Yildizel S., Tuskan Y., Kaplan G.Yildizel, SA, Tuskan, Y, Kaplan, G2023-05-092023-05-092017-01-012017.01.011687-8086https://hdl.handle.net/20.500.12597/13477This 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.trueArticle10.1155/2017/762018710.1155/2017/76201872-s2.0-85042098170WOS:00041896170000120171687-8094