- author = "Amir Hossein Alavi and Mahmoud Ameri and Amir Hossein Gandomi and Mohammad Reza Mirzahosseini",
- title = "Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method",
- journal = "Construction and Building Materials",
- year = "2011",
- volume = "25",
- number = "3",
- pages = "1338--1355",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Asphalt concrete mixture, Flow number, Simulated annealing, Marshall mix design, Regression analysis",
- ISSN = "0950-0618",
-
DOI = "
doi:10.1016/j.conbuildmat.2010.09.010",
- size = "18 pages",
- abstract = "A high-precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed constitutive model correlates the flow number of Marshall specimens with the percentages of filler, bitumen, voids in mineral aggregate, Marshall stability and flow. The comprehensive experimental database used for the development of the model was established upon a series of uniaxial dynamic creep tests conducted in this study. Generalised regression neural network and multiple regression-based analyses were performed to benchmark the GP/SA model. The contributions of the variables affecting the flow number were evaluated through a sensitivity analysis. A subsequent parametric study was carried out and the trends of the results were confirmed with the results of the experimental study. The results indicate that the proposed GP/SA model is effectively capable of evaluating the flow number of asphalt mixtures. The derived model is remarkably straightforward and provides an analysis tool accessible to practising engineers.",
-
notes = "a School of Civil Engineering, Iran University of
Science and Technology, Tehran, Iran
b College of Civil Engineering, Tafresh University, Tafresh, Iran
c Transportation Research Institute (TRI), Tehran, Iran",
