Abstract :

California Bearing Ratio is used for the evaluation of subgrade strengths during design of flexible pavements. However, determination of CBR value is time consuming, tedious, costly and laborious which highly affects the schedule of projects. To make a preliminary assessment of the suitability of soils required for a project, it is important to develop a prediction model for these engineering properties on the basis of laboratory tests which are quick to perform, less time consuming and cheap such as the tests for index properties of soils. This research evolves developing an efficient, simplified California Bearing Ratio (CBR) predictive model from soil index and compaction properties of fine-grained soils. The laboratory results indicated that samples used in this research lie in MH categories based on USCS and in the range of group A-7-5(16-65) based on AASHTO classification system. NCSS-12software is employed to investigate the significance of individual independent variables with the soaked CBR. One best model from each category with a very good statistical goodness of fit measures was selected. The developed models were validated against primary data. Moreover, the newly developed models are found to be by far better and can be used as a simple convenient tool to predict the CBR value of fine-grained soils in the study area. Among the various parameters derived from index properties and compaction characteristics, the Liquid Limit and Maximum Dry Density were found to be the most effective predictive parameter with R2 = 0.899. The comparative results showed that the variation between the experimental and predicted results for CBR falls within 7.86% confidence interval which is by far better than other empirical equations reviewed in this paper.