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In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil. Unlabelled Image • Different Artificial Intelligence (AI) methods were developed for the prediction of Cc. • Monte Carlo approach is proposed for the sensitivity analysis of the AI models. • RMSE, MAE, and R2 were used for the validation of the models. • SVM is the best method in comparison to ANN and ANFIS methods for the prediction of Cc. [ABSTRACT FROM AUTHOR]

• Prediction of soil consolidation coefficients (Cv) was done using Artificial Intelligent (AI). • Monte Carlo simulations were also performed estimate the robustness of the AI models. • RMSE, MAE, and R2 were used for validation of the models. • Novel ANN-BBO model was better compared to others for predicting the Cv. 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. [ABSTRACT FROM AUTHOR]