Open Access
Mustafa Alas1, Shaban Ismael Albrka Ali  2*
1Near EastUniversity  , Nicosia , North Cyprus
2Near EastUniversity  , Nicosia , North Cyprus
* Corresponding author:

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 ,

Published Date: 31 December 2018    | 448     13


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


Abdullah, S. M., 2009. Artificial Neural Network Model for Predicting Compressive Strength of Concrete. Tikrit Journal of Engineering Sciences, 16, 55-63.
Abedali, A. H., 2015. Predicting Complex Shear Modulus Using Artificial Neural Networks. Journal of Civil Engineering and Construction Technology, 6, 15-26.
Baldo, N., Manthos, E. & Pasetto, M., 2018. Analysis Of The Mechanical Behaviour Of Asphalt Concretes Using Artificial Neural Networks. Advances in Civil Engineering, 2018.
Derousseau, M., Kasprzyk, J. & Srubar, W., 2018. Computational Design Optimization of Concrete Mixtures: A Review. Cement and Concrete Research, 109, 42-53.
Dutta, S., Murthy, A. R., Kim, D. & Samui, P., 2017. Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling. CMC-Computers Materials & Continua, 53, 157-174.
El-Badawy, S., Abd El-Hakim, R. & Awed, A., 2018. Comparing Artificial Neural Networks With Regression Models For Hot-Mix Asphalt Dynamic Modulus Prediction. Journal of Materials in Civil Engineering, 30(7), 04018128.
Esfandiarpour, S. & Shalaby, A., 2017. Local Calibration of Creep Compliance Models of Asphalt Concrete. Construction and Building Materials, 132, 313-322.
Fang, C., Yu, R., Liu, S. & Li, Y., 2013. Nanomaterials Applied In Asphalt Modification: A Review. Journal of Materials Science & Technology, 29, 589-594.
Firouzinia, M. & Shafabakhsh, G., 2018. Investigation of the Effect of Nano-Silica on Thermal Sensitivity of HMA Using Artificial Neural Network. Construction and Building Materials, 170, 527-536.
Golzar, K., Jalali-Arani, A. & Nematollahi, M. 2012. Statistical Investigation On Physical–Mechanical Properties Of Base And Polymer Modified Bitumen Using Artificial Neural Network. Construction and Building Materials, 37, 822-831.
Huang, J., Pan, X., Dai, S.B. and Cai, Y., 2015. FEM analysis of dynamic behavior of asphalt pavement structure weakened by grassroots with account of hydraulic and vehicle load coupling effects. In IOP Conference Series: Materials Science and Engineering, 103, No. 1, p. 012038).
Jung, S. & Ghaboussi, J., 2006. Neural Network Constitutive Model for Rate-Dependent Materials. Computers & Structures, 84, 955-963.
Kok, B. V., Yilmaz, M., Sengoz, B., Sengur, A. & Avci, E., 2010. Investigation Of Complex Modulus Of Base And SBS Modified Bitumen With Artificial Neural Networks. Expert Systems with Applications, 37, 7775-7780.
Liu, J., Yan, K., Liu, J. & Zhao, X., 2018. Using Artificial Neural Networks To Predict The Dynamic Modulus Of Asphalt Mixtures Containing Recycled Asphalt Shingles. Journal of Materials in Civil Engineering, 30, 04018051.
Mohabeer, H., Soyjaudah, K. S. & Pavaday, N. Enhancing The Performance Of Neural Network Classifiers Using Selected Biometric Features., 2011, the Fifth International Conference on Sensors Technologies And Applications, 2011. 140-144.
Naderpour, H. & Mirrashid, M., 2018. An Innovative Approach for Compressive Strength Estimation of Mortars Having Calcium Inosilicate Minerals. Journal of Building Engineering, 19, 205-215.
Öztaş, A., Pala, M., Özbay, E. A., Kanca, E., Caglar, N. & Bhatti, M. A. 2006. Predicting The Compressive Strength And Slump Of High Strength Concrete Using Neural Network. Construction and Building Materials, 20, 769-775.
Sebaaly, H., Varma, S. & Maina, J. W., 2018. Optimizing Asphalt Mix Design Process Using Artificial Neural Network and Genetic Algorithm. Construction and Building Materials, 168, 660-670.
Specht, L. P., Khatchatourian, O., Brito, L. A. T. & Ceratti, J. A. P., 2007. Modeling of Asphalt-Rubber Rotational Viscosity by Statistical Analysis and Neural Networks.
Materials Research, 10, 69-74.
Tapkin, S., Çevik, A. & Uşar, Ü., 2009. Accumulated Strain Prediction Of Polypropylene Modified Marshall Specimens In Repeated Creep Test Using Artificial Neural Networks. Expert Systems with Applications, 36, 11186-11197.
Tasdemir, Y., 2009. Artificial Neural Networks for Predicting Low Temperature Performances of Modified Asphalt Mixtures. Indian Journal of Engineering and Materials Sciences, 4, 237-244
Venudharan, V. & Biligiri, K. P., 2017. Heuristic Principles to Predict the Effect Of Crumb Rubber Gradation On Asphalt Binder Rutting Performance. Journal of Materials in Civil Engineering, 29, 04017050.
Ziari, H., Amini, A., Goli, A. & Mirzaeiyan, D. 2018. Predicting Rutting Performance of Carbon Nano Tube (CNT) Asphalt Binders Using Regression Models and Neural
Networks. Construction and Building Materials, 160, 415-426.

Copyright © 2024 SETECH
Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TÜRKİYE