Prediction of Coefficient of Consolidation in Soil using Machine Learning Techniques

Abstract Coefficient of consolidation in the soil is the significant engineering properties and an important parameter for designing and auditing of geo-technical structures. Therefore, in this study, authors have proposed an efficient methodology to prediction the coefficient of consolidation using machine learning models namely Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Network based Fuzzy Inference System (ANFIS). Further, various feature selection techniques such as Least Absolute Shrinkage and Selection Operator algorithm (LASSO), Random Forests - Recursive Feature Elimination (RF-RFE), and Mutual information have also been applied. It has been observed that feature selection methods have enhanced the quality of prediction model by eliminating the irrelevant features and utilized only important features while building the prediction models. Experiments are performed on the dataset collected on the 534 soil samples from Ha Noi –Hai Phong highway project, Vietnam. Experimental results show the adequacy of the proposed model, and the hybrid approach ANFIS which is a fusion of ANN and fuzzy inference system includes complementary information of the uncertainty and adaptability. ANFIS along with LASSO feature selection method produces the coefficient of determination of 0.831 and thus provides the best prediction for the coefficient of consolidation of a soil as compared to other approaches.