Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

Authors:
Mehmet Yilmaz;Baha Vural Kok;Burak Sengoz;Abdulkadir Sengur;Engin Avci
Affiliations:
Firat University, Faculty of Engineering, Department of Civil Engineering, 23119 Elazig, Turkey;Firat University, Faculty of Engineering, Department of Civil Engineering, 23119 Elazig, Turkey;Dokuz Eylul University, Faculty of Engineering, Department of Civil Engineering, 35160 Izmir, Turkey;Firat University, Department of Electronic and Computer Education, 23119 Elazig, Turkey;Firat University, Department of Electronic and Computer Education, 23119 Elazig, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This study aims to model the complex modulus of base and ethylene-vinyl-acetate (EVA) modified bitumen by using Adaptive-Network-Based Fuzzy Inference System (ANFIS). The complex modulus of base and EVA polymer modified bitumen (PMB) samples were determined using dynamic shear rheometer (DSR). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with EVA copolymer at five different polymer contents. In ANFIS modeling, the bitumen temperature, frequency and EVA content are the parameters for the input layer and the complex modulus is the parameter for the output layer. The hybrid learning algorithm related to the ANFIS has been used in this study. The variants of the algorithm used in the study are two input membership functions and three input membership functions for each of the all inputs. The input membership functions are triangular, gbell, gauss2, and gauss. The results showed that EVA polymer modified bitumens display reduced temperature susceptibility than base bitumens. In the light of analysis the Adaptive-Network-Based Fuzzy Inference System and statistical methods can be used for modeling the complex modulus of bitumen under varying temperature and frequency. The analysis indicated that the training accuracy is improved by decreasing the number of input membership functions and the utilization of the two gauss input membership functions appeared to be most optimal topology. Besides, it is realized that the predicted complex modulus is closely related with the measured (actual) complex modulus.