Permanent deformation analysis of asphalt mixtures using soft computing techniques

This study presents two branches of soft computing techniques, namely multi expression programming (MEP) and multilayer perceptron (MLP) of artificial neural networks for the evaluation of rutting potential of dense asphalt-aggregate mixtures. Constitutive MEP and MLP-based relationships were obtained correlating the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of bitumen, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in this study. Relative importance values of various predictor variables of the models were calculated to determine the significance of each of the variables to the flow number. A multiple least squares regression (MLSR) analysis was performed to benchmark the MEP and MLP models. For more verification, a subsequent parametric study was also carried out and the trends of the results were confirmed with the experimental study results and those of previous studies. The observed agreement between the predicted and measured flow number values validates the efficiency of the proposed correlations for the assessment of the rutting potential of asphalt mixtures. The MEP-based straightforward formulas are much more practical for the engineering applications compared with the complicated equations provided by MLP.