Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2010.11.002 | Expert Syst. Appl. |
Keywords | Field | DocType |
rutting,marshall mix design,multi expression programming,subsequent parametric study,constitutive mep,mlp model,artificial neural network,rutting potential,marshall flow,soft computing technique,formulation,asphalt pavements,marshall specimen,previous study,permanent deformation analysis,experimental study result,flow number,flow number value,least square,multilayer perceptron,soft computing | Least squares,Data mining,Applied mathematics,Computer science,Regression analysis,Voids in mineral aggregate,Multilayer perceptron,Parametric statistics,Predictive modelling,Soft computing,Artificial neural network,Statistics | Journal |
Volume | Issue | ISSN |
38 | 5 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.48 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Reza Mirzahosseini | 1 | 5 | 0.48 |
| Alireza Aghaeifar | 2 | 5 | 0.48 |
| Amir Hossein Alavi | 3 | 1016 | 45.59 |
| Amir Hossein Gandomi | 4 | 1836 | 110.25 |
| Reza Seyednour | 5 | 5 | 0.48 |