Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

Abstract

The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties.