Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials

• Published in: Advances in civil engineering Vol. 2017; no. 2017; pp. 1 - 8
• Main Authors: Yıldızel, Sadık Alper, Kaplan, Gökhan, Tuskan, Yeşim
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2017; Hindawi; Hindawi Limited
• Subjects: Adaptive systems; Artificial neural networks; Blasting; Calcium carbonate; Civil engineering; Fiber reinforced materials; Glass fibers; Mathematical models; Sandblasting; Skid resistance; Tiling
• ISSN: 1687-8086; 1687-8094
• DOI: 10.1155/2017/7620187

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.