Akademik Veri Yönetim Sistemi
2nd INTERNATIONAL CONFERENCE ON CIVIL AND ENVIRONMENTAL ENGINEERING, Nevşehir, Türkiye, 8 - 10 Mayıs 2017, ss.147
Asphalt cement is one of the main component of the asphalt mixtures due to the its viscoelastic properties. The aging in asphalt cement occurs during the mixing and
construction process which is called short term aging and during the service life in the pavement that is known as long term aging. As a result of aging the viscoelastic
properties of asphalt cement is changed and age hardening can accelerate distresses such as fatigue, low temperature cracking and moisture damage. The long-term aging
prediction models have been developed in research studies over the years. The most well -known of them is the global aging system (GAS) model. GAS model is used in the
Mechanical Empirical Pavement Design Guide. In this study, multi-layer perceptrons (MLP) which is one of the artificial neural networks (ANN’s) techniques was used in modeling long term aging of asphalt cements. The
fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, was used for optimization of the network weights. The different ANN structures were tried in terms
of iterations and hidden layer numbers. The ANN (3,9,1) appeared to be most optimal topology for prediction of the aged viscosity value of asphalt cement. The ANN results
were compared to GAS model results. Experimental data obtained from three different studies reported in literature include asphalt pavements with different ages, loading
history and environmental conditions. The ANN model gave R2 coefficient of 0.99, which was higher than the value of 0.93 obtained using the GAS model. The root mean
square error of ANN model was lower than the GAS model, indicating that the developed ANN model is able to predict the aged viscosity value more accurately than GAS model.