期刊
CONSTRUCTION AND BUILDING MATERIALS
卷 25, 期 3, 页码 1338-1355出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2010.09.010
关键词
Asphalt concrete mixture; Flow number; Genetic programming; Simulated annealing; Marshall mix design; Regression analysis
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 Marshall 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. Generalized 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 practicing engineers. (C) 2010 Elsevier Ltd. All rights reserved.
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