Soft-computing techniques for prediction of soils consolidation coefficient

Abstract

Coefficient of consolidation (Cv) is an important parameter in the designing of civil engineering structures founded on soil. Determination of the Cv in the laboratory is beset with complexity, therefore several attempts have been made to correlate it with the index properties of soil. In this paper, various advanced soft computing approaches namely Biogeography-Based Optimization based Artificial Neural Networks (ANN-BBO), Artificial Neural Networks (ANN), Adaptive Network based Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM) were applied for quick and accurate prediction of the Cv of soft soil. For this, data of engineering properties of soil of Ha Noi–Hai Phong highway project of Vietnam was utilized as a case study for training and validating the models. Data pre-processing techniques namely correlation matrix and Principal Component Analysis (PCA) were applied in order to identify relevant variables for reducing data dimension while doing predictive analysis. Validation of the models was performed using statistical criteria namely Coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Monte Carlo simulation method was performed to estimate the robustness of the models. Comparison of results of these models indicated that all the studied models performed well but performance of the ANN-BBO model (R2 = 0.965, RMSE = 0.149, and MAE = 0.108) is the best in predicting the Cv of soil compared with other models such as ANFIS (R2 = 0.921, RMSE = 0.222, and MAE = 0.182), ANN (R2 = 0.922, RMSE = 0.302, and MAE = 0.178), and SVM (R2 = 0.949, RMSE = 0.199, and MAE = 0.112). Therefore, ANN-BBO can be used for better prediction of the Cv based on limited engineering parameters of soil.