THESIS ABSTRACT

Abstract

The quality of the construction materials used in pavement layers must be assessed prior to the construction of pavement. Some of the important properties governing the suitability and quality of a construction material include CBR, LAA, AIV, and PI value of the material. The base course material used must ensure the criteria’s set forth in Standard Specifications for Road and bridge Works, 2016 with amendment 2021. But there are no sufficient studies about relationship between CBR and thickness of base course with other physical properties of base course material. So, a relationship between the various properties of a construction material to the CBR value and pavement thickness can be very advantageous in a project to reduce time and for providing alternative check to design thickness. Hence, this study aims to determine the relationship between the CBR value to the various physical properties of a base material such as LAA, AIV, PI, OMC, and MDD. Additionally, a relation between the thicknesses of the base course and the independent properties was established. Twenty base course material samples were collected from different sources, and laboratory tests were performed to obtain the CBR, LAA, AIV, PI, MDD, and OMC values. The CBR value of sub-base of same sources were used to calculate the thickness of the base course using IRC recommended CBR method of flexible pavement design for traffic class E. Simple and multiple linear regression analysis were performed on the test results to determine the relationship. MDD and OMC values were not seen as a good fit for the regression model. Hence, the relationship obtained from the regression analysis are CBR = 131.625 – 0.59 LAA – 0.963 AIV – 0.804 PI (R2 =0.881) and thickness of base (cm) = -2.223 + 0.02 LAA + 0.441 AIV + 0.136 PI (R2 =0.76). Five sample sources were then used for the model validation. It was concluded that the equations can be useful for preliminary predictions of CBR value and thickness of base course for construction agencies.

Keywords

Base, CBR, LAA, AIV, PI, Thickness of base course, Linear Regression