Machine learning algorithm for the shear strength prediction of basalt-driven lateritic soil

The predictive models from different machine learning methods are useful tools for sensitive geo-mechanical properties determination. Many such models were previously established to manage time efficiency in a real-field scenario. The objective of this study is to assess and optimize three machine learning-based approaches, namely Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN), for predicting the performance of lateritic soil shear strength. The laboratory tests were accomplished to obtain the index parameters after collecting the representative lateritic soil samples along the Ratnagiri-Sangameshwar section of National Highway 66 in Maharashtra, India. To produce the training and test datasets for the prediction models, the intrinsic properties of the soil are documented, including unit weight, maximum dry density, liquid limit, plasticity index and optimal moisture content. Further, 102 lab-tested lateritic soil data of input parameters are implemented to perform the three supervised learning algorithms. The performance and accuracy of the studied methods were measured using the root mean square error (RMSE) and goodness of fit (R-2). The outcome demonstrates that the deep neural network model has the highest prediction accuracy for the residual soil shear strength among the three distinct proposed machine learning (ML) models.