Yildizel S.A.Tuskan Y.Kaplan G.2024-07-222024-07-22201716878086http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/15497This 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. © 2017 Sadik Alper Yildizel et al.EnglishAll Open Access; Gold Open AccessArticle10.1155/2017/7620187