SciELO - Scientific Electronic Library Online

vol.15 issue6Tensile properties of duplex UNS S32205 and lean duplex UNS S32304 steels and the influence of short duration 475 ºC agingEffect of non-solvents used in the coagulation bath on morphology of PVDF membranes author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Materials Research

Print version ISSN 1516-1439


TAPKıN, Serkan; CEVIK, Abdulkadir  and  OZCAN, Şenol. Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures. Mat. Res. [online]. 2012, vol.15, n.6, pp.865-883.  Epub Sep 25, 2012. ISSN 1516-1439.

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.

Keywords : Marshall design; static creep test; bitumen modification; polypropylene fibers; strain accumulation; artificial neural networks; closed form solutions.

        · text in English     · English ( pdf epdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License