Regression and Neural Network Models for California Bearing Ratio Prediction of Typical Granular Materials in Egypt

Abstract

California bearing ratio (CBR) is an important property used to express the quality and strength of the unbound granular materials and subgrade soils. It is one of the material inputs for the American Association State Highway Transportation Officials 1993 guide, and the Mechanistic Empirical Pavement Design Guide for the structural design of flexible pavements in case of the resilient modulus is not known. CBR is also conducted on the unbound materials for the quality control/quality assurance during construction. Because of its importance, this paper presents an attempt to develop simple and reliable CBR models based on routine material properties such as gradation, Atterberg limits and compaction properties using regression analysis (RA) and artificial neural networks (ANNs). Database of 207 CBR values was collected from the quality control reports prepared at the Highway and Airport Engineering Laboratory, Mansoura University. The collected CBR values were found to range between 26 and 98%. About 80% of the collected data were used for model development, while the remaining 20% were used for model validation in addition to 11 laboratory tested specimens. The developed model by RA and ANNs correlates CBR values with maximum dry density and diameter at 60% passing (D60). The prediction accuracy in terms of coefficient of determination ( $${R}^{2}$$ ) for the developed CBR model by both techniques was excellent, and the validation of the suggested model was satisfactory.