Polypropylene fibers are completely native modifiers and they do not have any dependence on abroad in case of technology. When viewed from this point, the modification of bituminous binders with polypropylene fibers is a very important step for our country's "economical" concerns. In this study, first of all, the physical and chemical effects of polypropylene fibers on bitumen were investigated. Next, the amount of "optimum" polypropylene fibers that has to be added into the mixture was determined. In order to determine it, first, static creep tests and Marshall tests were carried out and then, images of the polypropylene fiber added bituminous binders under fluorescence microscopy were researched. With the application of physical and mechanical tests to the Marshall specimens prepared with the optimum polypropylene amount that was obtained, optimum bitumen content was determined and finally economical analyses were carried out. By carrying out extensive analyses it was seen that the utilisation of polypropylene fibers improves the physical and mechanical properties of the resultant asphalt mixture mainly by enhancing the permanent deformation resistance. On the other hand, polypropylene modification results in 30% economy from bitumen which is a clear indication of the benefit in the mass production of asphalt concrete.
The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.
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