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SETSCI - Volume 3 (2018)
ISAS2018-Winter - 2nd International Symposium on Innovative Approaches in Scientific Studies, Samsun, Turkey, Nov 30, 2018

Mustafa Alas1, Shaban Ismael Albrka Ali  2*
1Near EastUniversity  , Nicosia , North Cyprus
2Near EastUniversity  , Nicosia , North Cyprus
* Corresponding author: saban@gmail.com
Published Date: 2019-01-14   |   Page (s): 1147-1157   |    6     3

ABSTRACT Complexity in the behavior of asphalt binders are further escalated with geopolymer (fly ash and the
alkali liquid) modification thus, making it difficult to predict the performance of the binder accurately. This study
employs artificial neural network modelling in order to predict complex shear modulus, storage modulus, loss
modulus and phase angle outcomes of experimental results from DSR oscillation tests under four separate
scenarios. The proposed artificial neural network models received test conditions (temperature and frequency) and
three different geopolymer concentrations (3, 5 and 7%.wt by the weight of bitumen) as the predictor parameters.
The variants of the optimal algorithms were Levenberg-Marquardt, Scaled conjugate gradient and Polak-Ribiere
conjugate gradient training algorithms with different combinations of network structures and tan-sig and log-sig as
activation functions. The coefficient of determination, covariance, and root mean squared error were used as
statistical measures of model prediction performance. Based on the statistical performance indicators LevenbergMarquardt algorithm with 3-5-1 network architecture and tan-sig as activation function was the best performing
model for predicting complex modulus with R2 values of 0.996 for training dataset and 0.971 for testing dataset
and RMSE values of 0.118 and 0.139 for training and testing datasets respectively. Further, it was observed that
the least efficient model was phase angle prediction model developed with the Polak-Ribiere conjugate gradient
training algorithm, 3-8-1 network architecture and log-sig as the activation function. The model yielded R2 values
of 0.909 and 0.829 for training and testing datasets respectively. Poor prediction performance for the testing
dataset was an indication that the model was unable to learn complexity in the data and that would perform below
0.90 significance level at predicting with untrained data.  
KEYWORDS Geopolymer modified asphalt binder; artificial neural networks; complex shear modulus; storage modulus; loss modulus; phase angle
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