Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming

Abstract

An essential factor in the design and in situ performance of asphalt concrete pavements is its elastic-stiffness properties. The objective of the present study, is to develop empirical equations to estimate indirect tensile stiffness modulus (ITSM) of bituminous mixture using the soft computing techniques of multi expression programming (MEP) and gene expression programming (GEP). The soft computing models were developed using experimental data points containing laboratory and field blended dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA) mixtures including three asphalt binders. The dominant parameters covered mix physical and volumetric properties, test temperatures, and the mix variables needed during quality control and assurance assessments. The soft computing models could successfully estimate the stiffness modulus with notable values of R values between 0.835 and 0.944. The GEP equations estimated the elastic stiffness modulus with a higher rate of accuracy when compared to the MEP, non-linear and linear regression estimates.