Issue 50

M. Ameri et alii, Frattura ed Integrità Strutturale, 50 (2019) 149-162; DOI: 10.3221/IGF-ESIS.50.14 150 concrete. Replacing 20% basalt in the mix design led to an increase in all the behavioral properties of the asphalt [1]. Morova in 2013 used the basalt fiber in the hot-mix asphalt concrete. In this research, the asphalt strength from 0-2% basalt was used in the mix design. The results of this study show that the use of basalt as 0.05 wt% of bitumen will have the best results [2]. Zheng in 2014 studied the fatigue of asphalt reinforced with basalt fiber, the results of which show that the basalt improves the behavioral properties of the asphalt, such as tensile strength, maximum curving tensile stress, curving stiffness modulus, and fatigue properties [3].Using ESEM analysis, Gao in 2018 analyzed the basalt-reinforced hot-mix asphalted concrete. In this study, it was shown that the scanning electron microscope is capable of examining the adhesion between fibers and asphalt in different compounds. It was found that the basalt fibers with adhesion between the asphalt components contribute to the increased strength properties of the made asphalt [4]. Lachance in 2016 evaluated the effect of glass in the mix design of hot-mix asphalt concrete. The purpose of the study was to investigate the possibility of using the recycled glass particles in asphalt mixtures and study the equivalent properties and performance instead of the conventional mixture. To this end, an asphalt mixture (ESG14) was first tested with different glass contents according to the design method of the International Institute of Quebec. Then, the performance of the samples against thermal cracking and the stiffness of the asphalt mixture were investigated with different glass contents [5]. Saltan in 2015 used the glass fiber in the hot-mix asphalt concrete. The glass wastes used for 4.0%, 4.5%, 5.0%, 5.5%, 6.0%, and 6.5% bitumen in the mix design of hot-mix asphalt concrete was found to be effective in improving the asphalt strength properties [6]. Arabani (2011) studied the dynamic properties of asphalt using glass wastes. The results of the research showed that the application of glass waste can improve the dynamic properties of asphalt [7]. The stiffness model used in the present study was obtained by replacing % 5, % 10, % 15 and 20% glass in the mix design at 5, 10 and 20°C. The results show that the temperature increase leads to a reduction in the stiffness modulus, and the replacement of 15% glass particles in the mix design, which leads to an increase in the stiffness modulus of the asphalt [8]. Ozer in 2018 used the neural network method to estimate the fatigue cracking using the accelerated testing in accordance with the mix design for the asphalt. In this research, a new numerical algorithm was used to estimate the behavioral properties of asphalt and the results showed that this method is appropriate for estimating the behavioral properties of asphalt [9]. The purpose of this study is to investigate the behavioral properties of the asphalt mixtures modified with the combination of basalt and glass fiber additives. For this purpose, it is tried to perform resilient modulus, dynamic creep, and indirect tension tests for finding the correct percentage of bitumen and obtain the optimal percentage of additive in each of the experiments. Then, the experimental results are estimated using the neural network models and the efficiency of neural network models is evaluated for the estimation of the results. The neural network used to estimate the behavioral properties of materials in laboratory research has recently been adopted by researchers. However, the conducted studies are in the field of concrete mix design and there is little research on the mix design and the prediction of behavioral properties. For example Kaur in 2000 used the fuzzy method to estimate the laboratory results. Input data to the neural network models in this research are the construction materials, pavement thickness, road age and traffic count as the input of the neural network and are intended to estimate the thickness of the asphalt. The Visual Basic environment is used to build the neural network models. After performing laboratory investigations, the neural network method is used to estimate the experimental results and to evaluate the robustness of the neural network in the estimation of the results [10]. Sodikov in 2005 emphasized the importance of prediction cost highway project with using neural network method. Lack of preliminary information, lack of database of road works costs, data amusingness, lack of an appropriate cost estimation methods are some major problem in high way project [11]. Tapkın in 2010 employed MLP to estimate physical and mechanical properties of the asphaltic mixture with PP fiber. In this study, neural network method was employed to predict laboratory test data such as Marshall Stability and flow tests [12]. In this research, due to the fact that the temperature of the asphalt wearing coarse affects its performance (stiffness and cracking on the asphalt surface), the neural network is used to estimate the temperature of the asphalt wearing coarse. The main goal of this study is to investigate the physical characteristics of the asphalt mixtures by combining basalt and glass fiber. To do this, marshal stability, resilient modulus, indirect tensile strength, and dynamic creep were done to determine the optimum percentage of these two modifiers, then laboratory test estimation was done through this method and the capability of ANFIS was evaluated. M ATERIALS n this research, for producing asphalt samples without additive and conducting the experimental test, limestone aggregates were used for making gravel and sand of each mixture. Moreover, stone powder was used as filler (material passing the sieve 200) [13]. The mentioned material was taken from Taloo (in east of Tehran- asphalt I