Comparison of Artifıcial Neural Networks and Response Surface Methodology in Stone Mastic Asphalt Using Waste Granite Filler

Murat Caner

Abstract

This study examined the modeling performance of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) using experimental data of mechanical and volumetric properties of stone mastic asphalt (SMA) samples. These samples were produced with Marshall Design method using different ratios of granite sludge filler (11-12%) and limestone filler (10%). The impact of percentage of bitumen, mineral filler rates and unit volume weights of samples were used as input parameters and Marshall Stability (MS) values were used as output parameter. Mechanical immersion tests were performed to examine moisture susceptibility on SMA samples that have different filler rates (10-11-12%). In order to examine the reliability of the obtained models error and regression analysis results were shown comparing model responses with the experimental results. 

Keywords

Marshall stability; Stone mastic asphalt; Response Surface; Neural network; Waste granite

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References

Do Vale, A. C., Casagrande, M. D. T. and Soares, J. B., Behavior of Natural Fiber in Stone Matrix Asphalt Mixtures Using Two Design Methods, J Mater Civ Eng 26 (2014), 457–465.

Akbulut, H., Gürer, C., Çetin, S., and Elmacı, A., Investigation of using granite sludge as filler in bituminous hot mixtures, Constr Build Mater, 36 (2012), 430–436.

Patel, K. A. and Brahmbhatt, P. K., A Comparative Study of the RSM and ANN Models for Predicting Surface Roughness in Roller Burnishing, Procedia Technol 23 (2016), 391–397.

Hafizi, A., Ahmadpour, A., Koolivand-Salooki, M., Heravi, M. M. and Bamoharram, F. F., Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H14[NaP5W30O110]/SiO2 catalyst, J Ind Eng Chem 19, (2013), 1981–1989.

Shojaeimehr, T., Rahimpour, F., Khadivi, M. A., and M. Sadeghi, A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA), J Ind Eng Chem 20 (2014), 870–880.

Sinha, K., Chowdhury, S., Das Saha, P., and Datta, S., Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana (Annatto) using response surface methodology (RSM) and artificial neural network (ANN), Ind Crops Prod 41 (2013), 165–171.

Pilkington, J. L., Preston, C., and Gomes, R. L., Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua, Ind Crops Prod 58 (2014), 15–24.

Geyikçi, F., Kiliç, E., Çoruh, S., and Elevli, S., Modelling of lead absorption from industrial sludge leachate on red mud by using RSM and ANN, Chem Eng J 183 (2012), 53–59.

Akbulut, H., Gürer, C., and Çetin, S., Use of volcanic aggregates in asphalt pavement mixes, Proc ICE - Transp 164 (2011), 111–123.

General Directory of Highways (TCK), Turkish State Highway Specifications. Ankara: Republic of Turkey, Ministry of Transport, (2013).

Montgomery, D. C., Design and analysis of experiment, Wiley, Hoboken, (2005).

Thepsonthi, T. and Özel, T., Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy, Int J Adv Manuf Technol 63 (2012), 903–914.

Myers, R. H. and Montgomery, D. C., Response surface methodology: process and product optimization using designed experiments, Wiley, Hoboken, (2002).

Caner, M., Gedik, E. and Keçebaş, A., Investigation on thermal performance calculation of two type solar air collectors using artificial neural network, Expert Syst Appl 38 (2011), 1668–1674.

Suzuki, K. et al, The characteristics of excitation system, In Advances in Power System Control, Operation and Management, APSCOM-91., International Conference on, (1991), 479–484.

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