**PREDICTION OF HIGH-TEMPERATURE PERFORMANCE OF GEOPOLYMER MODIFIED ASPHALT BINDER USING ARTIFICIAL NEURAL NETWORKS**

Mustafa Alas

^{1}, Shaban Ismael Albrka Ali

^{2}

^{*}

^{1}Near EastUniversity , Nicosia , North Cyprus

^{2}Near EastUniversity , Nicosia , North Cyprus

** Corresponding author: saban@gmail.com*

Presented at the 2nd International Symposium on Innovative Approaches in Scientific Studies (ISAS2018-Winter), Samsun, Turkey, Nov 30, 2018

SETSCI Conference Proceedings, 2018, 3, Page (s): 1147-1157 , https://doi.org/

**Published Date: **31 December 2018 |
1338 13

**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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