PREDICTION OF CALIFORNIA BEARING RATIO (CBR) USING INDEX PROPERTIES OF SOIL IN EKITI SENATORIAL DISTRICT

The design of pavement thickness is depending on the strength of subgrade soil expressed in terms of CBR (%). In the design of pavement, the suitability and stability of sub-grade materials are been determined before construction of pavement by using CBR test. However, in a large-scale road project, conducting laboratory tests by using the CBR test is very expensive and time consuming, which help in stability of civil engineering pavement design. California Bearing Ratio (CBR) value is an important soil parameter considered as main design input in the design of flexible pavements and runways of air fields. The design of pavement thickness determined depending on the strength of subgrade soil expressed in terms of CBR. This research aims to predict CBR using multiple linear regression. In this study, a survey was carried out in Ekiti South Senatorial Districts, and accordingly 30 sample locations were collected. All the samples were collected at average of a depth of 1.0m and analyzed for different Index properties such as sieve analysis, Atterberg’s limits, natural moisture content, Specific gravity, Compaction and Laboratory CBR in according with British Standards (BS 1377). Multiple linear regression was used to predict the CBR of the soil samples using the Index properties results. The specific gravity, liquid limit, Atterberg’s limits and natural moisture content varies from S.G is 2.14 to 2.77, Atterberg’s limits (LL= 21.85% to 51.10%, PL=11.2% to 30.3%, PI=2.61% to 30.72%), NMC=1.24% to 15.25% respectively. The particle size distribution curve shows that most of the soil are fine grained soils in respect to the Unified Soil Classification System (USCS) because 50% of the soil materials has particle sizes > 0.0075mm. To develop the intended correlation, model the procedures of data collection, laboratory test, correlation and regression analysis were done. The index soil properties considered for this study to establish correlations with CBR values are Percentage Passing sieve No.200, Liquid Limit (LL), Plastic Limit (PL), Plastic Index (PI), Maximum Dry Density (MDD) and Optimum Moisture Content (OMC). From the regression analysis result, the equations developed is CBR = 28.058 – 0.409LL – 0.273PI - 0.116PP200, with the coefficient of determination, R2 = 0.445 for multiple linear regression. Based on the result of regression analysis, fairly good correlation of CBR value is obtained with multiple parameters (LL, PL and PP200). Base on the outcome of the analysis the result shows that the prediction of CBR with the multiple linear regression method correlated well with the results obtained from the LL, PL and PP200. This study provides a valuable insight into the use of multiple linear regression for prediction for similar soil types in the region and since,