Improved Estimation of California Bearing Ratio Value from Dynamic Cone Penetrometer Test Data Using Hierarchical Bayesian Modeling

California Bearing Ratio (CBR) test provides a measure of soil subgrade strength, and thus, it is widely used for pavement design. However, in practice, the CBR value is often estimated through dynamic cone penetration (DCP) testing as its equipment is portable and easier to set up compared to the CBR testing equipment. Over years, a lot of effort has been put into developing CBR-DCP correlation models, most of which are based on regression analysis. Moreover, most of the models developed are based on a wide dataset compiled from different regions, which makes them incapable of accounting for the spatial variability of soil. In this paper, hierarchical Bayesian modeling (HBM) approach is adopted to develop region-specific CBR-DCP models considering spatial variability. For this purpose, a dataset of 783 CBR-DCP data pairs from 32 locations in Ohio is compiled from the LTPP InfoPave database. The developed models using HBM approach are then compared to a conventional regression model which is developed based on the entire dataset from all regions. The analysis showed that the region-specific HBM models outperform the conventional regression model. Additionally, because data sparsity is a major concern in geotechnical engineering, the performance of the developed HBM models is investigated in a new region where only a few data points are available. The results indicated that the HBM models outperform conventional regression models in new regions with very limited data.