Lateef_my2nd-repo

Predictive models were developed in this study using soft computing techniques including artificial neural network (ANN), M5P model tree (M5P) and random forest (RF) to predict laterite permeability coefficient and shear strength from index properties (specific gravity, linear shrinkage, liquid limit, plasticity index, fine sand content, and fines content). To achieve this goal, an experimental dataset obtained from laboratory analyses of three hundred laterite samples was divided into a model and a gaging dataset. The model dataset contains two hundred and forty data points, which were divided into training, testing and validation datasets, with 70% for training and 15% each for testing and validation of the proposed models. The gaging dataset contains sixty data points, which were used to evaluate and compare the proposed models’ performances with the existing models using the coefficient of determination, root mean squared error, mean absolute percentage error, and mean absolute error. The proposed models outperformed the existing models and provided satisfactory performance, with ANN models presenting the best performance, followed by RF and then M5P for both the permeability coefficient and the shear strength cases. This implies that the ANN models are the most reliable estimation for the prediction of lateritic soils’ permeability coefficient and shear strength. Thus, they can be used for practical purposes.