting OMC, with RMSE, MAE, OBJ, SI, a20-index, and R² values of 3.51, 2.31, 4.26, 0.202, 0.7, and 0.92%, respectively. However, for predicting MDD, the ANN model had the highest R² value (R² = 0.87), but the minimum RMSE (1.01), MAE (0.8), a20-index (0.998), and OBJ (1.07) were obtained from the MLR and LR models. Furthermore, sensitivity analyses revealed that the plastic limit significantly influences the OMC, while the gravel content plays a dominant role in predicting MDD.