Browsing by Author "Yildizel S., Tuskan Y., Kaplan G."

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    Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials
    (2017-01-01) Yildizel S., Tuskan Y., Kaplan G.; Yildizel, SA, Tuskan, Y, Kaplan, G
    This research focuses on the use of adaptive artificial neural network system for evaluating the skid resistance value (British Pendulum Number; BPN) of the glass fiber-reinforced tiling materials. During the creation of the neural model, four main factors were considered: fiber, calcium carbonate content, sand blasting, and polishing properties of the specimens. The model was trained, tested, and compared with the on-site test results. As per the comparison of the outcomes of the study, the analysis and on-site test results showed that there is a great potential for the prediction of BPN of glass fiber-reinforced tiling materials by using developed neural system.