Accumulated strain prediction of polypropylene modified marshall specimens in repeated creep test using artificial neural networks

Abstract

This study presents an application of artificial neural networks (ANN) for the prediction of repeated creep test results for polypropylene (PP) modified asphalt mixtures. Polypropylene fibers are used to modify the bituminous binder in order to improve the physical and mechanical properties of the resulting asphaltic mixture. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content were tested using universal testing machine (UTM-5P) in order to determine their rheological/creep, behavior under repeated loading. Different load values and loading patterns have been applied to the previously prepared specimens at a predetermined temperature. It has been shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing the increase of the service life of samples under repeated creep testing. The proposed ANN model uses the physical properties of standard Marshall specimens such as polypropylene type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids and repeated creep test properties such as rest period and pulse counts in order to predict the accumulated strain values obtained at the end of mechanical tests. Moreover parametric analyses have been carried out. The results of parametric analyses were used to evaluate the accumulated strain of the Marshall specimens subjected to repeated load creep tests in a quite well manner