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

The database contains a total 360 data points which was developed using data extracted from asphalt laboratories and plant mixtures. These mixes were designed using aggregates (limestone, sharp sand and filler) and asphalt binders (Trinidad Lake Asphalt - TLA and modified binders - MB). The asphalt mixtures are dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA). The variables in the dataset were chosen based on the requirements of existing dynamic modulus models as well as requirements for quality control and assurance (QC & QA) evaluation of asphalt concrete mixtures. The data was used to develop soft computing models using gene expression programming and multi expression programming techniques.