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Pro112

Comparison of artificial neural networks and response surface methodology in stone mastic asphalt using waste granite filler



Author(s): Sedat Çetin, Murat Caner, Cahit Gürer
Paper category: Proceedings
Book title: Proceedings on International RILEM Conference on Materials, Systems and Structures in Civil Engineering Conference segment on Moisture in Materials and Structures
Editor(s): Kurt Kielsgaard Hansen, Carsten Rode and Lars-Olof Nilsson
ISBN: 978-2-35158-178-0
e-ISBN: 978-2-35158-179-7
Publisher: RILEM Publications SARL
Publication year: 2016
Pages: 196 - 205
Total Pages: 10
Language : English


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.


Online publication : 2016
Publication type : full_text
Public price (Euros) : 0.00


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