Neural network modeling of SBS modified bitumen produced with different methods Baha Vural Kök a,⇑ , Mehmet Yilmaz a , Mehmet Çakirog ˘lu b , Necati Kulog ˘lu a , Abdulkadir S ßengür c a Fırat University, Department of Civil Engineering, Elazıg ˘, Turkey b Ondokuz Mayıs University, Department of Civil Engineering, Samsun, Turkey c Fırat University, Faculty of Technical Education, Electronics and Computer Education, Turkey highlights " Mixing polymers into bitumen has important consequences on the engineering properties. " Structural changes may be observed during processing of polymer-modified bitumens. " Complex modulus determination of PMBs is important to select the performance grade. " With the increase of experimental factors experimental trials increases exponentially. " Complex modulus predicted accurately with the developed ANN model. article info Article history: Received 30 January 2012 Received in revised form 17 December 2012 Accepted 18 December 2012 Available online 10 January 2013 Keywords: Bitumen Complex modulus Temperature Mixing rate Mixing time abstract Various types of polymers are added to bitumen in order to improve its properties under low and high temperatures. It is important to determine accurately the complex modulus of polymer-modified bitu- men samples (PMBs) in order to make a suitable mix design. Moreover the determination of the complex modulus is important in order to evaluate the efficiency of the additives. However the manufacture pro- cesses of PMBs involve many factors. This study aims to model the complex modulus of styrene–butadi- ene–styrene (SBS) modified bitumen samples that were produced by different methods using artificial neural networks (ANNs). PMB samples were produced by mixing a 160/220 penetration grade base bitu- men with 4% SBS Kraton D1101 copolymer at 18 different combinations of three mixing temperatures, three mixing times and two mixing rates. The complex modulus of PMBs was determined at five different test temperatures and at ten different frequencies. Therefore a total of 900 combinations were evaluated. Various different results were obtained for the same PMB produced at different conditions. In the ANN model, the mixing temperature, rate and time as well as the test temperature and frequency were the parameters for the input layer whereas the complex modulus was the parameter for the output layer. The most suitable algorithm and the number of neurons in the hidden layer were determined as Leven- berg–Marguardt with 3 neurons. It was concluded that, ANNs could be used as an accurate method for the prediction of the complex modulus of PMBs, which were produced using different methods. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Various types of additives are added to bitumen in order to im- prove the low and high temperature properties. Currently, the most commonly used polymer for bitumen modification is the sty- rene–butadiene–styrene SBS followed by other polymers such as ethylene vinyl acetate EVA, styrene butadiene rubber (SBR) and polyethylene [1]. SBS block copolymers are classified as elastomers that increase the elasticity of bitumen and they are probably the most appropriate polymers for bitumen modification by improving the temperature susceptibility of binder [2–4]. Mixing polymers into bitumen has important consequences on the engineering properties of bituminous binders. Thus, structural and chemical changes may be observed during processing of poly- mer-modified bitumens. Chemical compatibility and processing conditions are crucial to obtain suitable properties. Most polymers occur to be insoluble, in some degree, in the bitumen matrix, and phase separation may result. Lepe et al. tried different mixing rates such as 1200 rpm and 8200 rpm to produce polymer modified bitumen and concluded that a high energy mixing process is always necessary to stabilize and disperse a polymer 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.12.073 ⇑ Corresponding author. Tel.: +90 (424) 237 0000x5418; fax: +90 (424) 234 0114. E-mail addresses: bvural@firat.edu.tr (B.V. Kök), mehmetyilmaz@firat.edu.tr (M. Yilmaz), mehmetce@omu.edu.tr (M. Çakirog ˘lu), nkuloglu@firat.edu.tr (N. Kulog ˘lu), asengur@firat.edu.tr (A. S ßengür). Fuel 106 (2013) 265–270 Contents lists available at SciVerse ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel