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.本数据库共计收录360组数据点，其数据提取自沥青实验室及拌合厂混合料的实测资料。本次配制的混合料采用骨料（aggregates）：石灰石、棱角砂及矿粉，与沥青胶结料（asphalt binders）：特立尼达湖沥青（Trinidad Lake Asphalt, TLA）及改性沥青胶结料（modified binders, MB）制备而成。所涉及的沥青混合料涵盖密级配热拌沥青混合料（hot mix asphalt, HMA）与间断级配沥青玛蹄脂碎石混合料（stone matrix asphalt, SMA）。本数据集选取的变量，既契合现有动态模量（dynamic modulus）模型的参数要求，也满足沥青混凝土混合料的质量控制与质量保证（QC&QA）评估需求。该数据集被用于依托基因表达式编程（Gene Expression Programming）与多表达式编程（Multi Expression Programming）技术构建软计算模型。