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

Murat Caner


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. 


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

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Submitted: 2017-04-17 11:16:22
Published: 2017-12-12 13:20:45
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