The Development of Advanced Mathematical Models to Predict Compaction Parameters of FineGrained Soils in Terms of Atterberg Limits

Abstract

Compaction is an important engineering process that ensures the stability of soils by compressing them to a predefined strength. However, in most construction projects, particularly large-scale projects, achieving the appropriate compaction properties, such as optimum moisture content (OMC) and maximum dry density (MDD), it requires time and high cost. Predicting the compaction characteristics from the Atterberg limit, which involves simpler and faster testing techniques, becomes an important task in this scenario. The purpose of this study is to study the comparison of the multiple linear regression (MLR) method with the response surface method (RSM) and artificial neural network (ANN) to determine an accurate, efficient and simple technique to predict soil compaction parameters. For this research, 29 samples were subjected to a variety of laboratory testing. All of the parameters’ statistical relationships were analyzed. In this research, techniques are used, and the findings of these studies are discussed and analysed. To see the performance and accuracy of the model, the criteria for validation of the model used are based on the value of coefficient of determination (R2), absolute mean error (MAE), mean square error (MSE) and mean square root error (RMSE). A comparison with the test data revealed that the coefficient of determination (R2) of ANN model predictions was greater than those of other models. In addition, the findings indicate that the accuracy of ANN models are superior to the statistical models MLR and RSM.