Table of Contents Author Guidelines Submit a Manuscript
Advances in Civil Engineering
Volume 2017 (2017), Article ID 7620187, 8 pages
Research Article

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

1Faculty of Engineering, Department of Civil Engineering, Karamanoglu MehmetBey University, Karaman, Turkey
2Faculty of Engineering, Department of Civil Engineering, Manisa Celal Bayar University, Manisa, Turkey
3Faculty of Engineering, Department of Civil Engineering, Kastamonu University, Kastamonu, Turkey

Correspondence should be addressed to Sadik Alper Yildizel; rt.ude.umk@lezidliyas

Received 3 July 2017; Revised 17 October 2017; Accepted 29 October 2017; Published 19 December 2017

Academic Editor: Cumaraswamy Vipulanandan

Copyright © 2017 Sadik Alper Yildizel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.