Tajudin, A.N., Ni, L., Setyarini, P.S.E. and Darmawati, D.S. (2018) The Influence of CBR Value and Overloading on Flexible Pavement Mechanistic Response. Proceedings of the 11th Asia Pacific Transportation and the Environment Conference, Volume 186, 257-262.

has been cited by the following article:

• TITLE: Development of Empirical Models for the Estimation of CBR Value of Soil from Their Index Properties: A Case Study of the Ogbia-Nembe Road in Niger Delta Region of Nigeria

  AUTHORS: Jonathan O. Irokwe, Ify L. Nwaogazie, Samuel Sule

  KEYWORDS: Multiple Regression Model, Soil Index Properties, Analysis of Variance, California Bearing Ratio, Coefficient of Determination

  JOURNAL NAME: Open Journal of Civil Engineering, Vol.12 No.4, December 30, 2022

  ABSTRACT: This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil is a laborious operation that requires a longer time and materials leading to increased cost and schedule; this can be reduced by adopting an empirical-mathematical model that can predict the CBR using other simpler soil index properties such as Plastic Limit (PL), the Liquid Limit (LL), the Plasticity Index (PI) and the Moisture Content (MC), which are less laborious and take lesser time to obtain. Thirteen models were developed to understand the relationship between these soil index properties: the independent variable and the California Bearing Ratio (CBR): the dependent variable; Six linear, Six quadratic and One multiple linear regression models were developed for this relationship. Analysis of variance (ANOVA) on the thirteen models showed that the Optimum Moisture Content (OMC) and the Maximum Dry Density (MDD) are better independent variables for the prediction of the CBR value of Ogbia-Nembe soil generating a quadratic model and a multiple linear regression model having a better coefficient of determination R2 = 0.96 and 0.94 respectively, mean square error (MSE) of 0.74 and 1.152 respectively with Root mean square errors of 0.861 and 1.073 accordingly. These models were used to predict the CBR of the soil. The CBR values predicted by the model were further compared with those of the actual experimental test and found to be relatively consistent with minimal variance. This establishes that CBR of any soil can be predicted from the Index Property of the soil and this is more economical and takes lesser time and can be universally adopted for soil investigation.