A new method for CBR prediction using fuzzy set theory

California Bearing Ratio (CBR) values of the subgrade soils are key parameters in highway route survey and project design. However, in order to calculate the CBR value, time-consuming and specialized tests must be performed in the laboratory or in the field. In this study, a model based on Fuzzy set theory is proposed to predict the CBR value of soils. The geological characteristics of the fine-sized aggregate in soils are considered as the key parameter in the prediction model.For this purpose, novel Fuzzy membership functions are defined. Logarithmic interpolation approach, which takes into account the Atterberg limits and Proctor parameters of the soils, was used to determine the membership degrees of the gravel and sand sized aggregates forming the soil. The membership degrees of fine-sized aggregates were determined by basic sigmoid membership function according to the type of clay they contain. In order to verify the accuracy of the model, the calculated results were compared with the soaked CBR values obtained from laboratory tests and it was found that there was a high correlation (R2=0.86) between each other. Considering the obtained results together, it was determined that the effect of variables such as the geological class to which the aggregates forming the soil belong, which have the potential to affect the CBR value of the soils, on the results of the CBR prediction models can be eliminated by means of membership functions.